Compressive Sensing and Parallel Dynamic MRI

Compressive sensing (CS) is one of the important theories developed in signal processing in the recent decade, and MRI is it's one of, if not biggest, field of practical application. The use of compressive sensing can greatly reduce the number of measurements required during an MRI scan which result in faster acquisition. By the time I started researching this topic, the use of CS in MRI has already been researched, but it was limited to static subjects, ex. brain scans. However the real challenge of MRI is for the dynamic scans when the time of acquision is really limited, and the subject is changing in time, often moving.

 

As a part of this project, I have developed regularization methods for the reconstruction of temporally varying MRI scans from heavily incomplete samples (in Fourier domain). I have also developed new convex optimization approaches to be used for inverse problems regarding MRI which can be significantly parallelized and accelerated using GPUs.

This work formed the basis of my PhD. thesis, and helped me understand not only compressed sensing and inverse problems but also convex optimization and MRI. Having implemented all the algorithms that I used by myself, I also had immense experience on implementations with Matlablinear algebra and parallel processing.

Relevant Publications

  • Bilen, C. "Compressed sensing for dynamic parallel Magnetic Resonance Imaging", Polytechnic Institute of New York University, ProQuest Dissertations Publishing, 2013. 3549411 (PhD thesis)
  • Bilen, C.; Wang, Y.; Selesnick, I.W.; ”High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing”, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Special Issue on Circuits, Systems and Algorithms for Compressed Sensing, vol. 2, issue 3, p. 370-379, 2012
  • Bilen, C.; Wang, Y.; Selesnick, I.W.; ”Compressed Sensing for Moving Imagery in Medical Imaging”, arXiv : 1203.5772, 2012
  • Bilen, C.; Selesnick, I.W.; Wang, Y.; Otazo, R.; Sodickson, D.K., ”Combination of Compressed Sensing and Parallel Imaging with Adaptive Motion Compensation for Accelerated Dynamic MRI”, ISMRM 2013
  • Otazo, R.; Bilen, C.; Wang, Y.; Axel L.; Sodickson, D.K., ”Accelerated Dynamic MRI Using Multicoil Low-Rank Matrix Completion”, ISMRM 2013
  • Bilen, C.; Selesnick, I.W.; Wang, Y.; Otazo, R.; Sodickson, D.K., ”A Motion Compensating Prior for Dynamic MRI Reconstruction using Combination of Compressed Sensing and Parallel Imaging”, SPMB 2011
  • Bilen, C.; Selesnick, I.W.; Wang, Y.; Otazo, R.; Kim, D.; Axel, L.; Sodickson, D.K., “On compressed sensing in parallel MRI of cardiac perfusion using temporal wavelet and TV regularization”, ICASSP 2010