Hierarchical Reconstruction of 7T-like Images from 3T MRI Using Multi-level CCA and Group Sparsity.

Hierarchical Reconstruction of 7T-like Images from 3T MRI Using Multi-level CCA and Group Sparsity. Med Image Comput Comput Assist Interv. 2015 Oct;9350:659-666 Authors: Bahrami K, Shi F, Zong X, Shin HW, An H, Shen D Abstract Advancements in 7T MR imaging bring higher spatial resolution and clearer tissue contrast, in comparison to the conventional 3T and 1.5T MR scanners. However, 7T MRI scanners are less accessible at the current stage due to higher costs. Through analyzing the appearances of 7T images, we could improve both the resolution and quality of 3T images by properly mapping them to 7T-like images; thus, promoting more accurate post-processing tasks, such as segmentation. To achieve this method based on an unique dataset acquired both 3T and 7T images from same subjects, we propose novel multi-level Canonical Correlation Analysis (CCA) method and group sparsity as a hierarchical framework to reconstruct 7T-like MRI from 3T MRI. First, the input 3T MR image is partitioned into a set of overlapping patches. For each patch, the local coupled 3T and 7T dictionaries are constructed by extracting the patches from a neighboring region from all aligned 3T and 7T images in the training set. In the training phase, we have both 3T and 7T MR images scanned from each training subject. Then, these two patch sets are mapped to the same space using multi-level CCA. Next, each input 3T MRI patch is sparsely represented by the 3T dictionar...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research