.png){kind=icon}
返回
Rapid T1 quantification from high resolution 3D data with model-based reconstruction
.png){kind=icon}
OA
AI
DOI：10.1002/mrm.27502.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Purpose: Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model-based optimization framework in conjunction with undersampling 3D radial stack-of-stars data. Theory and Methods: High resolution 3D T-1 maps are generated from subsampled data by employing model-based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non-linear, non-differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss-Newton method. The importance of 3D-spectral regularization is demonstrated by a comparison to 2D-spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look-Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data. Results: Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T-1 maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions. Conclusions: The proposed algorithm is able to recover T-1 maps with an isotropic resolution of 1 mm(3) from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.
Keyword:
constrained reconstruction
inversion-recovery Look-Locker
imaging
model-based reconstruction
MRI
T1 quantification
variable flip angle
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
1.2W
被引数:
3.1W
机构
引用论文
.png){kind=icon}
Accelerating MR Parameter Mapping Using Sparsity-Promoting Regularization in Parametric DimensionOAAI
OAAI分享收藏Model-Based Acceleration of Parameter Mapping (MAP) for Saturation Prepared Radially Acquired DataOAAI
OAAI分享收藏Model-based T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASHOAAI
OAAI分享收藏
查看更多内容