用语义多样重建法发现加速MRI中被遗漏的病灶细节
Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions
- 生成与原始数据一致但语义多样的新图像，捕捉潜在病灶
- 在fastMRI+数据集上召回率提升，平均精度更高
- 适合临床医生排查误诊风险，尤其关注微小罕见病灶
近年来基于深度学习的加速MRI重建在高加速比下显著提升了图像质量。然而从临床角度看，图像质量只是次要问题；更重要的是，在严重欠采样数据中必须保留所有临床相关信息。本文表明，现有技术即使采用扩散模型重采样，仍可能无法重建微小或罕见病灶，导致误诊（假阴性）。为此我们提出“语义多样性重建”（Semantically Diverse Reconstructions, SDR），该方法在给定原始重建结果的基础上，生成多个与测量数据完全一致但语义上更具多样性的新图像。为自动评估SDR效果，我们在fastMRI+数据集上训练了目标检测器。实验显示，与原始重建相比，SDR显著降低了假阴性概率（提高召回率），并提升了平均精度。代码已公开于https://github.com/NikolasMorshuis/SDR。
原文摘要 · Abstract (English)
In recent years, accelerated MRI reconstruction based on deep learning has led to significant improvements in image quality with impressive results for high acceleration factors. However, from a clinical perspective image quality is only secondary; much more important is that all clinically relevant information is preserved in the reconstruction from heavily undersampled data. In this paper, we show that existing techniques, even when considering resampling for diffusion-based reconstruction, can fail to reconstruct small and rare pathologies, thus leading to potentially wrong diagnosis decisions (false negatives). To uncover the potentially missing clinical information we propose ``Semantically Diverse Reconstructions'' (\SDR), a method which, given an original reconstruction, generates novel reconstructions with enhanced semantic variability while all of them are fully consistent with the measured data. To evaluate \SDR automatically we train an object detector on the fastMRI+ dataset. We show that \SDR significantly reduces the chance of false-negative diagnoses (higher recall) and improves mean average precision compared to the original reconstructions. The code is available on https://github.com/NikolasMorshuis/SDR
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