SPS BISP TC Webinar: 14 February 2023, presented by Dr. Anubha Gupta

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News and Resources for Members of the IEEE Signal Processing Society

February 2023

SPS BISP TC Webinar: 14 February 2023, presented by Dr. Anubha Gupta

Webinar.jpg

Education & Resources

Upcoming SPS-BISP TC Webinar

Title: Tensor Decompositions in Functional Neuroimaging
Date: 14 February 2023
Time: 3:00 PM (CET) 
Duration: Approximately 1 Hour
Presenters: Dr. Anubha Gupta, (IIIT, Delhi)

Register for the Webinar

Abstract:

Radial sampling pattern is an important signal acquisition strategy in magnetic resonance imaging (MRI) owing to better immunity to motion-induced artifacts and less pronounced aliasing due to undersampling compared to the Cartesian sampling. These advantages of radial sampling can be exploited in acquisition of multidimensional signals such as High Angular Resolution Diffusion Imaging (HARDI), with tremendous scope of acceleration. Despite such benefits, gradient delays lead to samples being acquired from unknown miscentered radial trajectories, severely degrading the image reconstruction quality. In the present work, we propose CSR-PERT that is a joint framework, wherein these perturbed radial trajectories are estimated and utilized for image reconstruction from the compressively sensed measurements of 1) MRI data and 2) HARDI data. The proposed CSR-PERT method is tested on one real MRI dataset with trajectory deviations and is observed to perform better than the existing state-of-the-art method at high acceleration factors up to 8. To the best of our knowledge, this is the first work to address the problem of estimating perturbed trajectories using the compressively sensed MRI and HARDI data. The method is also tested for varying combinations of trajectory deviations and sampling proportions. It is observed to yield very good quality HARDI reconstruction for a wide variety of scenarios. We have also demonstrated the robustness of the proposed method on real datasets in clinical settings assuming perturbed as well as noisy trajectories.

Biography:

Anubha Gupta

Anubha Gupta received the B.Tech and M.Tech degrees in electronics and communication engineering from Delhi University, India, in 1991 and 1997, respectively. She received the Ph.D. degree in electrical engineering from Indian Institute of Technology (IIT), Delhi, India, in 2006. From 2008 to 2010, she received a second Master’s degree in education as a full-time student from the University of Maryland, College Park. She worked as Assistant Director with the Ministry of Information and Broadcasting, Government of India (through Indian Engineering Services) from 1993 to 1999 and, as faculty at NSUT-Delhi (2000-2008), and IIIT-Hyderabad (2011-2013), India.

Currently, she is working as a Professor at IIIT-Delhi, where she served as the Dean, Academic Affairs from June 2019 to June 2020. She has authored/co-authored more than 100 technical papers in scientific journals and conferences. She has published research papers in both engineering and education. A lot of exciting work is being taken up in her lab: SBILab (Lab: http://sbilab.iiitd.edu.in/index.html). Her research interests include applications of machine learning in cancer genomics, cancer imaging, biomedical signal, and image processing including fMRI, MRI, EEG, ECG signal processing, and Wavelets in deep learning.

Dr. Gupta is a senior member of the IEEE Signal Processing Society (SPS) and a member of the IEEE Women in Engineering Society. She is a technical committee member of BISP committee of IEEE SPS from January 2022 to December 2024.

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