Hyperpolarized 129Xenon MRI Ventilation Defect Quantification via Thresholding and Linear Binning in Multiple Pulmonary Diseases

Abstract

There is no agreed upon method for quantifying ventilation defect percentage (VDP) with high sensitivity and specificity from hyperpolarized (HP) gas ventilation MR images in multiple pulmonary diseases for both pediatrics and adults, yet identifying such methods will be necessary for future multi-site trials. Most HP gas MRI ventilation research focuses on a specific pulmonary disease and utilizes one quantification scheme for determining VDP. Here we sought to determine the potential of different methods for quantifying VDP from HP 129Xe images in multiple pulmonary diseases through comparison of the most utilized quantification schemes: linear binning and thresholding. HP 129Xe MRI was performed in a total of 176 subjects (125 pediatrics and 51 adults, age 20.98±16.48 years) who were either healthy controls (n=23) or clinically diagnosed with cystic fibrosis (CF) (n=37), lymphangioleiomyomatosis (LAM) (n=29), asthma (n=22), systemic juvenile idiopathic arthritis (sJIA) (n=11), interstitial lung disease (ILD) (n=7), or were bone marrow transplant (BMT) recipients (n=47). HP 129Xe ventilation images were acquired during a ≤16 second breath-hold using a 2D multi-slice gradient echo sequence on a 3T Philips scanner (TR/TE 8.0/4.0ms, FA 10-12°, FOV 300×300mm, voxel size≈3×3×15mm). Images were analyzed using 5 different methods to quantify VDPs: linear binning (histogram normalization with binning into 6 clusters) following either linear or a variant of a nonparametric nonuniform intensity normalization algorithm(N4ITK) bias-field correction, thresholding ≤60% of the mean signal intensity with linear bias-field correction, and thresholding ≤60% and ≤75% of the mean signal intensity following N4ITK bias-field correction. Spirometry was successfully obtained in 84% of subjects. All quantification schemes were able to label visually identifiable ventilation defects in similar regions within all subjects. The VDPs of control subjects were significantly lower (p<0.05) compared to BMT, CF, LAM, and ILD subjects for most of the quantification methods. No one quantification scheme was better able to differentiate individual disease groups from the control group. Advanced statistical modeling of the VDP quantification schemes revealed that in comparing controls to the combined disease group, N4ITK bias-field corrected 60% thresholding had the highest predictive efficacy, sensitivity, and specificity at the VDP cut-point of 2.3%. However, compared to the thresholding quantification schemes, linear binning was able to capture and label subtle low-ventilation regions in subjects with milder obstruction, such as subjects with asthma. The difference in VDP between healthy controls and patients varied between the different disease states for all quantification methods. Although N4ITK bias-field corrected 60% thresholding was superior in separating the combined diseased group from controls, linear binning is able to better label low-ventilation regions unlike the current, 60% thresholding scheme. For future clinical trials, a consensus will need to be reached on which VDP scheme to utilize, as there are subtle advantages for each for specific disease.