A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional...

Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning.

Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI_4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVI_Syn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (r_s) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI_4DCT and CTVI_Syn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI_4DCT or CTVI_Syn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model.

CTVI_Syn showed a mean rs value of 0.65 ± 0.04 compared to CTVI_4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients' RP-risk benefited from CTVI_4DCT-guided plans (Risk_{mean_4DCT_vs_Clinical}: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVI_Syn-guided plans (Risk_{mean_Syn_vs_Clinical}: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVI_Syn and CTVI_4DCT-guided plan (P > 0.05).

Using deep-learning techniques, CTVI_Syn generated from planning CT exhibited a moderate-to-high correlation with CTVI_4DCT. The CTVI_Syn-guided plans were comparable to the CTVI_4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.