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Computer Science

D-Index
34
Citations
7860
World Ranking
11944
National Ranking
4879

Overview

Mathews Jacob is affiliated with the University of Iowa in the United States. Their research primarily spans the fields of Medicine and Computer Science, with a significant focus on Radiology, Nuclear Medicine and Imaging as a subfield. Their work also encompasses Computer Vision and Pattern Recognition, Computational Mechanics, Biomedical Engineering, and Atomic and Molecular Physics, and Optics.

The scientist's research interests include several specialized topics, such as Advanced MRI Techniques and Applications, Medical Imaging Techniques and Applications, Sparse and Compressive Sensing Techniques, Medical Image Segmentation Techniques, Advanced Neuroimaging Techniques and Applications, Atomic and Subatomic Physics Research, and Radiomics and Machine Learning in Medical Imaging.

Mathews Jacob has contributed to various scholarly journals and conferences. Frequent venues for their publications include:

  • arXiv (Cornell University)
  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • IEEE Transactions on Medical Imaging
  • Magnetic Resonance in Medicine
  • IEEE Transactions on Computational Imaging

Their recent publications reflect an emphasis on imaging and reconstruction methods in magnetic resonance imaging (MRI). Selected papers include:

  • "J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction," 2020, IEEE Journal of Selected Topics in Signal Processing
  • "Deep learning for accelerated and robust MRI reconstruction," 2024, Magnetic Resonance Materials in Physics Biology and Medicine
  • "Dynamic Imaging Using a Deep Generative SToRM (Gen-SToRM) Model," 2021, IEEE Transactions on Medical Imaging
  • "qModeL: A plug-and-play model-based reconstruction for highly accelerated multi-shot diffusion MRI using learned priors," 2021, Magnetic Resonance in Medicine
  • "Computational MRI: Compressive Sensing and Beyond [From the Guest Editors]," 2020, IEEE Signal Processing Magazine

The scientist's work often involves collaboration, with frequent co-authors including Qing Zou, Sarv Priya, Prashant Nagpal, Aniket Pramanik, and Jyothi Rikhab Chand.

Best Publications

  • Design and Validation of a Tool for Neurite Tracing and Analysis in Fluorescence Microscopy Images

    E. Meijering;M. Jacob;J.-C.F. Sarria;P. Steiner

  • Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR

    Sajan Goud Lingala;Yue Hu;Edward DiBella;Mathews Jacob

  • MoDL: Model-Based Deep Learning Architecture for Inverse Problems

    Hemant K. Aggarwal;Merry P. Mani;Mathews Jacob

  • Design of steerable filters for feature detection using canny-like criteria

    M. Jacob;M. Unser

  • Blind Compressive Sensing Dynamic MRI

    S. G. Lingala;M. Jacob

  • Efficient energies and algorithms for parametric snakes

    M. Jacob;T. Blu;M. Unser

  • Higher Degree Total Variation (HDTV) Regularization for Image Recovery

    Yue Hu;M. Jacob

  • Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI

    Alessandro Daducci;Erick Jorge Canales-Rodriguez;Maxime Descoteaux;Eleftherios Garyfallidis

  • Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS).

    Merry Mani;Mathews Jacob;Douglas Kelley;Vincent Magnotta

  • Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples

    Greg Ongie;Mathews Jacob

  • Dynamic MRI Using SmooThness Regularization on Manifolds (SToRM)

    Sunrita Poddar;Mathews Jacob

  • J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction

    Hemant Kumar Aggarwal;Mathews Jacob

  • Efficient model-based quantification of left ventricular function in 3-D echocardiography

    Unknown

  • Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM

    Sampurna Biswas;Hemant K. Aggarwal;Mathews Jacob

  • Deformation Corrected Compressed Sensing (DC-CS): A Novel Framework for Accelerated Dynamic MRI

    Sajan Goud Lingala;Edward DiBella;Mathews Jacob

  • Nonlocal Regularization of Inverse Problems: A Unified Variational Framework

    Zhili Yang;M. Jacob

  • A Fast Algorithm for Convolutional Structured Low-Rank Matrix Recovery

    Gregory Ongie;Mathews Jacob

  • Generalized Higher Degree Total Variation (HDTV) Regularization

    Yue Hu;Greg Ongie;Sathish Ramani;Mathews Jacob

  • A Fast Majorize–Minimize Algorithm for the Recovery of Sparse and Low-Rank Matrices

    Yue Hu;S. G. Lingala;M. Jacob

  • Robust Reconstruction of MRSI Data Using a Sparse Spectral Model and High Resolution MRI Priors

    Ramin Eslami;Mathews Jacob

  • Convex Recovery of Continuous Domain Piecewise Constant Images From Nonuniform Fourier Samples

    Greg Ongie;Sampurna Biswas;Mathews Jacob

  • BSLIM: Spectral Localization by Imaging With Explicit $B_{0}$ Field Inhomogeneity Compensation

    I. Khalidov;D. Van De Ville;M. Jacob;F. Lazeyras

Frequent Co-Authors

Vincent A. Magnotta
Vincent A. Magnotta University of Iowa
Michael Unser
Michael Unser École Polytechnique Fédérale de Lausanne
Soura Dasgupta
Soura Dasgupta University of Iowa
Thierry Blu
Thierry Blu Chinese University of Hong Kong
Bradley P. Sutton
Bradley P. Sutton University of Illinois at Urbana-Champaign
Zhi-Pei Liang
Zhi-Pei Liang University of Illinois at Urbana-Champaign
Justin P. Haldar
Justin P. Haldar University of Southern California
Yoram Bresler
Yoram Bresler University of Illinois at Urbana-Champaign
Jong Chul Ye
Jong Chul Ye Korea Advanced Institute of Science and Technology
Weiyu Xu
Weiyu Xu University of Iowa

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