Uncommon presentation of a rare tumour - incidental finding in an asymptomatic patient: case report and comprehensive review of the literature on intrapericardial solitary fibrous tumours

BACKGROUND: A solitary fibrous tumour is a rare, mainly benign spindle cell mesenchymal tumour most commonly originating from the pleura. An intrapericardial location of a solitary fibrous tumour is extremely unusual. We present a case of an asymptomatic patient with a slow-growing massive benign cardiac solitary fibrous tumour. CASE PRESENTATION: A 37-year-old asymptomatic female patient was referred to our hospital with an enlarged cardiac silhouette found on her screening chest X-ray. The echocardiographic examination revealed pericardial effusion and an inhomogeneous mobile mass located in the pericardial sac around the left ventricle. Cardiac magnetic resonance (MRI) examination showed an intrapericardial, semilunar-shaped mass attached to the pulmonary trunk with an intermediate signal intensity on proton density-weighted images and high signal intensity on T2-weighted spectral fat saturation inversion recovery images. First-pass perfusion and early and late gadolinium-enhanced images showed a vascularized mass with septated, patchy, inhomogeneous late enhancement. Coronary computed tomography angiography revealed no invasion of the coronaries. Based on the retrospectively analysed screening chest X-rays, the mass had started to form at least 7 years earlier. Complete resection of the tumour with partial resection of the pulmonary trunk was performed. Histological evaluation of the septated, cystic mass revealed tumour cells forming an irregular patternless pattern; immunohistochemically, the cells tested positive for vimentin, CD34, CD99 and STAT6 but negative for keratin (AE1-AE3), CD31 and S100. Thus, the diagnosis of an intrapericardial solitary fibrous tumour was established. There has been no recurrence for 3 years based on the regular MRI follow-up. CONCLUSION: Intrapericardial SFTs, showing slow growth dynamics, can present with massive extent even in completely asymptomatic patients. MRI is exceedingly useful for characterizing intrapericardial masses, allowing precise surgical planning, and is reliable for long-term follow up

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS

Bezold–Jarisch reflex mediated syncope in pulmonary arterial hypertension: An illustrative case series

Abstract We present a novel description of Bezold–Jarisch Reflex (BJR) during cardiopulmonary exercise testing (CPET) in three young female patients with Group 1 pulmonary arterial hypertension (PAH). These three cases presented within 26 months, representing only 0.8% of 11,387 tests on patients with PAH undergoing CPET during this time frame

Data from: The demanding grey zone: sport indices by cardiac magnetic resonance imaging differentiate hypertrophic cardiomyopathy from athlete's heart

Background: We aimed to characterize gender specific left ventricular hypertrophy using a novel, accurate and less time demanding cardiac magnetic resonance (CMR) quantification method to differentiate physiological hypertrophy and hypertrophic cardiomyopathy based on a large population of highly trained athletes and hypertrophic cardiomyopathy patients. Methods and Results: Elite athletes (n=150,>18 training hours/week), HCM patients (n=194) and athletes with hypertrophic cardiomyopathy (n=10) were examined by CMR. CMR based sport indices such as maximal end-diastolic wall thickness to left ventricular end-diastolic volume index ratio (EDWT/LVEDVi) and left ventricular mass to left ventricular end-diastolic volume ratio (LVM/LVEDV) were calculated, established using both conventional and threshold-based quantification method. Whereas 47.5% of male athletes, only 4.1% of female athletes were in the grey zone of hypertrophy (EDWT 13-16mm). EDWT/LVEDVi discriminated between physiological and pathological left ventricular hypertrophy with excellent diagnostic accuracy (AUCCQ:0.998, AUCTQ:0.999). Cut-off value for LVM/LVEDVCQ<0.82 mm×m2/ml and for EDWT/LVEDViTQ<1.27 discriminated between physiological and pathological left ventricular hypertrophy with a sensitivity of 77.8% and 89.2%, a specificity of 86.7% and 91.3%, respectively. LVM/LVEDV evaluated using threshold-based quantification performed significantly better than conventional quantification even in the male subgroup with EDWT between 13-16mm (p<0.001). Conclusions: Almost 50% of male highly trained athletes can reach EDWT of 13 mm. CMR based sport indices provide an important tool to distinguish hypertrophic cardiomyopathy from athlete’s heart, especially in highly trained athletes in the grey zone of hypertrophy

raw database

Raw data regarding the paper entitled "The Demanding Grey Zone: Sport Indices by Cardiac Magnetic Resonance Imaging Differentiate Hypertrophic Cardiomyopathy from Athlete’s Heart" is presented in the current file. Codes applied in the database are presented in a separate spreadsheet. Abbreviations are listed in the manuscript

How are ECG parameters related to cardiac magnetic resonance images? Electrocardiographic predictors of left ventricular hypertrophy and myocardial fibrosis in hypertrophic cardiomyopathy

Abstract Background Structural myocardial changes in hypertrophic cardiomyopathy (HCM) are associated with different abnormalities on electrocardiographs (ECGs). The diagnostic value of the ECG voltage criteria used to screen for left ventricular hypertrophy (LVH) may depend on the presence and degree of myocardial fibrosis. Fibrosis can cause other changes in ECG parameters, such as pathological Q waves, fragmented QRS (fQRS), or repolarization abnormalities. Methods We investigated 146 patients with HCM and 35 healthy individuals who underwent cardiac magnetic resonance imaging (CMR; with late gadolinium enhancement [LGE] in HCM patients) and standard 12‐lead ECGs. On the ECG, depolarization and repolarization abnormalities, the Sokolow–Lyon index, the Cornell index, and the Romhilt–Estes score were evaluated. The left ventricular ejection fraction, volumes, and myocardial mass (LVM) were quantified. Myocardial fibrosis was quantified on LGE images. Results The sensitivity of the Romhilt–Estes score was the highest (75%), and this hypertrophy criterion had the strongest correlation with the LVM index (p < .0001; r = .41). The amount of fibrosis was negatively correlated with the Cornell index (p = .015; r = −.201) and the Sokolow–Lyon index (p = .005; r = −.23), and the Romhilt–Estes score was independent of fibrosis (p = .757; r = 0.026). fQRS and strain pattern predicted more fibrosis, while the Cornell index was a negative predictor of myocardial fibrosis (p < .0001). Among others, the strain pattern was an independent predictor of the LVM (p < .0001). Conclusion The Romhilt–Estes score is the most sensitive ECG criterion for detecting LVH in HCM patients, as myocardial fibrosis does not affect this criterion. The presence of fQRS and strain pattern predicts myocardial fibrosis

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS

Benchmarking off-the-shelf statistical shape modeling tools in clinical applications

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications. Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models