ArticleEuropean radiology2019
Neointimal formation after carotid artery stenting: phantom and clinical evaluation of model-based iterative reconstruction (MBIR).
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 14 citations in OpenAlex.
- Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience.BMC medical imaging · 2023Article
- Deep Learning-Based Reconstruction vs. Iterative Reconstruction for Quality of Low-Dose Head-and-Neck CT Angiography with Different Tube-Voltage Protocols in Emergency-Department Patients.Diagnostics (Basel, Switzerland) · 2022Article
- Carotid computed tomography angiography after cobalt-based alloy carotid artery stenting using ultra-high-resolution computed tomography with model-based iterative reconstruction.Radiology case reports · 2021Article
- Accuracy of thin-slice model-based iterative reconstruction designed for brain CT to diagnose acute ischemic stroke in the middle cerebral artery territory: a multicenter study.Neuroradiology · 2021Article
- Deep learning-based image restoration algorithm for coronary CT angiography.European radiology · 2019Article
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11 authors at 2 institutions in 1 country.
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Abstract
objectivesThe objective of this study was to investigate the usefulness of model-based iterative reconstruction (IR) for detecting neointimal formations after carotid artery stenting.
methodsIn a cervical phantom harbouring carotid artery stents, we placed simulated neointimal formations measuring 0.40, 0.60, 0.80 and 1.00 mm along the stent wall. The thickness of in-stent neointimal formations was measured on images reconstructed with filtered-back projection (FBP), hybrid IR (AIDR 3D), and model-based IR (FIRST). The clinical study included 43 patients with carotid stents. Cervical computed tomography (CT) images obtained on a 320-slice scanner were reconstructed with AIDR 3D and FIRST. Five blinded observers visually graded the likelihood of neointimal formations on AIDR 3D and AIDR 3D plus FIRST images. Carotid ultrasound images were the reference standard. We analysed results of visual grading by using a Jack-knife type receiver observer characteristics analysis software.
resultsIn the phantom study, the difference between the measured and the true diameter of the neointimal formations was smaller on FIRST than FBP or AIDR 3D images. In the clinical study, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AIDR 3D were 58%, 88%, 83%, 67% and 73%, respectively. For AIDR 3D plus FIRST images they were 84%, 78%, 80%, 82% and 81%, respectively. The mean area under the curve was significantly higher on AIDR 3D plus FIRST than AIDR 3D images (0.82 vs 0.72; p < 0.01).
conclusionsThe model-based IR algorithm helped to improve diagnostic performance for the detection of neointimal formations after carotid artery stenting. KEY POINTS: • Neointimal formations can be visualised more accurately with model-based IR. • Model-based IR improves the detection of neointimal formations after carotid artery stenting. • Model-based IR is suitable for follow up after carotid artery stenting.
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