Purpose: To develop a novel sequence for simultaneous quantification of T₁ and T₂ relaxation times in the myocardium based on the transient phase of the balanced steady‐state free precession.Methods: A new prototype sequence, named “cardiac balanced‐SSFP inversion recovery with interleaved sampling acquisition” (CABIRIA) was developed based on a single‐shot bSSFP readout following an inversion pulse. With this method, T₁ and T₂ values can be calculated from the analysis of signal evolution. The scan duration for a single slice in vivo was 8 heartbeats, thus feasible in a breath‐hold. The sequence was validated both in vitro by comparing it to conventional inversion recovery and multi‐echo spin‐echo methods and in 5 healthy volunteers by comparing it to the Modified Look‐Locker Inversion Recovery (MOLLI) sequence and to a T₂ quantification sequence based on multi‐T₂‐prepared bSSFP.Results: The method showed good agreement with conventional methods for both T₁ and T₂ measurements (concordance correlation coefficient ≥ 0.99) in vitro. In healthy volunteers the measured T₁ values were 1227 ± 68 ms and T₂ values 37.9 ± 2.4 ms, with similar inter‐ and intrasubject variability with respect to existing methods.Conclusion: The proposed CABIRIA method enables simultaneous quantification of myocardial T₁ and T₂ values with good accuracy and precision. Magn Reson Med 74:365–371, 2015.

Objective: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach. Material and methods: The application of quantitative imaging in neuromuscular diseases requires the availability of regions of interest (ROI) drawn on muscles to extract quantitative parameters. Up to now, manual drawing of ROIs has been considered the gold standard in clinical studies, with no clear and universally accepted standardized procedure for segmentation. Several automatic methods, based mainly on machine learning and deep learning algorithms, have recently been proposed to discriminate between skeletal muscle, bone, subcutaneous and intermuscular adipose tissue. We develop a supervised deep learning approach based on a unified framework for ROI segmentation. Results: The proposed network generates segmentation maps with high accuracy, consisting in Dice Scores ranging from 0.89 to 0.95, with respect to “ground truth” manually segmented labelled images, also showing high average performance in both mild and severe cases of disease involvement (i.e. entity of fatty replacement). Discussion: The presented results are promising and potentially translatable to different skeletal muscle groups and other MRI sequences with different contrast and resolution.

Imaging has become a valuable tool in the assessment of neuromuscular diseases, and, specifically, quantitative MR imaging provides robust biomarkers for the monitoring of disease progression. Quantitative evaluation of fat infiltration and quantification of the T2 values of the muscular tissue's water component (wT2) are two of the most essential indicators currently used. As each voxel of the image can contain both water and fat, a two-component model for the estimation of wT2 must be used. In this work, we present a fast method for reconstructing wT2 maps obtained from conventional multi-echo spin-echo (MESE) acquisitions and released as Free Open Source Software. The proposed software is capable of fast reconstruction thanks to extended phase graphs (EPG) simulations and dictionary matching implemented on a general-purpose graphic processing unit. The program can also perform more conventional biexponential least-squares fitting of the data and incorporate information from an external water-fat acquisition to increase the accuracy of the results. The method was applied to the scans of four healthy volunteers and five subjects suffering from facioscapulohumeral muscular dystrophy (FSHD). Conventional multi-slice MESE acquisitions were performed with 17 echoes, and additionally, a 6-echo multi-echo gradient-echo (MEGE) sequence was used for an independent fat fraction calculation. The proposed reconstruction software was applied on the full datasets, and additionally to reduced number of echoes, respectively, to 8, 5, and 3, using EPG and biexponential least-squares fitting, with and without incorporating information from the MEGE acquisition. The incorporation of external fat fraction maps increased the robustness of the fitting with a reduced number of echoes per datasets, whereas with unconstrained fitting, the total of 17 echoes was necessary to retain an independence of wT2 from the level of fat infiltration. In conclusion, the proposed software can successfully be used to calculate wT2 maps from conventional MESE acquisition, allowing the usage of an optimized protocol with similar precision and accuracy as a 17-echo acquisition. As it is freely released to the community, it can be used as a reference for more extensive cohort studies.

Caffeine is commonly used to combat high sleep pressure on a daily basis. However, interference with sleep–wake regulation could disturb neural homeostasis and insufficient sleep could lead to alterations in human gray matter. Hence, in this double-blind, randomized, cross-over study, we examined the impact of 10-day caffeine (3 × 150 mg/day) on human gray matter volumes (GMVs) and cerebral blood flow (CBF) by fMRI MP-RAGE and arterial spin-labeling sequences in 20 habitual caffeine consumers, compared with 10-day placebo (3 × 150 mg/day). Sleep pressure was quantified by electroencephalographic slow-wave activity (SWA) in the previous nighttime sleep. Nonparametric voxel-based analyses revealed a significant reduction in GMV in the medial temporal lobe (mTL) after 10 days of caffeine intake compared with 10 days of placebo, voxel-wisely adjusted for CBF considering the decreased perfusion after caffeine intake compared with placebo. Larger GMV reductions were associated with higher individual concentrations of caffeine and paraxanthine. Sleep SWA was, however, neither different between conditions nor associated with caffeine-induced GMV reductions. Therefore, the data do not suggest a link between sleep depth during daily caffeine intake and changes in brain morphology. In conclusion, daily caffeine intake might induce neural plasticity in the mTL depending on individual metabolic processes.

Objective: To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with laboratory and pulmonary findings. Methods: Structural MRI and T2 mapping of bilateral thigh muscles from patients with IIM and healthy volunteers were segmented using dedicated software based on a pre-trained convolutional neural network. Incremental and federated learning were implemented for continuous adaptation and improvement. Muscle T2 values derived from DL segmentation were compared between patients and healthy controls, and T2 values of patients were further analyzed with serum muscle enzymes, and interstitial lung disease (ILD) which was diagnosed and graded based on chest HRCT. Results: Overall, 64 patients (27 patients with dermatomyositis, 29 with polymyositis, and 8 with antisynthetase syndrome (ASS)) and 10 healthy controls were included. By using DL-based muscle segmentation, T2 values generated from T2 maps accurately differentiated patients from those of controls (p < 0.001) with a cutoff value of 36.4 ms (sensitivity 96.9%, and specificity 100%). In patients with IIM, muscle T2 values positively correlated with all the serum muscle enzymes (all p < 0.05). ILD score of patients with ASS was markedly higher than that of those without ASS (p = 0.011), while dissociation between the severity of muscular involvement and ILD was observed (p = 0.080). Conclusion: Automatic DL could be used to segment thigh muscles and help quantitatively assess muscular inflammation of IIM through T2 mapping. Key Points: • Muscle T2 mapping automatically segmented by deep learning can differentiate IIM from healthy controls. • T2 value, an indicator of active muscle inflammation, positively correlates with serum muscle enzymes. • T2 mapping can detect muscle disease in patients with normal muscle enzyme levels.