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New PhD Scholarships available now !!
Machine learning for biomedical image reconstruction.
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.
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.
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
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