用扩散模型指导的自适应采样，让MRI更快更准
Active MRI Acquisition with Diffusion Guided Bayesian Experimental Design
- 基于扩散模型和贝叶斯设计，动态选择最有信息量的采样点
- 在保持图像质量前提下，采样效率提升显著，支持多种分析任务
- 适合追求高速高质MRI的临床研究与医学影像分析场景
在临床MRI中，如何在不显著降低图像质量的前提下缩短扫描时间是一个关键挑战。这需要在减少原始k空间采样以加快采集速度与保留足够信息以实现高质量重建和分析之间取得平衡。为此，我们提出一种序列式贝叶斯实验设计（BED）方法，实现自适应、任务相关的最优测量选择。通过梯度优化设计参数，逐步选择能最大化目标图像后验分布信息增益的采样模式。本工作引入一种新的主动式BED流程，利用基于扩散的生成模型处理图像的高维特性，并采用随机优化策略，在满足采集约束与预算的前提下，从多种采样模式中选出最优方案。实验表明，该方法不仅可优化标准图像重建，还能同时提升各类图像分析任务的性能。在多个MRI采集任务中验证了其通用性与高效性。
原文摘要 · Abstract (English)
A key challenge in maximizing the benefits of Magnetic Resonance Imaging (MRI) in clinical settings is to accelerate acquisition times without significantly degrading image quality. This objective requires a balance between under-sampling the raw k-space measurements for faster acquisitions and gathering sufficient raw information for high-fidelity image reconstruction and analysis tasks. To achieve this balance, we propose to use sequential Bayesian experimental design (BED) to provide an adaptive and task-dependent selection of the most informative measurements. Measurements are sequentially augmented with new samples selected to maximize information gain on a posterior distribution over target images. Selection is performed via a gradient-based optimization of a design parameter that defines a subsampling pattern. In this work, we introduce a new active BED procedure that leverages diffusion-based generative models to handle the high dimensionality of the images and employs stochastic optimization to select among a variety of patterns while meeting the acquisition process constraints and budget. So doing, we show how our setting can optimize, not only standard image reconstruction, but also any associated image analysis task. The versatility and performance of our approach are demonstrated on several MRI acquisitions.
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