Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, the data acquisition process for MRI is inherently slow, which motivates undersampling and thus drives the need for accurate, efficient reconstruction methods from undersampled datasets. In this article, we describe the use of "plug-and-play" (PnP) algorithms for MRI image recovery. We first describe the linearly approximated inverse problem encountered in MRI. Then we review several PnP methods, where the unifying commonality is to iteratively call a denoising subroutine as one step of a larger optimizationinspired algorithm. Next, we describe how the result of the PnP method can be interpreted as a solution to an equilibrium equation, allowing convergence analysis from the equilibrium perspective. Finally, we present illustrative examples of PnP methods applied to MRI image recovery.

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Magnetic resonance imaging (MRI) is an established diagnostic imaging tool for investigating pediatric disease. MRI allows assessment of structure, function, and morphology in cardiovascular imaging, as well as tissue characterization in body imaging, without the use of ionizing radiation. For MRI in children, sedation and general anesthesia (GA) are often utilized to suppress patient motion, which can otherwise compromise image quality and diagnostic efficacy. However, evidence is emerging that use of sedation and GA in children might have long-term neurocognitive side effects, in addition to the short-term procedure-related risks. These concerns make risk-benefit assessment of sedation and GA more challenging. Therefore, reducing or eliminating the need for sedation and GA is an important goal of imaging innovation and research in pediatric MRI. In this review, the authors focus on technical and clinical approaches to reducing and eliminating the use of sedation in the pediatric population based on image acquisition acceleration and imaging protocols abbreviation. This paper covers important physiological and technical considerations for pediatric body MR imaging and discusses MRI techniques that offer the potential of recovering diagnostic-quality images from accelerated scans. In this review, the authors also introduce the concept of reporting elements for important indications for pediatric body MRI and use this as a basis for abbreviating the MR protocols. By employing appropriate accelerated and abbreviated approaches based on an understanding of the imaging needs and reporting elements for a given clinical indication, it is possible to reduce sedation and GA for pediatric chest, cardiovascular and abdominal MRI.

Purpose For the application of compressive sensing to parallel MRI, Poisson disk sampling (PDS) has been shown to generate superior results compared with random sampling methods. However, due to its limited flexibility to incorporate additional constraints, PDS is not readily extendible to dynamic applications. Here, we propose and validate a pseudo-random sampling technique that allows incorporating constraints specific to dynamic imaging. Methods The proposed sampling scheme, called variable density incoherent spatiotemporal acquisition (VISTA), is based on constrained minimization of Riesz energy on a spatiotemporal grid. Data from both a digital phantom and real-time cine were used to compare VISTA with uniform interleaved sampling (UIS) and variable density random sampling (VRS). The image quality was assessed qualitatively and quantitatively. Results VISTA improved the trade-off between noise and sharpness. Also, VISTA produced diagnostic quality images at an acceleration rate of 15, whereas UIS and VRS images degraded below the diagnostic threshold at lower acceleration rates. Conclusions VISTA generates spatiotemporal sampling patterns with high levels of uniformity and incoherence, while maintaining a constant temporal resolution. Using a small pilot study, VISTA was shown to produce diagnostic quality images at acceleration rates up to 15.

Our approach can be utilized to achieve fast reconstruction of large MRI datasets, thereby increasing the clinical utility of reconstruction techniques based on compressed sensing. Magn Reson Med 77:1505-1515, 2017. © 2016 International Society for Magnetic Resonance in Medicine.