Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammograms to train and validate our model, obtaining an accuracy of 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure

4-Dimensional Velocity Mapping Cardiac Magnetic Resonance of Extracardiac Bypass for Aortic Coarctation Repair

Abstract This report describes a case of a young lady who, following extracardiac bypass between ascending and descending aorta for severe aortic coarctation, underwent 4-dimensional flow cardiac magnetic resonance, a technique that, by 3-dimensional flow assessment over time (4-dimensional), allows not only quantification of flows but also wall shear stress. In this case, increased wall shear stress was observed in the conduit's acute angle (kinking) as well as at the distal anastomosis level. The authors postulate that increased wall shear stress could help identify and risk stratify adult congenital heart disease who could develop vascular complications in the future. ( Level of Difficulty: Intermediate.

Late plasma exosome microRNA-21-5p depicts magnitude of reverse ventricular remodeling after early surgical repair of primary mitral valve regurgitation

Introduction: Primary mitral valve regurgitation (MR) results from degeneration of mitral valve apparatus. Mechanisms leading to incomplete postoperative left ventricular (LV) reverse remodeling (Rev–Rem) despite timely and successful surgical mitral valve repair (MVR) remain unknown. Plasma exosomes (pEXOs) are smallest nanovesicles exerting early postoperative cardioprotection. We hypothesized that late plasma exosomal microRNAs (miRs) contribute to Rev–Rem during the late postoperative period. Methods: Primary MR patients (n = 19; age, 45–71 years) underwent cardiac magnetic resonance imaging and blood sampling before (T0) and 6 months after (T1) MVR. The postoperative LV Rev–Rem was assessed in terms of a decrease in LV end-diastolic volume and patients were stratified into high (HiR-REM) and low (LoR-REM) LV Rev–Rem subgroups. Isolated pEXOs were quantified by nanoparticle tracking analysis. Exosomal microRNA (miR)-1, –21–5p, –133a, and –208a levels were measured by RT-qPCR. Anti-hypertrophic effects of pEXOs were tested in HL-1 cardiomyocytes cultured with angiotensin II (AngII, 1 μM for 48 h). Results: Surgery zeroed out volume regurgitation in all patients. Although preoperative pEXOs were similar in both groups, pEXO levels increased after MVR in HiR-REM patients (+0.75-fold, p = 0.016), who showed lower cardiac mass index (–11%, p = 0.032). Postoperative exosomal miR-21-5p values of HiR-REM patients were higher than other groups (p < 0.05). In vitro, T1-pEXOs isolated from LoR-REM patients boosted the AngII-induced cardiomyocyte hypertrophy, but not postoperative exosomes of HiR-REM. This adaptive effect was counteracted by miR-21-5p inhibition. Summary/Conclusion: High levels of miR-21-5p-enriched pEXOs during the late postoperative period depict higher LV Rev–Rem after MVR. miR-21-5p-enriched pEXOs may be helpful to predict and to treat incomplete LV Rev–Rem after successful early surgical MVR

Multicentre multi-device hybrid imaging study of coronary artery disease: results from the EValuation of INtegrated Cardiac Imaging for the Detection and Characterization of Ischaemic Heart Disease (EVINCI) hybrid imaging population

AIMS: Hybrid imaging provides a non-invasive assessment of coronary anatomy and myocardial perfusion. We sought to evaluate the added clinical value of hybrid imaging in a multi-centre multi-vendor setting. METHODS AND RESULTS: Fourteen centres enrolled 252 patients with stable angina and intermediate (20-90%) pre-test likelihood of coronary artery disease (CAD) who underwent myocardial perfusion scintigraphy (MPS), CT coronary angiography (CTCA), and quantitative coronary angiography (QCA) with fractional flow reserve (FFR). Hybrid MPS/CTCA images were obtained by 3D image fusion. Blinded core-lab analyses were performed for CTCA, MPS, QCA and hybrid datasets. Hemodynamically significant CAD was ruled-in non-invasively in the presence of a matched finding (myocardial perfusion defect co-localized with stenosed coronary artery) and ruled-out with normal findings (both CTCA and MPS normal). Overall prevalence of significant CAD on QCA (>70% stenosis or 30-70% with FFR 640.80) was 37%. Of 1004 pathological myocardial segments on MPS, 246 (25%) were reclassified from their standard coronary distribution to another territory by hybrid imaging. In this respect, in 45/252 (18%) patients, hybrid imaging reassigned an entire perfusion defect to another coronary territory, changing the final diagnosis in 42% of the cases. Hybrid imaging allowed non-invasive CAD rule-out in 41%, and rule-in in 24% of patients, with a negative and positive predictive value of 88% and 87%, respectively. CONCLUSION: In patients at intermediate risk of CAD, hybrid imaging allows non-invasive co-localization of myocardial perfusion defects and subtending coronary arteries, impacting clinical decision-making in almost one every five subjects

A novel 3D Cartesian random sampling strategy for Compressive Sensing Magnetic Resonance Imaging

In this work we propose a novel acquisition strategy for accelerated 3D Compressive Sensing Magnetic Resonance Imaging (CS-MRI). This strategy is based on a 3D cartesian sampling with random switching of the frequency encoding direction with other K-space directions. Two 3D sampling strategies are presented. In the first strategy, the frequency encoding direction is randomly switched with one of the two phase encoding directions. In the second strategy, the frequency encoding direction is randomly chosen between all the directions of the K-Space. These strategies can lower the coherence of the acquisition, in order to produce reduced aliasing artifacts and to achieve a better image quality after Compressive Sensing (CS) reconstruction. Furthermore, the proposed strategies can reduce the typical smoothing of CS due to the limited sampling of high frequency locations. We demonstrated by means of simulations that the proposed acquisition strategies outperformed the standard Compressive Sensing acquisition. This results in a better quality of the reconstructed images and in a greater achievable acceleration

Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination