Concurrent multi-task learning-based deep convolutional neural networks for high resolution assessment of in-vivo cardiac microstructure

基于并行多任务学习的深度卷积神经网络，用于体内心脏微观结构的高分辨率评估

• 批准号: 2605686
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605686
• 关键词: Concurrent; multi; task; learning; based

Aim of the PhD Project:Develop a concurrent multi-task learning-based deep convolutional neural network (CNN) for data acquisition and image reconstruction of highly-undersampled spiral MRI data with minimal artefact.Deploy these methods for efficient, high-resolution in-vivo diffusion tensor cardiovascular magnetic resonance of cardiac microstructure.Validate these methods in controls and patients with myocardial infarction (MI).Project Description:The complex arrangement and dynamics of heart muscle cells (cardiomyocytes) and groups of cardiomyocytes known as sheetlets is vital to normal cardiac function. Diffusion tensor cardiovascular magnetic resonance (DT-CMR) is a unique MRI method providing information on microscopic tissue structures, based on measuring the self-diffusion of water. DT-CMR can infer the orientation of cardiomyocytes and sheetlets, which reorientate during contraction and provide measures sensitive to changes in extracellular space, membrane integrity and coherence of cardiomyocyte orientation. This novel method is increasingly used to investigate the microscopic changes underlying disease.As part of our ongoing investigations into cardiac microstructure at the Cardiovascular Magnetic Resonance Unit, The Royal Brompton Hospital, we have shown that acquisition of DT-CMR at multiple timepoints in the cardiac cycle is possible using the STEAM technique, which is a low signal to noise ratio (SNR) technique and therefore requires many signal averages at low resolution. As a result, pathologies affecting the right ventricle and atria, the thinned myocardium of chronically infarcted tissue and small focal changes cannot be adequately investigated.We recently developed a technique which samples data along two interleaved spiral paths to split the acquisition of higher resolution DT-CMR data across two heartbeats. However, interleaved DT-CMR techniques are sensitive to artefacts caused by localised changes in the magnetic field and motion between the two spirals. Despite these artefacts, we were able to increase the in-plane resolution of in-vivo DT-CMR acquisitions from 2.8x2.8mm2 to 1.8x1.8mm2[5].Unfortunately, acquiring two interleaves requires doubling the already long scan times, with typically twenty 18s breath holds required per slice and cardiac phase. In contrast, reconstructing data from a single interleave saves time, improves patient comfort and avoids some of the associated artefacts. Parallel imaging is frequently used to reduce the data required in MRI, but the associated loss of SNR, long computation times and the small field of view used in spiral STEAM DT-CMR are incompatible.In recent work we have established the effectiveness of deep convolutional neural networks (CNNs) in MRI reconstruction[6] and denoising DT-CMR datasets[7]. In simulations of spiral DT-CMR[8], we retrospectively undersampled DT-CMR images along spiral trajectories and trained a CNN using the fully sampled data as the ground truth. We demonstrated effective removal of aliasing artefacts with undersampling factors of up to 4 in this promising, but early stage pilot data. Here, we aim to build on these initial computational simulations and develop a clinically applicable in-vivo tool for CNN enabled efficient and robust high-resolution spiral DT-CMR in challenging patient cohorts. The student will develop a novel concurrent multi-task learning-based deep CNN for optimising data acquisition (i.e. seeking optimal spiral trajectories) and achieving high-fidelity image reconstruction (i.e. removal of undersampling and other artefacts) simultaneously. These methods will be tested in simulations and in-vivo using our state-of-the-art 3T Siemens Vida scanner at The Royal Brompton Hospital.

PHD项目的目标：开发一种基于并行多任务学习的深卷积神经网络(CNN)，用于以最少的伪影获取高度欠采样的螺旋MRI数据和图像重建。将这些方法应用于心脏微结构的高效、高分辨率体内扩散张量或心血管磁共振。在对照组和心肌梗死(MI)患者中验证这些方法。项目描述：心肌细胞(心肌细胞)和心肌细胞组的复杂排列和动力学对于正常的心功能至关重要。扩散张量心血管磁共振(DT-CMR)是一种独特的MRI方法，它基于测量水的自扩散来提供关于微观组织结构的信息。DT-CMR可以推测心肌细胞和心肌小片在收缩过程中重新定位的方向，并提供对细胞外空间、膜完整性和心肌细胞取向一致性变化敏感的指标。这种新的方法越来越多地被用于研究疾病背后的微观变化。作为我们在皇家布朗普顿医院心血管磁共振单位正在进行的心脏微结构研究的一部分，我们已经证明，使用STEAM技术可以在心脏周期的多个时间点获取DT-CMR，这是一种低信噪比(SNR)技术，因此需要在低分辨率下进行许多信号平均。因此，影响右室和心房、慢性心肌梗死组织变薄的心肌和微小的局灶性改变的病理机制无法得到充分的研究。我们最近开发了一种技术，沿着两个交错的螺旋路径采样数据，将高分辨率DT-CMR数据的采集分成两个心跳。然而，交错DT-CMR技术对两个螺旋之间的磁场和运动的局部变化引起的伪影很敏感。尽管有这些假象，我们还是能够将体内DT-CMR采集的平面内分辨率从2.8x2.8mm2提高到1.8x1.8mm2[5]。不幸的是，获得两个交错需要已经很长的扫描时间的两倍，通常每个切片和心脏时相需要20个18s的屏气。相比之下，从单个交错重建数据节省了时间，改善了患者的舒适度，并避免了一些相关的伪影。并行成像经常被用来减少MRI所需的数据量，但螺旋STEAM DT-CMR所使用的信噪比损失、计算时间长和视场小是不相容的。在最近的工作中，我们建立了深卷积神经网络(CNN)在MRI重建[6]和去噪DT-CMR数据集[7]中的有效性。在螺旋DT-CMR[8]的模拟中，我们沿着螺旋轨迹回溯性地欠采样DT-CMR图像，并使用完全采样的数据作为基本事实来训练CNN。在这个前景看好但处于早期阶段的试验数据中，我们展示了有效地去除混叠伪影，欠采样系数高达4。在这里，我们的目标是建立在这些初步的计算模拟的基础上，并开发一种临床适用的体内工具，用于CNN支持的高效和强大的高分辨率螺旋DT-CMR，以挑战患者队列。学生将开发一种基于并行多任务学习的深度CNN，用于优化数据采集(即寻找最佳螺旋轨迹)和同时实现高保真图像重建(即去除欠采样和其他伪影)。这些方法将在模拟和活体中使用我们在皇家布朗普顿医院最先进的3T西门子VIDA扫描仪进行测试。