Atlas-Based Neuroinformatics via MRI: Harnessing Information from Past Clinical Cases and Quantitative Image Analysis for Patient Care

Abstract

With the ever-increasing amount of anatomical information radiologists have to evaluate for routine diagnoses, computational support that facilitates more efficient education and clinical decision making is highly desired. Despite the rapid progress of image analysis technologies for magnetic resonance imaging of the human brain, these methods have not been widely adopted for clinical diagnoses. To bring computational support into the clinical arena, we need to understand the decision-making process employed by well-trained clinicians and develop tools to simulate that process. In this review, we discuss the potential of atlas-based clinical neuroinformatics, which consists of annotated databases of anatomical measurements grouped according to their morphometric phenotypes and coupled with the clinical informatics upon which their diagnostic groupings are based. As these are indexed via parametric representations, we can use image retrieval tools to search for phenotypes along with their clinical metadata. The review covers the current technology, preliminary data, and future directions of this field.

Tools for multiple granularity analysis of brain MRI data for individualized image analysis

“…The task of this ontology-based analysis is to compress the 1.2 million voxel spatial dimension, while losing a minimum amount of pathology information. This is an essential step, if we want to combine MRI-based anatomical features with non-image clinical information, such as demography, lifestyle, clinical symptoms, lab tests, etc., to improve our ability to stratify the heterogeneous patient groups or predict the outcomes [25]. …”

Atlas-based anatomical modeling and analysis of heart disease

“…It allows better personalization of patient diagnosis by taking into account the measurements derived from a clinically relevant group [8]. This population-based approach to measure clinical diagnosis has been well established in the brain domain [9]. However for the heart domain, statistical maps of regional cardiac shape and function are only recently being developed [5].…”

Big Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging

“…In genomics, databases have existed for many years for archiving and curating data [4]. In neuroscience, large repositories are available for specific disease groups or general atlases [5]. Efforts are now being made to facilitate data sharing in the cardiovascular domain [6].…”