About Me 

I am an Associate Professor in medical imaging, and I currently hold an Australian Research Council Industry Fellow. [Monash Profile] [ORCID] [RG] [Google

My main research areas:

I am affiliated with the following organisations:

I review for top tier journals including Nature Comms and Science and for a broad range of journals in biomedical imaging and image processing, and review for competitive grant schemes.  

Academic news 

Publications and Patents

A list of my publications are HERE

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

Parallel MRI + Deep learning

Pawar K. et al. (2021) Domain Knowledge Augmentation of Parallel MR Image Reconstruction using Deep Learning, CMIG https://doi.org/10.1016/j.compmedimag.2021.101968

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.

Point of Care MRI

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.


Peris H. et al. (2023) Nature Machine Intelligence, https://www.nature.com/articles/s42256-023-00682-w

Peris H. et al. (2023) MICCAI https://link.springer.com/chapter/10.1007/978-3-031-16443-9_16

Application in ALS 

Bhattarai, A. et al. (2020) MRI Iron Imaging in Amyotrophic Lateral Sclerosis, Journal of Magnetic Resonance Imaging, 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 Quant. Imaging Med. Surg (QIMS).

Review papers

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