UCSF

This project sought to create a deep learning model to detect and track MR-compatible catheter tips under Magnetic Resonance Imaging. Interventional MRI, or iMRI, has many advantages over traditional x-ray angiography methods, yet the path towards adoption is hindered by many obstacles, including the lack of easily visualizable catheter tips. The model, the Faster Region-based Convolutional Neural Network (Faster R-CNN), was chosen due to its well-balanced speed and accuracy over other model architectures. The dataset included MR images of passive and resonant catheter tips alone and as well as passive catheter tips in an abdominal aorta phantom. The Faster R-CNN was trained over many iterations and over the best run it was able to draw bounding boxes over the tip of the catheter with an overall mean average precision of 0.59 and overall average recall of 0.66. Further optimization of training parameters will be needed to create a model that can achieve a better mean average precision. This study opens the possibility of applying artificial intelligence models towards iMRI methods, which helps push towards the goal of proving the safety and efficacy of iMRI procedures. These foundational elements are critical to smoothing the adoption of iMRI for guiding endovascular procedures.