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Deep Learning in Medical Image Reconstruction
Research Group
Phaneendra K. Yalavarthy and Sriram Ganapathy
Computational & Data Sciences, IISc
and Department of Electrical Engineering, IISc
Reconstructions using the analytical back-projection algorithm. (a-c). Full bandwidth photoacoustic signal, (d-e) band-limited or limited bandwidth PA signal, and (g-i). Enhanced bandwidth or predicted PA signal using the deep learning.
Medical Image reconstruction is an applied area of inverse problems and typical reconstruction algorithms are either analytical or model-based one. The model-based image reconstruction algorithms are often provide quantitative information, thus they are often preferred in performing advanced clinical studies.
The project proposal aims to develop deep learning methods in the model-based image reconstruction framework to further enhance the capabilities of these reconstruction algorithms. Deployment of deep learning methods combined with sparse recovery techniques will also be taken up as part of this Ph.D. thesis topic to provide state-of-the-art reonctsruction algorithms.
Background needed: Linear Algebra (and/or) signal processing.
Basic Qualifications: B.E./B.Tech. in EE/ECE/IN/CS/IT/BME (or) M.Sc. (Mathematics/Physics)
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