Name
Title | ||
|---|---|---|
Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning |
Abstract | ||
|---|---|---|
Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the heart in free-breathing 3D coronary magnetic resonance angiography (CMRA). This reconstruction framework requires efficient and accurate estimation of non-rigid motion fields from undersampled images at different respiratory positions (or bins). However, state-of-the-art registration methods can be time-consuming. This article presents a novel unsupervised deep learning-based strategy for fast estimation of inter-bin 3D non-rigid respiratory motion fields for motion-corrected free-breathing CMRA. The proposed 3D respiratory motion estimation network (RespME-net) is trained as a deep encoder-decoder network, taking pairs of 3D image patches extracted from CMRA volumes as input and outputting the motion field between image patches. Using image warping by the estimated motion field, a loss function that imposes image similarity and motion smoothness is adopted to enable training without ground truth motion field. RespME-net is trained patch-wise to circumvent the challenges of training a 3D network volume-wise which requires large amounts of GPU memory and 3D datasets. We perform 5-fold cross-validation with 45 CMRA datasets and demonstrate that RespME-net can predict 3D non-rigid motion fields with subpixel accuracy (0.44 ± 0.38 mm) within ~10 seconds, being ~20 times faster than a GPU-implemented state-of-the-art non-rigid registration method. Moreover, we perform non-rigid motion-compensated CMRA reconstruction for 9 additional patients. The proposed RespME-net has achieved similar motion-corrected CMRA image quality to the conventional registration method regarding coronary artery length and sharpness. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TMI.2020.3029205 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Coronary Angiography,Coronary Vessels,Deep Learning,Heart,Humans,Image Processing, Computer-Assisted,Imaging, Three-Dimensional,Magnetic Resonance Angiography,Motion | Journal | 40 |
Issue | ISSN | Citations |
1 | 0278-0062 | 1 |
PageRank | References | Authors |
0.35 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haikun Qi | 1 | 1 | 0.68 |
| Niccolo Fuin | 2 | 1 | 0.35 |
| Gastao Cruz | 3 | 12 | 3.92 |
| Jiazhen Pan | 4 | 1 | 0.35 |
| Thomas Kustner | 5 | 33 | 6.58 |
| Aurélien Bustin | 6 | 16 | 3.89 |
| René Botnar | 7 | 19 | 4.24 |
| Claudia Prieto | 8 | 30 | 9.15 |