Robust quality-controlled quantitative stress perfusion cardiac MRI

PHD项目的目的：由于图像质量低、对比剂注射失败或缺乏对压力的反应，心脏MRI灌注的全自动评估可能会受到可靠性问题的影响。该项目将开发一系列人工智能启用的质量控制过程，使灌注心脏MRI在临床上更加健壮和可靠。项目描述：应激灌注心脏MRI未得到充分利用的主要原因是图像的视觉评估高度依赖于操作员的培训水平[1]。到目前为止，心脏MRI负荷灌注的定量分析仍主要是一种研究工具，但其临床翻译将是有利的，因为它可以自动化，使准确和用户独立的心肌灌注评估成为可能。我们小组最近还证明了定量应力灌注CMR的独立预后价值[2]。然而，这项工作仍然涉及几个步骤的人工交互，包括心肌分割。定量负荷灌注CMR的自动分析有可能彻底改变可疑冠状动脉疾病患者的管理。我们的团队率先使用深度学习来实现负荷灌注CMR的自动定量分析[3]。大规模部署这一解决方案的问题是，分析的目测和质量控制是不可行的。如果对于哪些病例分析失败并不明显，那么临床医生可能会得到失败或不准确的测量结果，因此得出错误的结论。我们的目标是通过开发一种使用深度神经网络的全自动图像质量控制(QC)工具来克服这一限制。深度学习在医学成像领域得到了广泛的应用，已经成为许多处理任务的事实上的标准。心脏MRI图像分析的趋势遵循了类似的路线，从重建到检测和分割任务，再到自动化诊断和预测，现在都使用深度学习。为此，我们设想开发三个QC过程，进行开发、验证并整合到临床工作流程中。动脉输入功能(AIF)分析：将使用机器学习方法来分类是否正确地将造影剂注射到患者体内。应激反应分析：一些患者对注射应激源药物没有预期的反应。将对算法进行训练和验证以识别这些患者。失败案例的检测。我们之前开发的自动处理流水线可能会失败，因为心脏被预测在错误的位置，或者预测的心脏形状不像预期的那样。将开发一个进一步的神经网络来标记这些案例。

Aim of the PhD Project:Fully automatic assessment of perfusion cardiac MRI can suffer from reliability issues due to low image quality, failed contrast injections, or a lack of response to stress. This project will develop a range of AI-enabled quality control processes to make perfusion cardiac MRI more robust and reliable in the clinic.Project Description:The main cause of the underutilisation of stress perfusion cardiac MRI is that visual assessment of the images is highly dependent on the level of training of the operators [1]. As yet, quantitative analysis of stress perfusion cardiac MRI remains primarily a research tool but its clinical translation would be advantageous as it can be automated, enabling accurate and user-independent assessment of myocardial perfusion. Our group has also recently demonstrated the independent prognostic value of quantitative stress perfusion CMR [2]. However, this work still involved several steps of manual interaction, including the segmentation of the myocardium. The automated analysis of quantitative stress perfusion CMR has the potential to revolutionise the management of patients with suspect coronary artery disease.Our group has pioneered the use of deep learning to achieve the automatic quantitative analysis of stress perfusion CMR [3]. The problem of deploying this solution at large scale is the visual inspection and quality control of the analysis is not feasible. If it is not obvious for which cases the analysis has failed, then clinicians may be presented with failed or inaccurate measurements and, therefore, draw the wrong conclusions. We aim to overcome this limitation by developing a fully automated image quality control (QC) tool using deep neural networks. Deep learning has been widely adopted in the field of medical imaging and has become the de facto standard for many processing tasks. Trends in cardiac MRI image analysis have followed a similar route where deep learning is now used for everything from reconstruction to detection and segmentation tasks to automating diagnostics and prognostics.To this end, we envisage the development of three QC processes to be developed, validated, and integrated into the clinical workflow.Analysis of the arterial input function (AIF): Machine learning methods will be used to classify whether the injection of the contrast agent into the patient was done correctly.Analysis of stress response: Some patients do not respond as expected to the injection of the stressor drug. Algorithms will be trained and validated to identify these patients.Detection of failed cases. Our previously developed automated processing pipeline can fail, in that the heart is predicted in the wrong location or the predicted shape of the heart is not as expected. A further neural network will be developed to flag these cases.