Case report: epipericardial fat necrosis, a rare cause of chest pain

Background Epipericardial fat necrosis (EFN) is a rare cause of chest pain which is often unrecognized. Case summary A 58 year-old male previously known with a transient ischaemic attack presented with a sharp, substernal chest pain. Pulmonary embolism was ruled out by computed tomography (CT) angiography. However, CT angiography revealed an inhomogeneous epipericardial mass. On cardiovascular magnetic resonance imaging (CMR) the mass had an inhomogeneous signal intensity without infiltration of surrounding tissue. Late gadolinium enhancement imaging showed subtle hyperenhancement. Tissue characterization by means of parametric mapping revealed very low native T1 relaxation times and increased T2 relaxation times. In conclusion, the epipericardial mass showed fibro-fatty inflammatory markers, suggestive of EFN. The chest pain resolved spontaneously. Follow up CT 3 months later showed a marked regression of the mass which confirmed the diagnosis EFN. Discussion EFN is a benign and self-limiting inflammatory cause of chest pain which can be diagnosed with multi-modality imaging and must not be overlooked in the differential diagnosis of patients with acute pleuritic chest pain.

Quantitative T1-relaxation corrected metabolite mapping of 12 metabolites in the human brain at 9.4 T

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive imaging modality that enables observation of metabolites. Applications of MRSI for neuroimaging applications has shown promise for monitoring and detecting various diseases. This study builds off previously developed techniques of short TR, 1H FID MRSI by correcting for T1-weighting of the metabolites and utilizing an internal water reference to produce quantitative (mmol kg-1) metabolite maps. This work reports and shows quantitative metabolite maps for 12 metabolites for a single slice. Voxel-specific T1-corrections for water are common in MRSI studies; however, most studies use either averaged T1-relaxation times to correct for T1-weighting of metabolites or omit this correction step entirely. This work employs the use of voxel-specific T1-corrections for metabolites in addition to water. Utilizing averaged T1-relaxation times for metabolites can bias metabolite maps for metabolites that have strong differences between T1-relaxation for GM and WM (i.e. Glu). This work systematically compares quantitative metabolite maps to single voxel quantitative results and qualitatively compares metabolite maps to previous works.

Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm

Pathological changes in the cortical lamina can cause several mental disorders. Visualization of these changes in vivo would enhance their diagnostics. Recently a framework for visualizing cortical structures by magnetic resonance imaging (MRI) has emerged. This is based on mathematical modeling of multi-component T1 relaxation at the sub-voxel level. This work proposes a new approach for their estimation. The approach is validated using simulated data. Sixteen MRI experiments were carried out on healthy volunteers. A modified echo-planar imaging (EPI) sequence was used to acquire 105 individual volumes. Data simulating the images were created, serving as the ground truth. The model was fitted to the data using a modified Trust Region algorithm. In single voxel experiments, the estimation accuracy of the T1 relaxation times depended on the number of optimization starting points and the level of noise. A single starting point resulted in a mean percentage error (MPE) of 6.1%, while 100 starting points resulted in a perfect fit. The MPE was <5% for the signal-to-noise ratio (SNR) ≥ 38 dB. Concerning multiple voxel experiments, the MPE was <5% for all components. Estimation of T1 relaxation times can be achieved using the modified algorithm with MPE < 5%.

T1 Relaxation Times in the Cortex and Thalamus Are Associated With Working Memory and Information Processing Speed in Patients With Multiple Sclerosis

Background: Cortical and thalamic pathologies have been associated with cognitive impairment in patients with multiple sclerosis (MS).Objective: We aimed to quantify cortical and thalamic damage in patients with MS using a high-resolution T1 mapping technique and to evaluate the association of these changes with clinical and cognitive impairment.Methods: The study group consisted of 49 patients with mainly relapsing-remitting MS and 17 age-matched healthy controls who received 3T MRIs including a T1 mapping sequence (MP2RAGE). Mean T1 relaxation times (T1-RT) in the cortex and thalami were compared between patients with MS and healthy controls. Additionally, correlation analysis was performed to assess the relationship between MRI parameters and clinical and cognitive disability.Results: Patients with MS had significantly decreased normalized brain, gray matter, and white matter volumes, as well as increased T1-RT in the normal-appearing white matter, compared to healthy controls (p &lt; 0.001). Partial correlation analysis with age, sex, and disease duration as covariates revealed correlations for T1-RT in the cortex (r = −0.33, p &lt; 0.05), and thalami (right thalamus: r = −0.37, left thalamus: r = −0.50, both p &lt; 0.05) with working memory and information processing speed, as measured by the Symbol-Digit Modalities Test.Conclusion: T1-RT in the cortex and thalamus correlate with information processing speed in patients with MS.

NI-3 Magnetic resonance relaxometry for tumor cell density imaging for glioma: An exploratory study via 11C-methionine PET and its validation via stereotactic tissue sampling

Objective: While visualization of non-enhancing tumors for glioma is crucial for planning the most appropriate surgical or non-surgical treatment of the disease, current MRI cannot achieve this goal. This study aims to test the hypothesis that quantitative and diffusion MRI can estimate tumor burden with the brain. Materials and Methods: Study 1: Ten patients who have undergone Methionine PET (Met-PET), quantitative MRI (qMRI), and diffusion MRI (DWI) were included for analysis. A cut-off of a tumor-to-normal ratio (T/Nr) 1.5 was set on Met-PET, and the values from qMRI and DWI were compared. Study 2: Seventy-nine stereo-tactically sampled tissues from 22 glioma patients were correlated with Met-PET, qMRI, and DWI measurements regarding tumor cell density. qMRI acquisition: Imaging was performed on either a 1.5 or 3 T MR scanner (Prisma or Aera; Siemens Healthcare, Erlangen, Germany). T1-relaxometry was achieved by first acquiring MP2RAGE images, then converting those images into T1-relaxation time maps. At the same time, T2-relaxometry was achieved by first acquiring multi-echo T2-weighted images and then converting those images into T2-relaxation time maps, with both relaxometries performed via Bayesian inference modeling (Olea Nova+; Canon Medical Systems, Tochigi, Japan). Results: Study 1 revealed that regions of 1850ms &lt; T1-relaxation time &lt; 3200ms and 115ms &lt; T2-relaxation time &lt; 225ms tended to be Met-PET T/Nr &gt; 1.5. DWI was not useful to separate areas between low and high Met-PET. Study 2 showed that regions of 1850ms &lt; T1-relaxation time &lt; 3200ms showed high tumor cell density than other areas (p=0.04). Conclusions: Our results supported the hypothesis that qMRI is useful for predicting the tumor load within the brain among glioma patients. T1-relaxation time was notably useful for this means. On the other hand, ADC measured from DWI was limited for tumor load prediction.

Quantification of contrast agent uptake in the hepatobiliary phase helps to differentiate hepatocellular carcinoma grade

This study aimed to assess the degree of differentiation of hepatocellular carcinoma (HCC) using Gd-EOB-DTPA-assisted magnetic resonance imaging (MRI) with T1 relaxometry. Thirty-three solitary HCC lesions were included in this retrospective study. This study's inclusion criteria were preoperative Gd-EOB-DTPA-assisted MRI of the liver and a histopathological evaluation after hepatic tumor resection. T1 maps of the liver were evaluated to determine the T1 relaxation time and reduction rate between the native phase and hepatobiliary phase (HBP) in liver lesions. These findings were correlated with the histopathologically determined degree of HCC differentiation (G1, well-differentiated; G2, moderately differentiated; G3, poorly differentiated). There was no significant difference between well-differentiated (950.2 ± 140.2 ms) and moderately/poorly differentiated (1009.4 ± 202.0 ms) HCCs in the native T1 maps. After contrast medium administration, a significant difference (p ≤ 0.001) in the mean T1 relaxation time in the HBP was found between well-differentiated (555.4 ± 140.2 ms) and moderately/poorly differentiated (750.9 ± 146.4 ms) HCCs. For well-differentiated HCCs, the reduction rate in the T1 time was significantly higher at 0.40 ± 0.15 than for moderately/poorly differentiated HCCs (0.25 ± 0.07; p = 0.006). In conclusion this study suggests that the uptake of Gd-EOB-DTPA in HCCs is correlated with tumor grade. Thus, Gd-EOB-DTPA-assisted T1 relaxometry can help to further differentiation of HCC.

Microstructural deficits in the thalamus of major depressive disorder

Macroscopic structural abnormalities in the thalamus and thalamic circuits have been shown to contribute to the neuropathology of major depressive disorder (MDD). However, cytoarchitectonic properties underlying these macroscopic abnormalities remain unknown. The purpose of this study was to identify systematic deficits of brain architecture in depression, from structural brain network organization to microstructural properties. A multi-modal neuroimaging approach including diffusion, anatomical and quantitative magnetic resonance imaging (MRI) was used to examine structural-related alternations in 56 MDD patients compared with 35 age- and sex-matched controls. Structural networks were constructed and analyzed using seed-based probabilistic tractography. Morphometric measurements, including cortical thickness and voxel-based morphometry (VBM), were evaluated across the whole brain. A conjunction analysis was then conducted to identify key regions showing common structural alternations across modalities. The microstructural properties, macromolecular tissue volume (MTV) and T1 relaxation times of identified key regions were then calculated. Results showed multiple alterations of structural connectivity within a set of subcortical areas and their connections to cortical regions in MDD patients. These subcortical regions included the putamen, thalamus and caudate, which are predominately involved in the limbic-cortical-striatal-pallidal-thalamic network (LCSPT). Structural connectivity was disrupted within and between large-scale networks, mainly including subcortical networks, default mode networks and salience/ventral attention networks. Consistently, these regions also exhibited widespread volume reductions in MDD patients, specifically the bilateral thalamus, left putamen and right caudate. Importantly, the microstructural properties, T1 relaxation time of left thalamus were increased and negatively correlated with its gray matter volume in MDD patients. The present work to date sheds light on the neuropathological disruptions of LCSPT circuit in MDD, providing the first multi-modal neuroimaging evidence for the macro-micro structural abnormalities of the thalamus in patients with MDD. These findings have implications in understanding the abnormal changes of brain structures across development of MDD.

The Water Polymorphism and the Liquid–Liquid Transition from Transport Data

NMR spectroscopic literature data are used, in a wide temperature-pressure range (180–350 K and 0.1–400 MPa), to study the water polymorphism and the validity of the liquid–liquid transition (LLT) hypothesis. We have considered the self-diffusion coefficient DS and the reorientational correlation time τθ (obtained from spin-lattice T1 relaxation times), measured, respectively, in bulk and emulsion liquid water from the stable to well inside the metastable supercooled region. As an effect of the hydrogen bond (HB) networking, the isobars of both these transport functions evolve with T by changing by several orders of magnitude, whereas their pressure dependence become more and more pronounced at lower temperatures. Both these transport functions were then studied according to the Adam–Gibbs model, typical of glass forming liquids, obtaining the water configurational entropy and the corresponding specific heat contribution. The comparison of the evaluated CP,conf isobars with the experimentally measured water specific heat reveals the full consistency of this analysis. In particular, the observed CP,conf maxima and its diverging behaviors clearly reveals the presence of the LLT and with a reasonable approximation the liquid–liquid critical point (LLCP) locus in the phase diagram.

Magnetic Resonance Relaxometry for Tumor Cell Density Imaging for Glioma: An Exploratory Study via 11C-methionine PET and Its Validation via Stereotactic Tissue Sampling

One of the most crucial yet challenging issues for glioma patient care is visualizing non-contrast-enhancing tumor regions. In this study, to test the hypothesis that quantitative magnetic resonance relaxometry reflects glioma tumor load within tissue and that it can be an imaging surrogate for visualizing non-contrast-enhancing tumors, we investigated the correlation between T1- and T2-weighted relaxation times, apparent diffusion coefficient (ADC) on magnetic resonance imaging, and 11C-methionine (MET) on positron emission tomography (PET). Moreover, we compared the T1- and T2-relaxation times and ADC with tumor cell density (TCD) findings obtained via stereotactic image-guided tissue sampling. Regions that presented a T1-relaxation time of >1850 ms but <3200 ms or a T2-relaxation time of >115 ms but <225 ms under 3 T indicated a high MET uptake. In addition, the stereotactic tissue sampling findings confirmed that the T1-relaxation time of 1850–3200 ms significantly indicated a higher TCD (p = 0.04). However, ADC was unable to show a significant correlation with MET uptake or with TCD. Finally, synthetically synthesized tumor load images from the T1- and T2-relaxation maps were able to visualize MET uptake presented on PET.