Vortical features for myocardial rotation assessment in hypertrophic cardiomyopathy using cardiac tagged magnetic resonance

A systematic review of multi-slice and multi-frame descriptors in cardiac MRI exams

Computer-Aided Diagnosis systems have been developed to help medical professional in their decision making routines towards a more accurate diagnosis. These systems process medical exams such as Magnetic Resonance (MRI) in order to quantify meaningful features. These can be used with similarity-measuring techniques in a Content-Based Image Retrieval context, or inputted into a machine learning classifier in order to support early disease detection. For cardiac MRIs, single slice descriptors have been proposed in the two-dimensional domain, shape descriptors have been proposed in the three-dimensional domain, and previous reviews have mapped these two descriptor categories. Nonetheless, no systematic review on these descriptors have looked at full cardiac MRI images sets. We have reviewed the literature by searching for descriptors that consider the whole slice set (multi-slice) or frames (multi-frame) in cardiac MRI exams. We discuss descriptors and techniques, the datasets that were used, and the different evaluation metrics. Finally, we highlight literature gaps and research opportunities.

Functional significance of intra-left ventricular vortices on energy efficiency in normal, dilated, and hypertrophied hearts

PURPOSE

To investigate the influence of changes in vortices within the left ventricle (LV) on energy efficiency (EE) in normal and diseased hearts.

METHODS

We performed vector flow mapping echocardiography in 36 normal participants (N), 36 patients with dilated cardiomyopathy (D), and 36 patients with LV hypertrophy (H). The circulation of the main anterior vortex was measured as a parameter of vortex strength. Energy loss (EL) was measured for one cardiac cycle, and EE was calculated as EL divided by stroke work (SW), which represents the loss of kinetic energy per unit of LV external work.

RESULTS

Circulation increased in the order of N, H, and D (N: 15 ± 4, D: 19 ± 8, H: 17 ± 6 × 10^-3 m² /s; analysis of variance [ANOVA] P < .01). Conversely, EE increased in the order of N, D, and H (N: 0.22 ± 0.07, D: 0.26 ± 0.16, H: 0.30 ± 0.16 10^-5 J/mm Hg mL m s; ANOVA P = .04), suggesting worst EE in group H. We found a positive correlation between circulation and SW only in group N, and positive correlation between circulation and EE only in diseased groups (D: R = 0.55, P < .01; H: R = 0.44, P < .01). Multivariable analyses revealed that circulation was the independent determinant of EE in groups D and H.

CONCLUSIONS

Enhanced vortices could be associated with effective increase in LV external work in normal hearts. Conversely, they were associated with loss of EE without an optimal increase in external work in failing hearts, regardless of the LV morphology.

Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction

Groupwise image (GW) registration is customarily used for subsequent processing in medical imaging. However, it is computationally expensive due to repeated calculation of transformations and gradients. In this paper, we propose a deep learning (DL) architecture that achieves GW elastic registration of a 2D dynamic sequence on an affordable average GPU. Our solution, referred to as dGW, is a simplified version of the well-known U-net. In our GW solution, the image that the other images are registered to, referred to in the paper as template image, is iteratively obtained together with the registered images. Design and evaluation have been carried out using 2D cine cardiac MR slices from 2 databases respectively consisting of 89 and 41 subjects. The first database was used for training and validation with 66.6–33.3% split. The second one was used for validation (50%) and testing (50%). Additional network hyperparameters, which are—in essence—those that control the transformation smoothness degree, are obtained by means of a forward selection procedure. Our results show a 9-fold runtime reduction with respect to an optimization-based implementation; in addition, making use of the well-known structural similarity (SSIM) index we have obtained significative differences with dGW with respect to an alternative DL solution based on Voxelmorph.