Low-Rank Atlas Image Analyses in the Presence of Pathologies

Abstract

We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes. Our framework combines a low-rank-plus-sparse image decomposition technique with an iterative, diffeomorphic, group-wise image registration method. At each iteration of image registration, the decomposition technique estimates a “healthy” version of each image as its low-rank component and estimates the pathologies in each image as its sparse component. The healthy version of each image is used for the next iteration of image registration. The low-rank and sparse estimates are refined as the image registrations iteratively improve. When that framework is applied to image-to-atlas registration, the low-rank image is registered to a pre-defined atlas, to establish correspondence that is independent of the pathologies in the sparse component of each image. Ultimately, image-to-atlas registrations can be used to define spatial priors for tissue segmentation and to map information across subjects. When that framework is applied to unbiased atlas formation, at each iteration, the average of the low-rank images from the patients is used as the atlas image for the next iteration, until convergence. Since each iteration’s atlas is comprised of low-rank components, it provides a population-consistent, pathology-free appearance. Evaluations of the proposed methodology are presented using synthetic data as well as simulated and clinical tumor MRI images from the brain tumor segmentation (BRATS) challenge from MICCAI 2012.

Groupwise registration of MR brain images with tumors

“…Inspired by the success of mask based and inpainting based strategies for pathological image registration (Brett et al, 2001, Stefanescu et al, 2004, Crinion et al, 2007, Sdika and Pelletier, 2009, Andersen et al, 2010, Parisot et al, 2012, Liu et al, 2014, Liu et al, 2015), we estimate a normal-appearance counterpart for each tumor image to be registered. Since brain tumor regions usually appear in different locations across subjects and with distinct image appearances, we adopt a LRSD method (Peng et al, 2012) to map pathological images to their normal appearance counterparts based on the LRSD’s low-rank part.…”

“…1. Instead of directly using the low-rank images as a normal-appearance counterpart for each brain tumor image (Liu et al, 2014, Liu et al, 2015), we identify tumor regions based on the LRSD’s sparse component using a graph cut technique (Boykov et al, 2001), and combine low-rank image information within the tumor regions and original image information outside of the tumor regions to obtain an inpainted version of the original tumor image.…”

“…We evaluated our method, referred to as DDGM+M-LRSD, based on both simulated and real brain tumor images, and compared it with the GIR method proposed by Liu et al (Liu et al, 2015). The latter method uses low-rank images as input images and a group average image is used as the group template, and therefore is referred to as AVG+LRSD.…”

“…Similar to the pathology simulation methods, the inpainting based methods also generate simulated images by replacing abnormal regions of pathological images with normal appearance information in order to make images to be registered comparable (Sdika and Pelletier, 2009, Liu et al, 2014). For example, the inpainting strategy was used to fill white matter lesions with normal white matter information (Sdika and Pelletier, 2009), and low-rank images with normal appearance were derived from pathological brain images for image registration (Liu et al, 2014, Liu et al, 2015). Although all these methods have demonstrated promising performance in pairwise image registration studies, the inpainting strategy might be a better choice for registering multiple pathological brain images to a common space in a groupwise image registration setting.…”

“…Recently, an inpainting based GIR method for pathological images was proposed to build a brain image atlas based on a group of pathological brain images (Liu et al, 2014, Liu et al, 2015). This method maps pathological brain images to their normal-appearance counterparts and uses their average image as the template image for achieving least biased registration of pathological brain images.…”