OUR TEAM

My research focuses on the integration of Magnetic Resonance Imaging (MRI),  machine learning, and deep learning. I am currently working on several projects that apply AI techniques to reduce MR image reconstruction time and computational burden while ensuring the quality of the MR reconstructed images remains uncompromised.

Ongoing Research Projects

• Modified VGG-16 based GROG Gridding for MR Image Reconstruction
• Deep Learning-Based Channel Compression for Accelerated Cartesian MRI Reconstruction

Journal Publications

• Arshad M, Qureshi M, Inam O, Omer H. “Transfer learning in deep neural network based under-sampled MR image reconstruction.” Magnetic Resonance Imaging. 2021 Feb 1;76:96-107. (Impact Factor=2.546)  https://doi.org/10.1016/j.mri.2020.09.018
• Arshad M, Qureshi M, Inam O, Omer H. “Transfer learning in deep neural network-based receiver coil sensitivity map estimation.” Magnetic Resonance Materials in Physics, Biology and Medicine. 2021 Oct; 34(5):717-28. (Impact Factor=2.533) https://doi.org/10.1007/s10334-021-00919-y
• Aamir F, Aslam I, Arshad M, Omer H. “Accelerated Diffusion-Weighted MR Image Reconstruction Using Deep Neural Networks.” Journal of Digital Imaging. 2022 Nov 4:1-3. (Impact Factor=4.903) https://doi.org/10.1007/s10278-022-  00709-5
• Aslam T, Najeeb F, Shahzad H, Arshad M, Omer H. “Motion Corrected DCE-MR Image Reconstruction using Deep Learning”, Applied Magnetic Resonance. 2024 (Impact Factor=1) https://doi.org/10.1007/s00723-024-01642-2
• Arshad M, Najeeb F, Khawaja R, Ammar A, Shafique M, Omer H. “Cardiac MR Image Reconstruction using Cascaded Hybrid Dual Domain Deep Learning Framework”, PLOS ONE.

Conference Publications

• Atif M, Arshad M, Bilal Y, Inam O, Shahzad H, Omer H, “Deep Learning-Based GRAPPA Operator Gridding for Non-Cartesian MR Image Reconstruction”. NVIDIA GTC conference, San Jose Convention Center (Silicon valley), March. 18-21, 2024
• Najeeb F, Arshad M, Shafique M, Omer H. “Dual Domain Deep Learning Framework for Cardiac MR Image Reconstruction”. Joint Annual Meeting and exhibition ISMRM & ISMRT, 03-08, Canada, June 2023 (Abstract-3084)
• Atif M, Arshad M, Bilal Y, Inam O, Shahzad H, Omer H, “GROG Gridding using Modified VGG-16 CNN model for Non-Cartesian MR Image Reconstruction.” Joint Annual Meeting and exhibition ISMRM & ISMRT, 03-08, Canada, June 2023 (Abstract-3111)​​​​​​​
• Arshad M, Qureshi M, Inam O, Omer H. “Deep Learning-Based Receiver Coil Sensitivity Map Estimation for SENSE Reconstruction using Transfer Learning.” 31st Joint Annual Meeting ISMRM-ESMRMB & SMRT, London, May. 07-12, 2022 (Abstract-1158)​​​​​​​
• Arshad M, Qureshi M, Inam O, Omer H. “Super Resolution of MR Images with Deep Learning Based k-space Interpolation.” 31st Joint Annual Meeting ISMRM- ESMRMB & SMRT, London, May. 07-12, 2022 (Abstract 1161)
• Khawaja R, Ammar A, Arshad M, Najeeb F, Omer H. “A Hybrid Dual Domain Deep Learning Framework for Cardiac MR Image Reconstruction.” 31st Joint Annual Meeting ISMRM-ESMRMB & SMRT, London, May. 07-12, 2022 (Abstract-4357)​​​​​​​
• Arshad M, Qureshi M, Inam O, Omer H. “PCA and U-Net based Channel Compression for Fast MR Image Reconstruction”. 29th ISMRM Annual Scientific Meeting (virtual), May. 15 – 20, 2021(Abstract-1752)​​​​​​​
• Arshad M, Qureshi M, Inam O, Omer H. “A Deep k-means Based Tissue Extraction from Reconstructed Human Brain MR Image”. 29th ISMRM Annual Scientific Meeting (virtual), May. 15 – 20, 2021 (Abstract 2398)​​​​​​​
• Arshad M, Qureshi M, Inam O, Omer H. “Channel Compression for Multiple Receiver Coil data in MRI” 37th ESMRMB Annual Scientific Meeting, Sep. 30 – Oct .02, 2020 (Abstract-L01.50)​​​​​​​
• Arshad M, Qureshi M, Inam O, Omer H. “Deep Learning based Accelerated MR Image Reconstruction via Transfer Learning” 28th ISMRM Annual Scientific Meeting, Sydney, April. 18 – 23, 2020 (Abstract-3491)​​​​​​​
• Arshad M, Qureshi M, Khattak I, Omer H. “Radial MR Image Reconstruction through Deep Learning” 36th Annual Meeting of ESMRMB, Oct 2019, Rotterdam/NL (AbstractL07.19)