Presentation IS-4003, A Cloud Based Medical Imaging Platform Using APU, by Kovey Kovalan at the AMD Developer Summit (APU13) Nov. 11-13, 2013.

1. VOXCELL®
-­‐
SUPERCOMPUTING
CLOUD
MEDICAL
IMAGING
PLATFORM
USING
GPU/APU
KOVEY
KOVALAN
&
SANKET
GAJJAR
KJAYA
MEDICAL

2. BUSINESS
CASE:
Opportunity
to
Improve
PaDent
Care

3. OPPORTUNITY
TO
IMPROVE
PATIENT
CARE
MEDICAL
IMAGING
MARKET
! US
spends
$100B
on
520,500,000
medical
scans
!
$3.5B
on
soTware
‒ RIS
CVIS
PACS
!
$1.8B
in
2010
!
3.5%
CAGR
‒ Image
Analysis
!
$1.7B
in
2012
!
7.1%
CAGR
! Why
Scan?
!
early
detecDon
!
survive
‒ e.g.
13M
cancer
paDents
alive
in
2012
! 30,000
radiologists
!
10
minutes/scan
!
limits
diagnosDc
outcome
! Survival
rate
could
be
increased
through
Dmely
physicians
and
paDent
interacDon
! Physicians
and
paDents
need
enhanced
visualizaDons,
computer
aided
diagnosis,
and
social
media
! KJAYA
Medical
has
a
soluDon
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4. MEDICAL
IMAGE
MANAGEMENT
IS
CURRENTLY
ON
PREMISES
PICTURE
ARCHIVING
AND
COMMUNICATION
SYSTEMS
(PACS)
Film
Warehouse
Digital
Warehouse
Onsite
PACS
Specialized
WorkstaDon
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5. CROWDED
MARKET
–
OLDER
TECHNOLOGY
CURRENT
PACS
MARKET
IS
FRAGMENTED
Onsite
PACS
Blue
Ocean
Markets
Cloud
Social
Media
Third
GeneraDon
PACS
Technology
Current
Technology
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6. CURRENT
CLOUD
PACS
MARKET
-­‐
LESS
THAN
1%
FOCUSED
ON
NON-­‐DIAGNOSTIC
USE
OF
IMAGE
SHARING
AND
OFFSITE
BACKUP
13%
3%
2%
1%
Cloud
Current
Cloud
accounts
about
1%
of
the
market
• $56m
in
2010
expected
to
grow
27%
CAGR
to
2018
• Mostly
in
archival
and
image
sharing
• Third
generaDon
PACS
on
cloud
in
its
infancy
81%
Onsite
Challenges
for
cloud
PACS
• Access
speeds
• DiagnosDc
quality
• Tools
to
manipulate
data
in
real
Dme
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7. PACS
FUTURE
ENTERPRISE
IMAGING
CLOUD
Onsite
PACS
Cloud
based
Enterprise
PACS
Third
generaMon
PACS
requirements
Current
RIS/PACS
• 91%
penetraDon
• 52%
older
than
5
years
• 21%
plan
to
replace
in
12
months
Cardiology
:
60%
have
no
PACS
Pathology:
90%
have
no
PACS
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• Enterprise
PACS
–
PaDent
centered,
mulD-­‐departmental,
integrated
image
management
plalorm
• Cloud
based
–
Strong
ROI,
distributed
mulD-­‐site
access
at
speeds
equal
to
on
site
PACS
• Image/report
sharing
with
referring
physicians
and
paDents
on
demand
• Higher
levels
of
funcDonality
-­‐
advanced
visualizaDon,
computer
aided
diagnosis
• IntegraDon
with
EHRs,
HIEs

8. VoXcell®
Cloud
On-­‐Demand
Cloud
CompuMng
for
Medical
Imaging

9. VOXCELL
DEMO
.
! Bullet
‒ Sub-­‐bullet
‒ TerDary
Bullet
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10. VoXcell®
Cloud
Technical
Detail

11. DIFFERENTIATED
APPROACH
:
GPU
CLOUD
GPU
CLOUD
BENEFITS
GPU
:
1100
GFLOPS
Real-­‐Dme
diagnosDc
quality
visualizaDons
•
On-­‐demand
and
real-­‐Dme
radiology
•
IntuiDve
results
for
ordering
physicians
•
Connect
with
paDents
CPU
:
90
GFLOPS
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Faster
and
Affordable
CAD
and
‘Big
Data’
AnalyDcs
•
Improve
accuracy
•
Less
radiaDon
to
paDents
by
reducing
unnecessary
use
of
imaging
•
Streamline
healthcare
and
reduce
costs

12. DIFFERENTIATED
APPROACH
:
GPU
CLOUD
HIGH
DEFINITION
VISUALIZATION
" CPU
Ray
CasDng
(Compromise
Quality
for
Speed)
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" VoXcell
GPU
Pre-­‐integrated
Texturing

13. DIFFERENTIATED
APPROACH
:
GPU
CLOUD
HIGH
DEFINITION
VISUALIZATION
" CPU
Ray
CasDng
(Compromise
Quality
for
Speed)
" VoXcell
GPU
Pre-­‐integrated
Texturing
" Real-­‐Dme
performance
requires
early
ray
terminaDon
once
opacity
is
reached
(25%)
!
results
in
hard
plasDc
looking
surfaces.
Transparent
surfaces
degrades
performance.
" Real-­‐Dme
performance
achieved
through
texture
mapping
polygons
!
results
in
soTer,
more
realisDc
surfaces
that
includes
interior
points.
Enables
transparent
surfaces
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14. DIFFERENTIATED
APPROACH
:
GPU
CLOUD
PREDICTIVE
INTELLIGENT
STREAMING
OVERCOMES
LARGE
DATA
ACCESS
SPEED
AND
LATENCY
OVER
INTERNET
" Use
GPU
to
manipulate
GB
of
paDent
data
remotely
without
transmiqng
data
to
end
user
" Access
visualizaDons
on
any
device
on-­‐demand
and
real-­‐Dme
" Streaming
visualizaDons
done
by
predicDng
next
frames
" Fast
FPS
from
GPU
enable
discarding
incorrectly
predicted
frames
and
generaDng
new
ones
" Predicted
frames
are
buffered
to
client
overcoming
latency
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15. DIFFERENTIATED
APPROACH
:
GPU
CLOUD
ARTIFICIAL
INTELLIGENCE
LEADS
TO
INTELLIGENT
VISUALIZATIONS®
" Pasern
RecogniDon
Using
ArDficial
Neural
Network
" HeurisDc
Search
Using
GeneDc
Algorithm
CPU
:
500s
GPU
:
10s
15K
Paserns
" Uses:
" Computer
Aided
Diagnosis
through
IntuiDve
VisualizaDons
" Cancer
or
Tumor
DetecDon
" SegmenDng
Body
Parts
" Intelligent
VisualizaDon®
R&D
ParDally
Funded
by
NaDonal
Science
FoundaDon
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GPU
is
3000x
over
CPU

16. IP
SUMMARY
:
SUPERCOMPUTING
CLOUD
COMPARISON
Legacy
PACS
ConvenMonal
Cloud
KJAYA’s
SupercompuMng
Cloud
PlaVorm
Transmits
raw
scans
to
end
users
Streams
visualizaDon
on
demand
Compromises
raw
scan
for
faster
transmission
•
Not
fit
for
diagnosis
•
Computer
Aided
Diagnosis
(CAD)
inaccuracy
HD
quality
without
transmiqng
raw
scan
•
FDA
510K
cleared
primary
diagnosDc
use
•
ArDficial
Intelligence
CAD
on
gaming
technology
Storage
servers
cannot
manipulate
or
analyze
large
data
–
not
scalable
Graphics
processors
for
large
scan
manipulaDon
and
analyDcs
Powerful
PC
workstaDon
to
run
clinical
app
Clinical
apps
run
on
any
device
CAD
lack
breadth
of
data
and
processing
power
CAD
on
vast
historical
and
powerful
processors
using
arDficial
intelligence
algorithms
on
GPU
Tools
limited
by
vendor
capability
Flexible
toolkit
>
App
store
for
medical
imaging
No
barriers
to
entry
Filed
patents
since
2009
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17. IP
:
PATENT
PENDING
PLATFORM
PATENT
APPLICATIONS
I. Secure
Cloud
SupercompuMng
based
Medical
Imaging
System
PCT/US2010/036355
for
“Method
and
System
for
Fast
Access
to
Advanced
VisualizaDon
of
Medical
Scans
Using
a
Dedicated
Web
Portal”
II. Hybrid
Cloud
for
Medical
Imaging
61/514,295
for
“Method
and
System
for
Fast
Access
to
Advanced
VisualizaDon
of
Medical
Scans
Using
Hybrid
Local
and
Dedicated
Web
Portal”
III. A
Scalable
Architecture
to
handle
large
amounts
of
data
and
users
11/672,581
for
"Method
and
System
for
Processing
a
Volume
VisualizaDon
Dataset
IV. ArMficial
Intelligence
on
GPU
for
3D
and
Computer
Aided
DetecMon
PCT/US11/45047
for
“AdapDve
VisualizaDon
for
Direct
Physician
Use”
V. Patent
Firm:
DeLio
&
Peterson,
New
Haven,
CT
(near
Yale
University)
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18. COMPETITIVE
LANDSCAPE
PACS
RIS
Intelligent
VisualizaDons®
(AI)
3D
on
any
PC
4D
on
any
PC
Image
Sharing
Archive
&
Disaster
Recovery
DiagnosDc
Quality
over
Internet
FDA
Cleared
PredicDve
Streaming
(not
downloading)
MulD
data
center
SupercompuDng
plalorm
.
KJAYA
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
CareStream
Y
Y
N
N
N
?
Y
N
Y
N
Y
N
TeraRecon*1
N
N
N
Y
N
N
N
?
Y
N
N
N
Shina*1
on
Amazon
Cloud
N
N
N
Y
N
N
N
N
Y
N
Y
N
vRAD
Y
N
N
N
N
N
Y
N
Y
N
Y
N
DICOM
Grid
Y
N
N
N
N
Y
Y
N
N
N
N
N
LifeImage*1
N
N
N
N
N
Y
N
N
N
N
N
N
AccelaRad
Y
N
N
N
N
Y
N
N
N
N
N
N
InsiteOne*1
N
N
N
N
N
N
Y
N
N
N
Y
N
BRIT
Y
Y
N
N
N
N
N
N
Y
N
N
N
MedWeb
Y
Y
N
N
N
N
N
N
Y
N
N
N
secureRAD
Y
N
N
N
N
N
?
N
?
N
N
N
ScImage
Y
N
N
N
N
N
?
N
?
N
N
N
NCS
Y
Y
N
N
N
N
?
N
Y
N
N
N
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19. INDUSTRY
INSIDER
RECOGNITIONS
"Most
cloud-­‐compuDng
services
don’t
offer
diagnosDc-­‐quality
images,
and
the
ones
that
do
typically
feature
lag
Dme,
slowing
the
process.
The
ability
to
quickly
process
and
transmit
diagnosDc-­‐level
images
sets
KJAYA
apart
in
this
regard."
Christopher
Gaerig,
Imaging
Economics
“Today’s
medical
environment
demands
efficient,
cost-­‐effecDve
workflow
and
VoXcell
delivers
the
tools
that
can
empower
faster
and
more
accurate
diagnosis
within
an
extremely
affordable
fee
structure."
Frost
&
Sullivan
“These
are
ambiDous
companies,
with
highly
innovaDve
products
and
business
development
strategies
that
will
enable
them
to
carve
out
a
place
in
global
markets....”
KJAYA’s
INVESTOR:
Enterprise
Ireland
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20. VOXCELL
CLOUD
IMPLEMENTATION

21. CLUSTER
COMPONENTS
BIG
DATA
CLUSTER
A
Node
24
TB
Storage
5
TFLOPS
AMD
GPU
2
CPU
Up
to
192GB
Memory
90,000
IOPS
Two
Nodes
48
TB
Storage
2
AMD
GPU
(10,000
GFLOPS)
4
CPU
(360
GFLOPS)
Up
to
384GB
Memory
180,000
IOPS
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22. CONTENT
HEADER
CONTENT
SUBHEADER
! Bullet
‒ Sub-­‐bullet
‒ TerDary
Bullet
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23. APU:
Natural
Progression

24. ARCHITECTURAL
COMPARISON
" CPU+GPU=APU
CPU
VERSUS
HYBRID
CLOUD
ConvenDonal
DiagnosMc
KJAYA’s
VoXcell®
Cloud
Non-­‐
DiagnosMc
" Not
on
" On
Access
demand
demand
DiagnosMc
" On
demand
Cloud
Cloud
CPU
Process
CPU
GPU
Data
RelaDonal
Database
Storage
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Big
Data
Clusters

25. WHY
APU?
REDUCED
POWER
CONSUMPTION
CPU
GPU
" 5A
" Mostly
dissipated
as
heat
! VS
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26. WHY
APU?
MANAGE
EVER-­‐EXPANDING
VOLUMES
OF
MEDICAL
IMAGING
DATA
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27. WHY
APU
WITH
HSA?
HETEROGENEOUS
UNIFORM
MEMORY
ACCESS
(HUMA)
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28. HUMA
USAGE
IN
GPU
BASED
VOLUME
RENDERING
PRE-­‐COMPUTED
CLASSIFICATION
VOLUME
" Intensity
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" {Bone,
Tissue,
Air}

29. GPU
BASED
MULTIDIMENSIONAL
TRANFER
FUNCTION
VOLUME
RENDERING
USING
PRE-­‐COMPUTED
CLASSIFICATION
VOLUME
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30. DISCLAIMER
&
ATTRIBUTION
The
informaDon
presented
in
this
document
is
for
informaDonal
purposes
only
and
may
contain
technical
inaccuracies,
omissions
and
typographical
errors.
The
informaDon
contained
herein
is
subject
to
change
and
may
be
rendered
inaccurate
for
many
reasons,
including
but
not
limited
to
product
and
roadmap
changes,
component
and
motherboard
version
changes,
new
model
and/or
product
releases,
product
differences
between
differing
manufacturers,
soTware
changes,
BIOS
flashes,
firmware
upgrades,
or
the
like.
AMD
assumes
no
obligaDon
to
update
or
otherwise
correct
or
revise
this
informaDon.
However,
AMD
reserves
the
right
to
revise
this
informaDon
and
to
make
changes
from
Dme
to
Dme
to
the
content
hereof
without
obligaDon
of
AMD
to
noDfy
any
person
of
such
revisions
or
changes.
AMD
MAKES
NO
REPRESENTATIONS
OR
WARRANTIES
WITH
RESPECT
TO
THE
CONTENTS
HEREOF
AND
ASSUMES
NO
RESPONSIBILITY
FOR
ANY
INACCURACIES,
ERRORS
OR
OMISSIONS
THAT
MAY
APPEAR
IN
THIS
INFORMATION.
AMD
SPECIFICALLY
DISCLAIMS
ANY
IMPLIED
WARRANTIES
OF
MERCHANTABILITY
OR
FITNESS
FOR
ANY
PARTICULAR
PURPOSE.
IN
NO
EVENT
WILL
AMD
BE
LIABLE
TO
ANY
PERSON
FOR
ANY
DIRECT,
INDIRECT,
SPECIAL
OR
OTHER
CONSEQUENTIAL
DAMAGES
ARISING
FROM
THE
USE
OF
ANY
INFORMATION
CONTAINED
HEREIN,
EVEN
IF
AMD
IS
EXPRESSLY
ADVISED
OF
THE
POSSIBILITY
OF
SUCH
DAMAGES.
ATTRIBUTION
©
2013
Advanced
Micro
Devices,
Inc.
All
rights
reserved.
AMD,
the
AMD
Arrow
logo
and
combinaDons
thereof
are
trademarks
of
Advanced
Micro
Devices,
Inc.
in
the
United
States
and/or
other
jurisdicDons.
SPEC
is
a
registered
trademark
of
the
Standard
Performance
EvaluaDon
CorporaDon
(SPEC).
Other
names
are
for
informaDonal
purposes
only
and
may
be
trademarks
of
their
respecDve
owners.
30
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