Wendy Kang’s research while affiliated with University of Auckland and other places

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an imaging methodology that uses blood as an endogenous contrast agent to quantify flow. One limitation of this method of capillary blood quantification when applied in the lung is the contribution of signals from non‐capillary blood. Intensity thresholding is one approach that has been proposed for minimizing the non‐capillary blood signal. This method has been tested in previous in silico modeling studies; however, it has only been tested under a restricted set of physiological conditions (supine posture and a cardiac output of 5 L/min). This study presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal “per‐slice” intensity threshold value using the individual components of the simulated ASL signal (signal arising independently from capillary blood as well as pulmonary arterial and pulmonary venous blood). The aim of this study was to assess whether the threshold value should vary with slice location, posture, or cardiac output. We applied an in silico modeling approach to predict the blood flow distribution and the corresponding ASL quantification of pulmonary perfusion in multiple sagittal imaging slices. There was a significant increase in ASL signal and heterogeneity (COV = 0.90 to COV = 1.65) of ASL signals when slice location changed from lateral to medial. Heterogeneity of the ASL signal within a slice was significantly lower (P = 0.03) in prone (COV = 1.08) compared to in the supine posture (COV = 1.17). Increasing stroke volume resulted in an increase in ASL signal and conversely an increase in heart rate resulted in a decrease in ASL signal. However, when cardiac output was increased via an increase in both stroke volume and heart rate, ASL signal remained relatively constant. Despite these differences, we conclude that a threshold value of 35% provides optimal removal of large vessel signal independent of slice location, posture, and cardiac output. Perfusion measurement in the lungs using arterial spin labeling magnetic resonance imaging is often contaminated with unwanted signals from large vessels. This modeling study developed a methodology for optimizing the amount of unwanted signals removed to improve perfusion measurements.

Specific ventilation imaging (SVI) proposes that using oxygen‐enhanced 1H MRI to capture signal change as subjects alternatively breathe room air and 100% O2 provides an estimate of specific ventilation distribution in the lung. How well this technique measures SV and the effect of currently adopted approaches of the technique on resulting SV measurement is open for further exploration. We investigated (1) How well does imaging a single sagittal lung slice represent whole lung SV? (2) What is the influence of pulmonary venous blood on the measured MRI signal and resultant SVI measure? and (3) How does inclusion of misaligned images affect SVI measurement? In this study, we utilized two patient‐based in silico models of ventilation, perfusion, and gas exchange to address these questions for normal healthy lungs. Simulation results from the two healthy young subjects show that imaging a single slice is generally representative of whole lung SV distribution, with a calculated SV gradient within 90% of that calculated for whole lung distributions. Contribution of O2 from the venous circulation results in overestimation of SV at a regional level where major pulmonary veins cross the imaging plane, resulting in a 10% increase in SV gradient for the imaging slice. A worst‐case scenario simulation of image misalignment increased the SV gradient by 11.4% for the imaged slice. In this study, we have assessed some of the underlying assumptions of an oxygen‐enhanced proton MRI technique to measure specific ventilation (SV) via in silico modeling. Simulation results show that imaging a single slice is representative of whole lung SV and the contribution of oxygen in the venous circulation on SV measurements is minimal. In addition, simulated misalignment of the 220 image set showed minimal impact on SV suggesting that this imaging technique is robust in healthy individuals.

The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare.

Gravity and matched airway/vascular tree geometries are both hypothesized to be key contributors to ventilation-perfusion (V/Q) matching in the lung, but their relative contributions are challenging to quantify experimentally. We used a structure-based model to conduct an analysis of the relative contributions of tissue deformation (the 'Slinky' effect), other gravitational mechanisms (weight of blood and gravitational gradient in tissue elastic recoil), and matched airway and arterial tree geometry to V/Q matching and therefore to total lung oxygen exchange. Our results showed that the heterogeneity in V and Q were lowest and the correlation between V and Q was highest when the only mechanism for V/Q matching was either tissue deformation or matched geometry. Heterogeneity in V and Q was highest and their correlation was poorest when all mechanisms were active (that is, at baseline). Eliminating the contribution of matched geometry did not change the correlation between V and Q at baseline. Despite the much larger heterogeneities in V and Q at baseline, the contribution of in-common (to V and Q) gravitational mechanisms provided sufficient compensatory V/Q matching to minimize the impact on oxygen transfer. In summary, this model predicts that during supine normal breathing under gravitational loading, passive V/Q matching is predominantly determined by shared gravitationally-induced tissue deformation, compliance distribution, and the effect of the hydrostatic pressure gradient on vessel and capillary size and blood pressures. Contribution from the matching airway and arterial tree geometries in this model is minor under normal gravity in the supine adult human lung.