AI enabled 3-fold accelerated in vivo Whole-Heart Diffusion Tensor Cardiac MR

博士项目的目的：3倍加速的全心脏在体扩散张量心脏磁共振（DT-CMR）。开发3D和同步多切片（SMS）DT-CMR采集开发和优化AI算法，以最大限度地减少3D和SMS DT-CMR图像中的伪影。将这些算法集成到CMR图像重建管道中，用于临床翻译。项目描述/背景：在体扩散张量心脏磁共振（DT-CMR）提供了一种非侵入性询问跳动心脏3D微结构的方法，这是其他临床测试所不允许的1，2。这种独特的创新技术提供了巨大的潜力，通过新的微观结构和功能评估，以改善临床诊断。DT-CMR数据采集目前效率非常低;在心动周期的一个时间点采集通过心脏的单个2D切片需要约10分钟。全心脏DT-CMR覆盖对于许多心脏疾病的准确诊断至关重要，例如以异质方式影响心脏的心肌梗死（MI）。因此，提高效率是DT-CMR临床诊断的关键。3D采集可以增加覆盖范围和SNR。目前，2D DT-CMR方法使用单次激发读出，其在一个心动周期中采集一个图像所需的所有数据。3D DT-CMR将需要分段读出，其在几个心动周期内采集一个体积所需的所有数据。与扩散编码相结合的分割导致相位误差伪影。此外，较长的3D读出导致失真伪影2。同时多切片（SMS）技术同时获取多个2D切片，但分别重建它们，极大地提高了成像效率。虽然SMS技术已成功用于神经成像3，但到目前为止，仅验证了SMS DT-CMR研究的概念4，5。由于心脏是比大脑小的器官，因此多个切片之间的距离较小。SMS算法在这些情况下通常会失败，并且来自一个切片的信息错误地最终出现在不同的切片上（“切片间泄漏”伪影）。人工智能（AI）算法可以从训练图像中学习复杂的关系或模式，并对新获取的图像进行准确的预测。AI算法可以被训练来重建高度欠采样的3D采集，校正失真伪影并最大限度地减少运动引起的相位误差。SMS也准备好从人工智能算法中受益，因为可以很容易地获得多切片训练数据集。体外DT-CMR数据可以提供一个没有心脏和呼吸运动的优秀测试用例，而体内DT-CMR数据可以在健康志愿者和患者队列中获得。在这个项目中，生成对抗网络和其他新颖的AI算法将被开发，以最大限度地减少/消除3D DT-CMR和SMS DT-CMR体外和体内数据中的伪影。这些新算法将被优化，以允许最大可能的加速因子，目标是3倍加速的全心脏覆盖（即，目前需要约90分钟的全心脏9切片协议将需要约30分钟，目标SMS加速因子为3）。这些AI使能的体内全心DT-CMR技术将被集成到临床CMR图像重建管道中，用于临床翻译。这些AI算法的重现性将在健康志愿者和MI患者中进行测试。

Aim of the PhD Project:3-times accelerated whole-heart in vivo diffusion tensor cardiac magnetic resonance (DT-CMR).Development of 3D and simultaneous multi-slice (SMS) DT-CMR acquisitionsDevelopment and optimisation of AI algorithms to minimise artefacts in 3D and SMS DT-CMR images.Integration of these algorithms in the CMR image reconstruction pipeline for clinical translation.Project Description / Background:In vivo diffusion tensor cardiac magnetic resonance (DT-CMR) provides a means for non-invasive interrogation of the 3D microarchitecture of the beating heart, which no other clinical test allows1,2. This unique and innovative technology offers tremendous potential to improve clinical diagnosis through novel microstructural and functional assessment. DT-CMR data acquisition is currently very inefficient; ~10 minutes are needed to acquire a single 2D slice through the heart at one timepoint in the cardiac cycle. Whole-heart DT-CMR coverage is essential for accurate diagnosis in many cardiac diseases such as myocardial Infarction (MI) which affects the heart in a heterogeneous fashion. Improved efficiency is therefore key to DT-CMR clinical translation.3D acquisitions allow increased coverage and SNR. Currently, 2D DT-CMR methods use single-shot readouts that acquire all the data needed for one image in one cardiac cycle. 3D DT-CMR would require segmented readouts that acquire all the data needed for one volume over several cardiac cycles. Segmentation combined with diffusion encoding leads to phase error artefacts. Furthermore, longer 3D readouts lead to distortion artefacts2.Simultaneous multi-slice (SMS) techniques acquire multiple 2D slices simultaneously but reconstruct them separately, improving imaging efficiency tremendously. While SMS techniques have been successfully used in neuroimaging3, so far only proof-of-concept SMS DT-CMR studies have been published4,5. Since the heart is a smaller organ than the brain, the distance between multiple slices is smaller. The SMS algorithm often fails in these circumstances and information from one slice erroneously ends up on a different slice ("interslice-leakage" artefact).Artificial Intelligence (AI) algorithms can learn complex relationships or patterns from training images and make accurate predictions on newly acquired images. AI algorithms can be trained to reconstruct highly undersampled 3D acquisitions, correct distortion artefacts and minimise motion-induced phase errors. SMS is also well poised to benefit from AI algorithms because multi-slice training data sets can easily be acquired. Ex vivo DT-CMR data can provide an excellent test case devoid of cardiac and respiratory motion, while in vivo DT-CMR data can be acquired in healthy volunteers and as well as patient cohorts.In this project, Generative Adversarial Networks and other novel AI algorithms will be developed to minimise/eliminate artefacts in 3D DT-CMR and SMS DT-CMR ex vivo and in vivo data. These novel algorithms will be optimised to allow for the maximum possible speed-up factors, aiming for 3-fold accelerated whole-heart coverage (i.e. a whole-heart 9-slice protocol which currently takes ~90 minutes would require ~30 minutes with the targeted SMS acceleration factor of 3). These AI enabled in vivo whole-heart DT-CMR techniques will then be integrated in the clinical CMR image reconstruction pipeline for clinical translation. Reproducibility of these AI algorithms will be tested in healthy volunteers and patients with MI.