Automated CMR Quantification of Pulmonary Artery Size-Based and Slow-Flow Metrics in Pulmonary Hypertension.
Authors
Affiliations (10)
Affiliations (10)
- School of Medicine & Population Health, The University of Sheffield, Sheffield S10 2TN, UK.
- Department of Radiological Sciences, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia.
- Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield S10 2TN, UK.
- Division of Image Processing, Department of Radiology, Leiden University Medical Centre, 2333 Leiden, The Netherlands.
- Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
- Radiological Sciences Department, College of Applied Medical Science, King Saud bin Abdulaziz University for Health Science, Riyadh 11426, Saudi Arabia.
- King Abdullah International Medical Research Center (KAIMRC), Riyadh 11426, Saudi Arabia.
- National Institute for Health and Care Research, Sheffield Biomedical Research Centre, Sheffield S10 2TN, UK.
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2TN, UK.
- Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2TN, UK.
Abstract
<b>Background/Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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). <b>Conclusions:</b> 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.