Future of Medical Physics and US Healthcare

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, VOLUME 14, NUMBER 5, 2013

The future of medical physics in the US health-care system
This issue, I would like to digress from the meaning of the JACMP and discuss with you some
personal thoughts on the US health-care system and the future of medical physics under the
Affordable Care Act. My credentials for discussing this topic include a newly minted PhD in
Health Management from the School of Public Health at the University of Louisville. I spent
many rewarding hours in classes, seminars, and lectures learning about the changes in US
health care and public health over the past 11 years. This is a very difficult topic to explore in
the limited space assigned to an editorial, but I would like to point out some themes that we
need to bear in mind as we look forward.
1. Efficiency — It is expected that the Affordable Care Act will add approximately 30 million
people to the roles of the insured. Health-care workers likely will be expected to absorb the
associated additional work with only modest increases in employment. Please keep in mind
that hundreds of billions of dollars are being diverted from the Medicare Program over the
next decade to fund the services provided to these additional patients and consumers. Now
add to this dynamic the large number of retiring baby-boom health-care workers and the loss
of a large base of knowledge and experience. It is projected that up to 30% of the health-care
workforce could retire within the next five years. While younger workers are usually less
expensive to providers, they also may be less efficient in the delivery of quality services.
Those health delivery systems that can deliver health services at community standard of care
quality will be at a competitive advantage. For example, if a cancer center can treat patients
presenting with a certain disease with fewer fractions than another while reporting similar
and competitive outcomes, the gatekeepers of the systems likely will seek out and reward a
more efficient provider.
2. Training — Barriers are increasing for those seeking to enter the heath-care workforce.
Medical physics MS programs are charging up to $20,000 per year for tuition. Student loans
for this training are running at a 7.5% annual interest rate. Financing a medical school education is even more challenging. If a student borrows $200,000 to go to medical school, then
moves on to complete a residency program, it may be years before a young physician can
begin to make a dent in this liability. Yes, some physicians earn significant financial rewards,
but such success is by no means guaranteed. Physicians of tomorrow may very well face
student loan debt larger than their mortgages. Although medical physics training pathways
are shorter and less expensive, financial barriers are both significant and growing.
3. Reimbursement — If you look at business trends, health-care providers are merging into
enormous entities composed of multiple hospitals and clinics. It may be that one primary
purpose for this trend is to become large enough to negotiate directly with the primary payers.
It is no secret the Center for Medicare and Medicaid Services (CMS) seems to be moving
away from the Current Procedural Terminology (CPT)-based fee-for-service reimbursement for outpatient health services. Each year, we see more services being bundled under
the broader Ambulatory Patient Classification (APC) categories, with the apparent ultimate
goal seeming to be to assign a dollar value for each International Classification of Disease
(ICD-9 or ICD-10) patient diagnosis. There are dozens of ICD codes for breast cancer (just
one example), and most other major cancers you can name. Once the relative values of
each code are assigned, it will be possible to name a single multiplier to the entire table to
determine the reimbursement of any patient presentation. The multiplier would be determined by the funds available and political realities. This approach could replace the current
fee-for-service reimbursement system. The bottom line is that the value of work performed
by medical physicists could easily get lost in the consolidation of the reimbursement and
payment system, along with the trend of health-care providers merging into larger and even
larger entities. We will need to redouble our efforts to be visible and relevant.
1 1

2 Mills: Editorial

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4. Competition — For example, a large not-for-profit US hospital chain and a hospital chain
from an Asian nation together are building a hospital in the Cayman Islands. Large investment
banks fund this multibillion dollar project. Ultimately the plans include a two thousand-bed
hospital, medical school, research center, as well as a biotechnology park and assisted living complex. It intends to seek Joint Commission International Accreditation. This is only
one of many such projects either being constructed or planned in the Caribbean and Central
America. The funding for such projects comes from those that anticipate there will be many
willing to travel for quality services offered at lower cost. Some aspects of the Cayman
I
slands project include the importation of health-care professionals from other countries with
no additional requirements to practice and no taxes on imported capital equipment, and the
purchase of equipment and supplies at the rates that hospital groups in Asia pay. Considering the proximity of the Cayman Islands to Florida, Texas, and the other Gulf Coast states,
it seems very likely to me those 2,000 beds might not be enough.
My conclusion from what I have seen in the literature and heard from my instructors is the
US health-care system will undergo enormous changes over the next few years. These will be
times of uncertainty and struggle for all health professions. Although medical physicists are
in some ways better equipped to weather the storm than others, we too might face significant
pressures. Are we ready for the challenge?
Michael D. Mills
Editor-in-Chief

Journal of Applied Clinical Medical Physics, Vol. 14, No. 5, 2013


AAPM Medical Physics Practice Guideline 1.a: CT Protocol
Management and Review Practice Guideline
The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and
professional practice of medical physics. The AAPM has more than 8,000 members
and is the principal organization of medical physicists in the United States.
The AAPM will periodically define new practice guidelines for medical physics
practice to help advance the science of medical physics and to improve the quality
of service to patients throughout the United States. Existing medical physics practice
guidelines will be reviewed for the purpose of revision or renewal, as appropriate,
on their fifth anniversary or sooner.
Each medical physics practice guideline represents a policy statement by the
AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council.
The medical physics practice guidelines recognize that the safe and effective
use of diagnostic and therapeutic radiology requires specific training, skills, and
techniques, as described in each document. Reproduction or modification of the
published practice guidelines and technical standards by those entities not providing these services is not authorized.

1. Introduction
The review and management of computed tomography (CT) protocols is a facility’s ongoing
mechanism of ensuring that exams being performed achieve the desired diagnostic image
quality at the lowest radiation dose possible while properly exploiting the capabilities of the
equipment being used. Therefore, protocol management and review are essential activities in
ensuring patient safety and acceptable image quality. These activities have been explicitly identified as essential by several states(1-2) regulatory and accreditation groups such as the American
College of Radiology (ACR) CT Accreditation program,(3) as well as the Joint Commission in
its Sentinel Event Alert,(4) among others. The AAPM considers these activities to be essential
to any quality assurance (QA) program for CT, and as an ongoing investment in improved
quality of patient care.
CT exam protocols are used to obtain the diagnostic image quality required for the exam,
while minimizing radiation dose to the patient and ensuring the proper utilization of the scanner features and capabilities. Protocol Review refers to the periodic evaluation of all aspects of
CT exam protocols. These parameters include acquisition parameters, patient instructions (e.g.,
breathing instructions), the administration and amounts of contrast material (intravenous, oral,
etc.), and postprocessing parameters. Protocol Management refers to the process of review,
implementation, and verification of protocols within a facility’s practice.
This is a complex undertaking in the present environment. The challenges in optimization
of dose and image quality are compounded by a lack of an automated mechanism to collect
and modify protocols system-wide. The manual labor involved in identifying, recording, and
compiling for review and subsequent implementation of all relevant parameters of active protocols is not inconsequential.(5) The clinical community needs effective protocol management
tools and efficient methods to replicate protocols across different scanners in order to ensure
consistent protocol parameters. The ability to quickly view and understand the myriad of CT
protocol parameters contained within a single exam type is critical to the success of protocol
review. The ability to quickly identify an outlier protocol parameter would also be hugely
beneficial to the CT protocol review process.
3 3

4 AAPM: Medical Physics Practice Guidelines

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This MPPG only applies to CT scanners used for diagnostic imaging. It is not applicable to
scanners used exclusively for:

a. Therapeutic radiation treatment planning or delivery;
b. Only calculating attenuation coefficients for nuclear medicine studies; or
c. Image guidance for interventional radiologic procedures.

2. Definitions
a. CT Protocol – the collection of settings and parameters that fully describe a CT examination.(6) Protocols may be relatively simple for some body part specific systems or
highly complex for full-featured, general-purpose CT systems.(7)
b. Qualified Medical Physicist –as defined by AAPM Professional Policy 1(8)
3. Staffing Qualifications and Responsibilities
a. The Protocol Review and Management Team

Protocol Review and Management requires a team effort; this team must consist of at
least a lead CT radiologist, the lead CT technologist, and qualified medical physicist
(QMP). In addition, a senior member of the facility administration team should also be
involved. This could be the Chief Medical or Administrative Officer for the facility, or
a dedicated Radiology Department Administrator/Manager, as determined by hospital
leadership. If a senior member of the facility administration team is not a member of
the Protocol Review and Management Team, there should be a clear delineation of
the reporting structure.

This team must be responsible for protocol design and review of all parameter settings.
Each team member brings different expertise and may have different responsibilities in
the Protocol Review and Management process. To be successful, it is very important
that the expectations of roles and responsibilities of each member are clearly described.
The ability to work together as a team will be an important attribute of each member of
this group. The flow chart in Appendix A is an example of how team members should
work together and in parallel during the process.(5) Additional examples of protocol
management based on one facility’s experience are discussed in References 9 and 10.
The team members, their qualifications and expectations are described below.
i. Qualified Medical Physicist (QMP)
The first Professional Policy of the AAPM provides a comprehensive definition
of a Qualified Medical Physicist (QMP).(8) The subfield of medical physics applicable for CT Protocol Management is Diagnostic Medical Physics. As stated by
the Policy, “a [QMP] is an individual who is competent to independently provide
clinical professional services in one or more of the subfields of medical physics”
and meets each of the following credentials:
a. “Has earned a master’s or doctoral degree in physics, medical physics, biophysics, radiological physics, medical health physics, or equivalent disciplines
from an accredited college or university; and
b. Has been granted certification in the specific subfield(s) of medical physics
with its associated medical health physics aspects by an appropriate national
certifying body and abides by the certifying body’s requirements for continuing education.”
c. For Diagnostic Medical Physics, the acceptable certifying bodies as of 2012
are: the American Board of Radiology, the American Board of Medical
Physics, and the Canadian College of Physicists in Medicine.



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ii. Responsibilities of the QMP

In the context of CT Protocol Management and Review, the QMP’s responsibilities
may vary, depending on the type of facility being supported; regardless, the QMP
must be involved in the review of all protocols. These considerations should be
balanced with adequate response times to facility inquiries.
A QMP’s time at a facility should include but not be limited to:
a. meeting with the CT Protocol Management and Review team;
b. clinical observation; phantom measurements;
c. side-by-side image review with radiologist(s);
d. artifact review with technologist(s) and/or radiologist(s);

and
e. discussion of equipment performance and operation, etc.
While regular dialogue is important, the QMP should also remember that facility
personnel themselves, in particular the Lead CT Radiologist, should lead the CT
Protocol Management and Review process; the QMP is an integral member of
the team. The QMP may elect to perform baseline dose measurements and image
quality tests at the outset of the project, particularly if the QMP does not have
personal historical experience with the scanner(s) in the facility.
iii. In-house QMP

For the in-house QMP, this ongoing CT protocol review project may consume
much of his/her time, so the QMP should be sure to adequately communicate with
his/her supervisor(s), with other team members, and with department/hospital
management in this regard. The facility should understand that the CT Protocol
Management and Review process is an ongoing investment in improved quality
of patient care.
In-house QMPs may be able to arrange more frequent meetings with CT Protocol
Management and Review team members than their consulting colleagues; six to
twelve meetings annually may be more appropriate for facilities with in-house
QMPs, with the meeting frequency likely decreasing as time goes on and the
facility’s protocols are sufficiently improved.
iv. Consulting QMP

It is important to note that CT Protocol Management and Review services are
above and beyond normal QMPs consulting services (e.g., the annual physics
survey), which have traditionally been limited to image quality, dosimetry, and
basic protocol review for a few selected examinations. Consultant QMPs should
make this clear to their clients, and negotiate their services appropriately.
QMPs providing consulting services should maintain regular dialogue with the
facility via convenient means (e.g., email, phone, and perhaps text message, if
appropriate). It may be beneficial to use a communication process that provides
a log of these interactions. It is recommended that the consulting QMP discuss
with each facility access to images, including, but not limited to, remote access to
the facility’s Picture Archiving and Communication System (PACS) for improved
consultative capabilities.
Consulting QMP’s should work with the facility to arrange mutually agreeable
times to visit the facility for CT protocol portfolio review activities. Three to four
visits annually may be reasonable.



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v. Qualifications and Expectation of the Lead CT Technologist

The American Society of Radiologic Technologists (ASRT) has developed a practice standard entitled The Practice Standards for Medical Imaging and Radiation
Therapy – Computed Tomography Practice Standards, effective June 19, 2011,
which describes the education and certification requirements and scopes of practice
for CT technologists.(11)
The Lead CT Technologist is expected to provide the interface between the patient,
staff, and the equipment. This includes workflow, the assembly and management
of the CT portfolio, and education of the technologist pool.
vi. Qualifications of the CT Radiologist

Facilities should refer to the ACR for guidance on the requirements for physicians
for accreditation or those in the Practice Guideline for Performing and Interpreting
CT(12) and CT Accreditation Program Requirements.(13)
The CT radiologist leads the CT Protocol Management and Review and defines
image quality requirements.(14)
4. The Protocol Management Review Process
It is important that the CT Protocol Review and Management team designs and reviews all new
or modified protocol settings for existing and new scanners to ensure that both image quality
and radiation dose aspects are appropriate. Each member of CT Protocol Management team
has a critical role related to his or her specific area of expertise for the evaluation, review, and
implementation of protocols. The following elements should be considered for inclusion in a
specific facilities’ protocol review process:
•

•

•

While performing the review process, the CT Protocol Management team should pay
particular attention to the oversight and review of existing protocols along with the
evaluation and implementation of new and innovative technologies that can improve
image quality and/or lower patient dose in comparison to the older protocol.
Particular attention should be paid to the specific capabilities of each individual scanner
(e.g., minimum rotation time, automatic exposure controls including both tube current
modulation, as well as kV selection technologies, iterative reconstruction, reconstruction algorithms, etc.) to ensure maximum performance of the system is achieved. In
addition, consideration should be made to consolidate protocols or remove legacy
protocols that may not be current or applicable any longer.
The review process should include a review of the most current literature such as
ACR practice guidelines,(12) AAPM protocol list,(7) and peer-reviewed journals, etc.,
to ensure state-of-the-art protocols are being utilized.

The following considerations are important during review of a protocol:
a. Recommendations for State and National Guidance

Local, state, and federal law or regulation varies greatly depending on the state in which
the facility is located. The QMP must be familiar with applicable federal law and the
specific requirements for the state or local jurisdiction where the facility is located.
Protocol review and management, while not always explicitly required by state law
or regulation, may often facilitate compliance with many provisions within state laws
and regulations relating to radiation dose from CT. Links to applicable state regulations
can be found at: http://www.aapm.org/government_affairs/licensure/default.asp.
b. Frequency of Review

The review process must be consistent with federal, state, and local laws and regulations. If there is no specific regulatory requirement, the frequency of protocol review


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should be no less frequent than 24 months. This review should include all new protocols added since the last review. However, the best practice would be to review a
facility’s most frequently used protocols at least annually.
c. Clinically Significant Protocols that Require Annual Review

For every facility there are protocols that are used frequently or could result in significant doses. If a facility performs the following six clinical protocols, the CT Protocol
Review and Management team must review these annually (or more frequently if
required by state or local regulatory body). Facilities that do not perform all of the
exams listed below must select additional protocols at their facility, either the most
frequently performed or higher-dose protocols, to a total of at least six for annual
review. The six clinical protocols requiring annual review are:
i. Pediatric Head (1 year old) (if performed at the institution)
ii. Pediatric Abdomen (5 year old; 40-50 lb. or approx. 20 kg) (if performed at the
institution)
iii. Adult Head
iv. Adult Abdomen (70 kg)
v. High Resolution Chest
vi. Brain Perfusion (if performed at the institution)
d. Protocol Naming

A facility should consider naming CT protocols in a manner consistent with the RadLex
Playbook ID.(15) This would provide a more consistent experience for patients and
referring physicians, and allow more direct comparison among various facilities. This
practice may also allow more direct utilization of the ACR Dose Index Registry(16) tools
and provide more efficient automated processes with postprocessing workstations. Also,
the standardization of protocol names between scanners, even when the scanners are
of different makes and models, is strongly encouraged. Appropriate protocol naming
will likely result in fewer technologist errors and allow more efficient comparison of
protocol parameters between scanners. A facility should consider incorporating version
dates in protocol names to easily confirm the latest approved version.
e. Permissions.
i. It is important that each facility establish a process for determining who has permission to access the protocol management systems. Each facility should decide and
document who has permission to change protocol parameters on the scanner(s). If
the scanner allows password protection of protocols, then the facility is encouraged
to use this important safety feature. Facilities should also decide how passwords
are protected and archived.
ii. Each facility should decide on the process of making protocol adjustments and the
frequency with which these adjustments should be made. This includes decisions
as to what approvals need to be secured before a protocol adjustment may be
made, and the documentation process (e.g., a change control log documenting the
rationale for each change, as well as who authorized or motivated the change).
iii. Each facility should consider how to most effectively utilize the NEMA XR 26
standard (Access Controls for Computed Tomography)(17) when these tools become
available on scanners at their facility.
f. Acquisition parameters including kV, mA, rotation time, collimation or detector
configuration, pitch, etc., should be reviewed to ensure they are appropriate for the
diagnostic image quality (noise level, spatial resolution, etc.) necessary for the clinical
indication(s) for the protocol, while minimizing radiation dose. For example, a slow


8

rotation time and/or low pitch value would not be appropriate for a chest CT exam
due to breath-hold issues.
i. The facility should explicitly review the expected Volume Computed Tomography
Dose Index (CTDIvol) values. For the limited set of protocols where reference
values are available, the CTDIvol values should be compared to the reference values of the ACR CT Accreditation Program,(3) Dose Reference Levels (DRLs),(18)
AAPM CT Protocols,(7) or other available reference values for the appropriate
protocols.

Note: These reference values may be exceeded for individual patient scans (such
as for a very large patient, or when the routine protocol is not used because of a
different clinical indication, or when the reference value only refers to a single
pass in a multipass study).
ii. For a facility’s routine protocol for a standard sized patient, the expected CTDIvol
values should be below these reference values.
g. Reconstruction parameters such as the width of the reconstructed image (image
thickness), distance between two consecutive reconstructed images (reconstruction
interval), reconstruction algorithm/kernel/filter, and the use of additional image planes
(e.g., sagittal or coronal planes, etc.) should also be reviewed to ensure appropriate
diagnostic image quality (noise level, spatial resolution, etc.) necessary for the clinical indication(s) for the protocol. For example, a high-resolution chest exam typically
generates thin (~ 1 mm) images using a sharp reconstruction filter.
h. Advanced dose reduction techniques should be considered when the use of such
techniques is consistent with the goals of the exam. Depending on the capabilities of
each specific scanner, consider use of the following, if they are available:
i. Automatic exposure control (e.g., tube current modulation or automatic kV selection) methods.
ii. Iterative reconstruction techniques.
i. Adjustments of acquisition parameters should be adjusted for patient size,
either through a series of manual adjustments or through the use of automatic techniques (such as tube current modulation methods that adjust for patient size).
j. Radiation dose management tools fall under two related but different categories,
and may provide CT dose data that can be used to determine facility reference dose
ranges.
i. Radiation dose management tools that identify when potentially high-radiation
dose scans are being prescribed should be implemented when available. This
includes dose reporting and tracking software, participation in dose registries,
and methods as described in the MITA XR25 standard (“Dose Check”).(19)
ii. Radiation dose management tools may be used to monitor doses and collect data
from routine exams. Statistical analysis of dose parameter values for a specific
exam or clinical indication (e.g., average CTDIvol for a routine noncontrast head)
can be provided. Participation in a national registry (such as the ACR Dose Index
Registry)(16) and use of commercial dose tracking products are now available for
this purpose.
k. Populating Protocols Across Scanners

Each facility should decide on the process by which protocol parameters are populated
across additional scanners (whether this is done manually or by copy/paste, if the
s
canners allow). The facility should decide whether there are ‘master’ or ‘primary’
scanners in the facility where manual protocol adjustments are to be made and archived,


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and that set of protocols moved to the other similar scanners, or if another strategy
will be employed.
l. Documentation

The CT Protocol Review and Management team should maintain documentation
of all changes to protocols, and historical protocols should be available for review.
Documentation should include the rationale for changes (e.g., improve temporal
resolution, reduce breath-hold time, reduce patient dose, etc.). The latest protocol
should be readily and obviously available to users during clinical protocol selection.
In some settings it may be helpful to maintain historical protocols on the scanner, in
a less conspicuous location or clearly labeled as a legacy protocol.

The facility should decide and document who is responsible for maintaining the overall
protocol description documentation. The facility should also describe whether the
protocol description documentation is accessible to others for reference, how often it
is updated, and how all protocols (on the scanners as well as the protocol description
documentation) are archived.

m. Periodic Vendor-specific Education/Refresher Sessions

The CT Protocol Management Process team is responsible for ensuring that each
member is adequately trained for protocol review on each scanner used at his or her
facility. Each member of the CT Protocol Management Process team should receive
refresher training no less than annually or when new technology is introduced that
substantially impacts image quality or dose to the patient.
i. Available educational resources should be considered in order to keep staff updated
on current best practices.
ii. Periodic refresher training should be scheduled for all members of the CT Protocol
Management Process team.
iii. Attendance should be taken at initial and all refresher-training sessions, and
consequences identified for failure to complete training.
n. Verification

Once a CT Protocol Management Process has been established, the CT Protocol Review
and Management team must institute a regular review process of all protocols to be
sure that no unintended changes have been applied that may degrade image quality or
unreasonably increase dose.

As a best practice, the CT Protocol Review and Management team should conduct a
random survey of specific exam types to verify that the protocols used are acceptable
and consistent with protocols specified above. This should involve a limited review
of recent patient cases to assess:
i. Acquisition and reconstruction parameters,
ii. Image quality, and
iii. Radiation dose.

5. Conclusion
CT protocol management and review is an important part of a CT facility’s operation and is
considered important by many state regulatory bodies, accrediting, and professional organizations. Protocol parameter control and periodic review will help maintain the facility’s image
quality to acceptable levels, and will serve to assure patient safety and continuous improvement
in the imaging practice.



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ACKNOWLEDGMENTS
This guideline was developed by the Medical Physics Practice Guideline Task Group-225 of
the Professional Council of the AAPM.
TG-225 Members:
Dianna D. Cody, Chair, PhD, FAAPM
Tyler S. Fisher, MS
Dustin A. Gress, MS
Rick Robert Layman, Jr., MS
Michael F. McNitt-Gray, PhD, FAAPM
Robert J. Pizzutiello, Jr., MS, FAAPM
Lynne A. Fairobent, AAPM Staff
AAPM Subcommittee on Practice Guidelines – AAPM Committee responsible for sponsoring
the draft through the process.
Joann I. Prisciandaro, PhD, Chair
Maria F. Chan, PhD, Vice-Chair Therapy
Jessica B. Clements, MS
Dianna D. Cody, PhD, FAAPM
Indra J. Das, PhD, FAAPM
Nicholas A. Detorie, PhD, FAAPM
Vladimir Feygelman, PhD
Jonas D. Fontenot, PhD
Luis E. Fong de los Santos, PhD
David P. Gierga, PhD
Kristina E. Huffman, MMSc
David W. Jordan, PhD
Ingrid R. Marshall, PhD
Yildirim D. Mutaf, PhD
Arthur J. Olch, PhD, FAAPM
Robert J. Pizzutiello Jr., MS, FAAPM, FACMP, FACR
Narayan Sahoo, PhD, FAAPM
J. Anthony Seibert, PhD, FAAPM, FACR
S. Jeff Shepard, MS, FAAPM, Vice-Chair Imaging
Jennifer B. Smilowitz, PhD
James J. VanDamme, MS
Gerald A. White Jr., MS, FAAPM
Ning J. Yue, PhD, FAAPM
Lynne A. Fairobent, AAPM Staff
References
1. AB 510, Radiation control: health facilities and clinics: records. 2011-2012 Reg. Sess. (CA 2012). An act to amend
Sections 115111, 115112, and 115113 of the Health and Safety Code, relating to public health, and declaring the
urgency thereof, to take effect immediately. Available from: http://leginfo.legislature.ca.gov/faces/billNavClient.
xhtml?bill_id=201120120AB510&search_keywords
2. Rules and Regulations – Radiation Control Program [Internet]. Austin, TX: Texas Department of State
Health Services (DSHS). Last updated 25 July 2013. Available from: http://www.dshs.state.tx.us/radiation/rules.
shtm#227
3. American College of Radiology. CT Accreditation Program. Available from: http://www.acr.org/Quality-Safety/
Accreditation/CT
4. The Joint Commission Sentinel Event Alert: Radiation risks of diagnostic imaging. Issue 47, August 24, 2011.
Available from: http://www.jointcommission.org/assets/1/18/SEA_47.pdf


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5. Siegelman JR, and Gress DA. Radiology stewardship and quality improvement: the process and costs of implementing a CT Radiation Dose Optimization Committee, in a medium sized community hospital system. J Am
Coll Radiol. 2013;10(6):416–22.
6. AAPM. CT Lexicon, ver. 1.3, 04/20/2012. Available from: http://www.aapm.org/pubs/CTProtocols/documents/
CTTerminologyLexicon.pdf
7. AAPM. CT scan protocols. Available from: http://www.aapm.org/pubs/CTProtocols/
8. AAPM. Professional Policy Statement, PP 1-H: Definition of A Qualified Medical Physicist. Available from:
http://www.aapm.org/org/policies/details.asp?id=316&type=PP
9. Siegle C, Kofler JM, Torkelson JE, Leitzen SL, McCollough CH. CT scan protocol management. Presented at
the Radiological Society of North America Scientific Assembly and Annual Meeting, Chicago, Illinois,
November 2004.
10. Kofler JM, McCollough CH, Vrieze TJ, Bruesewitz MR, Yu L, Leng S. Team-based methods for effectively creating, managing and distributing CT protocols. Presented at the Radiological Society of North America Scientific
Assembly and Annual Meeting, Chicago, Illinois, November, 2010.
11. ASRT. The practice standards for medical imaging and radiation therapy – computed tomography practice standards
(effective June 19, 2011). Available from: http://www.asrt.org/main/standards-regulations/practice-standards/
practice-standards
12. ACR. ACR practice guideline for performing and interpreting diagnostic computer tomography (CT). Available
from: http://acr.org/~/media/ACR/Documents/PGTS/guidelines/CT_Performing_Interpreting.pdf
13. ACR. CT accreditation program requirements. Available from: http://acr.org/~/media/ACR/Documents/
Accreditation/CT/Requirements.pdf
14. ACR. CT accreditation program clinical image quality guide. Available from: http://www.acr.org/~/media/ACR/
Documents/Accreditation/CT/ImageGuide.pdf
15. RSNA. RadLex playbook. Available from: http://rsna.org/RadLex_Playbook.aspx
16. ACR. National radiology data registry: dose index registry [website]. Available from: www.acr.org/nrdr
17. NEMA. Access controls for computed tomography: identification, interlocks, and logs. NEMA XR 26-2012.
Rosslyn, VA: National Electrical Manufacturers Association; 2012.
18. McCollough C, Branham T, Herlihy V, et al. Diagnostic reference levels from the ACR CT Accreditation Program.
J Am Coll Radiol. 2011;8(11):795–803.
19. NEMA. Computed tomography dose check. NEMA XR 25-2010. Rosslyn, VA: National Electrical Manufacturers
Association; 2010.



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APPENDIX
Appendix A: Example of how team members may work together and in parallel during the
process.



Evaluation of the sensitivity of two 3D diode array
dosimetry systems to setup error for quality
assurance (QA) of volumetric-modulated arc
therapy (VMAT)
Guangjun Li,1,2 Sen Bai,1,2 Nianyong Chen,1a Lansdale Henderson,3
Kui Wu,1,2 Jianghong Xiao,1,2 Yingjie Zhang,1,2 Qingfeng Jiang,1,2
Xiaoqin Jiang1,2

Department of Radiation Oncology,1 Cancer Center, West China Hospital, Sichuan
University, Chengdu, Sichuan, China; Center for Radiation Physics and Technology,2
Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China;
Department of Neuroscience,3 University of Virginia, Charlottesville, VA, USA
nchenyy@gmail.com

Received 28 November, 2011; accepted 8 April, 2013
The purpose of this study is to evaluate the sensitivities of 3D diode arrays to setup
error for patient-specific quality assurance (QA) of volumetric-modulated arc
therapy (VMAT). Translational setup errors of ± 1, ± 2, and ± 3 mm in the RL, SI,
and AP directions and rotational setup errors of ± 1° and ± 2° in the pitch, roll, and
yaw directions were set up in two phantom systems, ArcCHECK and Delta4, with
VMAT plans for 11 patients. Cone-beam computed tomography (CBCT) followed
by automatic correction using a HexaPOD 6D treatment couch ensured the position accuracy. Dose distributions of the two phantoms were compared in order to
evaluate the agreement between calculated and measured values by using γ analysis
with 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria. To determine the impact on setup
error for VMAT QA, we evaluated the sensitivity of results acquired by both 3D
diode array systems to setup errors in translation and rotation. For the VMAT QA
of all patients, the pass rate with the 3%/3 mm criteria exceeded 95% using either
phantom. For setup errors of 3 mm and 2°, respectively, the pass rates with the
3%/3 mm criteria decreased by a maximum of 14.0% and 23.5% using ArcCHECK,
and 14.4% and 5.0% using Delta4. Both systems are sensitive to setup error, and do
not have mechanisms to account for setup errors in the software. The sensitivity of
both VMAT QA systems was strongly dependent on the patient-specific plan. The
sensitivity of ArcCHECK to the rotational error was higher than that of Delta4. In
order to achieve less than 3% mean pass rate reduction of VMAT plan QA with
the 3%/3 mm criteria, a setup accuracy of 2 mm/1° and 2 mm/2° is required for
ArcCheck and Delta4 devices, respectively. The cumulative effect of the combined
2 mm translational and 1° rotational errors caused 3.8% and 2.4% mean pass rates
reduction with 3%/3 mm criteria, respectively, for ArcCHECK and Delta4 systems.
For QA of VMAT plans for nasopharyngeal cancer (NPC) using the ArcCHECK
system, the setup should be more accurate.
PACS numbers: 87.55.ne, 87.55.Qr, 87.55.km
Key words: VMAT, setup error, patient-specific QA, 3D diode array

a

Corresponding author: Nianyong Chen, Department of Radiation Oncology, Cancer Center, Sichuan University
West China School of Medicine/West China Hospital, 37 Guoxuexiang, Wuhou District, Chengdu, Sichuan
610041, P.R. China; phone: (86) 28 8542 2952; fax: (86) 28 8542 2952; email: nchenyy@gmail.com

13 13

14 Li et al.: Evaluation of 3D diode array dosimetry systems for VMAT QA

14

I. Introduction
Volumetric-modulated arc therapy (VMAT) is a new intensity-modulated radiotherapy (IMRT)
technology with single or multiple gantry arcs that achieves appropriate dose-target conformity
and permits critical organ sparing. VMAT delivers radiation via dynamic multileaf collimator
(MLC) motion, and allows for variable dose rates, gantry speed modulation, and collimator
rotation.(1) Thus far, VMAT has been used to treat various tumor sites, including head and
neck,(2-5) lung,(5-8) prostate,(4,5,9-12) rectum,(5,13) cervix uteri,(14) spinal metastases,(15,16) and
brain metastases.(17) However, the dose calculation and the implementation of VMAT plans are
highly complex. It is, therefore, essential to perform patient-specific quality assurance (QA)
of VMAT plans.(18)
Various types of dosimetry systems exist for dose verification, including gel dosimetry,(19,20)
water phantoms with film and ion chambers,(21) online 2D detector arrays,(22-25) Monte Carlobased frameworks,(26) and 3D diode arrays.(27-29) With the exception of the 3D diode arrays,
these QA systems are limited either by single-plane measurements of the dose distribution
or increased off-line processing time for measured data.(27) Two commercial 3D diode array
systems, ArcCHECK (Sun Nuclear, Melbourne, FL) and Delta4 (ScandiDos AB, Uppsala,
Sweden), were applied for dose verification in IMRT and VMAT.
In our clinical practice, we used ArcCHECK and Delta4 for QA of VMAT plans. During QA
measurements, the phantoms were positioned such that the crosslines on the surface aligned
with the room lasers. In practice, we noted that the registration errors (e.g., 2 mm) of the room
lasers and the radiation isocenter of the linacs affected the phantom positioning error, and thereby
obviously influenced the dose verification of VMAT. However, unlike most other commercial
systems, the analysis software available in the ArcCHECK and Delta4 systems is unable to
correct for positioning errors. Therefore, it is crucial to determine the sensitivity of the 3D
detector arrays to setup error for QA of VMAT plans. In one case, Letourneau et al.(27) assessed
the sensitivity of the prototype of the ArcCHECK dosimetry system to phantom translational
setup error in the right–left and anterior–posterior directions and the results demonstrated that
the diode array sensitivity to setup error is strongly dependent on the patient-specific VMAT
plans. However, the effect of the rotational setup error on ArcCHECK and other 3D detector
arrays with various detector positions is not fully understood. In this study, we examined the
sensitivities of ArcCHECK and Delta4 to translational and rotational setup errors in all directions for patient-specific QA of VMAT plans.
II. Materials and Methods
A. Patients’ plan selection
Eleven patients requiring VMAT plans of differing complexity for cancers, including esophageal,
prostate, cervix uteri, rectal, and nasopharyngeal cancer (NPC), were selected for this study.
The VMAT plans were designed using a commercial 3D treatment planning system (Pinnacle
v9.0, Philips Medical, Madison, WI) with a SmartArc optimization algorithm.(30) Patients’
characteristics and planning states are summarized in Table 1. The plans for NPC had two full
arcs with one control point per 4°, and the plans of other cancer sites had only one full arc
with one control point per 4°. All TPS calculations in this study were done with a dose grid
resolution of 2 mm.



15

Table 1. Patient characteristics and planning states. Using the SIB technique, two or three dose levels are defined for
each patient, except for patients with rectal cancer.

Number of
Dose
Monitor
Disease Site
Patients
Levels
Units
Esophageal
Prostate(c)
Cervix uteri
Rectal
NPC(d)

3
1
1
3
3

2
2
2
1
3

497±108
1316
906
1259±136
668±65

Average Field
Width
(mm)

Average Leaf
Travel(a)
(mm)

Average Leaf
Travel Speed(b)
(mm/s)

37±5
51
49
82±8
26±1

579±75
848
818
610±57
1418±121

3.8±0.6
5.4
5.0
4.2±0.3
4.5±0.3

a Average

leaf travel is calculated over all leaves, excluding leaves which remain closed over all treatment.
leaf travel speed is average leaf travel divided by delivery time.
c The prostate plan is a whole pelvis and prostate boost plan.
d The treatment volume for NPC included all sites: primary, upper, and lower neck with a single VMAT plan.
b Average

B. Measurement devices
Two 3D dosimetry systems, ArcCHECK and Delta4, were used for measurements. The
ArcCHECK dosimetry system,(27,31) consists of 1386 diodes, each with 0.8 × 0.8 mm2 active
measuring area, embedded in the cylindrical wall of the phantom. The Delta4 dosimetry system is
based on two crossing arrays including 1069 diodes in a fixed cylindrical geometry, providing full
coverage of the cross section for any beam direction.(28,29) The spatial locations of the detectors
are different between the two dosimetry systems. The dose distribution tested by ArcCHECK
forms a cylindrical distribution with a diameter of 21 cm, typically positioned in the region
surrounding the tumor target volume. In the Delta4 system, the dose distribution is measured
on the two intersected perpendicular planes that cut through the tumor target volume.
C. Delivery and patient-specific QA
The 11 VMAT plans were cast on the reference CT images of ArcCHECK and Delta4 phantoms
and the dose distributions were recalculated. Both 3D diode arrays were placed on the HexaPOD
6D robotic treatment couch (Elekta, Crawley, UK) for measurements. Beam attenuation by the
treatment couch has been considered in the plans by generating the couch’s model from the
contour of the couch, together with the density information. All tests were carried out using
an Elekta Synergy accelerator at the nominal energy of 6 MV X-rays with a 1 cm leaf width
MLCi and an RTD 7.01 controller system (Elekta). Before recording QA measurements, we
performed the quality control (QC) for the linac according to the TG142 report(32) ensuring the
coincidence of lasers with the isocenter was within a radius of 1 mm. To minimize the setup
error, cone-beam computed tomography (CBCT) and a HexaPOD robotic treatment couch
(HRTC) were used to set up the phantoms with 0.5 mm and 0.5° residual errors as prescribed
in the studies by Sharpe et al.(33) and Meyer et al.(34) The reference CT images were acquired
on a CT scanner with a slice thickness of 1 mm, and the resulting CBCTs possessed a voxel
resolution of 0.5 mm in all three dimensions of the reconstructed images. Registration between
the reference CT and CBCT was carried out automatically using an inbuilt method in XVI,
namely gray value match.
D. Setup error simulation
The ArcCHECK and Delta4 phantoms were translated respectively in the right–left (RL),
anterior–posterior (AP), and superior–inferior (SI) directions by ± 1, ± 2, and ± 3 mm and
rotated in the pitch, roll, and yaw directions by ± 1° and ± 2° using the 6D treatment couch.
Figure 1 shows the definition of pitch, roll, and yaw used in this study. The 11 VMAT plans
were separately delivered to the each of the two phantoms for dose verification; in total, 31
measurements (1 without positional error, 18 with translational errors, and 12 with rotational
errors) were performed for each patient plan with one dosimetry system. For the combined


16

Fig. 1. Coordinate system used in the study. Arrows indicate positive rotation with respect to each axis.

reproducibility of setup and measurement, the above procedure was repeated three times for
one rectal cancer case with the ArcCHECK and Delta4 systems, respectively. The intervals
between the reproducibility tests were more than one month.
We compared the measured dose distributions of each array with the calculated dose distributions generated by the planning system in order to analyze the effect of phantom-specific setup
errors on VMAT QA. We also analyze the cumulative effect of combined 2 mm translational
and 1° rotational errors, which could be figured out by the quadratic summation method.(32)
The pass rate of γ analysis was computed by comparing the calculated and measured dose distributions using 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria, respectively. Diode readings, or
“dose-values,” lower than 10% of the highest diode signal were ignored in the analysis. These
ignored readings reflect low-dose and low-gradient regions, typically located under the “jaws,”
where the diode response is less reliable and the signal-to-noise ratio presents a concern.(35) The
paired Student’s t-test was used for analysis of the results obtained from ArcCHECK and Delta4.
All tests were two-tailed with a p-value of < 0.05 considered the threshold for statistical significance. Statistical analysis was performed with the SPSS (v.14.0, Chicago, IL) program.
III. Results
A. QA for VMAT patients’ plans
QA results of the 11 VMAT plans tested with ArcCHECK and Delta4 are shown in Table 2. All
pass rates of γ analysis with the 3%/3 mm criteria are higher than 95% for both diode arrays.
Except for NPC, all pass rates of γ analysis with the 3%/2 mm and 2%/2 mm criteria are higher
than 95% and 90%, respectively. The mean pass rate of γ analysis with the 2%/2 mm criteria
for NPC by ArcCHECK was 84.7%. The lower results for NPC compared to the other cancer
sites are due to the target volume complexity and the differences in the geometrical position
of the diodes in ArcCHECK and Delta4 (see Fig. 2). A significant difference in the pass rate
with the 3%/3mm criteria between the two systems (p = 0.004) indicates that the pass rates of
Delta4 are higher than those of ArcCHECK.



17

Table 2. The QA results of 11 VMAT plans using ArcCHECK and Delta4 phantoms obtained without introducing
any setup error.

Cancer Site

γ (%)a of ArcCHECK
3%/3 mm
3%/2 mm
2%/2 mm

3%/3 mm

Esophageal
Prostate
Cervix uteri
Rectal
NPC

98.5±0.3
98.7
98.5
98.8±0.9
95.6±0.8

99.7±0.3
98.6
99.2
99.4±0.4
98.5±0.5

96.8±0.4
97.5
97.2
97.2±1.8
92.5±0.9

91.2±0.7
92.9
91.4
93.6±2.4
84.7±1.0

γ (%)a of Delta4
3%/2 mm
2%/2 mm
98.2±1.0
96.5
98.0
97.8±1.2
95.3±1.9

95.3±1.7
90.6
92.9
95.8±1.0
90.1±2.0

a Gamma

(γ) results are the percentage of points passing the gamma criterion of 3%/3 mm, 3%/2 mm, and 2%/2 mm,
respectively.

Fig. 2. The two systems measured the different section of the dose distribution because of the geometrical position of the
diodes. ArcCHECK and Delta4 diodes positioned on the circular line and the crossline, respectively.

B. Sensitivities of two diode arrays to translational setup error
Figure 3 shows the impact of the translational setup error on 11 patient-specific VMAT QA
plans. Setup error was separately introduced in the RL, SI, and AP directions, and the impact
was measured using ArcCHECK and Delta4. When the translational setup errors are ± 1, ± 2,
and ± 3 mm, respectively, the pass rates of γ analysis with the 3%/3 mm criteria decreased by
a maximum of 2.5%, 6.4%, and 14.0% for ArcCHECK and 2.5%, 6.9%, and 12.2% for Delta4
in the RL direction; 6.1%, 8.4%, and 13.4% for ArcCHECK and 1.6%, 6.3%, and 14.4% for
Delta4 in the SI direction; 2.0%, 4.5%, and 9.5% for ArcCHECK and 1.7%, 5.1%, and 10.5%
for Delta4 in the AP direction.
To further test the difference between the two dosimetry systems in sensitivity to setup error,
we compared all of their values for the reduction of γ analysis with the 3%/3 mm criteria in
each direction. Significant differences in the pass rate of γ analysis in the RL and SI directions
(p = 0.019 and < 0.001, respectively) indicate a higher sensitivity of ArcCHECK diodes than
Delta4 diodes to translational setup error in both directions; however, only a nominal difference
was observed in the AP direction between the two systems (p = 0.074).


18

Fig. 3. The impact of translational setup errors on dosimetric verification of 11 VMAT plans using ArcCHECK and Delta4
phantoms in (a) RL, (b) SI, and (c) AP directions. The simulated translational setup errors are 1, 2, 3, -1, -2, and -3 mm,
respectively. The decreased pass rates of γ analysis from the original results are assessed with the 3%/3 mm criteria.

The tested results also indicated that the pass rate of γ analysis was most affected by translation in the RL and AP directions for NPC and esophageal cancer, but only affected by translation in the SI direction for prostate cancer. For ArcCHECK, the maximum standard deviations
(error bars shown in Fig. 3) which were calculated for the three cases of each disease site for
each setup error were 4.0%, 3.3%, and 3.1%, respectively, for NPC, esophageal, and rectal
cancer; for Delta4 they were 3.8%, 4.5%, and 4.3%. These results indicate that the effects of
translational setup errors on VMAT QA are strongly dependent on patient-specific plans in
spite of the same disease site.
C. Sensitivities of two diode arrays to rotational setup error
Figure 4 shows the impact of the rotational setup error for 11 patient-specific VMAT QA.
Setup error was separately introduced in the pitch, roll, and yaw directions, and the impact was
measured using ArcCHECK and Delta4. When the rotational setup errors were ± 1° and ± 2°,
respectively, the pass rates of γ analysis with the 3%/3 mm criteria decreased by a maximum
of 5.5% and 9.9% for ArcCHECK and 2.5% and 5.0% for Delta4 in the pitch direction; 5.2%
and 19.2% for ArcCHECK and 1.8% and 4.9% for Delta4 in the roll direction; and 8.4% and
23.5% for ArcCHECK and 1.7% and 4.9% for Delta4 in the yaw direction. Significant differences between the two systems in all rotation directions (p < 0.001, = 0.001, and < 0.001 in the
pitch, roll, and yaw directions, respectively), indicate that the configuration of the ArcCHECK


19

Fig. 4. The impact of rotational setup errors on dosimetric verification of 11 VMAT plans using ArcCHECK and Delta4
phantoms in (a) pitch, (b) roll, and (c) yaw directions. The simulated rotational setup errors are 1°, 2°, -1°, and -2°, respectively. The decreased pass rates of γ analysis, compared to the original results, are assessed with the 3%/3 mm criteria.

system is more sensitive to rotational setup error than the Delta4 system when determining
VMAT QA, and emphasize the importance of accurate rotational positioning during measurements using ArcCHECK.
From the results gathered by the two systems, we observed the greatest impact on the pass
rate of γ analysis with the 3%/3 mm criteria in all directions for NPC and esophageal cancer.
For ArcCHECK, the maximum standard deviations (error bars shown in Fig. 4) were 5.8%,
3.4%, and 2.5%, respectively, for NPC, esophageal, and rectal cancer; for Delta4 they were
1.1%, 2.2%, and 1.6%. These results indicate that the effects of the rotational setup errors on
VMAT QA are strongly dependent on patient-specific plans in spite of the same disease site.
D. Influence of setup error on the pass rate of γ analysis with various criteria
Table 3 shows the impact of setup errors in translation and rotation on the pass rate of γ analysis
with various criteria attained by ArcCHECK and Delta4. Stricter gamma criteria resulted in a
greater impact of the setup error on the pass rate of γ analysis. For a translational setup error of
3 mm, the pass rates of γ analysis with the 2%/2 mm criteria decreased by an average of 13.2%
± 5.5% for ArcCHECK and by an average of 14.6% ± 6.7% for Delta4. For the rotational setup
error of 2°, the pass rates of γ analysis with the 2%/2 mm criteria decreased by an average of
14.5% ± 6.6% for ArcCHECK and by an average of 7.0% ± 3.7% for Delta4.


20

Table 3. Translational and rotational setup errors result in a decrease in the pass rate of γ analysis with various criteria
on the average and standard deviation.

Setup Error

3%/3 mm

ArcCHECK
3%/2 mm

2%/2 mm

3%/3 mm

Delta4
3%/2 mm

2%/2 mm

Translation
1 mm
0.7±1.0
1.4±1.5
2.0±2.0
0.6±0.6
1.6±1.5
2.1±2.2
2 mm
2.8±1.7
5.0±2.9
6.7±3.5
2.3±1.9
5.4±3.3
7.3±4.2
3 mm
6.9±3.3
10.0±4.9
13.2±5.5
6.1±3.8
11.1±5.5
14.6±6.7
Rotation

1°
1.8±1.9
3.7±2.9
4.9±3.7
0.5±0.8
1.5±1.5
2.5±2.6

2°
8.6±4.7
12.1±5.5
14.5±6.6
2.4±1.7
5.0±2.9
7.0±3.7

E. Combined reproducibility of setup and measurement
Figure 5 shows the standard deviation of pass rates with the 3%/3 mm criteria for the reproducibility tests for one rectal cancer case with the ArcCHECK and Delta4 systems, respectively.
The mean standard deviations were 0.59% for ArcCHECK and 0.44% for Delta4 for all reproducibility tests in this case, and the mean standard deviations for 1, 2, and 3 mm translational
setup errors, respectively, were 0.32%, 0.65%, and 0.83% for ArcCHECK and 0.12%, 0.41%,
and 0.88% for Delta4; for 1° and 2° rotational setup errors, respectively, they were 0.41% and
0.75% for ArcCheck and 0.29% and 0.54% for Delta4.

Fig. 5. The standard deviation of pass rates with the 3%/3 mm criteria for the reproducibility tests for one rectal cancer
case with the ArcCHECK and Delta4 systems, respectively. The tests contain all simulated (a) translational and (b) rotational setup errors for this case.



21

F. Cumulative effect of both translational and rotational error
Figure 6 shows the cumulative impact of 2 mm translational and 1° rotational setup errors for
the 11 patient-specific VMAT QA using ArcCHECK and Delta4, respectively. For ArcCHECK
system, the average decreased pass rates of γ analysis with the 3%/3 mm criteria in all directions were 3.4%, 3.1%, 3.3%, 2.9%, and 5.6%, respectively, for esophageal, prostate, cervix
uteri, rectal, and nasopharyngeal cancer, and for Delta4 the average decreased pass rates were
2.9%, 3.3%, 1.7%, 1.4%, and 3.0%, respectively.

Fig. 6. The cumulative impact of 2 mm translational and 1° rotational errors was evaluated for the pass rate reduction with
3%/3 mm criteria for both ArcCHECK and Delta4 systems. The calculated average and standard deviation of the pass rate
reduction contained the translational and rotational errors of combinations in all directions.

IV. DISCUSSION
Patient-specific dosimetric verification has been indispensable for IMRT QA. Furthermore,
achieving accurate QA results is critical in detecting discrepancies between delivery and planning. The characteristics of Delta4, as reported by Korreman et al.,(36) were determined by
comparing consecutive deliveries of the same plan. The results indicated a strong agreement
in all cases for the accumulated dose with dose deviations < 1% for all measurement points
and cases. Letourneau et al.(27) assessed the combined reproducibility of the ArcCHECK
dosimeter system response and the linear accelerator (Elekta Synergy) for VMAT with the
repeat delivery of the head and neck plans. The results demonstrated strong performance and
stability of both systems. In our study, we tested the combined reproducibility of setup and
measurement. The mean standard deviations were 0.59% (0.06%–1.27%) for ArcCHECK and
0.44% (0.06%–1.06%) for Delta4, which shows good reproducibility. However, the results
were slightly worse than the reports above because the pass rate altered more significantly as
the setup error increased.
Dosimetric verification in the study indicated that the QA of VMAT assessed by both
ArcCHECK and Delta4 met therapeutic quality requirements. However, it has been noted that
the pass rate of γ analysis for Delta4 was higher than ArcCHECK, referring to the results for
zero setup errors (p = 0.004). The reasons for the differences are as follows. First, ArcCHECK
and Delta4 dosimetry systems have very different spatial locations of diode detectors. Thus,
each system measures a different section of the total dose distribution and samples different
dose gradients (see Fig. 2). Compared to Delta4, dose distributions of ArcCHECK are more
complex. Moreover, ArcCHECK is more sensitive to the dose delivery errors and the angular
discretization effect because of the different diode locations.(37) Second, the dose error normalization was different. To establish the percent dose error normalization value, a different
method was used for each device, given the different arrangement of the detectors. The Delta4
results were normalized at the isocenter, while the ArcCHECK results were normalized to the


22

maximum measured dose in the detector ring. Moreover, the 10% dose cutoff threshold, below
which the voxel is excluded from analysis, may not mean the same on the ArcCHECK diode
surface as it does on the Delta4 diode planes.(37)
In all tests of the setup error simulation, the two 3D diode arrays exhibited extreme sensitivity
to translational and rotational setup errors in all axes for patient-specific QA results of VMAT
plans, while exhibiting strong dependence on the patient-specific plan. In general, we have
observed an impact of the translational setup error on the QA results of complex VMAT plans
and target volumes such as NPC, a cancer contained in the upper and lower neck regions. We
have also observed a marked influence of the rotational setup error on the QA results of VMAT
plans with long target volumes, such as esophageal cancer. In this paper, we tested three cases for
each of the sites of NPC, esophageal, and rectal cancers. Despite the same cancer site amongst
case triplets, a difference in sensitivity to setup errors was observed due to the variation of
patient-specific plans. Letourneau et al.(27) assessed the sensitivity of the prototype ArcCHECK
dosimetry system for phantom setup error after CBCT image-guided setup. Letourneau and
colleagues found specifically that the diodes’ sensitivity to setup error in the RL and AP directions were highly plan-dependent; the direction of the steepest dose gradients for a given plan
did not necessarily correspond with the direction of the phantom setup.
In addition, the results in Figs. 3 and 4 for both sets of setup errors indicate some differences in the order of 3%–4% reduction rates between the negative and positive setup errors.
The differences are mainly due to the following two reasons. Firstly, the residual setup errors
up to 0.5 mm and 0.5° were still present when CBCT and HRTC were used. Secondly, planspecific features along each translational and rotational setup directions were different, such
as the dose gradient orientation.
On the other hand, the respective sensitivities to setup error of ArcCHECK and Delta4 were
not uniform. Though the diode arrays demonstrated similar sensitivity to translational setup
error, ArcCHECK was slightly more sensitive than Delta4 for the gamma criteria 3%/3 mm,
likely measuring a section of the dose distribution with more dose gradients. In addition, compared to the translational setup error, ArcCHECK diodes were more sensitive to the rotational
setup error than Delta4, due to the difference in spatial locations between the two 3D diode
arrays. For the same rotational setup error, the diodes of the ArcCHECK system shift a greater
distance than those of Delta4.
The AAPM TG-142 report recommended that the tolerance of laser localization was 1.5 mm
for IMRT.(32) For both ArcCHECK and Delta4 systems, 1° rotational error could cause an
approximate error of 2 mm on the surface of the phantoms. Therefore, the cumulative effect of
the combined 2 mm translational and 1° rotational errors was evaluated, and the average pass
rates reduction with the 3%/3 mm criteria were 3.8% and 2.4% for ArcCHECK and Delta4 systems, respectively. The cumulative effect for ArcCHECK system was more obvious than Delta4
system mainly due to the higher sensitivity of ArcCHECK to the rotational error. Especially
for NPC tests using ArcCHECK, the cumulative effect was quite obvious; thus, the setup of
ArcCHECK should be more accurate than the other cancer sites. In addition, because of the
difference in the sensitivity between the two systems, their setup accuracy could be different.
As shown in Table 3, in order to achieve less than 3% mean pass rate reduction of VMAT plan
QA with the 3%/3 mm criteria, a setup accuracy of 2 mm/1° and 2 mm/2° are required for
ArcCheck and Delta4 devices, respectively.
V. Conclusions
In this study, both the ArcCHECK and Delta4 diode arrays showed high sensitivity to setup
errors, and sensitivity of both systems is strongly dependent on patient-specific plans. The sensitivity of ArcCHECK to the rotational error was higher than that of Delta4. In order to achieve
less than 3% mean pass rate reduction of VMAT plan QA with the 3%/3 mm criteria, a setup


23

accuracy of 2 mm/1° and 2 mm/2° is required for ArcCheck and Delta4 devices, respectively.
The cumulative effect of the combined 2 mm translational and 1° rotational errors caused
3.8% and 2.4% mean pass rates reduction with 3%/3 mm criteria, respectively, for ArcCHECK
and Delta4 systems. For QA of VMAT plans for NPC using the ArcCHECK system, the setup
should be more accurate.
Acknowledgments
Guangjun Li, Sen Bai, and Nianyong Chen contributed equally to this work. The Delta4 dosimetry
system was provided by Beijing HGPT Technology & Trade Co. Ltd. This work was partially
supported by the National Natural Science Foundation of China (Grant No. 81101697).
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Development of real-time motion verification system using
in-room optical images for respiratory-gated radiotherapy
Yang-Kyun Park,1,2 Tae-geun Son,3 Hwiyoung Kim,2 Jaegi Lee,4
Wonmo Sung,2 Il Han Kim,1,2 Kunwoo Lee,3 Young-bong Bang,5
and Sung-Joon Ye1,2,4,5a

Department of Radiation Oncology,1 Seoul National University Hospital, Seoul;
Interdisciplinary Program in Radiation Applied Life Science,2 Seoul National
University, Seoul; Department of Mechanical and Aerospace Engineering,3 Seoul
National University, Seoul; Program in Biomedical Radiation Sciences,4 Department of
Transdisciplinary Studies, Graduate School of Convergence Science and Technology,
Seoul National University, Seoul; Advanced Institutes of Convergence Technology,5
Seoul National University, Suwon, Korea
sye@snu.ac.kr

Received 5 October, 2012; accepted 15 April, 2013
Phase-based respiratory-gated radiotherapy relies on the reproducibility of patient
breathing during the treatment. To monitor the positional reproducibility of patient
breathing against a 4D CT simulation, we developed a real-time motion verification
system (RMVS) using an optical tracking technology. The system in the treatment
room was integrated with a real-time position management system. To test the
system, an anthropomorphic phantom that was mounted on a motion platform
moved on a programmed breathing pattern and then underwent a 4D CT simulation with RPM. The phase-resolved anterior surface lines were extracted from the
4D CT data to constitute 4D reference lines. In the treatment room, three infrared
reflective markers were attached on the superior, middle, and inferior parts of the
phantom along with the body midline and then RMVS could track those markers
using an optical camera system. The real-time phase information extracted from
RPM was delivered to RMVS via in-house network software. Thus, the real-time
anterior–posterior positions of the markers were simultaneously compared with
the 4D reference lines. The technical feasibility of RMVS was evaluated by repeating the above procedure under several scenarios such as ideal case (with identical
motion parameters between simulation and treatment), cycle change, baseline shift,
displacement change, and breathing type changes (abdominal or chest breathing).
The system capability for operating under irregular breathing was also investigated
using real patient data. The evaluation results showed that RMVS has a competence
to detect phase-matching errors between patient’s motion during the treatment and
4D CT simulation. Thus, we concluded that RMVS could be used as an online
quality assurance tool for phase-based gating treatments.
PACS number: 87.55.Qr
Key words: gated radiotherapy, external marker tracking, quality assurance,
4D CT
I. Introduction
Respiration-induced motion has been a significant challenge in radiotherapy for thoracic and
abdominal tumors.(1) To manage this motion, the respiratory gating technique was introduced
and evaluated in previous studies.(2,3) In this technique, radiation is controlled by a beam delivery
a

Corresponding author: Sung-Joon Ye, 101 Daehak-ro Jongno-gu, Seoul, Korea, 110-744; phone:
(82) (2) 2072 2819; fax: (82) (2) 741 2819; email: sye@snu.ac.kr

25 25

26 Park et al.: Real-time motion verification

26

system within a particular portion of the patient’s breathing cycle (so-called gating window).(4)
The tumor motion only within the gating window is taken into account in both treatment planning and delivery processes. Therefore, with this technique tumor margins can be reduced and,
thus, tumor dose escalation is enabled without compromising normal tissue sparing.(5)
One widely used gating system with external marker-based monitoring is the real-time position management (RPM) system (Varian Medical Systems, Palo Alto, CA). Several studies have
been performed to evaluate efficacy of RPM.(6,7) The RPM system provides two alternative
methods to define the gating window: phase-based gating and amplitude-based gating.(8) It has
been reported that amplitude-based gating results in lower residual motion than phase-based
gating.(7,9) However, in some institutions, phase-based gating is preferred for two reasons:
(1) phase-based gating provides a stable duty cycle, whereas the amplitude-based gating suffers from baseline shifts;(3,9) and (2) some specific CT systems correlate images only in terms
of the respiration phase.(8,10)
In the RPM phase-based gating technique, the reproducibility of respiratory motion (e.g.,
displacement according to the respiratory phase) between simulation and treatment fractions
is essential. However, in routine clinical practice, the RPM system with a phase-based mode
has not provided any solution to quantitatively compare two displacements during the delivery
and the CT simulation. The best way to verify the reproducibility of the respiratory motion is
to use X-ray imaging,(4) which results in excessive radiation exposure if the acquisitions are
performed frequently during treatment.
To verify the reproducibility of the external marker position, several methods using noninvasive optical tracking were proposed. Wong et al.(11) used the ExacTrac system (BrainLAB
AG, Feldkirchen, Germany) to monitor a patient’s abdominal surface positions during the deep
inhalation breath-hold (DIBH) technique. Venkat et al.(12) developed an audiovisual biofeedback
system using a single infrared (IR) reflective marker to improve and verify the reproducibility
of external marker positions between simulation and treatment. On the other hand, Plathow
et al.(13) demonstrated that the correlation between internal tumor motion and external marker
motion was highly dependent on the breathing type such as abdominal breathing and thoracic
breathing. This finding supported the idea that motion monitoring with a single external marker
could not provide sufficient tracking information for tumor motion.(14,15) Therefore, to improve
the internal–external correlation, several studies have proposed multiple external marker tracking(16) or markerless surface monitoring,(17-19) rather than single external marker tracking. For
tracking of multiple markers or a patient’s surface, commercial products such as ExacTrac
and GateRT (VisionRT, London, UK) are available on the market. However, so far no study
has attempted to use 4D CT data as the reference of motion monitoring to check the positional
reproducibility of the multiple external markers or patient’s external surface.
This study aimed to develop a quality assurance technique to quantitatively compare a
patient’s external surface motion between 4D CT simulation and treatment for RPM phase-based
gating. The developed technique involved stereocamera-based optical tracking, surface extraction from 4D CT simulation data, and a phase synchronization method with RPM. Phantom
experiments were performed using a programmable respiratory motion platform to evaluate
the performance of our system.
II. Materials and Methods
A. System overview
A schematic illustration of the proposed quality assurance method for RPM phase-based gating
is shown in Fig. 1. A conventional RPM-based gating technique uses a single IR camera, an IR
reflective marker bock, and a workstation connected to the beam delivery system. In order to
acquire real-time images of patient surface motion according to the signals of the RPM system,
the developed system consisted of two wall-mounted stereocameras and multiple IR markers


27

Fig. 1. A schematic representation for the developed quality assurance method using real-time motion verification system
(RMVS).

on the patient’s anterior surface, and a phase synchronization program (PSP). The motion error
calculator (MEC) was programmed to quantitatively compare acquired real-time images of the
multiple IR markers with phase-matched reference images extracted from 4D CT simulation
data. Details of the developed system are given in the Materials & Methods Sections C, D, E
and F below.
B. Anthropomorphic phantom and motion platforms
An anthropomorphic phantom (Alderson Research Laboratories, New York, NY) and two different types of motion platform (A and B) were used to evaluate the system. Platform A was
composed of an acrylic stage and two linear actuators that were approximately 40 cm apart along
the superior–inferior direction. The actuators oscillated the stage between two positions in the
anterior–posterior direction. The two oscillation positions and the cycle can be programmed.
In normal mode, these two actuators were synchronized to keep the stage horizontal. However,
by fixing one actuator in a certain position, the motion platform can also simulate asymmetric
motions, such as abdominal and chest breathing. On the other hand, platform B is a fully programmable motion platform which can simulate arbitrary motions in 3D space, such as patient
respiratory data. The platform is composed of polycarbonate panels and four stepping motors
to simulate 3D tumor and external marker motion. The positional accuracy of the platform had
been evaluated by using a high-resolution laser sensor (RF603, RIFTEK, Minsk, Belarus), of
which spatial resolution is 0.03 mm and temporal resolution is 0.01 ms. It was determined to
be 0.2 mm.(20) In this study, most of the phantom experiments were performed with platform A
because of its simplicity in operation and unique feature of asymmetric motions for breathing
type change simulations. Platform B was used to evaluate the accuracy of the optical tracking
system and to simulate real patient respiratory data.



28

C. Stereocamera system and IR marker tracking
An IR-based stereocamera enabled us to monitor the phantom’s respiratory motion in real time.
The system hardware consisted of two charge-coupled device (CCD) cameras (HVR2300C, Hi
Vision System, Korea) with universal serial bus (USB) 2.0 interface, 2 IR filters (B+W 092,
Schneider-Kreuznach, Bad Kreuznach, Germany), multiple IR light-emitting diodes (LEDs), and
a personal computer (PC) with a 2.8 GHz central processing unit (CPU). A custom-fabricated
frame housing the cameras, IR filters, and IR LEDs was mounted on the inferior wall of the
treatment room. The stereocamera system was calibrated with a checkerboard template and free
software (Camera Calibration Toolbox, Imperial College, London, UK). The calibration procedures were performed by following Zhang’s method.(21) Three IR reflective markers (Scotchlite
154 TM 3000X, 3M, St. Paul, MN) having a diameter of 6 mm were attached on the phantom
surface along the body midline, even though the system can track multiple external markers
simultaneously independent of their positions and number. In our previous studies using the
same marker tracking method, the tracking accuracy was found to be 0.4 ± 0.4 mm and 0.2 ±
0.4 mm for 3D and vertical direction, respectively.(22,23)
D. Extraction of 4D CT-based reference lines
Phase-resolved anterior body midlines were extracted from the 4D CT simulation data and used
as vertical displacement references denoted as “4D reference lines”. A workflow to obtain the
4D reference lines is shown in Fig. 2. Ten phase image sets of the phantom were acquired from
a 4D CT scanner (Big Bore Brilliance, Philips Medical Systems, Bothell, WA) equipped with
the RPM system. The acquired images were transferred to a treatment planning system (Eclipse,
Varian Medical Systems, Palo Alto, CA), and external body surfaces were automatically contoured in the system by using a CT number threshold of -450 HU. These body contours were
then exported into DICOM-RT structure (RS) files. Using in-house DICOM processing software,
ten sets of anterior surface midlines tagged by unique respiratory phase values were extracted

Fig. 2. A flow chart demonstrating how to generate the 4D reference lines from 4D CT simulation data.


29

from the RS files. The initially generated ten sets of body midlines were linearly interpolated
to create 100 sets of data so that each line was assigned integer phase values ranging from 0
to 99. These 100 body midlines were defined as “4D reference lines” in this study. Finally, the
complete sets of the 4D reference lines were exported to a text file.
E. Respiratory phase synchronization with RPM
In RPM-phase based gating, beam-on and -off are controlled by the RPM real-time calculated
phase information. It has been reported that the real-time calculated phase is error-prone and
the retrospective phase calculation using a RPM log file (called “vxp file”) is more accurate.(8)
However, despite of its imperfectness, the real-time calculated phase was our choice for the
online verification for detecting any errors by monitoring in real time the relationship between
phase and displacement. Therefore, to compare current positions of the tracked markers with
the reference line at the same phase taken from the 100 sets of 4D reference lines, the phase
synchronization program (PSP) was developed to provide this phase information in real time.
The PSP was installed on the RPM workstation and operated simultaneously with RPM software
version 1.7.5. As the RPM software provided a clock-shaped interface for displaying respiratory
phase values, the PSP set the ROI at the center of the “clock” and processed the image of the
ROI in real time, as shown in Fig. 3. Finally, the phase value calculated by the software was
then transferred to the RMVS through a LAN.

Fig. 3. An illustration of the procedure to acquire the respiratory phase value from the RPM workstation. The PSP processed
the ROI set on the “phase clock” and transferred the calculated phase value to the RMVS over a network in real time.

F. Motion error calculator
The motion error calculator (MEC) is a software module integrated into the RMVS. Three types
of input data were required to run the MEC. The first data were the 3D positions of the external
markers tracked by the stereocamera system in real time, the second was the 4D reference lines,
and the third was the current phase value acquired from the PSP in real time. First, the MEC


30

imports a text file containing the 100 sets of 4D reference lines. Secondly, current 3D position
data of the tracked external markers were acquired by the stereocamera system in real time and
simultaneously transferred into the MEC. Thirdly, the MEC received the RPM-generated current
phase information from PSP and dynamically selected the reference line corresponding to the
current phase value. Finally, displacement error between the external marker positions and the
reference line was calculated and displayed on an operator’s monitor screen in real time. Even
though the stereocamera system can track 3D coordinates of the markers, only the z-axis value
was used to calculate errors. The x-axis value was not used because the 4D reference line was
assumed to be a body midline having the x-axis value of 0. On the other hand, the y-axis value
was used to find the corresponding projected points of the tracked marker on the 4D reference
line. The MEC calculated singed and absolute errors, which are given as follows:
Signed error(Δi) = Pi(t)z − z4DCT( (t), Pi(t)y

(1)

Absolute error = | Δi |
(2)
where i is the marker index, Δ is the signed error for ith marker, Pi(t)y and Pi(t)z are the y and z
component of the 3D position of ith marker at time t, respectively. z4DCT is the anterior–posterior
position of the 4D reference lines, and ϕ (t) is the integer value of the respiratory phase at time
t. The average error for all markers can be defined as an overall error as follows:

1 N

(3)
Overall error (signed) =
Δ
N i=1 i

Σ

1 N

(4)
Overall error (absolute) =
|Δ |
N i=1 i

Σ

where N is the number of markers. If the motion error of individual markers exceeds a userdefined tolerance, the MEC can display a warning message to the operator.
The proposed point-to-line matching method allowed us to remove the necessity of an external marker during 4D CT scan. Even though a corresponding projected point on the reference
line is not exactly matched with a tracked point (especially when considering deformation of
patient surface), the proposed error metrics can provide quantitative error values when there
are some problems in positional reproducibility between simulation and treatment.
G. Phantom evaluation
G.1 Evaluation of system accuracy and precision
Prior to the phantom experiments with various motion parameters, the overall accuracy and
precision of the system was evaluated. Even though the accuracy of the stereocamera system
and platform B had been evaluated in our previous studies, a test experiment with an irregular
breathing pattern was performed by using both stereocamera and platform B. Three IR markers
on the RANDO phantom (The Phantom Laboratory, Salem, NY) were tracked in this experiment.
On the other hand, to evaluate the accuracy of platform A, a test motion with a displacement of
31 mm and a cycle of 3.1 s was simulated with four equidistant markers and solid water slabs
(one more marker and flat surface phantom in this initial evaluation). The motion parameters
were then compared with the tracking data acquired by the stereocamera system. Using the
test motion, the accuracy of RPM and 4D CT was also evaluated for the comparison purpose.


31

Finally, the accuracy of phase synchronization was evaluated by comparing the respiratory
phase data of RPM and RMVS to confirm that both systems have the same phase value when
the same positional data is given.
G.2 Evaluation of interfractional changes in breathing motion
Three IR reflective markers in an interval of 6 cm were attached on the RANDO phantom
surface (superior, middle, and inferior markers), and a RPM marker block was placed beside
the middle marker. The phantom (mounted on platform A) oscillated regularly according to a
reference motion pattern (“normal breathing” hereafter, for convenience) having a displacement of 20 mm, and a cycle of 3.1 s for all three markers. The phantom then underwent a 4D
CT scan and 4D reference lines were prepared as described in Section D above. In a treatment
room, various motion scenarios were simulated to test whether our system could detect the
abnormalities when the motion pattern was changed from that of CT simulation. Figure 4 shows
the experimental setup in the treatment room. Five different motion scenarios were designed to
simulate possible clinical situations. They included ideal (identical motion parameters between
CT simulation and treatment), cycle change, baseline shift, displacement change, and breathing type change cases. Figure 5 shows the five motion scenarios graphically. In the ideal case,
motion errors were expected to be zero. Even in the cycle change scenario, no motion errors
were expected because the RPM phase-based system cannot account for any systematic changes
in the breathing cycle. In contrast, the baseline shift, displacement change, and breathing type
change scenarios were expected to exhibit significant motion errors in our developed system.
In total, eight sets of phantom experiments, the parameters of which are listed in Table 1, were
performed in the treatment room. Each experiment took 100 s. Mean signed errors (MSEs) and
mean absolute errors (MAEs) between external marker positions and 4D reference lines were
evaluated for each experiment.

Fig. 4. Experimental setup for system evaluation on a motion phantom. Two types of motion platform were used for the
simulations of breathing type changes (platform A) and irregular breathing (platform B).



32

Fig. 5. Five motion scenarios were taken into account in the phantom experiments: (a) ideal, (b) cycle change, (c) baseline
shift, (d) displacement change, and (e) breathing type change.
Table 1. Various motion parameters of the phantom experiments.

Scenario

Experiment
No.
Description

Displacement (mm)
Baseline
(mm)
Superior
Middle
Inferior

Cycle
(s)

Ideal case

1

Ideal case

0

20

20

20

3.1

Cycle change

2

Longer cycle

0

20

20

20

4.7

Baseline shift

3
4

Offset -5
Offset +5

-5
5

20
20

20
20

20
20

3.1
3.1

Smaller
5
0
10
10
10
3.1

displacement
Displacement
change

Larger
6
0
40
40
40
3.1

displacement

Abdominal
7
0
14.6
20
25.4
3.1
Breathing type

breathing

change
Chest
8
0
25.4
20
14.6
3.1

breathing



33

G.3 Evaluation of intrafractional changes in breathing motion
Evaluation for intrafractional motion changes was simply performed by using a combination
of phantom motion sequences including normal breathing, abdominal breathing, chest breathing, offset -5 mm, and larger displacement patterns. Approximately 60 s was allotted for each
sequence. Between any two consecutive sequences, approximately 5 s of motor initialization
time was inserted. The most important difference from the interfractional change experiment
was that a respiratory model initially established by the RPM system was gradually changed
during the experiment, resulting in a beam interruption signal produced by the RPM predictive
filter. Therefore, the duty cycle was expected to be changed from the initial value (30%). The
threshold of the predictive filter was set to be a default value.(20)
G.4 Evaluation for real patient respiratory motion
To evaluate the capabilities of our system in more realistic situation, additional experiments were
performed using the motion platform B in conjunction with two patients’ respiratory data. The
input data for the platform were made by processing RPM log files that were acquired during
the 4D CT simulation and one treatment session of each patient. It should be noted that only
anterior–posterior movement was simulated in this experiment because the RPM system could
support vertical tracking only. The RANDO phantom mounted on the platform underwent 4D
CT scans while the platform reproduced the patient’s simulation session. Finally, the RMVS
was tested while the phantom reproduced the patient respiratory motion in treatment room by
the same procedures as described above.
III. Results
A. Compatibility between RMVS and RPM
The phantom experiment demonstrated that all of the functions of the developed system (i.e.,
RMVS) were successfully executed. Without any interference the system worked together with
RPM as intended. The phase values generated by RPM were successfully transferred to the
system with a minimum time delay of approximately 60 ms. A possible source of time delay
could be the TCP/IP communication between RPM and RMVS. As the time delay existed
consistently, a constant phase shift value was applied to MEC.
B. Evaluation results for system accuracy and precision
As seen in Fig. 6, the respiratory curve obtained from the stereocamera system perfectly coincided with that of the input data for platform B. The mean absolute error for 300 s of simulation
calculated for a single marker was 0.2 ± 0.2 mm, which validated our previous findings on the
accuracy of the system. Table 2 summarized the results of accuracy evaluation for platform A
and 4D CT. It was demonstrated that platform A also moved accurately as programmed when
compared to the tracking result of the stereocamera system. However, the 4D CT showed a
slight underestimate of the displacement, which would be propagated to the error in our system.
Details on inaccuracy of 4D CT imaging follows in the Discussion Section below.
In phase synchronization, phase values of RMVS coincided well with those of the RPM
system, which implied that the PSP worked well with both systems (Fig. 7). The mean absolute
error for 180 s calculated for all markers was 1.4 ± 3.5%. It should be noted that a constant
phase offset had been applied to the RMVS to account for the systematic time delay.



34

Fig. 6. The programmed motion of platform B coincided perfectly with the marker motion obtained from the stereocamera
system, confirming the accuracy of the systems.
Table 2. Motion parameters of the reference motion independently measured by stereocamera system, 4D CT, and
RPM.

Programmed
tereocameraa
S
4D CTb
RPMa,c

Displacement
(mm)

Mid position
(mm)

Cycle
(s)

31.0
30.9±0.1
30.3±0.4
31.7±0.0

22.5
22.3±0.1
22.6±0.1
23.2±0.0

3.1
3.1±0.1
N/A
3.1±0.0

a Mean

value of ten cycles and four marker positions.
value of four positions corresponding to each marker position on 4D CT.
c The baseline was corrected by using a reference position.
RPM = real-time position management system; 4D CT = FOUR-dimensional computed tomography.
b Mean

Fig. 7. Phase comparison between the RPM and the RMVS. Good agreement in two phase values demonstrated that the
phase synchronization was successfully carried out and the selected value for the constant phase offset was optimal.



35

C. Phantom evaluation results
Figure 8 shows the 4D CT images of the phantom with the normal breathing. For the comparison
purpose, 4D CT images from abdominal and chest breathing motions are also illustrated. It was
observed that even though the breathing type changed, the movement of the central region of
the phantom was similar to that of the reference breathing, which would be a potential pitfall
of single marker-based monitoring.

Fig. 8. Phantom sagittal images from 4D CT sets acquired during reference motion, abdominal breathing, and chest
breathing. The dotted line was set parallel to the middle surface point on the end-of-exhale phase image.

C.1 Evaluation of interfractional changes in breathing motion
From these experiments, it was confirmed that RPM phase-based gating didn’t provide any
warning or interruption signals, even if there were significant interfractional changes in respiratory motion pattern. This supports the usefulness of our system as a respiratory QA tool. For
each experiment, MSEs and MAEs between external marker positions and 4D reference lines
were calculated. These are summarized in Tables 3 and 4 and graphically shown in Fig. 9.
MSE mode was useful to detect baseline shifts with their direction (overall errors of -5.0 ± 0.9
and 5.1 ± 0.9 mm for experiments 3 and 4, respectively). In MAE mode, the system detected
errors even in the ideal and cycle change cases (overall errors of 0.8 ± 0.5, and 0.7 ± 0.5 mm for
experiments 1 and 2, respectively), which turned out to be the systematic error in our system.
Relatively large errors and deviations were observed in the displacement change experiments
compared to the ideal case (overall errors of 2.7 ± 1.2, and 5.9 ± 3.6 for experiments 5, and 6,
respectively). For breathing type change cases, the errors of the middle marker were relatively
small (0.9 ± 0.7 and 1.5 ± 1.0 for experiment 7 and 8, respectively), compared to those of the
inferior and superior markers (inferior marker error of 2.5 ± 1.7 for experiment 7 and superior


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