ArticleJapanese journal of radiology2015
Model-based iterative reconstruction for detection of subtle hypoattenuation in early cerebral infarction: a phantom study.
Article in Japanese journal of radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 31 citations in OpenAlex.
- Advantages of deep learning reconstruction algorithm in ultra-high-resolution CT for the diagnosis of pancreatic cystic neoplasm.Japanese journal of radiology · 2025Article
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- Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies.Diagnostics (Basel, Switzerland) · 2023Article
- Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease.European radiology · 2022Article
- Model-Based Iterative Reconstruction (MBIR) for ASPECT Scoring in Acute Stroke Patients Selection: Comparison to rCBV and Follow-Up Imaging.Tomography (Ann Arbor, Mich.) · 2022Article
- Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations.The neuroradiology journal · 2021Article
- Deep Learning-based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases.Radiology. Artificial intelligence · 2019Article
- Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.European radiology · 2019Article
- A "one-stop-shop" 4D CTA protocol using 320-row CT for advanced imaging in acute ischemic stroke: a technical note.European radiology · 2019Article
- "Hyperdense artery sign" in early ischemic stroke: diagnostic value of model-based reconstruction approach in comparison with standard hybrid iterative reconstruction algorithm.Neuroradiology · 2018Observational
- Article
- Diagnostic performance of reduced-dose CT with a hybrid iterative reconstruction algorithm for the detection of hypervascular liver lesions: a phantom study.European radiology · 2017Article
- Enhanced gray-white matter differentiation on non-enhanced CT using a frequency selective non-linear blending.Neuroradiology · 2016Article
- Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging.Neuroradiology · 2016Article
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6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeModel-based iterative reconstruction (MBIR) was recently shown to enable dose reduction in computed tomography (CT). The detectability of low-contrast lesions was assessed on CT images reconstructed with MBIR compared with the conventional filtered back-projection (FBP) method. MATERIALS AND
methodsA phantom simulating brain gray matter containing small lesions mimicking early cerebral infarctions was scanned at tube currents of 50, 100, 200, and 400 mA. Images were reconstructed by use of both methods. Round regions were cropped from the reconstructed images, half with a lesion, the other half without. Eight radiologists reviewed the images and scored the certainty of lesion detection on a 5-point scale. Overall performance was analyzed by use of a receiver operating characteristic curve.
resultsFor the tube currents investigated, the analysis showed that the mean areas under the curves for the reviewers were 0.65, 0.70, 0.82, and 0.83 for FBP and 0.70, 0.76, 0.78, and 0.90 for MBIR. For each current, there was no significant difference between the areas under the curves for the different reconstruction methods (p = 0.32, 0.24, 0.49, and 0.17).
conclusionFor the small, low-contrast lesions in the phantom model used in this study, no significant difference between detectability was observed for MBIR and FBP.
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