Abstract
Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in inpainting these lesions, most existing methods operate cross-sectionally or lack 3D anatomical continuity. We present a novel pseudo-3D longitudinal inpainting framework based on Denoising Diffusion Probabilistic Models (DDPM). Our approach utilizes multi-channel conditioning to incorporate longitudinal context from distinct visits (t_1, t_2) and extends Region-Aware Diffusion (RAD) to the medical domain, focusing the generative process on pathological regions without altering surrounding healthy tissue. We evaluated our model against state-of-the-art baselines on longitudinal brain MRI from 93 patients. Our model significantly outperforms the leading baseline (FastSurfer-LIT) in terms of perceptual fidelity, reducing the Learned Perceptual Image Patch Similarity (LPIPS) distance from 0.07 to 0.03 while effectively eliminating inter-slice discontinuities. Furthermore, our model demonstrates high longitudinal stability with a Temporal Fidelity Index of 1.024, closely approaching the ideal value of 1.0 and substantially narrowing the gap compared to LIT's TFI of 1.22. Notably, the RAD mechanism provides a substantial gain in efficiency; our framework achieves an average processing time of 2.53 min per volume, representing approximately 10x speedup over the 24.30 min required by LIT. By leveraging longitudinal priors and region-specific denoising, our framework provides a highly reliable and efficient preprocessing step for the study of progressive neurodegenerative diseases. A derivative dataset consisting of 93 pre-processed scans used for testing will be available upon request after acceptance. Code will be released upon acceptance.
Abstract (translated)
准确的脑部MRI纵向分析常常因病变随时间演变而受阻，这会偏移自动化神经影像管线。虽然深度生成模型在修复这些病灶方面显示出潜力，但大多数现有方法要么是横断面操作，要么缺乏3D解剖连续性。我们提出了一种基于去噪扩散概率模型（DDPM）的新型伪三维纵向修复框架。我们的方法利用多通道条件，整合来自不同时间点(t_1, t_2)的纵向信息，并将区域感知扩散(RAD)机制扩展至医学领域，专注于病变区域的生成过程，而不改变周围的健康组织。 我们在93名患者的纵向脑部MRI上评估了该模型，与最先进的基准方法相比，我们的模型在感知保真度方面显著优于FastSurfer-LIT基线模型，将学习到的感知图像块相似性(LPIPS)距离从0.07降至0.03，并有效消除了层间不连续性。此外，我们的模型展示了高度的时间稳定性，时间保真指数为1.024，接近理想值1.0，并显著缩小了与LIT TFI(1.22)的差距。 值得注意的是，RAD机制提供了巨大的效率提升；我们的框架实现了每个体积平均处理时间为2.53分钟，相比LIT所需的24.30分钟大约快了十倍。通过利用纵向先验和区域特异性去噪，我们提供的预处理步骤对于进行渐进性神经退行性疾病的研究是高度可靠且高效的。 在被接受后，我们将提供一个包含93个预处理扫描的衍生数据集供测试使用，并将在获得许可后发布代码。
URL
https://arxiv.org/abs/2603.05693
