Model-based Magnetic Resonance Image (MRI) Reconstruction and its Applications
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Magnetic Resonance Imaging (MRI) is a powerful non-invasive imaging technique widely used in medical diagnostics. However, the inherent slowness of MRI acquisition has led to significant research efforts in accelerating the imaging process. Model-based reconstruction methods have emerged as a prominent approach to achieve this acceleration while maintaining image quality.
These model-based reconstruction methods involve three key steps: establishing a model (either deterministic or probabilistic) for the acquired data, creating a corresponding inverse problem using prior knowledge, and finding the optimal estimator through appropriate optimization algorithms and computational methods. Commonly used priors in MRI include sparsity and low-rank characteristics, supported by theories such as compressed sensing.
My research aims to investigate improved MRI image reconstruction methods and explore their applications. By advancing these techniques, we can potentially enhance image quality, reduce scan times, and expand the clinical utility of accelerated MRI in medical diagnosis and treatment planning.
Reference: (†: equal contribution)
[1] T. H. Kim, B. Bilgic, D. Polak, K. Setsompop, J. P. Haldar. Wave-LORAKS: Combining Wave Encoding with Structured Low-Rank Matrix Modeling for More Highly Accelerated 3D Imaging. Magnetic Resonance in Medicine, 81:1620-1633, 2019. https://onlinelibrary.wiley.com/doi/10.1002/mrm.27511
[2] R. A. Lobos, T. H. Kim, W. S. Hoge, J. P. Haldar. Navigator-free EPI Ghost Correction with Structured Low-Rank Matrix Models: New Theory and Methods. IEEE Transactions on Medical Imaging, 37:2390- 2402, 2018. https://ieeexplore.ieee.org/document/8329142/
[3] B. Bilgic† , T. H. Kim† , C. Liao, M. K. Manhard, L. L. Wald, J. P. Haldar, K. Setsompop. Improving Parallel Imaging by Jointly Reconstructing Multi-Contrast Data. Magnetic Resonance in Medicine, 80: 619-632, 2018. https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27076 (co-1st author)
[4] T. H. Kim, K. Setsompop, J. P. Haldar. LORAKS Makes Better SENSE: Phase-Constrained Partial Fourier SENSE Reconstruction without Phase Calibration. Magnetic Resonance in Medicine, 77:1021- 1035, 2017. http://onlinelibrary.wiley.com/doi/10.1002/mrm.26182
Deep Learning Methods for Accelerated MRI and Inverse Problems
Recent advancements in artificial intelligence have led to the application of artificial neural networks (ANNs) and deep learning (DL) techniques in MRI reconstruction. These approaches show promise in accelerating MRI acquisition while maintaining image quality. However, the direct application of generic DL models to medical imaging poses significant challenges. A critical concern is the phenomenon of 'hallucination,' where ANNs might generate non-existent lesions or erase existing ones, potentially leading to misdiagnosis.
My research focuses on developing robust ANN-based MRI reconstruction methods utilizing physics-based DL approaches. By incorporating domain-specific knowledge and physics constraints into the training process, we aim to mitigate the risks associated with generic DL models. This approach aims to ensure the reliability and clinical validity of reconstructed MRI images.
Reference:
[5] T. H. Kim, Z. Zhang, J. Cho, B. Gagoski, J. P. Haldar, B. Bilgic. Robust multi-shot EPI with untrained artificial neural networks: Unsupervised scan-specific deep learning for blip up-down acquisition (BUDA). International Society for Magnetic Resonance in Medicine Annual Meeting, 2021, p. 224.
https://index.mirasmart.com/ISMRM2021/PDFfiles/0224.html
[6] T. H. Kim, J. P. Haldar. Learning how to interpolate Fourier data with unknown autoregressive structure. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, 2019. https://ieeexplore.ieee.org/abstract/document/9048755
[7] T. H. Kim, J. P. Haldar. Learning-based computational MRI reconstruction without big data: From structured low-rank matrices to recurrent neural networks. Wavelets and Sparsity XVIII, Proceedings of SPIE, San Diego, 2019. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11138/1113817/Learning-based-computational-MRI-reconstruction-without-big-data--from/10.1117/12.2527584.full?SSO=1
[8] T. H. Kim, P. Garg, J. P. Haldar. LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive Reconstruction in k-Space. arXiv:1904.09390
Numerical Algorithms for ill-posed Inverse Problems
Image reconstruction is fundamentally an inverse problem that involves deriving the desired image from a limited set of observations. However, modern computers face challenges in directly computing large-scale inverse mappings due to their complexity. As a result, numerical methods have become prevalent in various fields such as optimal control, deep learning, and constrained optimization. My objective is to explore novel numerical algorithms aimed at improving computational efficiency in solving large-scale inverse problems.
[9] T. H. Kim, J. P. Haldar. Efficient Iterative Solutions to Complex-Valued Nonlinear Least-Squares Problems with Mixed Linear and Antilinear Operators. Optimization and Engineering, 23:749-768, 2022. https://link.springer.com/article/10.1007/s11081-021-09604-4
Image Quality Assessment
To evaluate image reconstruction, it is essential to employ metrics for assessing image quality. Commonly used metrics include Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), which are typically applied when a ground-truth image is available. However, these individual metrics measure only specific properties of images and may not comprehensively represent all image characteristics. Recognizing this limitation, my research aims to investigate novel techniques for more comprehensive image quality assessment.
[10] T. H. Kim, J. P. Haldar. The Fourier Radial Error Spectrum Plot: A more nuanced quantitative evaluation of image reconstruction quality. IEEE International Symposium on Biomedical Imaging, Washington, DC, 2018. pp. 61–64. https://ieeexplore.ieee.org/document/8363523/
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