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<p>Computed tomography pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis, but patients with iodinated contrast allergies or renal insufficiency are often ineligible. CT-derived perfusion (CTP) is a novel, non-contrast method to quantify pulmonary perfusion from an inhale/exhale CT image pair (4DCT). The resulting CT-<i>P</i> information can be used to identify hypo-perfused regions associated with PE. This pilot study introduces a thresholding approach that estimates the number of lung lobes with perfusion deficits according to optimally selected CTP thresholds. The number of lobes indicated as low-functioning provides a score to categorize patients as PE-positive, negative, or inconclusive. We trained and validated the model on a retrospective dataset of 123 suspected PE patients, achieving 72% accuracy, 75% sensitivity, and 69% specificity, with 17% of cases inconclusive. To our knowledge, this is the first PE diagnostic model from non-contrast 4DCT, showing the feasibility of non-contrast PE diagnosis strategies.</p>

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Diagnoses of pulmonary embolism from non-contrast 4DCT using image processing-derived quantitative perfusion scores

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Hsu-Ting Kuo,
  • Yi-Kuan Liu,
  • Debarghya Chaki,
  • Girish Nair,
  • Danielle Turner-Lawrence,
  • Craig Stevens,
  • Jorge Cisneros,
  • Edward Castillo

摘要

Computed tomography pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis, but patients with iodinated contrast allergies or renal insufficiency are often ineligible. CT-derived perfusion (CTP) is a novel, non-contrast method to quantify pulmonary perfusion from an inhale/exhale CT image pair (4DCT). The resulting CT-P information can be used to identify hypo-perfused regions associated with PE. This pilot study introduces a thresholding approach that estimates the number of lung lobes with perfusion deficits according to optimally selected CTP thresholds. The number of lobes indicated as low-functioning provides a score to categorize patients as PE-positive, negative, or inconclusive. We trained and validated the model on a retrospective dataset of 123 suspected PE patients, achieving 72% accuracy, 75% sensitivity, and 69% specificity, with 17% of cases inconclusive. To our knowledge, this is the first PE diagnostic model from non-contrast 4DCT, showing the feasibility of non-contrast PE diagnosis strategies.