用稀疏2D切片重建高质量3D脑部MRI，仅需12.5%切片数据
MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slices
- 多核纹理感知损失预测缺失切片，保留精细解剖结构
- 在12.5%稀疏切片下实现全分辨率3D重建，无幻觉现象
- 适用于临床快速磁共振成像，提升患者体验
磁共振成像（MRI）采集耗时长、患者负担重，扫描时间越长越易产生运动伪影，降低图像质量并常需重复扫描。为应对这一挑战，我们提出新框架MK-ResRecon与IdentityRefineNet3D，仅需12.5%的轴向切片即可重建高保真3D MRI体积。MK-ResRecon采用多核纹理感知损失预测缺失中间切片，保留细微解剖细节；IdentityRefineNet3D将预测切片与原始稀疏切片联合优化为统一3D体积，获得平滑解剖结构。模型在大规模T1序列增强后脑部MRI数据集上训练，并在异质脑部MRI队列中评估。该方法提供准确、无幻觉、可泛化且经临床验证的3D MRI重建方案，为快速、患者友好的磁共振成像开辟可行路径。
原文摘要 · Abstract (English)
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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