Background: 3D printed patient-specific coronary models have the ability to enable repeatable benchtop experiments under controlled blood flow conditions. This approach can be applied to CT-derived patient geometries to emulate coronary flow and related parameters such as Fractional Flow Reserve (FFR). Methods: This study uses 3D printing to compare such benchtop FFR results with a non-invasive CT-FFR research software algorithm and catheter based invasive FFR (I-FFR) measurements. Fifty-two patients with a clinical indication for I-FFR underwent a research Coronary CT Angiography (CCTA) prior to catheterization. CT images were used to measure CT-FFR and to generate patient-specific 3D printed models of the aortic root and three main coronary arteries. Each patient-specific model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for two coronary outflow rates ('normal', 250 ml min −1 ; and 'hyperemic', 500 ml min −1 ) by adjusting the model's distal coronary resistance. Results: Pearson correlations and ROC AUC were calculated using invasive I-FFR as reference. The Pearson correlation factor of CT-FFR and B-FFR-500 was 0.75 and 0.71, respectively. Areas under the ROCs for CT-FFR and B-FFR-500 were 0.80 (95%CI: 0.70-0.87) and 0.81 (95%CI: 0.64-0.91) respectively. Conclusion: Benchtop flow simulations with 3D printed models provide the capability to measure pressure changes at any location in the model, for ultimately emulating the FFR at several simulated physiological blood flow conditions. Clinical Trial Registration: https://clinicaltrials.gov/show/ NCT03149042 REVISED

Cardiac amyloidosis (CA) is a common and potentially fatal infiltrative cardiomyopathy. Contrast-enhanced cardiac MRI (CMR) is used as a diagnostic tool. However, utility of CMR for the comprehensive analysis of biventricular strains and strain rates is not reported as extensively as echocardiography. In addition, RV strain analysis using CMR has not been described previously. Objectives: We sought to study the global and regional indices of biventricular strain and strain rates in endomyocardial biopsy (EMB)-proven, genotyped cases of CA. Methods: A database of 80 EMBs was curated from 2012 to 2019 based on histology. A total of 19 EMBs positive for CA were subjected to further tissue-characterization with histology, and compared with four normal biopsy specimens. Samples were genotyped for ATTR- or AL-subtypes. Five patients, with both echocardiography and contrast-enhanced CMR performed 72-h apart, were subjected to comprehensive analysis of biventricular strain and strain-rates. Results: Histology confirmed that the selected samples were indeed positive for cardiac amyloid. Echocardiography showed reduced global and regional left-ventricular (LV) longitudinal strain indices. CMR with tissue-characterization of LV showed global reductions in circumferential, radial and longitudinal strains and strain-rates, following a general trend with the echocardiographic findings. The basal right-ventricular (RV) segments had reduced circumferential strains with no changes in longitudinal strain. Conclusions: In addition to providing a clinical diagnosis of CA based on contrast clearance-dynamics, CMR can be a potent tool for accurate functional assessment of global and regional changes in strain and strain-rates involving both LV and RV. Further studies are warranted to validate and curate the strain imaging capacity of CMR in CA.

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Purpose: Coronary computed tomography angiography (CTA) has one of the highest diagnostic sensitivities for detection of the significance of coronary artery disease (CAD); however, sensitivity is moderate and may result in increased catheterization rates. We performed an efficacy study to determine whether a trained machine learning algorithm that uses coronary CTA data may improve CAD diagnosis accuracy. Methods: Sixty-four-patient image datasets based on coronary CTA were retrospectively collected to generate eight views considering 45°increments around the coronary artery centerline. The dataset was randomly split into training and testing cohorts. Invasive FFR measurements were used as ground truth labels. A convolutional neural network (CNN) was trained and the model's capacity to predict severity of CAD was assessed on the testing cohort. Classification accuracy and area under the receiver operating characteristic curve (AUROC) analysis were performed. Similar CAD severity classification accuracy and AUROC analyses were performed using only percent diameter stenosis (%DS) and CT-derived FFR performed by 13 operators with various levels of expertise. Results: Classification accuracy over the test cohort was 80.9% using the trained network and 72.4% using the user-operated CT-derived FFR software. AUROC over the test cohort was 0.862 using the trained network, 0.807 using %DS, and 0.758 using the human-operated CT-derived FFR software. Conclusions: A trained neural network compared noninferiorly in-terms of classification accuracy and AUROC with human operators of a CT-derived FFR software, and in-terms of AUROC with clinical decision-making using %DS.

Gonadoblastomas are known to develop in dysgenetic gonads, especially so, if Y chromosome material is present. A 20-years- old girl who noticed breast development since the age of 12 years presented with primary amenorhoea, distension of lower abdomen and intermittent pain for two months. She had breakthrough bleeding with six months of estrogen replacement. Tanner breast stage was fi ve and pubic hair stage was also fi ve. Examination revealed a mass in the lower abdomen extending into hypogastrium, umbilical and lumbar regions. Her gonadotropin levels were grossly elevated. Karyotyping showed 46XY. CT scan of abdomen showed a 17X11 cm mass in the pelvis without visible gonads. Surgical excision of the mass along with bilateral salpingophorectomy was performed. Histopathology revealed the mass to be a dysgerminoma, while the right gonad lodged gonadoblastoma. She was diagnosed as a rare case of Swyer syndrome. Keywords: Dysgerminoma, gonadoblastoma, Swyer syndrome, XY gonadal dysgenesis.