Evaluation of image registration algorithms for 3D phase-resolved functional lung (PREFUL) ventilation MR imaging in healthy volunteers and COPD patients

Science and Research

PURPOSE: To assess the influence of the registration algorithms on the repeatability of 3D phase-resolved functional lung (PREFUL) ventilation MRI. METHODS: 23 healthy volunteers and 10 patients with chronic obstructive pulmonary disease underwent MRI using 3D PREFUL under tidal breathing. The registration of dynamically acquired data to a fixed image was executed using single-step, stepwise and group-oriented registration (GOREG) approaches. Advanced Normalization Toolkit (ANTs) and Forsberg image-registration package were used for the registration. Image registration algorithms were tested for differences and evaluated by the repeatability analysis of ventilation parameters using coefficient of variation (CoV), intraclass-correlation coefficient, Bland-Altman plots and correlation to spirometry. Also, the registration time and image quality were computed for all registration approaches. RESULTS: Very strong to strong correlations (r range: 0.917-0.999) were observed between ventilation parameters derived using various registration approaches. Median CoV values of cross-correlation (CC) parameter were significantly lower (all P<=0.0054) for ANTs GOREG registration when compared to single-step and stepwise ANTs registration. Majority of comparisons between COPD patients and age-matched healthy volunteers showed agreement among the registration approaches. The repeatability of regional ventilation (RVent) based ventilation defect percentage (VDP(RVent) ) and VDP(CC) was significantly higher (both P<=0.0054) for Forsberg GOREG registration, when compared to ANTs GOREG registration. All 3D PREFUL-derived ventilation parameters correlated with FEV(1) and FEV(1) /FVC (all|r|>0.40, all P<0.03). The image sharpness of RVent maps was statistically elevated (all P<0.001) using GOREG registration when compared to single-step and stepwise registration approaches using ANTs. The best computational performance was achieved with Forsberg GOREG registration. CONCLUSION: The GOREG scheme improves the repeatability and image quality of dynamic 3D PREFUL ventilation parameters. Registration time can be ~10-fold reduced to 9 minutes using the Forsberg method with equal or even improved repeatability and comparable PREFUL ventilation results compared to the ANTs method.