Abstract
Purpose: To develop and validate DeepMocor, a deep learning–based method for motion-compensated 4-dimensional magnetic resonance fingerprinting (4D-MRF) reconstruction to accelerate conventional 4D-MRF reconstruction, enabling more efficient clinical treatment planning.
Methods and Materials: 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 magnetic resonance imaging scanner. DeepMocor involves motion field initialization, motion field refinement, and final 4D-MRF reconstruction. A 3-fold cross-validation strategy was employed for training and testing. Performance was evaluated against 2 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 difference (AMD) and Pearson correlation coefficients (PCC) in the superior-inferior and anterior-posterior directions. The Wilcoxon signed rank test was used for comparison with P < .05.
Results: For T1 maps, DeepMocor demonstrates PSNR of 25.49 ± 1.30, SSIM of 0.84 ± 0.03, MAPE of 3.5% to 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% to 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 superior-inferior direction and AMD of 0.32 ± 0.37 mm with PCC of 0.94 ± 0.06 in the anterior-posterior 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.
Conclusions: The proposed DeepMocor method enables a 24-fold acceleration compared to the conventional reference method, highlighting its potential for liver radiation therapy planning.
| Original language | English |
|---|---|
| Journal | International Journal of Radiation Oncology Biology Physics |
| DOIs | |
| Publication status | E-pub ahead of print - 30 Oct 2025 |
Bibliographical note
Publisher Copyright:© 2025
Funding
Disclosures: none. This work was supported in part by the National Natural Science Foundation of China Young Scientist Fund (grant number 82202941); the General Research Fund (grant numbers 15102219, 15104822, 15104323); the Health and Medical Research Fund (HMRF 10211606); and the Health Bureau, Innovation and Technology Support Program (ITS/049/22FP).
