MRI scanners introduce geometric distortion into their images. This distortion, which can be as much as several
millimeters, is problematic when the image is being used for radiation therapy or precision neurosurgical planning.
CIRS builds several grid phantoms for geometric distortion characterization. We collaborated with CIRS’s engineering
team to design and develop software to characterize an MRI scanner’s geometric distortion and track it over time.
Our role on the project involved:
Uploading, parsing, and managing raw imaging (DICOM) data.
Developing and validating a precise fiducial localization algorithm.
Developing and validating a registration algorithm that performs well in the presence of distortion, noise, false
positives, and false negatives.
Designing, training, and validating a convolutional neural network (deep learning algorithm) to reject false positives.
The performance of this stage ensures that the full process can be automated.
Designing and developing a web application user interface and associated PDF reports.
Through out the project we worked closely with the CIRS engineering team.