TRD3 - Image Reconstruction

TRD3 - 图像重建

• 批准号: 10549856
• 负责人: Mehmet Akcakaya
• 金额: $ 22.19万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10549856
• 关键词: 3-Dimensional; Acceleration; Address; Algorithms; Anatomy; Brain imaging; Calibration; Cardiac; Clinical; Data; Data Set; Databases; Dependence; Development; Diffusion; Dimensions; Elements; Exhibits; Formulation; Fourier Transform; Human; Image; Joint repair; Joints; Learning; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Maps; Measurement; Measures; Methods; Modeling; Morphologic artifacts; Motion; Noise; Perfusion; Phase; Physiological; Play; Protocols documentation; Radial; Research; Residual state; Resolution; Role; Rotation; Sampling; Scanning; Scheme; Slice; System; Techniques; Technology; Training; Variant; computerized tools; connectome; convolutional neural network; cost; data space; deep learning; high resolution imaging; image reconstruction; improved; mathematical model; multidimensional data; novel; novel strategies; optical sensor; reconstruction; resilience; respiratory; sensor; simulation; spatiotemporal; temporal measurement; usability

Project Summary/Abstract Image reconstruction from raw measurements is an inverse problem of fundamental importance in MRI. The basic formulation for such reconstructions involve a k-space sampled uniformly on a Cartesian grid at greater than the Nyquist rate, which is Fourier transformed to generate the desired image. However, this acquisition- reconstruction strategy is often difficult to perform in practical research and clinical settings, as it leads to long scan times, necessitating trade-offs in spatial and temporal resolutions. This observation has led to the development of multiple reconstruction strategies over the last few decades, including partial Fourier imaging, parallel imaging, non-Cartesian acquisitions and compressed sensing, where the reconstruction goes beyond a simple Fourier transform, and often involves careful mathematical modeling of the MR system and images. The aforementioned developments aim to address a continuous need for faster imaging, improved resolutions and robustness, both in clinical and research settings. However, as the existing methods reach the limits of resolution and acceleration achievable in the presence of system and physiological limitations, new reconstruction strategies are needed to improve image quality for various acquisition strategies. In this TRD, we seek to develop new image reconstruction techniques for enabling fast high-resolution acquisitions, improving noise resilience, allowing for different encoding strategies, while increasing robustness to underlying physiological and system variations. Our developments for fast high-resolution imaging include improved strategies for k-space interpolation reconstruction in Cartesian imaging, as well as new self- calibrated techniques for three-dimensional non-Cartesian imaging. For the former, we extend the liner shift- invariant convolutional interpolation approaches for reconstructing multi-coil data in two ways: i) Scan-specific deep learning without training databases for non-linear estimation of missing k-space data, in simultaneous multi-slice, parallel and partial Fourier imaging, ii) Region-specific shift-variant linear kernels for highly- accelerated volumetric parallel imaging. For non-Cartesian acquisitions, our self-calibration is used to estimate radius- and rotation-specific interpolation kernels, without additional ACS data. We also tackle the problem of improving non-Fourier encoded acquisitions, such as spatiotemporal encoding, and devise fast matrix sparsifying approaches to enable regularized reconstructions without high computational burden. To further improve reconstruction fidelity in multi-dimensional acquisitions, we propose the local use of high-order tensor models, along with an information theoretic approach for parameter-free regularization. Finally, we consider imaging in the presence of physiological and system variations, such as motion and B0 inhomogeneities, which are especially pronounced at ultrahigh field strengths, and develop a self-consistency based framework for nonlinear inversion, which utilizes improved initialization from external sensors or sequence elements.

项目摘要/摘要 原始测量结果的图像重建是MRI中基本重要性的反向问题。这 此类重建的基本公式涉及在更大的笛卡尔网格上均匀采样的K空间 而不是Nyquist速率，该速率是傅立叶变换以生成所需图像的。但是，此收购 - 重建策略通常在实践研究和临床环境中很难执行，因为它导致长期 扫描时间，需要在空间和时间分辨率方面进行权衡。这种观察导致了 在过去的几十年中，制定多种重建策略，包括部分傅立叶成像， 平行成像，非牙犯的采集和压缩感应，重建超出了 简单的傅立叶变换，通常涉及MR系统和图像的仔细数学建模。这 上述发展旨在满足对更快成像，改进决议和的持续需求 在临床和研究环境中，鲁棒性。但是，随着现有方法达到限制 在存在系统和生理局限性的情况下可以实现的解决和加速 需要重建策略来改善各种获取策略的图像质量。 在此TRD中，我们试图开发新的图像重建技术，以实现快速的高分辨率 采集，提高噪声弹性，允许不同的编码策略，同时提高鲁棒性 潜在的生理和系统变化。我们的快速高分辨率成像的发展包括 改进了笛卡尔成像中K空间插值重建的策略，以及新的自我 三维非现行成像的校准技术。对于前者，我们延长了衬里班次 - 不变的卷积插值方法，用于通过两种方式重建多型线圈数据：i）特定于扫描 无需培训数据库的深度学习即可同时估算丢失K空间数据的非线性估计 多板板，平行和部分傅立叶成像，ii）高度 - 高度的区域特异性偏差线性内核 加速的体积平行成像。对于非牙犯的收购，我们的自我校准用于估计 半径和旋转特异性插值内核，没有其他ACS数据。我们还解决了 改善非概念编码的收购，例如时空编码，并设计快速矩阵 稀疏的方法可以实现不高计算负担的正规重建。进一步 提高多维采购中的重建保真度，我们建议局部使用高级张量 模型，以及无参数正则化的信息理论方法。最后，我们考虑 在存在生理和系统变化的情况下进行成像，例如运动和B0不均匀性，它们 在超高野外优势下特别明显，并为基于自稳定的框架 非线性反演，它利用了外部传感器或序列元素的初始化改进。