Ultra-Fast Dynamic MRI for Lung Tumor Tracking Based On Compressed Sensing and Low-Rank Decomposition in the Spatial-Temporal Domain
M Sarma*, P Hu, D Ennis, A Thomas, P Lee, K Sheng, UCLA School of Medicine, Los Angeles, CAWE-G-WAB-4 Wednesday 4:30PM - 6:00PM Room: Wabash Ballroom
Purpose: Dynamic MRI is an attractive tool for internal organ motion monitoring but its speed is yet to be improved for real time 3D imaging. The purpose of the study is to accelerate the imaging acquisition using a hybrid compressed-sensing and low rank decomposition method (k-t SLR) exploiting data sparsity in the spatial-temporal domain.
Methods: Six retrospective lung cancer patients were included in the study. For each subject, 120 continuously dynamic 2D MR images were acquired using a fast steady state precession imaging sequence. To test the feasibility of accelerated MR acquisition, fully sampled k-space data were down-sampled by 5-20 folds before reconstruction. The under-sampled low rank spatial-temporal matrix was recovered by a k-t SLR method computing the matrix of minimum nuclear norm that fits the data. The k-t SLR results were compared against conventional total variation (TV) reconstruction of individual dynamic MR frames.
Results: High imaging fidelity and low noise levels were achieved with the k-t SLR method even at the most aggressive down-sampling ratio while TV reconstruction showed substantial loss of details and subsequently greater tracking errors. The k-t SLR method resulted in an average normalized mean square error less than 0.05 as opposed to 0.23 by using the TV reconstruction on individual frames. In addition to the high correlation coefficients (>0.90) in the tumor trajectories automatically tracked from the original images and the k-t SLR reconstructed images, less than 6% points showed tracking errors greater than 1 mm with 20 fold down-sampling, as opposed to the lower correlation coefficient (0.87) and 15% tracking error from using TV alone with 10 fold data down-sampling.
Conclusion: We have demonstrated the potential to significantly reduce the amount data samples required for lung MR image reconstruction in the radiotherapy context where long imaging acquisition of a repetitive anatomic process is performed.
Funding Support, Disclosures, and Conflict of Interest: The study is supported in part by NIH 1R21CA144063 and R21CA161670