公开(公告)号：US20170035319A1

公开(公告)日：2017-02-09

申请号：US15227825

申请日：2016-08-03

Applicant: University of Virginia Patent Foundation ,  Beth Israel Deaconess Medical Center, Inc.

Inventor： Li Zhao ,  Craig Meyer

IPC: A61B5/055 ,  A61B5/00 ,  G06K9/46 ,  G06T11/00 ,  G06K9/62 ,  A61B5/026 ,  G06T7/00

CPC classification number: A61B5/055 ,  A61B5/0042 ,  A61B5/0263 ,  G01R33/4826 ,  G01R33/5611 ,  G01R33/56366 ,  G06K2209/05

Abstract: Methods, systems, and computer-readable media for rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction are disclosed. In one embodiment, a method includes acquiring magnetic resonance data associated with an area of interest of a subject. The magnetic resonance data includes associated with arterial spin labeling (ASL) of the area of interest. The method also includes performing image reconstruction on the acquired resonance data. The image reconstruction includes compressed sensing enforcing a model-based sparsity constraint, where the model-based sparsity constraint is based on an ASL signal prototype dictionary.

Abstract translation: 公开了用于基于稀疏模型的图像重建的用于快速3D动态动脉自旋标记的方法，系统和计算机可读介质。 在一个实施例中，一种方法包括获取与受试者的感兴趣区域相关联的磁共振数据。 磁共振数据包括与感兴趣区域的动脉自旋标记（ASL）相关联。 该方法还包括对获取的共振数据执行图像重构。 图像重构包括强制基于模型的稀疏约束的压缩感知，其中基于模型的稀疏约束基于ASL信号原型字典。

公开(公告)号：US10561337B2

公开(公告)日：2020-02-18

申请号：US15227825

申请日：2016-08-03

Applicant: University of Virginia Patent Foundation

Inventor： Li Zhao ,  Craig Meyer

IPC: A61B5/055 ,  A61B5/026 ,  A61B5/00 ,  G01R33/563 ,  G01R33/561 ,  G01R33/48

Abstract: Methods, systems, and computer-readable media for rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction are disclosed. In one embodiment, a method includes acquiring magnetic resonance data associated with an area of interest of a subject. The magnetic resonance data includes associated with arterial spin labeling (ASL) of the area of interest. The method also includes performing image reconstruction on the acquired resonance data. The image reconstruction includes compressed sensing enforcing a model-based sparsity constraint, where the model-based sparsity constraint is based on an ASL signal prototype dictionary.