Intelligent Quality Control using AutoBundles

DIPY is a brain imaging and diffusion MRI software analysis library built in Python. Diffusion MRI is a unique non-invasive MRI technique that is used to study the structural connectivity of the brain. DIPY is already heavily tested however now we would like to allow it to use advanced and innovative quality control that works along large pipelines and across many datasets. Which is currently not available. In the field of diffusion MRI there is large amount of different algorithms that work sequentially from the input to the output. The output is usually a visual approximation of the white matter. As a first step we would like to create a system that directly checks the final output and generates reports if something is missing. To do so we are developing a new algorithm called AutoBundles which can quickly segment the tractograms and report which pathways are available in the dataset.

Use of FutureSystems

Future systems will provide the infrastructure for running the quality control experiments.

Scale of Use

At first we will need to have access to a few nodes. Some of them will need GPUs. We need the GPUs for remote 3D visualization (primary reason) or for deep learning tasks (secondary reason). Also the IO datasets used or created will be initially a few Terabytes. We hope to be able to run our quality control scripts once a day every day. Some of the execution pipelines will take from a few hours to many hours (e.g. 10 hours) per node.

At first stage we will not need many nodes but as more datasets are included then we will need more processing power.