Accelerated susceptibility-driven positive contrast MRI reconstruction based on primal-dual optimization with minimal parameter tuning

Background: Metallic interventional devices such as brachytherapy seeds and stents, are extensively utilized in clinical settings. However, these devices generate significant susceptibility artifacts in conventional magnetic resonance imaging (MRI), manifesting as signal voids that impede precise visualization. Susceptibility-driven positive contrast MRI (PC-MRI) mitigates this limitation by solving regularized ℓ1-norm minimization problems to reconstruct positive contrast images. The conventional nonlinear conjugate gradient (CG) algorithm, commonly employed for solving such non-smooth convex optimization problems, encounters challenges, including slow convergence rates, sensitivity to initial solutions and parameter selection, and difficulties in achieving optimal imaging reconstruction due to ill-posed inversion problems. This study aimed to develop and evaluate an accelerated primal-dual (PD) optimization framework with graphics processing unit (GPU) parallelization to overcome the limitations of the conventional CG algorithm for susceptibility-driven PC-MRI reconstruction. The proposed method seeks to solve the exact ℓ1-minimization problem without smoothing approximations. Methods: The efficacy of the method was evaluated through computational simulations, phantom experiments, and in vivo studies. Quantitative assessments included convergence behavior, full width at half maximum (FWHM), signal-to-noise ratio (SNR), and reconstruction time. Statistical significance was determined using paired t-tests, with a significance threshold set at P