Publications

2025

• Venkatesh Vadaddi, Raji Susan Mathew, and Phaneendra K. Yalavarthy, "ISDU-QSMNet: Iteration Specific Denoising with Unshared Weights for Improved QSM Reconstruction," NMR in Biomedicine 38(11), e70152 (2025). doi: 10.1002/nbm.70152)

• [Ashwin RajKumar*, Naveen Paluru*], Raji Susan Mathew, Prathamesh Shenai, Dana Abdeen, Nicholas Laycock, and Phaneendra K. Yalavarthy, "Unsupervised Machine Learning for Automated Corrosion Staging Using Optical Microscopy Images," npj Materials Degradation, 9, 83 (2025). [*Equal Contribution]. (doi: 10.1038/s41529-025-00635-1)

2024

• Vadaddi Venkatesh, Raji Susan Mathew, and Phaneendra K. Yalavarthy, "SpiNet-QSM: Model-based Deep Learning with Schatten p-norm Regularization for Improved Quantitative Susceptibility Mapping," Magnetic Resonance Materials in Physics, Biology and Medicine (Special Issue on The role of artificial intelligence in MRI/MRS acquisition and reconstruction) 37(3), 411-427 (2024). (doi: 10.1007/s10334-024-01158-7).

• Naveen Paluru, Raji Susan Mathew, and Phaneendra K. Yalavarthy, "DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain," NMR in Biomedicine, 37(9), e5163 (2024). (doi: 10.1002/nbm.5163).

• Karan R. Gujarati, Lokesh Bathala, Vaddadi Venkatesh, Raji Susan Mathew, and Phaneendra K. Yalavarthy, "Transformer-based Automated Segmentation of the Median Nerve in Ultrasound Videos of Wrist-to-Elbow Region ," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 71(1), 56-69, 2024. (doi: 10.1109/TUFFC.2023.3330539). 

• Raji Susan Mathew, Naveen Paluru and Phaneendra K. Yalavarthy, "Model-Resolution based Deconvolution for Improved Quantitative Susceptibility Mapping," NMR in Biomedicine, 37(2), e5055, 2024. (doi: https://doi.org/10.1002/nbm.5055 ).

Before 2024

• Raji Susan Mathew and Joseph Suresh Paul, “Automated regularization parameter selection using continuation based proximal method for compressed sensing MRI”, IEEE Transactions on Computational Imaging. 6,  1309-1319, Aug. 2020. (doi: 10.1109/TCI.2020.3019111)

• Raji Susan Mathew and Joseph Suresh Paul, “Sparsity Promoting Adaptive Regularization for Compressed Sensing Parallel MRI", IEEE Transactions on Computational Imaging.  4(1), 147-59, Mar. 2018. (doi: 10.1109/TCI.2017.2787911)

• Raji Susan Mathew and Joseph Suresh Paul, “A Frequency-Dependent Regularization for Autocalibrating Parallel MRI Using the Generalized Discrepancy Principle,” IEEE Transactions on Computational Imaging. 3(4), 891-900, Dec. 2017. (doi: 10.1109/TCI.2017.2707979)

• Raji Susan Mathew and Joseph Suresh Paul, “Improving image quality in low snr parallel acquisition using a weighted least squares GRAPPA reconstruction,” Journal of Imaging and Interventional Radiology. 8(2), 16-22, 2016.

• Thomas F. Apotheker, Siraj T. M., Raji Susan Mathew, and Navchetan Awasthi, “Context-QSMnet: Enhancing Generalization in Quantitative Susceptibility Mapping with Context-Aware Deep Neural Networks,” ICVGIP 2025. doi:10.1145/3774521.3774574.

• Goutham Raj, Raji Susan Mathew, and Linga Reddy Cenkaramaddi, “Aerial Vehicle Classification Using a Custom Lightweight CNN: A Deep Learning Approach,” IEEE CONECCT 2025.

• Raji Susan Mathew and Joseph Suresh Paul, “A Quantitative Comparison of the Role of Parameter Selection for Regularization in GRAPPA-Based Autocalibrating Parallel MRI,” In Proceedings of 3rd International Conference on Computer Vision and Image Processing 2020 (pp. 35-45). Springer, Singapore.

• Raji Susan Mathew and Joseph Suresh Paul, “Combination of global and nonlocal sparse regularization priors for MR image reconstruction”, In TENCON 2019- 2019 IEEE Region 10 Conference (TENCON) 2019 Oct 17 (pp. 2680-2684). IEEE.

• Raji Susan Mathew and Joseph Suresh Paul, “Adaptive Fast Composite Splitting Algorithm for MR Image Reconstruction,” In International Conference on Machine Intelligence and Signal Processing 2019 Sep 7 (pp. 161-171). Springer, Singapore.

• Joseph Suresh Paul and Raji Susan Mathew, Regularized Image Reconstruction in Parallel MRI with MATLAB, CRC press, Taylor & Francis Group, LLC. October 2019.

• Raji Susan Mathew*, Naveen Paluru*, and Phaneendra K. Yalavarthy, Artificial Intelligence in Healthcare - India Case Study, Biotechnology in India - Reworking a Strategy, Springer Nature, (pp. 193-215). Singapore: Springer Nature Singapore, 2024. (doi: https://doi.org/10.1007/978-981-97-0896-3_8).

• Joseph Suresh Paul, Raji Susan Mathew and M. S. Renjith, Theory of Parallel MRI and Cartesian SENSE Reconstruction: Highlight. In Medical Imaging in Clinical Applications, (pp. 311-328). Springer International Publishing, 2016.

• Aaqilah AJ, Sunil Kumar, C. Kesavadas, and Raji Susan Mathew, "Probabilistic Modeling for Quantitative Susceptibility Mapping: a VAE UNET Approach", Poster Presentation, 11th Annual Scientific Meeting ISMRM Indian Chapter, IIT Hyderabad, Feb, (2024).

• Raji Susan Mathew, Naveen Paluru, and Phaneendra K. Yalavarthy, "Magnitude-Weighting-Free Approach Enhances Vein Definition in Nonlinear Quantitative Susceptibility Mapping Reconstruction", Poster Presentation, The 2nd ISMRM Workshop on Accessible MRI, New Delhi, Feb, (2024).

• Raji Susan Mathew, Naveen Paluru and Phaneendra K. Yalavarthy, "Model-Resolution based Deconvolution for Improved Quantitative Susceptibility Mapping," Poster Presentation, Medical Image Computing Workshop, Organized by IISc Bangalore, 24-25 Feb, (2023).

• Naveen Paluru, Raji Susan Mathew,  and Phaneendra K. Yalavarthy, "TTC-QSM : Model Based Test Time Correction for Improved Quantitative Susceptibility Mapping", Poster Presentation, Medical Image Computing Workshop, Organized by IISc Bangalore, 24-25 Feb, (2023).

• Vaddadi Venkatesh, Raji Susan Mathew,  and Phaneendra K. Yalavarthy, "SpiNet-QSM: Model-based Deep Learning with Schatten p‐norm regularization for Quantitative Susceptibility Mapping", Poster Presentation, Medical Image Computing Workshop, Organized by IISc Bangalore, 24-25 Feb, (2023).

• Ashish Verma, Raji Susan Mathew, Phaneendra K. Yalavarthy," A Deep Learning based Back Project Filter Method for Limited Angle Computed Tomography", HTC Tomographic Challenge (2022). [Github].[Certificate].