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Background: Intrauterine growth restriction (IUGR) is associated with prenatal changes in cardiac shape and function that persist in childhood and may be associated to an increased cardiovascular mortality in adulthood. Ultrasound and MRI provide information at organ level, but their resolution is too low. Microscopy provides detail at cellular and subcellular level, but an integrated evaluation at organ level is extremely challenging. We hypothesize that X-ray phase-contrast synchrotron radiation-based micro-CT could provide information regarding detailed cardiac anatomy to better understand the cardiac remodeling in IUGR.Methods: A validated model of IUGR in New Zealand rabbit was used. Fetal hearts were formalin-fixed and ethanol-dehydrated. 3D X-ray phase-contrast synchrotron radiation-based micro-CT was performed at 7.43μm isotropic resolution (ESRF-ID19). Standard morphometric measurements were performed in a transverse view. Fiber orientation could be recognised and the change in fibers' angle was quantified. The distribution and local diameters of the coronary vessels tree were studied.Results: IUGR fetuses showed smaller and more globular hearts with thicker myocardial walls (Fig A, B) and coronary artery vessels dilatation (Fig C, D). A change in fiber orientation in IUGR hearts was observed, suggesting an increase of circumferential over longitudinal orientation.Conclusions: Our data suggest that cardiac remodeling due to IUGR affects fiber orientation and the geometry of the coronary vascular tree. These changes might explain the later increased risk of cardiovascular disease. Moreover, the unique novel technique used enables the acquisition of high resolution datasets of whole hearts at myofiber resolution, providing structural information at both organ and microscopic level. [ABSTRACT FROM AUTHOR]

Nondestructive evaluation (NDE) is a set of techniques used for material inspection and defect detection without causing damage to the inspected component. One of the commonly used nondestructive techniques is called ultrasonic inspection. The acquisition of ultrasonic data was mostly automated in recent years, but the analysis of the collected data is still performed manually. This process is thus very expensive, inconsistent, and prone to human errors. An automated system would significantly increase the efficiency of analysis, but the methods presented so far fail to generalize well on new cases and are not used in real-life inspection. Many of the similar data analysis problems were recently tackled by deep learning methods. This approach outperforms classical methods but requires lots of training data, which is difficult to obtain in the NDE domain. In this work, we train a deep learning architecture EfficientDet to automatically detect defects from ultrasonic images. We showed how some of the hyperparameters can be tweaked in order to improve the detection of defects with extreme aspect ratios that are common in ultrasonic images. The proposed object detector was trained on the largest dataset of ultrasonic images that was so far seen in the literature. In order to collect the dataset, six steel blocks containing 68 defects were scanned with a phased-array probe. More than 4000 VC-B-scans were acquired and used for training and evaluation of EfficientDet. The proposed model achieved 89.6% of mean average precision (mAP) during fivefold cross validation, which is a significant improvement compared to some similar methods that were previously used for this task. A detailed performance overview for each of the folds revealed that EfficientDet-D0 successfully detects all of the defects present in the inspected material.

Background: In the era of increasingly successful corrective interventions in patients with congenital heart disease (CHD), global and regional myocardial remodeling are emerging as important sources of long-term morbidity/mortality. Changes in organization of the myocardium in CHD, and in its mechanical properties, conduction, and blood supply, result in altered myocardial function both before and after surgery. To gain a better understanding and develop appropriate and individualized treatment strategies, the microscopic organization of cardiomyocytes, and their integration at a macroscopic level, needs to be completely understood. The aim of this study is to describe, for the first time, in 3 dimensions and nondestructively the detailed remodeling of cardiac microstructure present in a human fetal heart with complex CHD., Methods and Results: Synchrotron X-ray phase-contrast imaging was used to image an archival midgestation formalin-fixed fetal heart with right isomerism and complex CHD and compare with a control fetal heart. Analysis of myocyte aggregates, at detail not accessible with other techniques, was performed. Macroanatomic and conduction system changes specific to the disease were clearly observable, together with disordered myocyte organization in the morphologically right ventricle myocardium. Electrical activation simulations suggested altered synchronicity of the morphologically right ventricle., Conclusions: We have shown the potential of X-ray phase-contrast imaging for studying cardiac microstructure in the developing human fetal heart at high resolution providing novel insight while preserving valuable archival material for future study. This is the first study to show myocardial alterations occur in complex CHD as early as midgestation.

Experimental studies on isolated cardiomyocytes from different animal species and human hearts have demonstrated that there are regional differences in the Ca2+ release, Ca2+ decay and sarcomere deformation. Local deformation heterogeneities can occur due to a combination of factors: regional/local differences in Ca2+ release and/or re-uptake, intra-cellular material properties, sarcomere proteins and distribution of the intracellular organelles. To investigate the possible causes of these heterogeneities, we developed a two-dimensional finite-element electromechanical model of a cardiomyocyte that takes into account the experimentally measured local deformation and cytosolic [Ca2+] to locally define the different variables of the constitutive equations describing the electro/mechanical behaviour of the cell. Then, the model was individualised to three different rat cardiac cells. The local [Ca2+] transients were used to define the [Ca2+]-dependent activation functions. The cell-specific local Young's moduli were estimated by solving an inverse problem, minimizing the error between the measured and simulated local deformations along the longitudinal axis of the cell. We found that heterogeneities in the deformation during contraction were determined mainly by the local elasticity rather than the local amount of Ca2+, while in the relaxation phase deformation was mainly influenced by Ca2+ re-uptake. Our electromechanical model was able to successfully estimate the local elasticity along the longitudinal direction in three different cells. In conclusion, our proposed model seems to be a good approximation to assess the heterogeneous intracellular mechanical properties to help in the understanding of the underlying mechanisms of cardiomyocyte dysfunction.

Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.

Unlabelled: The objective of this study was to determine the feasibility of using a fast (short-duration) transmission computed tomogram (TCT), acquired immediately before or after the emission CT, to correct for photon attenuation in cardiac SPECT., Methods: The asymmetric fanbeam geometry with a 99mTc line source was used to acquire TCTs after conventional cardiac emission CT imaging on a triple-head SPECT system. The TCTs were reconstructed to generate patient-specific attenuation maps, which were used with an iterative maximum likelihood algorithm to reconstruct attenuation-corrected cardiac SPECT studies. The results of attenuation correction based on TCTs as short as 1 min were compared with long-duration transmission imaging for a phantom and several human studies., Results: Attenuation correction based on asymmetric fanbeam TCT significantly improves the uniformity of images of a uniform tracer distribution in a cardiac-thorax phantom configured to simulate a large patient. By using a high-activity line source and a rapid camera rotation, a suitable attenuation map for this phantom can be obtained from a 4-min TCT. A similar result is obtained for patients with thorax widths of <40 cm., Conclusion: A sequential imaging protocol for acquiring a fast TCT can be used for attenuation correction of cardiac SPECT imaging. The sequential TCT can be acquired without significantly extending the duration of the imaging study. This method provides a way to perform attenuation correction on existing triple-head SPECT systems without extensively modifying the system.