Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia
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Subject motion is a major challenge in functional magnetic resonance imaging studies (fMRI) of the fetal brain and placenta during maternal hyperoxia. We propose a motion correction and volume outlier rejection method for the correction of severe motion artifacts in both fetal brain and placenta. The method is optimized to the experimental design by processing different phases of acquisition separately. It also automatically excludes high-motion volumes and all the missing data are regressed from ROI-averaged signals. The results demonstrate that the proposed method is effective in enhancing motion correction in fetal fMRI without large data loss, compared to traditional motion correction methods.
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Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia
- 1. Robust Motion Correction and Outlier Rejection of in vivo Functional MR Images of the Fetal Brain and Placenta during Maternal Hyperoxia Paper 9417-23 Wonsang You, Ahmed Serag, Iordanis E. Evangelou, Nickie Niforatos-Andescavage, Catherine Limperopoulos The SPIE Medical Imaging conference Conference 9417 Biomedical Applications in Molecular, Structural, and Functional Imaging Session 5: fMRI 25 February 2015 at 2:40 - 3:00 PM Children's National Medical Center, George Washington University (United States) 1
- 2. Background : Placenta The placenta plays a critical role for oxygenation of the fetus. The placenta provides maternal-fetal transit of essential metabolites and clearance of toxic substances. 2 There is a paucity of tools available to reliably assess in vivo placental health and function. Existing clinical tools for placental assessment remain insensitive in predicting and assessing placental well-being.
- 3. Background: Hyperoxia Study Changes in fetal brain and placental oxygenation during maternal hyperoxia have been explored using fMRI. BOLD signal is significantly degraded by diverse factors – fetal motion, respiration, cardiac pulsation, amniotic fluid. Available tools do not successfully mitigate these factors. Objective : to develop a robust preprocessing pipeline dedicated to the fetal brain and placenta. 3
- 4. Paradigm 4 Baseline 2 min 100% Oxygen supply (Hyperoxia) 4 min Return to baseline 3:36 min
- 5. Methods 5 Regions of interest (ROIs) Mean signals over an ROI Time points Hyperoxia Placenta Brain 360 340 300 320 280 240 260 50 100 150 200 250 AveragedBOLDsignal Placenta Brain
- 6. Challenge : Fetal Motion 6 FMRI data are significantly affected by fetal movement. Global motion correction (GMC) Local motion correction (LMC) Volume outlier rejection (VOR) Missing data recovery (MDR) The proposed preprocessing pipeline Bias field correction (BFC) Brain Placenta 1 2 3 4
- 7. Design-optimized Motion Correction 7 Global motion correction Local motion correction Fetal motion varies over phases and ROIs. 1 2 Motion correction separately in each ROI. Motion correction separately in each phase. Brain Placenta Baseline Hyperoxia Return to baseline time …… Each volume is registered to phase-specific t-mean volume. Mean volume Field of view (FOV) is restricted to dilated ROI.
- 8. ProbabilisticVolume Outlier Rejection 8 Typical motion correction is not successful for some volumes with large motion. Wrongly registered volumes can be detected based on the outlier probability of BOLD signal. Large motion Small motion Large motion leads to large number of voxels in non- overlapping part. A non-overlapping voxel is likely to have an outlier in its BOLD signal. 𝑃 𝑂 = 𝑃 𝑂 𝐵 𝑃 𝐵 + 𝑃 𝑂 𝐹 𝑃(𝐹) 𝑃 𝑂 𝐵 high 𝑚 ∝ 𝑃 𝐵 Bayes rule → used as the Volume Outlier Score (VOS) A B C Outlier score can be obtained by using the Bayes rule.
- 9. Outlier Rejection and Missing Data Recovery 9 Volume index Intensity Augmented signal Data imputation by regression and Gaussian uncertainty Thresholding Volume outlier score Detecting outlier volumes by thresholding the volume outlier scores Outlier volumes Hyperoxia
- 10. Results : Outlier Rejection 10 Before volume outlier rejection After volume outlier rejection 0 • Median absolute residual variation (MARV) was reduced after volume outlier rejection. • It means most voxels have less noisy BOLD signals. 20 10 0 30 40 50 60 70 20 10 40 60 70 Less noise
- 11. Results : Motion Correction 11 0 2 4 6 8 10 12 FLIRT Proposed • The number of outliers in structural similarity between each volume and template was reduced. • The number of temporal outliers in ROI-averaged time series was reduced. 2 4 6 8 Raw GMC LMC VOR
- 12. Applications 12 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 25 27 29 31 33 35 37 39 Gestational week Correlation analyses after applying the preprocessing pipeline. Demonstrated linearly increasing connectivity between the placenta and fetal brain.
- 13. Conclusion 1 Design-optimized multi-stage motion correction Effective for stimulus-based fMRI of the moving fetus. Volume outlier rejection and missing data recovery Imperfect motion correction can be compensated by automatic outlier rejection and statistical data imputation. Effective for imaging data whose subject motion cannot be well corrected. 13
- 14. Conclusion 2 Our work offers important technical advances for reliable preprocessing of stimulus-based fMRI studies in the living fetus. This work lays the foundation for – non-invasive functional assessment of the fetal brain- placental unit, – fetal brain oxygenation in response to maternal oxygen therapy in high-risk pregnancies. 14
Notes de l'éditeur
- My talk is about how to overcome serious fetal motion to analyze functional MRI data of the fetal brain and placenta during maternal hyperoxia.
- In conclusion, we developed a robust preprocessing technique including not only design-optimized multi-stage motion correction but also volume outlier rejection and missing data recovery. The design-optimized multi-stage motion correction is effective for stimulus-based fMRI of the moving fetus. On the other hand, imperfect motion correction can be compensated by automatic outlier rejection and statistical data imputation. Therefore, it is effective for imaging data whose subject motion cannot be well corrected. We tested the applicability of the proposed methods to pediatric data analyses. After applying the proposed preprocessing pipeline, the correlation analyses show linearly increasing trend of connectivity between the placenta and fetal brain. Thank you for your attention.
- In conclusion, we developed a robust preprocessing technique including not only design-optimized multi-stage motion correction but also volume outlier rejection and missing data recovery. The design-optimized multi-stage motion correction is effective for stimulus-based fMRI of the moving fetus. On the other hand, imperfect motion correction can be compensated by automatic outlier rejection and statistical data imputation. Therefore, it is effective for imaging data whose subject motion cannot be well corrected. We tested the applicability of the proposed methods to pediatric data analyses. After applying the proposed preprocessing pipeline, the correlation analyses show linearly increasing trend of connectivity between the placenta and fetal brain. Thank you for your attention.
- What is the effect of maternal hyperoxia on the functional relationship between the placenta and the fetal brain? Our primary scientific question is how the maternal hyperoxia affects the functional relationship between the placenta and fetal brain. To answer this question, we built the maternal hyperoxia study design as shown here. After two minutes resting state, 100% oxygen was supplied for four minutes. An EPI sequence was acquired including both the placenta and fetal brain. Then, the regions of interest were manually segmented, and BOLD signals were averaged over each ROI for post-processing analyses.
- What is the effect of maternal hyperoxia on the functional relationship between the placenta and the fetal brain? Our primary scientific question is how the maternal hyperoxia affects the functional relationship between the placenta and fetal brain. To answer this question, we built the maternal hyperoxia study design as shown here. After two minutes resting state, 100% oxygen was supplied for four minutes. An EPI sequence was acquired including both the placenta and fetal brain. Then, the regions of interest were manually segmented, and BOLD signals were averaged over each ROI for post-processing analyses.
- However, the functional MRI data are significantly affected by fetal movement. Let’s see an example with this video. As you see, the brain moves a lot, but its motion is significantly different from placental motion. For this reason, the traditional motion correction tools do not work well. To overcome the problem of fetal motion artifact, we proposed the following preprocessing pipeline. Briefly, it consisted of five steps including as bias field correction, global motion correction, local motion correction, volume outlier rejection, and missing data recovery.
- Fetal motion is heterogeneous throughout the stimulus phases and ROIs. Therefore our goal was to optimize motion correction for the stimulus design. Our proposed method consists of two consecutive steps of global motion correction and local motion correction. In the global motion correction step, an EPI sequence is separated into three phases, and motion correction is performed independently in each phase. In other words, a template is defined as phase-specific mean volume, and each volume is registered to the phase-specific template. For the local motion correction step, each volume is separated into brain-masked volume and plancenta-masked volume, and motion correction is performed independently in each ROI-masked volume. One important difference with normal motion correction is that the field of view is limited to an ROI and its neighborhood.
- However, Motion correction sometimes fails for certain high motion volumes. The wrongly registered volumes, so called the outlier volumes, can be automatically detected based on the temporal variation of BOLD signals, that is, by using the outlier probability of BOLD signals. As you seen in these right-hand figures, high motion leads to large number of voxels in the non-overlapping part. It means that the probability of an ROI voxel belonging to the background is monotonically increasing with higher object motion. Therefore, the probability P(B) can be used as the volume outlier score. On the other hand, as shown on the left-hand figures, a non-overlapping voxel is likely to have an outlier in its BOLD signal. This means that the posterior probability P(O|B) of a background voxel having an outlier is high. By using these empirical assumptions, the volume outlier scores can be computed by using the Bayes rule.
- Finally, as shown in the top-right figure, the volume outliers can be detected by thresholding the volume outlier scores. The left figure is an example of volume outlier rejection. As you see, the volume outlier rejection produces missing data points in the ROI-averaged time series. The existence of missing data can affect the reliability of data analyses in post-processing. To solve this problem, we developed a technique to estimate the missing data statistically. As shown in the bottom-right figure, the missing data can be imputed by regression with Gaussian uncertainty.
- The performance of proposed methods was evaluated with 16 subjects. Median absolute residual variation (MARV) was significantly reduced after volume outlier rejection. As shown in the left figure, it means that the BOLD signals became less noisy in most voxels. To validate the performance of motion correction, we computed structural similarity between each volume and template. As you see in the top-right figure, the number of outliers in structural similarity was exponentially reduced after each preprocessing step. Also, as compared the performance with the traditional motion correction tool called FLIRT. The bottom-right figure shows that the number of temporal outliers in ROI-averaged time series was significantly reduced.
- The performance of proposed methods was evaluated with 16 subjects. Median absolute residual variation (MARV) was significantly reduced after volume outlier rejection. As shown in the left figure, it means that the BOLD signals became less noisy in most voxels. To validate the performance of motion correction, we computed structural similarity between each volume and template. As you see in the top-right figure, the number of outliers in structural similarity was exponentially reduced after each preprocessing step. Also, as compared the performance with the traditional motion correction tool called FLIRT. The bottom-right figure shows that the number of temporal outliers in ROI-averaged time series was significantly reduced.
- We tested the applicability of the proposed methods to pediatric data analyses. After applying the proposed preprocessing pipeline, the correlation analyses show linearly increasing trend of connectivity between the placenta and fetal brain.
- In conclusion, we developed a robust preprocessing technique including not only design-optimized multi-stage motion correction but also volume outlier rejection and missing data recovery. The design-optimized multi-stage motion correction is effective for stimulus-based fMRI of the moving fetus. On the other hand, imperfect motion correction can be compensated by automatic outlier rejection and statistical data imputation. Therefore, it is effective for imaging data whose subject motion cannot be well corrected. We tested the applicability of the proposed methods to pediatric data analyses. After applying the proposed preprocessing pipeline, the correlation analyses show linearly increasing trend of connectivity between the placenta and fetal brain. Thank you for your attention.
- In conclusion, we developed a robust preprocessing technique including not only design-optimized multi-stage motion correction but also volume outlier rejection and missing data recovery. The design-optimized multi-stage motion correction is effective for stimulus-based fMRI of the moving fetus. On the other hand, imperfect motion correction can be compensated by automatic outlier rejection and statistical data imputation. Therefore, it is effective for imaging data whose subject motion cannot be well corrected. We tested the applicability of the proposed methods to pediatric data analyses. After applying the proposed preprocessing pipeline, the correlation analyses show linearly increasing trend of connectivity between the placenta and fetal brain. Thank you for your attention.