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

• URL: http://arxiv.org/abs/2309.09954v1
• Date: Mon, 18 Sep 2023 17:26:22 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: In MRI reconstruction, ill-posed inverse problems arise, where a satisfactory closed-form analytical solution is not available. We propose vSHARP (supervised Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems. We present a comparative analysis of our experimental results with state-of-the-art approaches, highlighting the superior performance of vSHARP.
• Score: 7.694990352622926
• 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 supervised and self-supervised 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 the proposed model by applying it to the task of accelerated Parallel MRI Reconstruction on two distinct datasets. We present a comparative analysis of our experimental results with state-of-the-art approaches, highlighting the superior performance of vSHARP.

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