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Mangrove forests reduce wave attack along tropical and sub-tropical coastlines, decreasing the wave loads acting on coastal protection structures. Mangrove belts seaward of embankments can therefore lower their required height and decrease their slope protection thickness. Wave reduction by mangroves depends on tree frontal surface area and stability against storms, but both aspects are often oversimplified or neglected in coastal protection designs. Here we present a framework to evaluate how mangrove belts influence embankment designs, including mangrove growth over time and failure by overturning and trunk breakage. This methodology is applied to Sonneratia apetala mangroves seaward of embankments in Bangladesh, considering forest widths between 10 and 1000 m (cross-shore). For water depths of 5 m, wave reduction by mangrove forests narrower than 1 km mostly affects the slope protection and the bank erodibility, whereas the required embankment height is less influenced by mangroves. Sonneratia apetala trees experience a relative maximum in wave attenuation capacity at 10 years age, due to their large submerged canopy area. Once trees are more than 20 years old, their canopy is emergent, and most wave attenuation is caused by trunk and roots. Canopy emergence exposes mangroves to wind loads, which are much larger than wave loads, and can cause tree failure during cyclones. These results stress the importance of including tree surface area and stability models when predicting coastal protection by mangroves. [ABSTRACT FROM AUTHOR]

Background: Absolute myocardial perfusion imaging (MPI) is beneficial in the diagnosis and prognosis of patients with suspected or known coronary artery disease. However, validation and standardization of perfusion estimates across centers is needed to ensure safe and adequate integration into the clinical workflow. Physical myocardial perfusion models can contribute to this clinical need as these can provide ground-truth validation of perfusion estimates in a simplified, though controlled setup. This work presents the design and realization of such a myocardial perfusion phantom and highlights initial performance testing of the overall phantom setup using dynamic single photon emission computed tomography. Results: Due to anatomical and (patho-)physiological representation in the 3D printed myocardial perfusion phantom, we were able to acquire 22 dynamic MPI datasets in which 99mTc-labelled tracer kinetics was measured and analyzed using clinical MPI software. After phantom setup optimization, time activity curve analysis was executed for measurements with normal myocardial perfusion settings (1.5 mL/g/min) and with settings containing a regional or global perfusion deficit (0.8 mL/g/min). In these measurements, a specific amount of activated carbon was used to adsorb radiotracer in the simulated myocardial tissue. Such mimicking of myocardial tracer uptake and retention over time satisfactorily matched patient tracer kinetics. For normal perfusion levels, the absolute mean error between computed myocardial blood flow and ground-truth flow settings ranged between 0.1 and 0.4 mL/g/min. Conclusion: The presented myocardial perfusion phantom is a first step toward ground-truth validation of multimodal, absolute MPI applications in the clinical setting. Its dedicated and 3D printed design enables tracer kinetic measurement, including time activity curve and potentially compartmental myocardial blood flow analysis. [ABSTRACT FROM AUTHOR]

4D phase contrast magnetic resonance imaging (PC-MRI) allows for the visualization and quantification of the cerebral blood flow. A drawback of software that is used to quantify the cerebral blood flow is that it oftentimes assumes a static arterial luminal area over the cardiac cycle. Quantifying the lumen area pulsatility index (aPI), i.e. the change in lumen area due to an increase in distending pressure over the cardiac cycle, can provide insight in the stiffness of the arteries. Arterial stiffness has received increased attention as a predictor in the development of cerebrovascular disease. In this study, we introduce software that allows for measurement of the aPI as well as the blood flow velocity pulsatility index (vPI) from 4D PC-MRI. The internal carotid arteries of seven volunteers were imaged using 7 T MRI. The aPI and vPI measurements from 4D PC-MRI were validated against measurements from 2D PC-MRI at two levels of the internal carotid arteries (C3 and C7). The aPI and vPI computed from 4D PC-MRI were comparable to those measured from 2D PC-MRI (aPI: mean difference: 0.03 (limits of agreement: −0.14 – 0.23); vPI: 0.03 (−0.17–0.23)). The measured blood flow rate for the C3 and C7 segments was similar, indicating that our proposed software correctly captures the variation in arterial lumen area and blood flow velocity that exists along the distal end of the carotid artery. Our software may potentially aid in identifying changes in arterial stiffness of the intracranial arteries caused by pathological changes to the vessel wall. [ABSTRACT FROM AUTHOR]

The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how 'normal' tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed. [ABSTRACT FROM AUTHOR]

Photoacoustic (optoacoustic) imaging can visualize vasculature deep in tissue using the high contrast of hemoglobin to light, with the high-resolution possible with ultrasound detection. Since angiogenesis, one of the hallmarks of cancer, leads to increased vascularity, photoacoustics holds promise in imaging breast cancer as shown in proof-of-principle studies. Here for the first time, we investigate if there are specific photoacoustic appearances of breast malignancies which can be related to the tumor vascularity, using an upgraded research imaging system, the Twente Photoacoustic Mammoscope. In addition to comparisons with x-ray and ultrasound images, in subsets of cases the photoacoustic images were compared with MR images, and with vascular staining in histopathology. We were able to identify lesions in suspect breasts at the expected locations in 28 of 29 cases. We discovered generally three types of photoacoustic appearances reminiscent of contrast enhancement types reported in MR imaging of breast malignancies, and first insights were gained into the relationship with tumor vascularity. [ABSTRACT FROM AUTHOR]