Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions

Purpose: A time-efficient strategy to acquire high-quality multi-contrast images is to reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause leakage of uncommon features among contrasts, compromising diagnostic utility. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Theory: Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. Methods: The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. Results: The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms. Conclusion: The proposed compressive sensing method performs fast reconstruction of multi-channel multi-contrast MRI data with improved image quality. It offers reliability against feature leakage in joint reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio

Ultrafast 3d spin-echo acquisition improves gadolinium-enhanced mri signal contrast enhancement

Long scan times of 3D volumetric MR acquisitions usually necessitate ultrafast in vivo gradient-echo acquisitions, which are intrinsically susceptible to magnetic field inhomogeneities. This is especially problematic for contrast-enhanced (CE)-MRI applications, where non-negligible T 2 & z.ast; effect of contrast agent deteriorates the positive signal contrast and limits the available range of MR acquisition parameters and injection doses. To overcome these shortcomings without degrading temporal resolution, ultrafast spin-echo acquisitions were implemented. Specifically, a multiplicative acceleration factor from multiple spin echoes (??32) and compressed sensing (CS) sampling (??8) allowed highly-accelerated 3D Multiple-Modulation- Multiple-Echo (MMME) acquisition. At the same time, the CE-MRI of kidney with Gd-DOTA showed significantly improved signal enhancement for CS-MMME acquisitions (??7) over that of corresponding FLASH acquisitions (??2). Increased positive contrast enhancement and highly accelerated acquisition of extended volume with reduced RF irradiations will be beneficial for oncological and nephrological applications, in which the accurate in vivo 3D quantification of contrast agent concentration is necessary with high temporal resolution.open0

Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM) and region-of-interest susceptibility measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey matter susceptibility than some existing methods, when the acquisition was accelerated four times. The proposed DCRNet also dramatically shortened the reconstruction time by nearly 10 thousand times for each scan, from around 80 hours using conventional approaches to only 30 seconds.Comment: 10 figure

¹⁹F-MRI of inhaled perfluoropropane for quantitative imaging of pulmonary ventilation

PhD ThesisMRI of exogenous imaging agents offers a safely repeatable modality to assess regional pulmonary ventilation. A small number of studies have validated the safety and potential utility of 19F imaging of inhaled thermally polarised perfluoropropane. However, the relative scarcity of signal in restrictive breath hold length acquisition times inhibits translation of this technique to clinical application. This work presents methods used to maximise the attainable image quality of inhaled perfluoropropane. Novel quantitative measures of ventilation and perfusion have been investigated and discussed. A preliminary healthy volunteer study was conducted to verify the efficacy of the imaging technique and to assess perfluoropropane wash-in and wash-out rates. Quantitative assessment of the suitability of four RF coil designs was performed, comparing power efficiency with loading and signal homogeneity within the sensitive volume of each coil. The 3D spoiled gradient echo sequence was simulated, accounting for the power performance of the chosen birdcage coil design, for calculation of acquisition parameter values required to achieve the highest SNR in a fixed acquisition period for 19F-MRI of inhaled perfluoropropane. Studies on resolution phantoms and healthy volunteers assessed the performance of the optimised imaging protocol, in combination with a compressed sensing technique that permitted up to three-fold acceleration. Two novel lung-representative phantoms were fabricated and used to investigate the behaviour of the MR properties of inhaled perfluoropropane with changing structural and magnetic environments. Finally, a method for lengthening the T2* of inhaled perfluoropropane by susceptibility matching the alveolar tissue to the inhaled gas by intravenous injection of a highly paramagnetic contrast agent is presented. Initial development work was conducted in phantoms and rodents before translation to healthy volunteers. This technique offers the potential to concurrently acquire images reflecting both pulmonary ventilation and perfusion