Compressed Quantitative MRI

压缩定量 MRI

• 批准号: EP/M019802/1
• 负责人: Mike Davies
• 金额: $ 80.76万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM019802%2F1
• 关键词: Compressed; Quantitative; MRI

The proposed research will provide the first proof-of-principle for a new family of Compressed Quantitative Magnetic Resonance Imaging (CQ-MRI), able to rapidly acquire a multitude of physical parameter maps for the imaged tissue from a single scan. MRI is the pre-eminent imaging modality in clinical medicine and neuroscience, providing valuable anatomical and diagnostic information. However, the vast majority of MR imaging is essentially qualitative in nature providing a `picture' of the tissue while not directly measuring its physical parameters. In contrast, quantitative MRI aims to measure properties that are intrinsic to the tissue type and independent of the scanner and scanning protocol. Unfortunately, due to excessively long scan times, Quantitative MRI is not usually included in standard protocols.The proposed research is based on a combination of a new acquisition philosophy for Quantitative MRI, called Magnetic Resonance Fingerprinting, and recent advances in model-based compressed sensing theory to enable rapid simultaneous acquisition of the multiple parameter maps. The ultimate goal of the research will be to produce a full CQ-MRI scan capability with a scan time not substantially longer than is currently needed for a standard MRI scan.

这项拟议的研究将为一种新的压缩定量磁共振成像(CQ-MRI)家族提供第一个原理证明，该家族能够从一次扫描中快速获得成像组织的多个物理参数图。磁共振成像是临床医学和神经科学中最重要的成像手段，提供有价值的解剖和诊断信息。然而，绝大多数磁共振成像本质上是定性的，提供了组织的“图像”，而不是直接测量其物理参数。相比之下，定量MRI的目标是测量组织类型固有的、独立于扫描仪和扫描方案的属性。遗憾的是，由于扫描时间过长，定量MRI通常不包括在标准协议中。该研究结合了一种新的定量MRI采集理念--磁共振指纹识别，以及基于模型的压缩感知理论的最新进展，以实现多参数地图的快速同时采集。这项研究的最终目标将是生产一种完整的CQ-MRI扫描能力，扫描时间不会比目前标准MRI扫描所需的时间长很多。

项目成果

Inexact Gradient Projection and Fast Data Driven Compressed Sensing

• DOI: 10.1109/tit.2018.2841379
• 发表时间: 2017-05
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Mohammad Golbabaee;M. Davies

Accelerating T2* Mapping with Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI).

使用最大似然估计 (MLE) 和并行成像 (PI) 加速 T2* 映射。

• 发表时间: 2017
• 作者: Bano W.

Greedy approximate projection for magnetic resonance fingerprinting with partial volumes

部分体积磁共振指纹的贪婪近似投影

• DOI: 10.1088/1361-6420/ab356d
• 发表时间: 2020
• 期刊: Inverse Problems
• 影响因子: 2.1
• 作者: Duarte R

Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting.

用于加速笛卡尔 MR 指纹识别的多次回波平面成像：传统螺旋 MR 指纹识别的替代方案。

• DOI: 10.1016/j.mri.2019.04.014
• 发表时间: 2019
• 期刊: Magnetic resonance imaging
• 影响因子: 2.5
• 作者: Benjamin AJV

Diagnostic quality assessment of IR-prepared 3D magnetic resonance neuroimaging accelerated using compressed sensing and k-space sampling order optimization.

使用压缩感知和 k 空间采样顺序优化加速了 IR 准备的 3D 磁共振神经成像的诊断质量评估。

• DOI: 10.1016/j.mri.2020.08.025
• 发表时间: 2020
• 期刊: Magnetic resonance imaging
• 影响因子: 2.5
• 作者: Benjamin AJV