TY - JOUR
T1 - Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver
AU - Lønning, Kai
AU - Caan, Matthan W.A.
AU - Nowee, Marlies E.
AU - Sonke, Jan Jakob
N1 - Funding Information:
This work was supported by an institutional grant of the Dutch Cancer Society and of the Dutch Ministry of Health, Welfare and Sport. The authors would also like to thank Tessa van de Lindt for support in data acquisition and Chaoping Zhang for help with this manuscript.
Funding Information:
This work was supported by an institutional grant of the Dutch Cancer Society and of the Dutch Ministry of Health, Welfare and Sport. The authors would also like to thank Tessa van de Lindt for support in data acquisition and Chaoping Zhang for help with this manuscript.
Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/1
Y1 - 2024/1
N2 - Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T2-weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol.
AB - Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T2-weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol.
KW - Accelerated dynamic MRI reconstruction
KW - Deep learning
KW - MR guided liver SBRT
KW - Recurrent inference machines
UR - http://www.scopus.com/inward/record.url?scp=85185261467&partnerID=8YFLogxK
U2 - 10.1016/j.compmedimag.2024.102348
DO - 10.1016/j.compmedimag.2024.102348
M3 - Article
C2 - 38368665
AN - SCOPUS:85185261467
SN - 0895-6111
VL - 113
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
M1 - 102348
ER -