Subvoxel vessel wall thickness measurements of the intracranial arteries using a convolutional neural network

Vessel wall thickening of the intracranial arteries has been associated with cerebrovascular disease and atherosclerotic plaque development. Visualization of the vessel wall has been enabled by recent advancements in vessel wall MRI. However, quantifying early wall thickening from these MR images is difficult and prone to severe overestimation, because the voxel size of clinically used acquisitions exceeds the wall thickness of the intracranial arteries. In this study, we aimed for accurate and precise subvoxel vessel wall thickness measurements. A convolutional neural network was trained on MR images of 34 ex vivo circle of Willis specimens, acquired with a clinically used protocol (isotropic acquired voxel size: 0.8 mm). Ground truth measurements were performed on images acquired with an ultra-high-resolution protocol (isotropic acquired voxel size: 0.11 mm) and were used for evaluation. Additionally, we determined the robustness of our method by applying Monte Carlo dropout and test time augmentation. Lastly, we applied our method on in vivo images of three intracranial aneurysms to measure their wall thickness. Our method shows resolvability of different vessel wall thicknesses, well below the acquired voxel size. The method described may facilitate quantitative measurements on MRI data for a wider range of clinical applications

Dynamic brain ADC variations over the cardiac cycle and their relation to tissue strain assessed with DENSE at high-field MRI

PURPOSE: The ADC of brain tissue slightly varies over the cardiac cycle. This variation could reflect physiology, including mixing of the interstitial fluid, relevant for brain waste clearance. However, it is known from cardiac diffusion imaging that tissue deformation by itself affects the magnitude of the MRI signal, leading to artificial ADC variations as well. This study investigates to what extent tissue deformation causes artificial ADC variations in the brain. THEORY AND METHODS: We implemented a high-field MRI sequence with stimulated echo acquisition mode that simultaneously measures brain tissue deformation and ADC. Based on the measured tissue deformation, we simulated the artificial ADC variation by combining established theoretical frameworks and compared the results with the measured ADC variation. We acquired data in 8 healthy volunteers with diffusion weighting b = 300 and b = 1000 s/mm2 . RESULTS: Apparent diffusion coefficient variation was largest in the feet-to-head direction and showed the largest deviation from the mean ADC at peak systole. Artificial ADC variation estimated from tissue deformation was 1.3 ± 0.37·10-5 mm2 /s in the feet-to-head direction for gray matter, and 0.75 ± 0.29·10-5 mm2 /s for white matter. The measured ADC variation in the feet-to-head direction was 5.6·10-5  ± 1.5·10-5 mm2 /s for gray matter and 3.2·10-5  ± 1.0·10-5 mm2 /s for white matter, which was a factor of 3.5 ± 0.82 and 3.4 ± 0.57 larger than the artificial diffusion variations. The measured diffusion variations in the right-to-left/anterior-to-posterior direction were a factor of 1.5 ± 1.0/1.7 ± 1.4 and 2.0 ± 0.91/2.5 ± 0.94 larger than the artificial diffusion variations for gray matter and white matter, respectively. CONCLUSION: Apparent diffusion coefficient variations in the brain likely largely reflect physiology

Automated Assessment of Cerebral Arterial Perforator Function on 7T MRI

BACKGROUND: Blood flow velocity and pulsatility of small cerebral perforating arteries can be measured using 7T quantitative 2D phase contrast (PC) MRI. However, ghosting artifacts arising from subject movement and pulsating large arteries cause false positives when applying a previously published perforator detection method. PURPOSE: To develop a robust, automated method to exclude perforators located in ghosting artifacts. STUDY TYPE: Retrospective. SUBJECTS: Fifteen patients with vascular cognitive impairment or carotid occlusive disease and 10 healthy controls. FIELD STRENGTH/SEQUENCE: 7T/cardiac-gated 2D PC MRI. ASSESSMENT: Perforators were automatically excluded from ghosting regions, which were defined as bands in the phase-encoding direction of large arteries. As reference, perforators were manually excluded by two raters (T.A., J.J.M.Z.), based on perforator location with respect to visible ghosting artifacts. The performance of both censoring methods was assessed for the number of (Nincluded ), mean velocity (Vmean ), and pulsatility index (PI) of the included perforators. STATISTICAL TESTS: For within-method comparisons, inter- and intrarater reliability were assessed for the manual method, and test-retest reliability was assessed for both methods from repeated 2D PC scans (without repositioning). Intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CIs) were determined for Nincluded , Vmean , and PI for all within-method comparisons. The ICC to compare between the two methods was determined with the use of both (test-retest) scans using a multilevel nonlinear mixed model. RESULTS: The automated censoring method showed a moderate to good ICC (95% CI) vs. manual censoring for Nincluded (0.73 [0.58-0.87]) and Vmean (0.90 [0.84-0.96]), and a moderate ICC for PI (0.57 [0.37-0.76]). The test-retest reliability of the manual censoring method was considerably lower than the interrater and intrarater reliability, indicating that scanner noise dominates the uncertainty of the analysis. DATA CONCLUSION: The proposed automated censoring method can reliably exclude small perforators affected by ghosting artifacts. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 1

Cerebral hemodynamics during atrial fibrillation: computational fluid dynamics (CFD) analysis of lenticulostriate arteries using 7T high-resolution magnetic resonance imaging

Atrial fibrillation (AF) is the most common cardiac arrhythmia, inducing irregular and faster heart beating. Aside from disabling symptoms - such as palpitations, chest discomfort, and reduced exercise capacity - there is growing evidence that AF increases the risk of dementia and cognitive decline, even in the absence of clinical strokes. Among the possible mechanisms, the alteration of deep cerebral hemodynamics during AF is one of the most fascinating and least investigated hypotheses. Lenticulostriate arteries (LSAs) - small perforating arteries perpendicularly departing from the anterior and middle cerebral arteries and supplying blood flow to basal ganglia - are especially involved in silent strokes and cerebral small vessel diseases, which are considered among the main vascular drivers of dementia. We propose for the first time a computational fluid dynamics analysis to investigate the AF effects on the LSAs hemodynamics by using 7 T high-resolution magnetic resonance imaging (MRI). We explored different heart rates (HRs) - from 50 to 130 bpm - in sinus rhythm and AF, exploiting MRI data from a healthy young male and internal carotid artery data from validated 0D cardiovascular-cerebral modeling as inflow condition. Our results reveal that AF induces a marked reduction of wall shear stress and flow velocity fields. This study suggests that AF at higher HR leads to a more hazardous hemodynamic scenario by increasing the atheromatosis and thrombogenesis risks in the LSAs region

Blood Flow Velocity Pulsatility and Arterial Diameter Pulsatility Measurements of the Intracranial Arteries Using 4D PC-MRI

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

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23 февраля 2011 года исполнилось 75 лет со дня рождения главного инженера Днепродзержинской ГЭС — Кучерявого Владислава Семеновича.15 июня 2011 г. исполняется 70 лет ученому — гидроэнергетику, доктору технических наук, начальнику отдела расчетного обоснования ПАО "Укргидропроект", профессору, заведующему кафедрой гидротехнического строительства Харьковского государственного технического университета строительства и архитектуры Александру Исааковичу Вайнбергу

An anomaly detection approach to identify chronic brain infarcts on MRI

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