High-quality Four-dimensional Magnetic Resonance Fingerprinting (HQ-4DMRF) Reconstruction for Liver Cancer Radiotherapy.

[PURPOSE] To develop a high-quality four-dimensional magnetic resonance fingerprinting (HQ-4DMRF) framework with temporal low-rank constrained motion compensation for precise tumor motion management in liver radiotherapy.

[METHODS] HQ-4DMRF integrated four key innovations: 1) an automated internal respiratory navigator to track organ motion without external sensors; 2) a results-driven phase sorting algorithm to dynamically redistribute MRF dynamics across respiratory phases; 3) a novel temporal low-rank-constrained four-dimensional registration (Tel4dReg) algorithm to simultaneously compute all inter-phase deformation vector fields (DVFs) by leveraging low-rank respiratory motion properties and enforcing spatiotemporal regularization; 4) and an iterative motion-compensated optimization algorithm to reconstruct motion-resolved 4D tissue map. HQ-4DMRF was validated in 24 patients with hepatocellular carcinoma. All patients underwent a free-breathing abdominal MRF scan using a multi-slice two-dimensional FISP sequence. The motion measurement accuracy of HQ-4DMRF was assessed through inter-phase structural repeatability (ISR). ISR quantified the structural consistency in tissue maps across motion phases using the structural similarity index (SSIM), local cross-correlation (LCC), and textural feature intraclass correlation coefficient (TFICC) for tumor.

[RESULTS] The HQ-4DMRF demonstrated superior precision in motion measurement versus conventional 4DMRF techniques (p<0.001), with ISR-SSIM/-LCC/-TFICC of 0.82±0.06/0.36±0.07/0.75±0.20 for T1, 0.89±0.05/0.29±0.06/0.84±0.24 for T2, and 0.80±0.06/0.38±0.06/0.91±0.12 for PD maps. Compared with using conventional pair-wised registration methods, the Tel4dReg improved motion measurement accuracy by an average of 8.5%-12.5% (SSIM), 9.1%-36.2% (LCC), and 8.2%-17.1% (TFICC). The respiratory curve derived from automated internal respiratory navigator showed strong agreement with manual measurements (Pearson correlation coefficient [PCC]=0.90±0.12) and demonstrated consistent performance across different anatomical regions (PCC=0.83±0.13). The result-driven phase sorting enhanced the 4DMRF performance by 7.2%.

[CONCLUSION] The HQ-4DMRF framework presents a comprehensive solution to critical challenges in 4DMRF. Clinical validation in HCC patients demonstrates significant improvements in liver tumor motion characterization. These advances not only enhance the precision of radiotherapy planning through more accurate motion modeling but also establish HQ-4DMRF as a promising platform for 4D quantitative MRI in oncologic applications.