A validated patient-specific FSI model for vascular access in haemodialysis

The flow rate inside arteriovenous fistulas is many times higher than physiological flow and is accompanied by high wall shear stress resulting in low patency rates. A fluid–structure interaction finite element model is developed to analyse the blood flow and vessel mechanics to elucidate the mechanisms that can lead to failure. The simulations are validated against flow measurements obtained from magnetic resonance imaging data

Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT–SEQUOIA

Background Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. Purpose To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. Methods A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. Results Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6–1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0–1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00–1.00] and 0.99 [0.93–1.00] for calcium and uptake values, respectively). Conclusions We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring

Diffusion Tensor Imaging of the Cerebellum and Eyeblink Conditioning in Fetal Alcohol Spectrum Disorder

Background: Prenatal alcohol exposure is related to a wide range of neurocognitive effects. Eyeblink conditioning (EBC), which involves temporal pairing of a conditioned with an unconditioned stimulus, has been shown to be a potential biomarker of fetal alcohol exposure. A growing body of evidence suggests that white matter may be a specific target of alcohol teratogenesis, and the neural circuitry underlying EBC is known to involve the cerebellar peduncles. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that has proven useful for assessing central nervous system white matter integrity. This study used DTI to examine the degree to which the fetal alcohol-related deficit in EBC may be mediated by structural impairment in the cerebellar peduncles. Methods: Thirteen children with fetal alcohol spectrum disorder (FASD) and 12 matched controls were scanned using DTI and structural MRI sequences. The DTI data were processed using a voxelwise technique, and the structural data were used for volumetric analyses. Prenatal alcohol exposure group and EBC performance were examined in relation to brain volumes and outputs from the DTI analysis. Results: Fractional anisotropy (FA) and perpendicular diffusivity group differences between alcohol-exposed and nonexposed children were identified in the left middle cerebellar peduncle. Alcohol exposure correlated with lower FA and greater perpendicular diffusivity in this region, and these correlations remained significant even after controlling for total brain and cerebellar volumes. Conversely, trace conditioning performance was related to higher FA and lower perpendicular diffusivity in the left middle peduncle. The effect of prenatal alcohol exposure on trace conditioning was partially mediated by lower FA in this region. Conclusions: This study extends recent findings that have used DTI to reveal microstructural deficits in white matter in children with FASD. This is the first DTI study to demonstrate mediation of a fetal alcohol-related effect on neuropsychological function by deficits in white matter integrity. Copyright © 2011 by the Research Society on Alcoholism.Article in Pres