🤖 AI Summary
This study addresses the challenges of prolonged MRI scan times, susceptibility to motion artifacts, and the need for repeated acquisitions by proposing a two-stage framework—MK-ResRecon and IdentityRefineNet3D—that enables high-quality 3D brain MRI reconstruction from only 12.5% sparse 2D axial slices. The method uniquely integrates a multi-kernel texture-aware loss with an end-to-end 3D joint optimization strategy: the former employs a multi-kernel convolutional residual network to predict missing slices while preserving fine anatomical details, and the latter fuses original and predicted slices to produce smooth, coherent 3D volumes. Validated on a large-scale, heterogeneous dataset of T1-weighted contrast-enhanced brain MRIs, the approach achieves hallucination-free, high-fidelity reconstructions that generalize clinically under extremely sparse input conditions, substantially reducing scan duration and enhancing patient comfort.
📝 Abstract
Magnetic Resonance Imaging (MRI) acquisition remains a time-intensive and patient-straining process, as prolonged scan dura- tions increase the likelihood of motion artifacts, which degrade image quality and frequently require repeated scans. To address these chal- lenges, we propose a novel framework with two models MK-ResRecon and IdentityRefineNet3D to reconstruct high-fidelity 3D MRI volumes from sparsely sampled 2D slices-requiring only 12.5% of the axial slices for full resolution 3D reconstruction. MK-ResRecon predicts missing in- termediate 2D slices using a multi-kernel texture-aware loss, preserving fine anatomical details. IdentityRefineNet3D refines the predicted slices and the original sparse slices as a single 3D volume to obtain a smooth anatomical structure. We train the models on a large T1-sequence POST- contrast brain MRI dataset and evaluate on a large heterogeneous brain MRI cohort. The work provides accurate, hallucination-free, generaliz- able and clinically validated framework for 3D MRI reconstruction from highly sparse inputs and enables a clinically viable path towards faster and more patient-friendly MRI imaging.