标题： 基于物理约束的从1.5T图像估计3T MR图像 显示英文标题

标题： Estimation of 3T MR images from 1.5T images regularized with Physics based Constraint

摘要： 有限的高场强MRI扫描仪（如7T、11T）的可及性促使了后处理方法的发展，以提高低场强图像的质量。 现有的几种后处理方法已经证明了将3T图像提升为7T类似图像的可行性[3,18]。 人们观察到，由于图像质量较差，例如，将1.5T和更高场强（HF，3T）图像之间的函数映射比3T和7T图像之间的函数映射更复杂[10]。 除了[10]之外，还没有方法被提出用于改善<=1.5T的MRI图像。 此外，大多数现有方法[3,18]包括[10]都需要示例图像，并且通常依赖于LF和HF图像之间的像素到像素的对应关系，而这些对应关系对于<=1.5T图像通常是不准确的。 本文的重点是解决1.5T图像质量改进的无监督框架，避免对示例图像和相关图像配准的昂贵需求。 假设LF和HF图像通过线性变换（LT）相关联。 未知的HF图像和未知的LT是在交替最小化框架中估计的。 此外，提出了一种基于物理的约束，该约束提供了一个额外的非线性函数，以在LF和HF图像之间建立联系，从而在估计的HF图像中实现所需的高对比度。 实验结果表明，所提出的方法提供了处理后的1.5T图像，即估计的3T类似图像，其图像质量得到改善，并且与解决类似问题的现有方法相比具有可比的优势。 图像质量的改进也被证明相比扫描仪获取的1.5T图像能够提供更好的组织分割和体积量化。 显示英文摘要

摘要： Limited accessibility to high field MRI scanners (such as 7T, 11T) has motivated the development of post-processing methods to improve low field images. Several existing post-processing methods have shown the feasibility to improve 3T images to produce 7T-like images [3,18]. It has been observed that improving lower field (LF, <=1.5T) images comes with additional challenges due to poor image quality such as the function mapping 1.5T and higher field (HF, 3T) images is more complex than the function relating 3T and 7T images [10]. Except for [10], no method has been addressed to improve <=1.5T MRI images. Further, most of the existing methods [3,18] including [10] require example images, and also often rely on pixel to pixel correspondences between LF and HF images which are usually inaccurate for <=1.5T images. The focus of this paper is to address the unsupervised framework for quality improvement of 1.5T images and avoid the expensive requirements of example images and associated image registration. The LF and HF images are assumed to be related by a linear transformation (LT). The unknown HF image and unknown LT are estimated in alternate minimization framework. Further, a physics based constraint is proposed that provides an additional non-linear function relating LF and HF images in order to achieve the desired high contrast in estimated HF image. The experimental results demonstrate that the proposed approach provides processed 1.5T images, i.e., estimated 3T-like images with improved image quality, and is comparably better than the existing methods addressing similar problems. The improvement in image quality is also shown to provide better tissue segmentation and volume quantification as compared to scanner acquired 1.5T images.