Experimental Assessment of Two Non-Contrast MRI Sequences Used for Computational Fluid Dynamics: Investigation of Consistency Between Techniques

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

Differential hemodynamics between arteriovenous fistulas with or without intervention before successful use

A significant number of arteriovenous fistulas (AVFs) fail to maturate for dialysis. Although interventions promote maturation, functional primary patency loss is higher for AVFs with interventions (assisted maturation) than AVFs without interventions (un-assisted maturation). Although blood flow-associated hemodynamics have long been proposed to affect AVF remodeling, the optimal hemodynamic parameters for un-assisted maturation are unclear. Additionally, AVF maturation progress is generally not investigated until 6 weeks after AVF creation, and the examination is focused on the AVF's venous limb. In this exploratory study, patients (n = 6) underwent magnetic resonance imaging (MRI) at 1 day, 6 weeks, and 6 months after AVF creation surgery. Before successful use for hemodialysis, three AVFs required intervention and three did not. MRI of the AVFs were used to calculate lumen cross-sectional area (CSA) and perform computational fluid dynamics (CFD) to analyze hemodynamics, including velocity, wall shear stress (WSS), and vorticity. For the venous limb, the no-intervention group and intervention group had similar pre-surgery vein diameter and 1-day post-surgery venous CSA. However, the no-intervention group had statistically larger 1-day venous velocity (0.97 ± 0.67 m/s; mean ± SD), WSS (333 ± 336 dyne/cm2) and vorticity (1709 ± 1290 1/s) than the intervention group (velocity = 0.23 ± 0.10 m/s; WSS = 49 ± 40 dyne/cm2; vorticity = 493.1 ± 227 1/s) (P < 0.05). At 6 months, the no-intervention group had statistically larger venous CSA (43.5 ± 27.4 mm2) than the intervention group (15.1 ± 6.2 mm2) (P < 0.05). Regarding the arterial limb, no-intervention AVF arteries also had statistically larger 1-day velocity (1.17 ± 1.0 m/s), WSS (340 ± 423 dyne/cm2), vorticity (1787 ± 1694 1/s), and 6-month CSA (22.6 ± 22.7 mm2) than the intervention group (velocity = 0.64 ± 0.36 m/s; WSS = 104 ± 116 dyne/cm2, P < 0.05; vorticity = 867 ± 4551/s; CSA = 10.7 ± 6.0 mm2, P < 0.05). Larger venous velocity, WSS, and vorticity immediately after AVF creation surgery may be important for later lumen enlargement and AVF maturation, with the potential to be used as a tool to help diagnose poor AVF maturation earlier. However, future studies using a larger cohort are needed to validate this finding and determine cut off values, if any.