Academic radiology · 2024
Pulmonary 129Xe MRI: CNN Registration and Segmentation to Generate Ventilation Defect Percent with Multi-center Validation.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Mozaffaripour Ali, Matheson Alexander M, Rahman Omar, Sharma Maksym, Kooner Harkiran K, McIntosh Marrissa J, Rayment Jonathan, Eddy Rachel L, Svenningsen Sarah, Parraga Grace
Аннотация
RATIONALE AND OBJECTIVES: Hyperpolarized 129Xe MRI quantifies ventilation-defect-percent (VDP), the ratio of 129Xe signal-void to the anatomic 1H MRI thoracic-cavity-volume. VDP is associated with airway inflammation and disease control and serves as a treatable trait in therapy studies. Semi-automated VDP pipelines require time-intensive observer interactions. Current convolutional neural network (CNN) approaches for quantifying VDP lack external validation, which limits multicenter utilization. Our objective was to develop an automated and externally validated deep-learning pipeline to quantify pulmonary 129Xe MRI VDP.
MATERIALS AND METHODS: 1H and 129Xe MRI data from the primary site (Site1) were used to train and test a CNN segmentation and registration pipeline, while two independent sites (Site2 and Site3) provided external validation. Semi-automated and CNN-based registration error was measured using mean-absolute-error (MAE) while segmentation error was measured using generalized-Dice-similarity coefficient (gDSC). CNN and semi-automated VDP were compared using linear regression and Bland-Altman analysis.
RESULTS: Training/testing used data from 205 participants (healthy volunteers, asthma, COPD, long-COVID; mean age=54 ± 16y; 119 females) from Site1. External validation used data from 71 participants. CNN and semi-automated 1H and 129Xe registrations agreed (MAE=0.3°, R2 =0.95 rotation; 1.1%, R2 =0.79 scaling; 0.2/0.5px, R2 =0.96/0.95, x/y-translation; all p < .001). Thoracic-cavity and ventilation segmentations were also spatially corresponding (gDSC=0.92 and 0.88, respectively). CNN VDP correlated with semi-automated VDP (Site1 R2/ρ = .97/.95, bias=-0.5%; Site2 R2/ρ = .85/.93, bias=-0.9%; Site3 R2/ρ = .95/.89, bias=-0.8%, all p < .001).
CONCLUSION: An externally validated CNN registration/segmentation model demonstrated strong agreement with low error compared to the semi-automated method. CNN and semi-automated registrations, thoracic-cavity-volume and ventilation-volume segmentations were highly correlated with high gDSC for the datasets.
MATERIALS AND METHODS: 1H and 129Xe MRI data from the primary site (Site1) were used to train and test a CNN segmentation and registration pipeline, while two independent sites (Site2 and Site3) provided external validation. Semi-automated and CNN-based registration error was measured using mean-absolute-error (MAE) while segmentation error was measured using generalized-Dice-similarity coefficient (gDSC). CNN and semi-automated VDP were compared using linear regression and Bland-Altman analysis.
RESULTS: Training/testing used data from 205 participants (healthy volunteers, asthma, COPD, long-COVID; mean age=54 ± 16y; 119 females) from Site1. External validation used data from 71 participants. CNN and semi-automated 1H and 129Xe registrations agreed (MAE=0.3°, R2 =0.95 rotation; 1.1%, R2 =0.79 scaling; 0.2/0.5px, R2 =0.96/0.95, x/y-translation; all p < .001). Thoracic-cavity and ventilation segmentations were also spatially corresponding (gDSC=0.92 and 0.88, respectively). CNN VDP correlated with semi-automated VDP (Site1 R2/ρ = .97/.95, bias=-0.5%; Site2 R2/ρ = .85/.93, bias=-0.9%; Site3 R2/ρ = .95/.89, bias=-0.8%, all p < .001).
CONCLUSION: An externally validated CNN registration/segmentation model demonstrated strong agreement with low error compared to the semi-automated method. CNN and semi-automated registrations, thoracic-cavity-volume and ventilation-volume segmentations were highly correlated with high gDSC for the datasets.