Automatic myocardium strain quantification in MR synthetic images with Deep Leaning.

Accurate quantification of myocardium strain in magnetic resonance images is important to correctly diagnose and monitor cardiac diseases. Currently, available methods to estimate motion are based on tracking brightness pattern differences between images. In cine-MR images, the myocardium interior presents an inhered homogeneity, which reduces the accuracy in estimated motion, and consequently strain. Neural networks have recently been shown to be an important tool for a variety of applications, including motion estimation. In this work, we investigate the feasibility of quantifying myocardium strain in cardiac resonance synthetic images using motion generated by a compact and powerful network called Pyramid, Warping, and Cost Volume (PWC). Using the motion generated by the neural network, the radial myocardium strain obtained presents a mean average error of 12.30% +- 6.50%, and in the circumferential direction 1.20% +-0.61 %, better than the two classical methods evaluated. Clinical Relevance- This work demonstrates the feasibility of estimating myocardium strain using motion estimated by a convolutional neural network.

Improving the generalization of deep learning methods to segment the left ventricle in short axis MR images.

Cardiovascular disease is one of the major health problems worldwide. In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for the evaluation of the function and structure of the left ventricle (LV). More recently, deep learning methods have been used to segment LV with impressive results. On the other hand, this kind of approach is prone to overfit the training data, and it does not generalize well between different data acquisition centers, thus creating constraints to the use in daily routines. In this paper, we explore methods to improve the generalization in the segmentation performed by a convolutional neural network. We applied a U-net based architecture and compared two different pre-processing methods to improve uniformity in the image contrast between five cross-dataset training and testing. Overall, we were able to perform the segmentation of the left ventricle using multiple cross-dataset combinations of train and test, with a mean endocardium dice score of 0.82.Clinical Relevance- This work improves the result between the cross-dataset evaluation of the left ventricle segmentation, reducing the constraints for daily clinical adoption of a fully-automatic segmentation method.

Effects of transmembrane potential and pH gradient on the cytochrome c-promoted fusion of mitochondrial mimetic membranes.

The present study investigated the effects of ΔΨ and ΔpH (pH gradient) on the interaction of cytochrome c with a mitochondrial mimetic membrane composed of phosphatidylcholine (PC), phosphatidylethanolamine (PE), and cardiolipin (CL) leading to vesicle fusion. ΔpH generated by lowered bulk pH (pH(out)) of PCPECL liposomes, with an internal pH (pH(in)) of 8.0, favored vesicle fusion with a titration sigmoidal profile (pK(a) ~ 6.9). Conversely, ΔpH generated by enhanced pH(in) of PCPECL at a pH(out) of 6.0 favored the fusion of vesicles with a linear profile. We did not observe a significant amount of liposome fusion when ΔpH was generated by lowered pH(in) at a pH(out) of 8.0. At bulk acidic pH, ΔΨ generated by Na⁺ gradient also favored cyt c-promoted vesicle fusion. At acidic and alkaline pH(out), the presence of ΔpH and ΔΨ did not affect cytochrome c binding affinity measured by pyrene quenching. Therefore, cytochrome c-mediated PC/PE/CL vesicle fusion is dependent of ionization of the protein site L (acidic pH) and the presence of transmembrane potential. The effect of transmembrane potential is probably related to the generation of defects on the lipid bilayer. These results are consistent with previous reports showing that cytochrome c release prior to the dissipation of the ΔΨ(M) blocks inner mitochondrial membrane fusion during apoptosis.