MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slices

• URL: http://arxiv.org/abs/2605.03432v1
• Date: Tue, 05 May 2026 07:12:55 GMT
• Title: MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slices
• Abstract summary: We propose two models to reconstruct high-fidelity 3D MRI volumes from sparsely sampled 2D slices.<n>We refine the predicted slices and the original sparse slices as a single 3D volume to obtain a smooth anatomical structure.<n>The work provides accurate, hallucination-free, generaliz- able and clinically validated framework for 3D MRI reconstruction from highly sparse inputs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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