Partitioning the Right Ventricle Into 15 Segments and Decomposing Its Motion Using 3D Echocardiography-Based Models: The Updated ReVISION Method.

Three main mechanisms contribute to global right ventricular (RV) function: longitudinal shortening, radial displacement of the RV free wall (bellows effect), and anteroposterior shortening (as a consequence of left ventricular contraction). Since the importance of these mechanisms may vary in different cardiac conditions, a technology being able to assess their relative influence on the global RV pump function could help to clarify the pathophysiology and the mechanical adaptation of the chamber. Previously, we have introduced our 3D echocardiography (3DE)-based solution-the Right VentrIcular Separate wall motIon quantificatiON (ReVISION) method-for the quantification of the relative contribution of the three aforementioned mechanisms to global RV ejection fraction (EF). Since then, our approach has been applied in several clinical scenarios, and its strengths have been demonstrated in the in-depth characterization of RV mechanical pattern and the prognostication of patients even in the face of maintained RV EF. Recently, various new features have been implemented in our software solution to enable the convenient, standardized, and more comprehensive analysis of RV function. Accordingly, in our current technical paper, we aim to provide a detailed description of the latest version of the ReVISION method with special regards to the volumetric partitioning of the RV and the calculation of longitudinal, circumferential, and area strains using 3DE datasets. We also report the results of the comparison between 3DE- and cardiac magnetic resonance imaging-derived RV parameters, where we found a robust agreement in our advanced 3D metrics between the two modalities. In conclusion, the ReVISION method may provide novel insights into global and also segmental RV function by defining parameters that are potentially more sensitive and predictive compared to conventional echocardiographic measurements in the context of different cardiac diseases.

Age, BMI and diabetes as independent predictors of brain hypoperfusion.

BACKGROUND: Cerebral blood flow abnormalities are supposed to be potential risk factors for developing cognitive dysfunction in the general population. Aging, obesity and type 2 diabetes mellitus are associated with perfusion abnormalities leading to cognitive impairment, neurodegeneration and future development of dementia. In our study, we aimed at identifying independent factors that contribute to the appearance of regional brain perfusion changes besides those that are already known. MATERIAL AND METHODS: Forty-three type 2 diabetic and twenty-six obese patients were enrolled. After the intravenous administration of 740 MBq 99mTc-hexamethylpropylene amine oxime (HMPAO), all subjects underwent brain perfusion SPECT imaging applying AnyScan S Flex dual-head gamma camera (Mediso, Hungary). Using Philips Achieva 3T scanner brain resting-state functional MRI was also performed. The SPECT and MRI images were co-registered and transformed to the MNI152 atlas space so that data of the following standard volumes of interest (VOIs) could be obtained: frontal lobe, parietal lobe, temporal lobe, occipital lobe, limbic region, cingulate, insula, basal ganglia, cerebrum, limbic system and brain stem. Using the SPSS 25 statistical software package, general linear regression analysis, Student's t-test, and Mann-Whitney U-test were applied for statistical analyses. RESULTS: Multivariate linear analysis identified that BMI and age are significantly (p < 0.0001) associated with perfusion, and patient group was slightly above threshold (p = 0.0524). We also found that the presence of diabetes was an independent significant predictor of normalized regional brain perfusion only in the insula (p < 0.001). Other independent predictors of normalized regional brain perfusion were: age in the insula (p < 0.001) and in the limbic region (p < 0.01), and BMI in the brain stem (p < 0.01). CONCLUSIONS: Age and BMI proved to be general, and diabetes regional predictor of brain hypoperfusion. BMI appeared to be a novel factor affecting brain perfusion. In one specific region, the insula, we detected a difference between the obese and the diabetic group. These findings may be significant in the understanding of the development of cognitive impairment in metabolic diseases.

Fully automatic segmentation of right and left ventricle on short-axis cardiac MRI images.

Cardiac magnetic resonance imaging (CMR) is a widely used non-invasive imaging modality for evaluating cardiovascular diseases. CMR is the gold standard method for left and right ventricular functional assessment due to its ability to characterize myocardial structure and function and low intra- and inter-observer variability. However the post-processing segmentation during the functional evaluation is time-consuming and challenging. A fully automated segmentation method can assist the experts; therefore, they can do more efficient work. In this paper, a regression-based fully automated method is presented for the right- and left ventricle segmentation. For training and evaluation, our dataset contained MRI short-axis scans of 5570 patients, who underwent CMR examinations at Heart and Vascular Center, Semmelweis University Budapest. Our approach is novel and after training the state-of-the-art algorithm on our dataset, our algorithm proved to be superior on both of the ventricles. The evaluation metrics were the Dice index, Hausdorff distance and volume related parameters. We have achieved average Dice index for the left endocardium: 0.927, left epicardium: 0.940 and right endocardium: 0.873 on our dataset. We have also compared the performance of the algorithm to the human-level segmentation on both ventricles and it is similar to experienced readers for the left, and comparable for the right ventricle. We also evaluated the proposed algorithm on the ACDC dataset, which is publicly available, with and without transfer learning. The results on ACDC were also satisfying and similar to human observers. Our method is lightweight, fast to train and does not require more than 2 GB GPU memory for execution and training.