Childhood to adulthood: Accounting for age dependence in healthy‐reference distributions in ¹²⁹Xe gas‐exchange MRI

Abstract

Purpose Xenon‐129 (129Xe) gas‐exchange MRI is a pulmonary‐imaging technique that provides quantitative metrics for lung structure and function and is often compared to pulmonary‐function tests. Unlike such tests, it does not normalize to predictive values based on demographic variables such as age. Many sites have alluded to an age dependence in gas‐exchange metrics; however, a procedure for normalizing metrics has not yet been introduced. Theory We model healthy reference values for 129Xe gas‐exchange MRI against age using generalized additive models for location, scale, and shape (GAMLSS). GAMLSS takes signal data from an aggregated heathy‐reference cohort and fits a distribution with flexible median, variation, skewness, and kurtosis to predict age‐dependent centiles. This approach mirrors methods by the Global Lung Function Initiative for modeling pulmonary‐function test data and applies it to binning methods widely used by the 129Xe MRI community to interpret and quantify gas‐exchange data. Methods Ventilation, membrane‐uptake, red blood cell transfer, and red blood cell:membrane gas‐exchange metrics were collected on 30 healthy subjects over an age range of 5 to 68 years. A GAMLSS model was fit against age and compared against widely used linear and generalized‐linear binning 129Xe MRI analysis schemes. Results All 4 gas‐exchange metrics had significant skewness, and membrane‐uptake had significant kurtosis compared to a normal distribution. Age has significant impact on distribution parameters. GAMLSS‐binning produced narrower bins compared to the linear and generalized‐linear binning schemes and distributed signal data closer to a normal distribution. Conclusion The proposed “proof‐of‐concept” GAMLSS‐binning approach can improve diagnostic accuracy of 129Xe gas‐exchange MRI by providing a means of modeling voxel distribution data against age.

Functional gas exchange measures on 129Xe MRI and spectroscopy are associated with age, sex, and BMI in healthy subjects

“…We observed a clear age-related decrease in the RBC:M ratio, as also observed by Plummer et al ( 20 ) and strikingly similar to the preliminary results reported by Collier et al ( 22 ), who estimated an age coefficient of −0.004/yr. vs. our finding of −0.005/yr.…”

“…Recent work by Plummer et al has sought to address this gap by modeling age-related changes in 129 Xe image intensity histograms, with the goal of creating a tailored reference histogram for a given set of patient attributes ( 20 ). Here, we take an alternative approach ( 21 ) akin to that adopted by Collier et al ( 22 ) and use multivariate linear models to evaluate age-related changes in 129 Xe MRI and spectroscopy, including the RBC:M, RBC:Gas, Mem:Gas, RBC chemical shift, and RBC amplitude oscillations.…”

Optimized quantitative mapping of cardiopulmonary oscillations using hyperpolarized 129Xe gas exchange MRI: Digital phantoms and clinical evaluation in CTEPH

“…Moreover, using dynamic spectroscopy with multichannel receive coils presents a promising avenue of providing modest localization of oscillations without the types of artifacts that affect the key‐hole approach used here 39 . Future work would benefit from the incorporation of additional relevant demographic/clinical information, such as fingertip optical hemoglobin measurements, 40 age, 41 weight, and height, into the reference distribution. Although this work demonstrated that RBC oscillation heterogeneity is prevalent in patients with CTEPH and improved by PTE surgery, the spatial accuracy with which regions of low RBC oscillations can be localized remains unclear.…”

Study on Varicella-Zoster Virus Antibody Levels among Children Aged 1–7 Years in Changzhou, China

“…A GAMLSS was used to estimate VZV-IgG reference values and draw centile charts [ 13 , 14 , 15 ]. The GAMLSS requires a parametric distribution assumption for the response variable and models the distribution function of the explanatory variables using a nonparametric smoothing function, making the GAMLSS a semi-parametric regression type model [ 16 ] with the formula shown below: where μ represents the mean, σ represents the variance, υ represents the skewness, and τ represents the kurtosis.…”