vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems

• URL: http://arxiv.org/abs/2309.09954v2
• Date: Tue, 30 Jul 2024 15:54:49 GMT
• Title: vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems
• Authors: George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen,
• Abstract summary: vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems) is a novel Deep Learning (DL)-based method for solving ill-posed inverse problems arising in Medical Imaging (MI) For data consistency, vSHARP unrolls a differentiable gradient descent process in the image domain, while a DL-based denoiser, such as a U-Net architecture, is applied to enhance image quality. Our comparative analysis with state-of-the-art methods demonstrates the superior performance of vSHARP in these applications.
• Score: 7.043932618116216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical Imaging (MI) tasks, such as accelerated parallel Magnetic Resonance Imaging (MRI), often involve reconstructing an image from noisy or incomplete measurements. This amounts to solving ill-posed inverse problems, where a satisfactory closed-form analytical solution is not available. Traditional methods such as Compressed Sensing (CS) in MRI reconstruction can be time-consuming or prone to obtaining low-fidelity images. Recently, a plethora of Deep Learning (DL) approaches have demonstrated superior performance in inverse-problem solving, surpassing conventional methods. In this study, we propose vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems arising in MI. vSHARP utilizes the Half-Quadratic Variable Splitting method and employs the Alternating Direction Method of Multipliers (ADMM) to unroll the optimization process. For data consistency, vSHARP unrolls a differentiable gradient descent process in the image domain, while a DL-based denoiser, such as a U-Net architecture, is applied to enhance image quality. vSHARP also employs a dilated-convolution DL-based model to predict the Lagrange multipliers for the ADMM initialization. We evaluate vSHARP on tasks of accelerated parallel MRI Reconstruction using two distinct datasets and on accelerated parallel dynamic MRI Reconstruction using another dataset. Our comparative analysis with state-of-the-art methods demonstrates the superior performance of vSHARP in these applications.

Related papers

• Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network [7.043932618116216]
  We propose a deep learning-based reconstruction method for 2D dynamic multi-contrast, multi-scheme, and multi-acceleration MRI. Our approach integrates the state-of-the-art vSHARP model, which utilizes half-quadratic variable splitting and ADMM optimization.
  arXiv  Detail & Related papers  (2024-11-02T15:59:35Z)
• BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution [52.47005445345593]
  BlindDiff is a DM-based blind SR method to tackle the blind degradation settings in SISR. BlindDiff seamlessly integrates the MAP-based optimization into DMs. Experiments on both synthetic and real-world datasets show that BlindDiff achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-15T11:21:34Z)
• Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction [15.444386058967579]
  There are still problems with dynamic MRI k-space reconstruction based on Compressed Sensing (CS) In this paper, we propose a novel robust lowrank dynamic MRI reconstruction optimization model via highly under-sampled Fourier Transform (DFT) Experiments on dynamic MRI data demonstrate the superior performance proposed method in terms of both reconstruction accuracy and time complexity.
  arXiv  Detail & Related papers  (2023-10-23T13:34:59Z)
• Deep Cardiac MRI Reconstruction with ADMM [7.694990352622926]
  We present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of cardiac imaging. Our method optimize in both the image and k-space domains, allowing for high reconstruction fidelity.
  arXiv  Detail & Related papers  (2023-10-10T13:46:11Z)
• Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver [26.87738024952936]
  In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements. We propose an LU algorithm with shallow equilibrium regularizers (L) These implicit models are as expressive as deeper convolutional networks, but far more memory efficient during training.
  arXiv  Detail & Related papers  (2022-10-10T19:50:37Z)
• Denoising Diffusion Restoration Models [110.1244240726802]
  Denoising Diffusion Restoration Models (DDRM) is an efficient, unsupervised posterior sampling method. We demonstrate DDRM's versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization.
  arXiv  Detail & Related papers  (2022-01-27T20:19:07Z)
• Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction [3.058685580689605]
  We present a novel Deep Learning-based Inverse Problem solver applied to the task of accelerated MRI reconstruction. The RecurrentVarNet consists of multiple blocks, each responsible for one unrolled iteration of the gradient descent algorithm for solving inverse problems. Our proposed method achieves new state of the art qualitative and quantitative reconstruction results on 5-fold and 10-fold accelerated data from a public multi-channel brain dataset.
  arXiv  Detail & Related papers  (2021-11-18T11:44:04Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning [70.57193072829288]
  Low-rank Multi-view Subspace Learning has shown great potential in cross-view classification in recent years. Existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously. We propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy.
  arXiv  Detail & Related papers  (2020-03-22T03:57:38Z)
• Kullback-Leibler Divergence-Based Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation [152.609322951917]
  We come up with a Kullback-Leibler (KL) divergence-based Fuzzy C-Means (FCM) algorithm by incorporating a tight wavelet frame transform and a morphological reconstruction operation. The proposed algorithm works well and comes with better segmentation performance than other comparative algorithms.
  arXiv  Detail & Related papers  (2020-02-21T05:19:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.