The primary bottleneck for interventional MRI is accurately positioning instruments in the correct position relative to the lesion. The instruments, operated by hand or robots, move unpredictably and currently require a careful and iterative process of move - scan - move - scan until the position is satisfactory. Conversely, the lesion moves much less and can generally be assumed to stay within a region.
Guiding a percutaneous needle towards the region of interest in a prostate biopsy may take several minutes per biopsy. Cryoablation interventions, for example, use several needles and may take hours to position correctly. MRI-guided interventions can be made faster and more accurate by developing solutions allowing for real-time acquisitions, greatly improving current time-consuming processes.
Based on previous work showing dynamic deep-learning algorithms capable of reconstructing highly undersampled acquisitions, we modified a CRNN-MRI algorithm to reconstruct the sagittal and transverse images of a pelvis containing a biopsy needle in a dynamic fashion. In this project, we will integrate an existing segmentation AI into our reconstruction algorithm, employ various experiments to improve its performance, and subsequently integrate a trained model into simulation to show its performance in a simulated environment.
The student is invited to come up with other experiments that show improvement over the base model, exceptional students may propose using a different base algorithm if they think it would be better.
The dataset consists of 142 patients and 144 cases. These are biopsies, with each case containing at least 5 sagittal or transverse pairs of shape 256x256x5. The total number of files is 1820.
For model development, we provide access to our deep learning GPU cluster, SOL.
- Students Artificial Intelligence, Data Science, Computer Science, Bioinformatics, Biomedical Engineering or similar in the final stages of their Master education.
- You should be proficient in Python programming and have a theoretical understanding of deep learning architectures.
- Experience with medical images is beneficial.
- Project duration: 6 months, starting early 2023
- Location: Radboud University Medical Center
- For more information, please contact Stan Noordman