Moving Radiology Forward with HPC at Boston Children’s Hospital

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In this video from the Intel HPC Developer Conference, Simon Warfield from Boston Children’s Hospital describes how radiology is being transformed with 3D and 4D MRI technology powered by AI and HPC.

Simon Warfield is working to use AI to better diagnose various forms of damage in the human brain including concussions, autism spectrum disorder and Pediatric Onset Multiple Sclerosis. The end goal will be a tool that allows clinicians to look directly at the brain rather than attempting to diagnose problems—and efficacy of treatment—by somewhat subjective assessments of cognitive behavior.

Collecting and Processing the Data

Professor Warfield and his team have created a Diffusion Compartment Imaging (DCI) technique, which is able to extract clinically relevant information about soft tissues in the brain. DCI is an improvement to Diffusion-Weighted Imaging (DWI) that works by using pulsing magnetic field gradients during an MRI scan to measure the atomic-scale movements of water molecules (called Brownian motion). The differential magnitude and direction of these movements provides the raw data needed to identify microstructures in the brain and to diagnose the integrity and health of neural tracts and other soft-tissue components.

However, there are several challenges to make DCI a clinically useful tool. For example, it can take about an hour to perform a single DCI scan when aiming at a very high spatial resolution, or about 10 minutes for most clinical applications. Before optimization, it took over 40 hours to process the tens of gigabytes of resulting water diffusion data.

Such long processing time made DCI completely unusable in many emergency situations, and further it made Diffusion Compartment Imaging challenging to fit into the workflow of today’s radiology departments. After optimization, it now takes about an hour to process the data.

Professor Warfield and Boston Children’s Hospital have been an Intel Parallel Computing Center (IPPC) for several years. This gave Professor Warfield’s team an extraordinary opportunity to optimize their code.

The results of the optimization work were transformative, providing a remarkable 75X performance improvement when running on Intel Xeon processors and a 161X improvement running on Intel Xeon Phi processors. A complete Diffusion Compartment Imaging study can now be completed in 16 minutes on a workstation, which means Diffusion Compartment Imaging can now be used in emergency situations, in a clinical setting, and to evaluate the efficacy of treatment. Even better, higher resolution images can be produced because the optimized code scales.

Volume Inferencing to Find Brain Damage

Data processing isn’t the only challenge with using Diffusion Compartment Imaging in clinical settings as a scan typically generates tens to hundreds of images. Many radiologists are finding it difficult to keep up with the increasing volume of work.

Professor Warfield believes AI is a solution to this problem. The vision is to train a model that can automatically and quickly sort through hundreds of images to pinpoint those that differ from comparable images of a healthy brain. The goal is to provide the radiologist with the essential highlights of a complex study, identifying not only the most relevant images, but also pinpointing the critical areas on those images.

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