MRI provides a non-invasive diagnostic platform to quantify the physical and physiological attributes of skeletal muscle at rest and in response to exercise. MR relaxation parameters (T1, T2 and T2*) are characteristic of tissue composition and metabolic properties. With the recent advent of quantitative techniques that allow rapid acquisition of T1, T2 and T2* maps, we posited that an integrated treadmill exercise-quantitative relaxometry paradigm can rapidly characterize exercise-induced changes in skeletal muscle relaxation parameters. Accordingly, we investigated the rest/recovery kinetics of T1, T2 and T2* in response to treadmill exercise in the anterior tibialis, soleus and gastrocnemius muscles of healthy volunteers, and the relationship of these parameters to age and gender. Thirty healthy volunteers (50.3 ± 16.6 years) performed the Bruce treadmill exercise protocol to maximal exhaustion. Relaxometric maps were sequentially acquired at baseline and for approximately 44 minutes post-exercise. Our results show that T1, T2 and T2* are significantly and differentially increased immediately post-exercise among the leg muscle groups, and these values recover to near baseline within 30–44 minutes. Our results demonstrate the potential to characterize the kinetics of relaxation parameters with quantitative mapping and upright exercise, providing normative values and some clarity on the impact of age and gender.
Background--Stress cardiac magnetic resonance (CMR) has typically involved pharmacologic agents. Treadmill CMR has shown utility in single-center studies but has not undergone multicenter evaluation.
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.
Background Water–fat separation is a postprocessing technique most commonly applied to multiple‐gradient‐echo magnetic resonance (MR) images to identify fat, provide images with fat suppression, and to measure fat tissue concentration. Recently, Numerous advancements have been reported. In contrast to early methods, the process of water–fat separation has become complicated due to multiparametric analytic models, optimization methods, and the absence of a unified framework for diverse source data. Purpose To determine the feasibility and performance of MRI water–fat separation and parametric mapping via deep learning (DL) with a range of inputs. Study Type Retrospective data usage. Population/Subjects Ninety cardiac MR examinations from normal control, acute, subacute, and chronic myocardial infarction subjects were obtained, providing 1200 multiple gradient‐echo acquisitions. Field Strength/Sequence 1.5 T/2D multiple gradient‐echo pulse sequence Assessment Ground‐truth training and validation water–fat separation were obtained using a graph cut method with R2*, off‐resonance correction, and a multipeak fat spectrum. U‐Net DL training with single and multiecho, complex, and magnitude inputs were compared using quantitative and three‐observer subjective analysis. Statistical Tests DL methods' image structural similarity, and quantitative proton density fat fraction (PDFF), R2*, and off‐resonance quantitative values were statistically compared with the GraphCut reference standard using Student's t‐test and Pearson's correlation. Results Myocardial fat deposition in chronic myocardial infarction and intramyocardial hemorrhage in acute myocardial infarction were well visualized in the DL results. Predicted values for R2*, off‐resonance, water, and fat signal intensities were well correlated with a conventional model‐based water fat separation (R2 ≥ 0.97, P < 0.001) with appropriate inputs. DL parameter maps had a 14% higher signal‐to‐noise ratio (P < 0.001) when compared with a conventional method. Data Conclusion DL water–fat separation is feasible with a wide range of inputs, while R2* and off‐resonance mapping requires multiple echoes and complex images. With appropriate inputs, DL provides quantitative and subjective results comparable to conventional model‐based methods. Level of Evidence: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:655–665.
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