Physics and Imaging in Radiation Oncology · Published 2026-05-01 · DOI 10.1016/j.phro.2026.101002
Background and purpose: Quantitative T1 mapping is a major building block in several multiparametric magnetic resonance imaging (MRI) protocols intended for adaptive radiation therapy. The implementation of these protocols is challenging in anatomical sites that experience large physiological motion. The purpose of this study was to implement and validate motion-resolved quantitative T1 mapping on a 1.5 T MRI linear accelerator (MR-Linac) combining non-Cartesian k-space sampling trajectories with compressed sensing (CS) reconstruction. Materials and methods: Four 3-dimensional non-Cartesian k-space trajectories were evaluated: kooshball and stack-of-stars sampling using half- and full-spoke coverage. A variable flip angle acquisition was performed using the spoiled gradient-echo sequence. Gradient delay timing was optimized to minimize trajectory-induced artifacts. Eight CS reconstruction strategies were tested using spatial/spatiotemporal regularization operators. Reconstructions were evaluated and sorted by spatial resolution, bias, and variability. Motion-resolved T1 mapping was validated using two standard phantoms, one healthy volunteer, and one kidney cancer patient using respiratory self-gating and phase-sorted reconstruction. Results: All non-Cartesian T1 maps demonstrated high repeatability and low longitudinal bias in phantom studies, with coefficients of variation below 3.3%. Spatiotemporal regularization preserved spatial resolution and quantitative accuracy at undersampling factors up to 20-fold. In human subjects, non-Cartesian T1 mapping provided improved accuracy and reduced variability in mobile abdominal tissues compared to Cartesian acquisitions. Conclusions: Quantitative T1 mapping using non-Cartesian trajectories and CS reconstruction is feasible on a 1.5 T MR-Linac. The proposed approach enables accurate motion-resolved quantitative imaging within clinically practical acquisition times, establishing a foundation for multiparametric MRI in adaptive radiotherapy.
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