Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the \'type 1\' ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.

T1, T2 and T1ρ are well-recognized parameters for quantitative cardiac MRI. Simultaneous estimation of these parameters allows for comprehensive myocardial tissue characterization, such as myocardial fibrosis and edema. However, conventional techniques either quantify the parameters individually with separate breath-hold acquisitions, which may result in unregistered parameter maps, or estimate multiple parameters in a prolonged breath-hold acquisition, which may be intolerable to patients. We propose a free-breathing multi-parametric mapping (FB-MultiMap) technique that provides co-registered myocardial T1, T2 and T1ρ maps in a single efficient acquisition.
The proposed FB-MultiMap performs electrocardiogram-triggered single-shot Cartesian acquisition over 16 consecutive cardiac cycles, where inversion, T2 and T1ρ preparations are introduced for varying contrasts. A diaphragmatic navigator was used for prospective through-plane motion correction and the in-plane motion was corrected retrospectively with a group-wise image registration method. Quantitative mapping was conducted through dictionary matching of the motion corrected images, where the subject-specific dictionary was created using Bloch simulations for a range of T1, T2 and T1ρ values, as well as B1 factors to account for B1 inhomogeneities. The FB-MultiMap was optimized and validated in numerical simulations, phantom experiments, and in vivo imaging of 15 healthy subjects and six patients with suspected cardiac diseases.
The phantom T1, T2 and T1ρ values estimated with FB-MultiMap agreed well with reference measurements with no dependency on heart rate. In healthy subjects, FB-MultiMap T1 was higher than MOLLI T1 mapping (1218 ± 50 ms vs. 1166 ± 38 ms, p < 0.001). The myocardial T2 and T1ρ estimated with FB-MultiMap were lower compared to the mapping with T2- or T1ρ-prepared 2D balanced steady-state free precession (T2: 41.2 ± 2.8 ms vs. 42.5 ± 3.1 ms, p = 0.06; T1ρ: 45.3 ± 4.4 ms vs. 50.2 ± 4.0, p < 0.001). The pathological changes in myocardial parameters measured with FB-MultiMap were consistent with conventional techniques in all patients.
The proposed free-breathing multi-parametric mapping technique provides co-registered myocardial T1, T2 and T1ρ maps in 16 heartbeats, achieving similar mapping quality to conventional breath-hold mapping methods.

OBJECTIVE: The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water-fat separation using rosette MRF at 0.55 T.
METHODS: Spiral and rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in T1 and T2 quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects.
RESULTS: In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in T1 and T2 measurements. In addition, rosette MRF enables water-fat separation and can generate water- and fat- specific T1 maps, T2 maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm3 within the acquisition time of 15 s.
CONCLUSIONS: It is feasible to measure T1 and T2 simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water-fat separation along with T1 and T2 quantification with no time penalty.

Alteration in myocardial tissue, such as myocardial fibrosis, edema, inflammation, or accumulation with amyloid, lipids, or iron, has an important role in the cardiac remodeling that leads to diastolic and/or systolic dysfunction and the development of chronic heart failure, increasing the risk of adverse cardiovascular events. Thus, the early detection of changes at myocardial tissue level has great diagnostic and prognostic potential. The gold standard technique to assess these myocardial alterations is endomyocardial biopsy. However, this has been limited to a few patients due to the invasive nature, sampling errors, and its inability to assess the entire myocardium. Cardiovascular magnetic resonance (CMR) has emerged as the gold standard imaging not only for assessing cardiac volume, function quantification, and viability but also for noninvasive myocardial tissue characterization over the past decade. Its ability to characterize myocardial tissue composition is unique among noninvasive imaging modalities in cardiovascular disease. Currently, multi-parametric myocardial characterization with T1, T2, and extracellular volume has the potential to identify and track diffuse pathology in various diseases. In this review article, we present the role of established and emerging CMR techniques in myocardial tissue characterization, with an emphasis on T1 and T2 mapping, in clinical practice.

Standard relaxation time quantification using phase-cycled balanced steady-state free precession (bSSFP), eg, motion-insensitive rapid configuration relaxometry (MIRACLE), is subject to a considerable underestimation of tissue T1 and T2 due to asymmetric intra-voxel frequency distributions. In this work, an artificial neural network (ANN) fitting approach is proposed to simultaneously extract accurate reference relaxation times (T1 , T2 ) and robust field map estimates ( B 1 + , ΔB0 ) from the bSSFP profile.
Whole-brain bSSFP data acquired at 3T were used for the training of a feedforward ANN with N = 12, 6, and 4 phase-cycles. The magnitude and phase of the Fourier transformed complex bSSFP frequency response served as input and the multi-parametric reference set [T1 , T2 , B 1 + , ∆B0 ] as target. The ANN predicted relaxation times were validated against the target and MIRACLE.
The ANN prediction of T1 and T2 for trained and untrained data agreed well with the reference, even for only four acquired phase-cycles. In contrast, relaxometry based on 4-point MIRACLE was prone to severe off-resonance-related artifacts. ANN predicted B 1 + and ∆B0 maps showed the expected spatial inhomogeneity patterns in high agreement with the reference measurements for 12-point, 6-point, and 4-point bSSFP phase-cycling schemes.
ANNs show promise to provide accurate brain tissue T1 and T2 values as well as reliable field map estimates. Moreover, the bSSFP acquisition can be accelerated by reducing the number of phase-cycles while still delivering robust T1 , T2 , B 1 + , and ∆B0 estimates.

Multisystem iron poisoning is a major concern for long-term beta-thalassemia management. Quantitative MRI-based techniques routinely show iron overload in heart, liver, endocrine glands and kidneys. However, data on the brain are conflicting and monitoring of brain iron content is still matter of debate.
This 3T-MRI study applied a well validated high-resolution whole-brain quantitative MRI assessment of iron content on 47 transfusion-dependent (mean-age: 36.9 ± 10.3 years, 63% females), 23 non-transfusion dependent (mean-age: 29.2 ± 11.7 years, 56% females) and 57 healthy controls (mean-age: 33.9 ± 10.8 years, 65% females). Clinical data, Wechsler Adult Intelligence Scale scores and treatment regimens were recorded. Beside whole-brain R2* analyses, regional R2*-values were extracted in putamen, globus pallidum, caudate nucleus, thalamus and red nucleus; hippocampal volumes were also determined.
Regional analyses yielded no significant differences between patients and controls, except in those treated with deferiprone that showed lower R2*-values (p<0.05). Whole-brain analyses of R2*-maps revealed strong age-R2* correlations (r2=0.51) in both groups and clusters of significantly increased R2*-values in beta-thalassemia patients in the hippocampal formations and around the Luschka foramina; transfusion treatment was associated with additional R2* increase in dorsal thalami. Hippocampal formation R2*-values did not correlate with hippocampal volume; hippocampal volume did not differ between patients and controls. All regions with increased R2*-values shared a strict anatomical contiguity with choroid plexuses suggesting a blooming effect as the likely cause of R2* increase, in agreement with the available histopathologic literature evidence.
According to our MRI findings and the available histopathologic literature evidence, concerns about neural tissue iron overload in beta-thalassemia appear to be unjustified.

To present a stimulated-echo based mapping (STEM) approach for simultaneous T1 , T2 , and ADC mapping.
Diffusion-weighted stimulated-echo images with various combinations of mixing time (TM), TE, and b-value were acquired to enable simultaneous mapping of T1 , T2 , and ADC. The proposed STEM method was performed by densely sampling the TM-TE-b space in a phantom and in brain and prostate of healthy volunteers. T1 , T2 , and ADC from STEM were compared to reference mapping methods. Additionally, protocol optimization was performed to enable rapid STEM acquisition within 2 min by sparsely sampling the TM-TE-b space. The T1 , T2 , and ADC measurements from rapid acquisitions were compared to the densely sampled STEM for evaluation. Finally, a patient with biopsy-proven high-risk prostate cancer was imaged to demonstrate the ability of STEM to differentiate cancer and healthy tissues.
Relative to the reference measurements, densely sampled STEM provided accurate quantitative T1 , T2 , and ADC mapping in phantoms (R2  = 0.999, slope between 0.97-1.03), as well as in brain and prostate. Further, the T1 , T2 , and ADC measurements from the optimized rapid STEM acquisitions agreed closely with densely sampled STEM. Finally, STEM showed decreased T2 and ADC in prostate cancer compared to healthy prostate tissue.
STEM provides accurate simultaneous mapping of T1 , T2 , and ADC. This method may enable rapid and accurate multi-parametric tissue characterization for clinical and research applications.

There are a number of diseases which can increase left ventricular myocardial wall thickness through a number of different mechanisms. Multi-parametric mapping techniques are a new addition to the cardiovascular magnetic resonance (CMR) armoury with a number of potential clinical roles. In this review article, we will explore the role of imaging in left ventricular hypertrophy, and particularly developments in CMR. We focus on ability of CMR to characterize myocardial tissue using multiparametric mapping (native T1, T2 and extracellular volume mapping), to bridge from the microscopic histological domain and into the clinical domain of non-invasive imaging.

Obesity-related structural brain alterations point to a consistent reduction in gray matter with increasing body mass index (BMI) but changes in white matter have proven to be more complex and less conclusive. Hence, more recently diffusion tensor imaging (DTI) has been employed to investigate microstructural changes in white matter structure. Altogether, these studies have mostly shown a loss of white matter integrity with obesity-related factors in several brain regions. However, the variety of these obesity-related factors, including inflammation and dyslipidemia, resulted in competing influences on the DTI indices. To increase the specificity of DTI results, we explored specific brain tissue properties by combining DTI with quantitative multi-parameter mapping in lean, overweight and obese young adults. By means of multi-parameter mapping, white matter structures showed differences in MRI parameters consistent with reduced myelin, increased water and altered iron content with increasing BMI in the superior longitudinal fasciculus, anterior thalamic radiation, internal capsule and corpus callosum. BMI-related changes in DTI parameters revealed mainly alterations in mean and axial diffusivity with increasing BMI in the corticospinal tract, anterior thalamic radiation and superior longitudinal fasciculus. These alterations, including mainly fiber tracts linking limbic structures with prefrontal regions, could potentially promote accelerated aging in obese individuals leading to an increased risk for cognitive decline.