AI-accelerated 3D gradient echo versus ultrashort echo time MRI for lung nodule detection and measurement.

Background: Ultrashort echo time (UTE) sequences are widely used in pulmonary MRI due to favorable lung parenchyma signal but are limited by acquisition time and complex reconstruction. Purpose: To compare an AI-enhanced 3D gradient echo sequence with a UTE sequence for lung nodule detection and Lung-RADS classification, using CT as the reference standard. Materials and Methods: In this single-center observational study, patients with at least one CT-detected lung nodule underwent pulmonary MRI. MRI included a respiratory-gated AI-accelerated 3D gradient-echo sequence (CSAI-GRE) and a respiratory-gated 3D UTE sequence, both acquired with comparable scan times. Three radiologists independently assessed image quality, nodule detection, and Lung-RADS classification. CT served as the reference standard. Statistical analyses included paired tests for image quality, mixed-effects modeling for lesion-level detection, and Results: This study included 54 patients (mean age, 65 ± 12 years; 23 women [43%]) with 97 pulmonary nodules. Subjective image quality was higher for CSAI-GRE than UTE (mean score, 4.30 vs 3.91; Conclusion: CSAI-GRE and UTE enabled reliable pulmonary nodule detection. CSAI-GRE provided superior subjective image quality and slightly higher detection performance than UTE. However, the Lung-RADS reclassification rates suggest that MRI-based Lung-RADS categorization may not yet be sufficiently robust for routine clinical application.