用扩散模型+低秩正则，实现低剂量稀疏视角CT高保真重建。
Diffusion Low Rank Hybrid Reconstruction for Sparse View Medical Imaging
- 融合扩散生成先验与多正则化约束，提升重建质量。
- 在8/4/2视角下，SSIM等指标优于现有方法，纹理和边缘更清晰。
- 适合临床低剂量成像，计算高效，可扩展至3D重建。
本文提出TV-LoRA方法，用于低剂量稀疏视角CT重建，结合扩散生成先验（NCSN++与SDE建模）与多正则化约束（各向异性TV与核范数，即LoRA），在ADMM框架中实现。为解决极端稀疏视角下的病态问题与纹理丢失，该方法融合生成与物理约束，并采用基于2D切片的策略，结合FFT加速与张量并行优化以实现高效推理。在AAPM-2016、CTHD和LIDC数据集上，$N_{\mathrm{view}}=8,4,2$ 的实验表明，TV-LoRA在SSIM、纹理恢复、边缘清晰度与伪影抑制方面持续优于基准方法，展现出强鲁棒性与泛化能力。消融实验证明了LoRA正则与扩散先验的互补作用，且FFT-PCG模块带来显著加速。总体而言，扩散+TV-LoRA实现了高保真、高效的3D CT重建，在低剂量、稀疏采样场景中具有广泛临床应用前景。
原文摘要 · Abstract (English)
This work presents TV-LoRA, a novel method for low-dose sparse-view CT reconstruction that combines a diffusion generative prior (NCSN++ with SDE modeling) and multi-regularization constraints, including anisotropic TV and nuclear norm (LoRA), within an ADMM framework. To address ill-posedness and texture loss under extremely sparse views, TV-LoRA integrates generative and physical constraints, and utilizes a 2D slice-based strategy with FFT acceleration and tensor-parallel optimization for efficient inference. Experiments on AAPM-2016, CTHD, and LIDC datasets with $N_{\mathrm{view}}=8,4,2$ show that TV-LoRA consistently surpasses benchmarks in SSIM, texture recovery, edge clarity, and artifact suppression, demonstrating strong robustness and generalizability. Ablation studies confirm the complementary effects of LoRA regularization and diffusion priors, while the FFT-PCG module provides a speedup. Overall, Diffusion + TV-LoRA achieves high-fidelity, efficient 3D CT reconstruction and broad clinical applicability in low-dose, sparse-sampling scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。