Abstract / Summary
Background/Objectives: Pulmonary artery (PA) dilatation and slow-flow are imaging features of pulmonary hypertension (PH), but their quantitative assessment on routine cardiovascular magnetic resonance (CMR) remains limited. This study aimed to develop and evaluate an automated CMR pipeline for quantifying PA size-based and slow-flow metrics from routine white-blood (WB) and black-blood (BB) acquisitions. Methods: A total of 205 CMR acquisitions (106 WB and 99 BB) were used for model development and independent testing. Separate deep learning nnU-Net-based segmentation models were developed for WB and BB acquisitions using a two-stage cascaded framework. Automated measurements included PA volume and PA slow-flow. The trained models were subsequently applied to a larger cohort of 1471 patients using available WB and BB acquisitions to assess automated measurement extraction and agreement with clinical reports. Results: Automated PA volumetric measurements demonstrated excellent agreement with manual reference measurements for both the WB and BB models (ICC 0.94 and 0.93, respectively), with high segmentation accuracy (Dice 0.86 and 0.80, respectively). Automated slow-flow measurements also demonstrated excellent volumetric agreement (ICC 0.94), despite lower spatial overlap (Dice 0.61). Automated slow-flow classification demonstrated 83% agreement with qualitative clinical reporting (κ = 0.58). Conclusions: Automated PA volumetric quantification from routine CMR is feasible. Automated slow-flow quantification showed promising performance but requires validation across scanner vendors and BB acquisition protocols before broader clinical application.