Early detection of many life-threatening diseases (e.g., prostate and breast cancer) within at-risk population can improve clinical outcomes and reduce cost of care. While numerous disease-specific "screening" tests that are closer to Point-of-Care (POC) are in use for this task, their low specificity results in unnecessary biopsies, leading to avoidable patient trauma and wasteful healthcare spending. On the other hand, despite the high accuracy of Magnetic Resonance (MR) imaging in disease diagnosis, it is not used as a POC disease identification tool because of poor accessibility. The root cause of poor accessibility of MR stems from the requirement to reconstruct high-fidelity images, as it necessitates a lengthy and complex process of acquiring large quantities of high-quality k-space measurements. In this study we explore the feasibility of an ML-augmented MR pipeline that directly infers the disease sidestepping the image reconstruction process. We hypothesise that the disease classification task can be solved using a very small tailored subset of k-space data, compared to image reconstruction. Towards that end, we propose a method that performs two tasks: 1) identifies a subset of the k-space that maximizes disease identification accuracy, and 2) infers the disease directly using the identified k-space subset, bypassing the image reconstruction step. We validate our hypothesis by measuring the performance of the proposed system across multiple diseases and anatomies. We show that comparable performance to image-based classifiers, trained on images reconstructed with full k-space data, can be achieved using small quantities of data: 8% of the data for detecting multiple abnormalities in prostate and brain scans, and 5% of the data for knee abnormalities. To better understand the proposed approach and instigate future research, we provide an extensive analysis and release code.

翻译：在风险人群中对多种危及生命的疾病（如前列腺癌和乳腺癌）进行早期检测，可改善临床预后并降低医疗成本。尽管多种更接近即时检测（Point-of-Care, POC）的疾病特异性"筛查"测试已投入使用，但其较低的特异性会导致不必要的活检，造成可避免的患者创伤和医疗资源浪费。另一方面，尽管磁共振成像在疾病诊断中具有高准确性，但由于可及性差，它并未被用作即时疾病识别工具。磁共振可及性差的主要原因在于需要重建高保真图像，这需要耗时且复杂的流程来获取大量高质量的k空间测量数据。本研究探索了一种绕过图像重建步骤、直接推断疾病诊断的机器学习增强型磁共振管道的可行性。我们假设：相较于图像重建，疾病分类任务可用极少量的定制化k空间子集数据完成。为此，我们提出一种执行两项任务的方法：1）识别能最大化疾病识别准确度的k空间子集；2）直接利用所识别的k空间子集推断疾病，绕过图像重建步骤。我们通过测量系统在多种疾病和解剖结构上的性能来验证假设。结果表明，使用少量数据即可达到与基于全k空间数据重建图像训练的影像分类器相当的诊断性能：在前列腺和脑部扫描的多重异常检测中仅需8%的数据，在膝关节异常检测中仅需5%的数据。为深入理解所提方法并推动后续研究，我们提供了详尽分析并公开了相关代码。