关注细节：利用语义多样化重建揭示加速MRI中具有临床意义的图像细节
Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions
AI总结：
本文提出语义多样化重建方法SDR，通过生成与实测数据一致但语义更多样的MRI重建图像，揭示加速MRI中易被遗漏的小而罕见病理，显著降低假阴性诊断风险。
摘要：
近年来，基于深度学习的加速MRI重建在图像质量方面取得了显著提升，在高加速倍数下也表现出令人印象深刻的结果。然而，从临床角度看，图像质量只是次要的；更为重要的是，在从高度欠采样数据进行的重建中，所有具有临床意义的信息都应得到保留。本文表明，现有技术——即使考虑了基于扩散的重建中的重采样——也可能无法重建出小而罕见的病理结构，从而导致潜在的误诊决策（假阴性）。为了揭示可能缺失的临床信息，我们提出了“语义多样化重建”（Semantically Diverse Reconstructions, SDR）方法：给定一个原始重建结果，该方法生成具有增强语义变异性的新型重建图像，同时所有生成结果都与实测数据完全一致。为了自动评估SDR，我们在fastMRI+数据集上训练了一个目标检测器。我们证明，与原始重建相比，SDR显著降低了假阴性诊断的概率（更高的召回率），并提高了平均精度均值。代码可在https://github.com/NikolasMorshuis/SDR获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
