Joint reconstruction of multi-contrast images: compressive sensing reconstruction using both joint and individual regularization functions

In many clinical settings, multi-contrast images of a patient are acquired to maximize complementary information. With the underlying anatomy being the same, the mutual information in multi-contrast data can be exploited to improve image reconstruction, especially in accelerated acquisition schemes such as Compressive Sensing (CS). This study proposes a CS-reconstruction algorithm that uses four regularization functions; joint L1-sparsity and TV-regularization terms to exploit the mutual information, and individual L1-sparsity and TV-regularization terms to recover unique features in each image. The proposed method is shown to be robust against leakage-of-features across contrasts, and is demonstrated using simulations and in-vivo experiments

Statistically segregated k-space sampling for accelerating multiple-acquisition MRI

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that in turn can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically-segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially, while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo T2-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressedsensing reconstructions of multiple-acquisition datasets

Compressed multi-contrast magnetic resonance image reconstruction using augmented lagrangian method

In this paper, a Multi-Channel/Multi-Contrast image reconstruction algorithm is proposed. The method, which is based on the Augmented Lagrangian Method uses joint convex objective functions to utilize the mutual information in the data from multiple channels to improve reconstruction quality. For this purpose, color total variation and group sparsity are used. To evaluate the performance of the method, the algorithm is compared in terms of convergence speed and image quality using Magnetic Resonance Imaging data to FCSA-MT [1], an alternative approach on reconstructing multi-contrast MRI data

Graft materials in Peyronie's disease surgery: A comprehensive review

Objective. To discuss the currently used graft materials in Peyronie's surgery