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Motion Correction of Abdominal Diffusion-Weighted MRI Using Internal Motion Vectors
Michael Bush1, Thomas Vahle2, Uday Krishnamurthy1, Thomas Benkert2, Xiaodong Zhong1, Bradley Bolster1, Paul Kennedy3, Octavia Bane3, Bachir Taouli3, and Vibhas Deshpande1
1Siemens Medical Solutions USA, Inc., Malvern, PA, United States, 2Siemens Healthcare GmbH, Erlangen, Germany, 3The Department of Radiology and Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mt. Sinai, New York, NY, United States

Synopsis

Respiratory motion is a significant problem in MRI and is further amplified in abdominal imaging. Traditional methods to correct for motion in abdominal DWI significantly increase overall scan time. In this work, motion vectors derived from non-rigid registration of low b-value diffusion volumes are used to correct for motion in higher b-values, where low signal to noise and contrast make intra and inter b-value registration challenging. Initial results suggest the proposed method can produce images similar in quality to respiratory gating, while maintaining reduced acquisition times.

Introduction

Respiratory motion is a significant problem in abdominal MRI (1). Many solutions currently exist to compensate for respiratory motion, including breath-holds (2), navigator gating (3–8), extra-dimensional MRI (9,10), and image registration (11–17). In the case of diffusion-weighted imaging, respiratory gating significantly extends the exam duration. As an alternative, image registration can be used; however, commonly applied 2D registration ignores through-plane motion which can have a significant effect on axial views. 3D image registration requires binning the individual DWI averages into discrete motion states prior to registration. However, low signal at higher b-values and varied contrast between b-values makes intra- and inter-b-value image registration challenging, respectively. In this study, we propose the use of pre-defined motion vectors derived from intra-registration of the low-b-value volumes (18,19) for motion correction of the higher b-values, resulting in significantly reduced scan time compared to prospectively gated acquisitions while maintaining similar image quality.

Methods

DWI iMoCo
All sampled averages of b50 are binned into 5 discrete respiratory motion states using a respiratory bellows for motion estimation and a hybrid binning strategy as shown in Figure 1; slices are organized with respect to slice position, and all binned averages are combined to create a contiguous b50 volume for each motion state. Each motion state is then registered to the end-expiratory volume using a rigid translation to remove bulk motion. To correct for non-rigid motion, each motion state is additionally registered to the end-expiratory volume using a diffeomorphic non-parametric algorithm (18,19), correcting for through- and in-plane motion for the final b50 volume. The rigid and non-rigid translations are stored with respect to each motion state and used to warp the b800 motion states. Resultant motion-corrected volumes are then aligned, and ADC maps are generated using a mono-exponential fit.
Acquisitions and Protocols
For all healthy and patient volunteers, a single dataset consisting of 16 3D-diagonal diffusion weighted averages for both b50 and b800 was acquired in an axial plane allowing for retrospective down-sampling of the number of averages to be used in the final reconstruction. Four test cases were reconstructed, denoted by the number of averages included as (a-b), where a and b are the number of averages for b50 and b800, respectively:
1.) Free breathing (1-5) (=1 b50 average, 5 b800 averages)
2.) End-expiratory gated (16-16)
3.) Free breathing (8-5)
4.) DWI iMoCo (8-5)
Motion vectors were calculated using the b50 averages and applied to the b800 images for correction. This method was applied in Test Case 4, where the 8 averages acquired for b50 are used to create motion vectors for application to the 5 b800 averages. In Test Case 2, end-expiratory gating is applied retrospectively to all 16 acquired averages, resulting in an average inclusion of (~7-6) motion-compensated averages of b50 and b800, respectively. The proposed method was first evaluated in a motion phantom (MP) experiment on a 3T system (MAGNETOM Prisma Fit, Siemens Healthcare, Erlangen, Germany), where a respiratory waveform was used to drive a programmable motion stage (Vital Biomedical) in the superior-inferior (SI) direction. Then, two healthy volunteers and 3 research subjects with liver disease and suspected portal hypertension (evaluated invasively by transjugular biopsy with hepatic venous pressure gradient (HVPG) measurements) were recruited for inclusion in this study. Healthy volunteer scans were performed on a 3T system (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany), and patient scans were performed on a 1.5T system (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). This study was approved by the local IRB, and written consent was obtained from each subject.

Results and Discussion

Reformatted coronal views from the MP experiment are shown in Figure 2. Free-breathing views show significant distortion and distension in the superior-inferior (SI) direction corresponding to the applied motion of the phantom; misaligned b50 and b800 images then produce similarly distorted and distended ADC maps. Increasing the averages included for b50 from one to 8 reduces the motion effect but does not eliminate the resultant blurring. iMoCo views show significantly reduced motion artifacts, while allowing for a reduced required acquisition time (TA = 1:47 min) compared to Gated (TA = 4:00 min) by reducing the number of averages included in the final reconstruction. Figure 3 shows original axial views for the MP and healthy volunteer experiments. Similar results can be observed, where free-breathing views show in-plane distortion that is improved with the use of iMoCo while maintaining a shorter acquisition time. Reformatted coronal views from the patient volunteer experiments are shown in Figure 4. Respiratory patterns in these subjects were highly variable, resulting in missing slices in the gated views. iMoCo maintains good image quality by using all acquired averages, ensuring no slice locations are missing. Figure 5. shows original axial views for the patient volunteers, further illustrating the ability of iMoCo to reduce motion artifacts.

Conclusion

iMoCo has reduced the effects of respiratory motion, while maintaining similar image quality to the end-expiratory-gated gold standard. These results imply that the use of iMoCo could increase the clinical applicability of free breathing quantitative diffusion imaging of the abdomen, by improving the workflow efficiency of the acquisition and the reproducibility of the resultant ADC maps.

Acknowledgements

No acknowledgement found.

References

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Figures

Figure 1. Flow diagram of the iMoCo process. Redundant sampling of the b50 volumes results in well-filled motion states, allowing for accurate non-rigid registration. Motion vectors produced by the non-rigid registration are then applied to the remaining b-value volumes, which do not require redundant sampling.

Figure 2. Reformatted Coronal Views of the Motion Phantom (MP) Experiments (Voxel size 1.5x1.5x5.0 mm3, PAT 2, Matrix Size 128x104x35, FOV 380 mm, TR 6.1 s, TE 56 ms, b50-12 avgs, b800-12 avgs, BW 2300 Hz/Px). Images were acquired axially, while motion was applied in the superior-inferior direction, resulting in significant through-plane motion visible in both free-breathing datasets. iMoCo produced similar quality results to the Gated reference standard, while requiring a significantly shorter required acquisition time (~1:47 to ~4:00 min).

Figure 3. Motion Phantom (MP) and Healthy Volunteer ADC Maps (Voxel size 1.5x1.5x5.0 mm3, PAT 2, Matrix Size 128x104x35, FOV 380 mm, TR 6.1 s, TE 56 ms, b50-16 avgs, b800-16 avgs, BW 2300 Hz/Px). Motion Phantom diffusion values are most comparable between the gold-standard Gated and iMoCo maps.

Figure 4. Reformatted Coronal Clinical Volunteer ADC Maps (Voxel size 1.5x1.5x6.0 mm3, PAT 2, Matrix Size 128x104x35, FOV 380 mm, TR 6.5 s, TE 69 ms, b50-16 avgs, b800-16 avgs, BW 1502 Hz/Px).

Figure 5. Clinical Volunteer ADC Maps (Voxel size 1.5x1.5x6.0 mm3, PAT 2, Matrix Size 128x104x35, FOV 380 mm, TR 6.5 s, TE 69 ms, b50-16 avgs, b800-16 avgs, BW 1502 Hz/Px).

Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)
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