ANATOMYWORKS, LLC — Department of Health and Human Services SBIR Phase II: 108

Description

DESCRIPTION provided by applicant In this project we will develop a commercial resource for the automated analysis of brain anatomy based on MRI This product is based on the whole brain parcellation algorithm with the following unique features First it is based on a cutting edge multi atlas approach in which we will incorporate rich atlas resources from Dr Moriandapos s lab at the Johns Hopkins University JHU Second our multi atlas approach is based on advanced diffeomorphic image transformation and multi atlas probability fusion recently developed by Dr Miller at JHU These CPU intensive algorithms combined with a large atlas inventory require highly parallelized computational resources We therefore will develop a fully portable and scalable cloud based architecture such that many users can have access at minimum costs Third we will develop a flexible architecture to define brain structures with multiple anatomical criteria providing a very unique multi granularity analysis which provides an anatomy centric and intuitive interface for clinical use Fourth we extend the analysis to diffusion tensor imaging DTI by incorporating a unique approach to multi contrast image transformation and probability fusion Last but not least these algorithms can convert a set of multiple MR images to a quantitative and standardized Anatomical Matrix which allows us to perform image data structurization searching and individualized analysis of anatomical phenotypes Aim To establish a cloud based servicing architecture We will develop a scalable and portable architecture for cloud based computation Parallel processing is required to achieve fast computation for the multi atlas calculations The algorithms accept DICOM data from four major vendors and apply a parcellation tool that identifies brain structures Aim To establish a web based interface for non corporate users To make our advanced image analysis tools widely available for research communities we will create a web based interface and provide the service at a minimum cost $ data Aim To implement a data visualization interface with ontology based multi granularity analysis Our image analysis pipeline is a departure from conventional voxel based automated analysis Our structure based analysis reduces the anatomical dimension to much lower scales However there are multiple ways to perform the structure based information reduction The ontology based analysis provides a novel way to perform hierarchical anatomical interpretation of the structure based analysis Aim To increase the number of atlases and cases in the database for interpretation support Through the collaboration with JHU we have access to a large inventory of research and clinical data including controls and various patient groups To create reference data we will process these data and establish a background database against which users can compare and interpret their data PUBLIC HEALTH RELEVANCE We will develop software for automated analysis of brain MR images This software provides quantitative assessment of brain anatomical status of various brain disease patients