I am a researcher at Monash University in Australia and Head of Imaging Analysis at Monash Biomedical Imaging. [Monash Profile] [ORCID] [RG] [Google]
My main research areas are the development of novel MR and MR-PET methods including data acquisition, image reconstruction and multimodality data analysis, and application of machine learning in the medical imaging domain. I am also active in translating fundamental medical imaging development and scientific discoveries into real-world applications.
I am affiliated with the following organisations:
I am an ISMRM annual meeting committee member (2018-2021).
I am an area chair for IEEE ISBI 2022.
I am a member of the following societies: (i) IEEE EMBC, (ii) Society for Neuroscience, (iii) SNMMI, (iv) MICCAI.
I review for a broad range of journals in biomedical imaging and image processing, and review for competitive grant schemes.
Congratulations to Mevan Ekanayake for winning Visualise Your Thesis at Monash University and will represent Monash to compete internationally in Oct 2022!
I am leading a successful multi-institute grant application funded by Australian National Imaging Facility to establish a National Low Field Mobile MRI network! watch this space as several new positions will be available!!
An imaging scientist position is available to apply [Position filled]
Congratulations Viswanath on receiving the prestigious award of IIT Bombay, the "Naik and Rastogi Award for Excellence in Ph.D. Research" for the year 2019-2021!!
Two new PhD scholarship opportunities !! [link] [one filled]
Congratulations Viswanath on a successful PhD defence seminar - excellent work fully recognised by accessors!!
My webinar on Machine learning for biomedical image reconstruction. [link]
Succesful ARC DP project on Biophysics-informed deep learning framework for magnetic resonance imaging (# DP210101863) !!
Great to be part of this successful grant - ARDC, Australian Cancer Data Network: distributed learning from clinical data.
Congratulations on Viswanath's 2020 ISMRM Magna award!
MoCoNET:deep learning motion correction
Pawar K. et al. (2020) Suppressing Motion Artefacts in MRI using an Inception-Resnet Network with Motion Simulation Augmentation, NMR in Biomed. https://doi.org/10.1002/nbm.4225.
Pawar K. et al. (2020) Clinical Utility of Deep Learning Motion Correction for T1 weighted MPRAGE MR Images. European Journal of Radiology. 10.1016/j.ejrad.2020.109384
Pixel Classification for image reconstruction
Pawar K. et al. (2019) A Deep Learning Framework for Transforming Image Reconstruction into Pixel Classification. IEEE Access. https://ieeexplore.ieee.org/document/8931762
Low Dose PET imaging
Sudarshan, Upadhyay, et al (2021), Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data, Medical Image Analysis.
Multimodal functional MRI and functional PET Analysis models
Li, S et al (2020) Analysis of continuous infusion functional PET (fPET) in the human brain, NeuroImage. https://doi.org/10.1016/j.neuroimage.2020.116720
Li, S et al (2021), Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method, NeuroImage, https://doi.org/10.1016/j.neuroimage.2020.117603
Sudarshan, P.S. et al (2021) Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET. NeuroImage.
Joint MRI and PET image reconstruction methods
Sudarshan, P.S. et al (2020) Joint MRI-PET image reconstruction using a joint dictionary, Medical Imaging Analysis. https://doi.org/10.1016/j.media.2020.101669
Sudarshan, P.S. et al (2018) Joint PET+MRI Patch-based Dictionary for Bayesian Random Field PET Reconstruction, 2018 International conference on medical image computing and Computer Assisted Intervention (MICCAI), Granada, September 16-20, 2018.
Deep learning MR-PET attenuation correction
Pozaruk, A. et al (2020) augmented deep learning model for improved quantitative accuracy of MR based PET attenuation correction in prostate cancer, European Journal of Nuclear Medicine and Molecular Imaging (EJNMMI). https://doi.org/10.1007/s00259-020-04816-9.
Application in ALS
Bhattarai, A. et al. MRI Iron Imaging in Amyotrophic Lateral Sclerosis, Journal of Magnetic Resonance Imaging, Nov 2020. https://doi.org/10.1002/jmri.27530
Bhattarai, A. et al. (2020) Serial assessment of iron in the motor cortex in limb-onset Amyotrophic Lateral Sclerosis using Quantitative Susceptibility Mapping, accepted Quant. Imaging Med. Surg (QIMS).
Chen, Z et al., (2018) From simultaneous to synergistic MR-PET brain imaging: a review of hybrid MR-PET imaging methodologies, Human Brain Mapping, 39(12):5126-5144, Dec 2018. https://doi.org/10.1002/hbm.24314
Bhattarai, A. et al. (2020), MRI Iron Imaging in Amyotrophic Lateral Sclerosis, Journal of Magnetic Resonance Imaging, https://doi.org/10.1002/jmri.27530
Cameron D. Pain, et al. (2021), Deep Learning Based Image Reconstruction and Post-Processing Methods in Positron Emission Tomography for Low-Dose Imaging and Resolution Enhancement, European Journal of Nuclear Medicine and Molecular Imaging (EJNMMI), https://doi.org/10.1007/s00259-022-05746-4
Zhaolin Chen, et al. (2022), Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging – State-of-the-art and challenges, Journal of Digital Imaging, https://link.springer.com/article/10.1007/s10278-022-00721-9