Deep Learning-based Motion-Compensated Reconstruction for Accelerating Four-Dimensional Magnetic Resonance Fingerprinting.
Authors
Affiliations (7)
Affiliations (7)
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China.
- St. Paul's Hospital, Hong Kong SAR, 999077, China.
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong SAR, 999077, China.
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China.
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, 999077, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China. Electronic address: [email protected].
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China. Electronic address: [email protected].
Abstract
To develop and validate DeepMocor, a deep learning-based method for motion-compensated four-dimensional magnetic resonance fingerprinting (4D-MRF) reconstruction to accelerate conventional 4D-MRF reconstruction, enabling more efficient clinical treatment planning. This prospective study enrolled 19 hepatocellular carcinoma patients (mean age, 62 years; 14 males) between June 2021 and October 2024. Abdominal free-breathing raw k-space data were acquired using a 3T MRI scanner. DeepMocor involves motion field initialization, motion field refinement, and final 4D-MRF reconstruction. A three-fold cross-validation strategy was employed for training and testing. Performance was evaluated against two alternatives (Stage-I&III-only; Stage-III-only) in terms of image quality, tissue property accuracy, tumor-to-tissue contrast, and tumor motion measurement. Image quality was assessed by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Tissue property accuracy was evaluated by Mean Absolute Percentage Error (MAPE). Tumor-to-tissue contrast was quantified by Contrast-to-Noise Ratio (CNR) of the tumor region and the surrounding area. Tumor motion tracking was assessed by Average Motion Discrepancy (AMD) and Pearson Correlation Coefficients (PCC) in the superior-inferior (SI) and anterior-posterior (AP) directions. The Wilcoxon signed rank test was used for comparison with P < 0.05. For T1 maps, DeepMocor demonstrates PSNR of 25.49 ± 1.30, SSIM of 0.84 ± 0.03, MAPE of 3.5%-5.9%, and CNR of 6.14 ± 3.54. For T2 maps, DeepMocor achieves PSNR of 25.57 ± 1.24, SSIM of 0.88 ± 0.02, MAPE of 3.1%-15.8%, and CNR of 8.42 ± 13.72. DeepMocor achieves AMD of 0.62 ± 0.86 mm with PCC of 0.96 ± 0.07 in the SI direction and AMD of 0.32 ± 0.37 mm with PCC of 0.94 ± 0.06 in the AP direction. DeepMocor shows superior performance across most metrics compared to Stage-III-only and a subset of metrics compared to Stage-I&III-only significantly. The proposed DeepMocor method enables a 24-fold acceleration compared to the conventional reference method, highlighting its potential for liver radiotherapy planning.