Reliable AI for Medical Image Reconstruction

项目摘要/摘要 深度神经网络在从计算机到计算机的各种学科中都取得了广泛的经验成功 视觉到自然语言处理。然而，在诸如磁共振成像(MRI)的医学成像中， 各种挑战，包括缺乏高质量的培训数据、缺乏对腐败/离群值的稳健性以及 列车和测试时间之间的分配偏差，以及记录的可靠性和可信性的缺乏阻碍了 人工智能的广泛使用和适应。该项目开发基于深度学习的新架构、算法和 应对这些挑战的培训机制为MRI重建创造了一个新的工具包，即 健壮、可靠、值得信赖，但可以跨多个医院系统进行协作和私下培训。 使用这个新的工具包，该项目解决了三个主要的MRI重建挑战(1)减少采购量 通过更高的加速系数缩短时间，(2)即使在低强度的磁场下也能实现高质量的重建 领域，以及(3)处理运动伪影。与几个肌肉骨骼(MSK)部分合作 这个项目还包括收集、管理和发布新的数据集和开放源码 旨在解决这些关键挑战的重建软件。这也将有助于吸引来自 机器学习/人工智能社区进一步改进这一重要的医学成像模式。 医疗保健影响：该项目将显著增强核磁共振成像这一重要的诊断工具。(1) 采集时间的缩短将同时提高诊断的准确性和患者的舒适度。 (2)低场强扫描仪引起的噪声/非线性伪影的减少将导致尺寸和 磁共振扫描仪的重量。这最终可能允许在护理点或紧急情况下使用磁共振成像 在床边，也打开了过多的新用例。(3)运动伪影的减少增加了 对各种新疾病和新情况的诊断准确性，使MRI能够实现新的诊断用例。另外， (1)允许更多的患者使用相同的机器进行扫描，以及(2)降低磁铁和 磁共振扫描仪的操作空间。这可以显著降低患者成本，从而增加获得 具有重要诊断意义的医学成像设备。此外，对MSK数据收集的关注可以大大 有助于准确诊断病理，如膝部、唇部和旋转部的半月板撕裂或球粒陨石 CU肩部撕裂，手腕韧带撕裂。对臂丛神经病的特别关注也是 由于臂丛是一种复杂的解剖结构，具有明显的医疗保健价值 为上肢、肩部和上胸部提供神经支配的关键功能。臂丛神经 核磁共振研究将使这一复杂的解剖结构具有低/常规的老年强度的更详细的信息。 这在患者治疗中至关重要，特别是在创伤环境中。

Project Abstract/Summary Deep neural networks have enjoyed wide empirical success in a variety of disciplines ranging from computer vision to natural language processing. However, in medical imaging such as Magnetic Resonance Imaging (MRI), a variety of challenges including lack of high-quality training data, lack of robustness to corruption/outliers and distribution shifts between train and test time as well as documented lack of reliability and trustworthiness impede the wide use and adaptation of AI. This project develops new deep learning-based architectures, algorithms and training mechanisms that that deals with these challenges creating a new toolkit for MRI reconstruction that is robust, reliable, trustworthy yet can be trained collaboratively and privately across multiple hospital systems. Using this new toolkit the project addresses three major MRI reconstruction challenges (1) reducing acquisition time via higher acceleration factors, (2) enabling high quality reconstruction even with low-intensity magnetic elds, and (3) dealing with motion artifacts. In collaboration with Musculoskeletal (MSK) sections of a few major universities, this project also involves gathering, curating and releasing new datasets and open source reconstruction software aimed at addressing these key challenges. This will also help attract further research from the machine learning/AI community to further improve this important medical imaging modality. Healthcare Impact: This project will signi cantly enhance MRI which is an important diagnostic tool. (1) reductions in the acquisition time will simultaneously increases both the accuracy of diagnosis and patient comfort. (2) the reduction of noise/nonlinear artifacts caused by low- eld scanners will lead to a reduction in the size and weight of the MR scanner. This may eventually allow MRI to be used at point of care or for emergency scenarios at the bedside and also open up a plethora of new use cases. (3) reductions in the motion artifacts increases the accuracy of diagnosis for a variety of new diseases and conditions enabling new diagnostic use cases for MRI. Also, (1) allows more patients to receive a scan using the same machine and (2) lowers the cost of the magnet and the space of operation of MR scanners. This can signi cantly reduce patient cost and thus increase the access to this diagnostically important medical imaging modality. Furthermore, the focus on MSK data collection can greatly facilitate accurate diagnosis of pathology such as subtle meniscal tears or chondrites in the knee, labral and rotator cu tears in the shoulder, and ligament out tears in the wrist. The particular focus on brachial plexopathy is also expected to have signi cant healthcare bene ts as the briachial plexus is an intricate anatomic structure with the critical function of providing innervation to the upper extremity, shoulder, and upper chest. The brachial plexus MRI study will enable great detail of this intricate anatomical structure with low/conventional eld strengths which is of paramount importance in patient treatment, especially in traumatic settings.