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CDS Seminar (28th January 2021) : Adaptive Regularization Techniques for MR Image Reconstruction

28 Jan @ 10:00 AM -- 11:00 AM

Department of Computational and Data Sciences
Research Seminar (Online)

SPEAKER: Dr. Raji Susan Mathew, PhD scholar, Indian Institute of Information Technology and Management-Kerala

TITLE:  “Adaptive Regularization Techniques for MR Image Reconstruction”

Date & Time: January 28, 2021, 10:00 AM.

Venue: Online

ABSTRACT:

Magnetic resonance imaging (MRI) is a non-invasive medical imaging modality for the visualization of soft tissues. Despite the capability of providing high-resolution images, the difficulties associated with lengthy acquisition time necessitates reconstruction of the final image from a limited number of k-space samples. The reconstruction procedures can be either linear methods in k-space or image domain, or non-linear approaches that utilize the compressed sensing (CS) theory. As the aforementioned reconstruction procedures fall under the broad class of ill-posed inverse problems, the effective incorporation of the prior information, called regularization, is necessary for obtaining stable and meaningful solutions. However, the accuracy of regularized output depends on the regularization parameter choice. In this talk, we will focus on the adaptive estimation of the regularization parameters for k-space as well as sparsity promoting methods. Particularly, we will discuss a frequency dependent regularization approach for k-space based image recovery to mitigate the problems associated with a single filter calibration. For sparsity promoting methods, we will explore the estimation of an iteration-dependent parameter for continuation, outlined as an optimization problem by inclusion of an extra prior to the CS MRI cost function.

 

BIOGRAPHY

Dr. Raji Susan Mathew, PhD Scholar in Medical Image Computing and Signal Processing Lab, Indian Institute of information Technology and Management-Kerala.

 

Host Faculty: Prof.  Phaneendra Kumar Yalavarthy

Details

Date:
28 Jan
Time:
10:00 AM -- 11:00 AM

Venue

Online