High-quality 4D anatomical and functional MRI for abdominal tumor motion management.

Abstract

e16013 Background: 4D-MRI offers superior soft-tissue contrast for abdominal motion management but is often limited by the trade-off between image quality and scan duration. Moreover, existing techniques are largely restricted to anatomical imaging, lacking the integration of functional data. This study proposes a deep learning framework to generate high-quality (HQ) 4D anatomical and functional MRI from low-quality (LQ) inputs via accurate motion estimation and reconstruction, aiming to significantly enhance tumor tracking reliability and precision in Image-Guided Radiotherapy (IGRT). Methods: A total of 169 abdominal MRI datasets with complete liver coverage were partitioned into training and internal validation cohorts (8:2). Additionally, an independent external test cohort (n = 12) was acquired. The model employed a 3D U-Net-based architecture, utilizing HQ static 3D-MRI and LQ 4D-MRI inputs to synthesize HQ 4D anatomical MRI, HQ 4D functional MRI, and corresponding deformation vector fields (DVFs). To ensure spatial consistency, DWI was aligned via a hierarchical cross-contrast registration (HCR) pipeline. Image quality was quantified using Full Width at Half Maximum (FWHM) and CNR, while motion consistency was assessed via liver centroid trajectories. Results: In the validation and testing cohorts, quantitative motion analysis demonstrated consistent sub-voxel accuracy. For every individual patient and across all motion directions, the mean 3D trajectory errors were consistently < 1 mm. Furthermore, the maximum error for each case remained below the original voxel dimensions (Training:1.56×1.56×3.0mm³; Testing: 2.68×2.68×2.7mm³). For a representative test case, Figure 1 presents the motion tracking curves and quality metrics (CNR, FWHM). Correspondingly, Figure 2 displays the generated HQ images (T1, T2, and DWI). As shown in Figure 2, while the tumor was indistinguishable on anatomical sequences, the generated HQ-DWI delineated the lesion, validating the model's capability to recover functional information for target definition. Conclusions: The proposed framework successfully reconstructs HQ 4D anatomical and functional MRI from LQ inputs while maintaining precise motion information. This personalized, multi-parametric 4D-MRI approach demonstrates feasibility for fast and reliable motion management, potentially enabling high-precision IGRT for abdominal cancers.