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.

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心肌梗死（MI）。项目描述：心肌细胞的复杂排列和动力学（心肌细胞）和被称为骨膜的心肌细胞组对正常的心脏功能至关重要。扩散张量心血管磁共振（DT-CMR）是一种独特的MRI方法，可根据测量水的自扩散提供有关微观组织结构的信息。 DT-CMR可以推断出心肌细胞和毛坯的方向，在收缩过程中重新定位，并为细胞外空间的变化，膜完整性和心肌细胞方向的相干性提供敏感的措施。这种新颖的方法越来越多地用于调查疾病的微观变化。作为我们正在进行的对心血管磁共振单元（Royal Brompton医院）对心脏微观结构的调查的一部分，我们已经表明，使用蒸汽技术的噪声率很低的信号和SNR，我们表明，DT-CMR在多个信号技术中可能需要在多个心脏周期中进行多个时间点（因此）。结果，影响右心室和心房的病理，慢性梗塞组织的稀薄心肌和小焦点变化无法得到充分研究。我们最近开发了一项技术，该技术沿着两种交织的螺旋路径采样数据，以分散两种心跳加热的高分辨率DT-CMR数据的采集。然而，交织的DT-CMR技术对磁场的局部变化和两个螺旋之间的运动引起的伪影敏感。尽管有这些人工制品，但我们能够将Vivo In-Vivo DT-CMR采集的平面分辨率从2.8x2.8mm2增加到1.8x1.8mm2 [5]。相比之下，从单个交流中重建数据可节省时间，改善患者的舒适度并避免一些相关的人工制品。并行成像经常用于减少MRI所需的数据，但是SNR的相关损失，较长的计算时间和螺旋蒸汽DT-CMR中使用的小视野是不相容的。在最近的工作中，我们确定了深卷卷神经网络（CNN）在MRI重建中的有效性[6] [6]] [6]和demisising DT-CMR DataSets [7]。在螺旋DT-CMR [8]的模拟中，我们沿着螺旋轨迹回顾性地采样了DT-CMR图像，并使用完全采样的数据作为地面真理训练了CNN。我们证明了在这个有前途但早期的试点数据中，有效地删除了具有多达4的底采样因子的混叠伪像。在这里，我们旨在基于这些初始计算模拟，并开发一种适用于CNN的临床上的体内工具，以实现有挑战性的患者队列中有效且可靠的高分辨率螺旋DT-CMR。该学生将开发一种新型的基于多任务的基于多任务的深层CNN，以优化数据获取（即寻求最佳的螺旋轨迹）并同时实现高保真图像重建（即去除底漆和其他人工伪像）。这些方法将在模拟和体内进行测试，并使用我们的最先进的3T西门子VIDA扫描仪在皇家Brompton医院进行测试。