The introduction of compressed sensing for increasing imaging speed in MRI has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than that are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This paper presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and non-linear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the paper discusses current challenges and future opportunities.

Purpose Conventional fat/water separation techniques require that patients hold breath during abdominal acquisitions, which often fails and limits the achievable spatial resolution and anatomic coverage. This work presents a novel approach for free-breathing volumetric fat/water separation. Theory and Methods Multi-echo data are acquired using a motion-robust radial stack-of-stars 3D GRE sequence with bipolar readout. To obtain fat/water maps, a model-based reconstruction is employed that accounts for the off-resonant blurring of fat and integrates both compressed sensing and parallel imaging. The approach additionally enables generation of respiration-resolved fat/water maps by detecting motion from k-space data and reconstructing different respiration states. Furthermore, an extension is described for dynamic contrast-enhanced fat-water-separated measurements. Results Uniform and robust fat/water separation is demonstrated in several clinical applications, including free-breathing non-contrast abdominal examination of adults and a pediatric subject with both motion-averaged and motion-resolved reconstructions, as well as in a non-contrast breast exam. Furthermore, dynamic contrast-enhanced fat/water imaging with high temporal resolution is demonstrated in the abdomen and breast. Conclusion The described framework provides a viable approach for motion-robust fat/water separation and promises particular value for clinical applications that are currently limited by the breath-holding capacity or cooperation of patients.

Purpose Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model‐based optimization framework in conjunction with undersampling 3D radial stack‐of‐stars data. Theory and Methods High resolution 3D T 1 maps are generated from subsampled data by employing model‐based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non‐linear, non‐differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss‐Newton method. The importance of 3D‐spectral regularization is demonstrated by a comparison to 2D‐spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look‐Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data. Results Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T 1 maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions. Conclusions The proposed algorithm is able to recover T 1 maps with an isotropic resolution of 1 mm 3 from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.

This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping. Methods: An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetizationprepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver). Results: ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R 2 = 1.0), brain (R 2 = 0.96), and liver (R 2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R 2 = 1.0), brain (R 2 = 0.99), and liver (R 2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers. Conclusion: This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.

The GRASP reconstruction can be substantially accelerated using GROG. This framework is promising toward broader clinical application of GRASP and other iterative non-Cartesian reconstruction methods. Magn Reson Med 80:286-293, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

The purpose of this study was to evaluate oxygen‐enhanced pulmonary imaging at 0.55 T with 3D stack‐of‐spirals ultrashort‐TE (UTE) acquisition. Oxygen‐enhanced pulmonary MRI offers the measurement of regional lung ventilation and perfusion using inhaled oxygen as a contrast agent. Low‐field MRI systems equipped with contemporary hardware can provide high‐quality structural lung imaging by virtue of the prolonged T2*. Fortuitously, the T1 relaxivity of oxygen increases at lower field strengths, which is expected to improve the sensitivity of oxygen‐enhanced lung MRI. We implemented a breath‐held T1‐weighted 3D stack‐of‐spirals UTE acquisition with a 7 ms spiral‐out readout. Measurement repeatability was assessed using five repetitions of oxygen‐enhanced lung imaging in healthy volunteers (n = 7). The signal intensity at both normoxia and hyperoxia was strongly dependent on lung tissue density modulated by breath‐hold volume during the five repetitions. A voxel‐wise correction for lung tissue density improved the repeatability of percent signal enhancement maps (coefficient of variation = 34 ± 16%). Percent signal enhancement maps were compared in 15 healthy volunteers and 10 patients with lymphangioleiomyomatosis (LAM), a rare cystic disease known to reduce pulmonary function. We measured a mean percent signal enhancement of 9.0 ± 3.5% at 0.55 T in healthy volunteers, and reduced signal enhancement in patients with LAM (5.4 ± 4.8%, p = 0.02). The heterogeneity, estimated by the percent of lung volume exhibiting low enhancement, was significantly increased in patients with LAM compared with healthy volunteers (11.1 ± 6.0% versus 30.5 ± 13.1%, p = 0.01), illustrating the capability to measure regional functional deficits.

Purpose:To develop an isotropic high-resolution stack-of-spirals UTE sequence for pulmonary imaging at 0.55 Tesla by leveraging a combination of robust respiratory-binning, trajectory correction, and concomitant-field corrections. Methods:A stack-of-spirals golden-angle UTE sequence was used to continuously acquire data for 15.5 minutes. The data was binned to a stable respiratory phase based on superoinferior readout self-navigator signals. Corrections for trajectory errors and concomitant field artifacts, along with image reconstruction with conjugate gradient SENSE, were performed inline within the Gadgetron framework. Finally, data were retrospectively reconstructed to simulate scan times of 5, 8.5, and 12 minutes. Image quality was assessed using signal-to-noise, image sharpness, and qualitative reader scores. The technique was evaluated in healthy volunteers, patients with coronavirus disease 2019 infection, and patients with lung nodules. Results:The technique provided diagnostic quality images with parenchymal lung SNR of 3.18 ± 0.0.60, 4.57 ± 0.87, 5.45 ± 1.02, and 5.89 ± 1.28 for scan times of 5, 8.5, 12, and 15.5 minutes, respectively. The respiratory binning technique resulted in significantly sharper images (p < 0.001) as measured with relative maximum derivative at the diaphragm. Concomitant field corrections visibly improved sharpness of anatomical structures away from iso-center. The image quality was maintained with a slight loss in SNR for simulated scan times down to 8.5 minutes. Inline image reconstruction and artifact correction were achieved in <5 minutes.

Dynamically phase-cycled radial bSSFP has the potential for banding-free bSSFP imaging in a short scan time, in the presence of severe field inhomogeneities and at high resolution.