ArticleNeuroradiology2016
Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Thin-Slice Brain CT Image Quality and Lesion Detection Evaluation in Deep Learning Reconstruction Algorithm.Clinical neuroradiology · 2025Article
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- Development and validation of the effective CNR analysis method for evaluating the contrast resolution of CT images.Physical and engineering sciences in medicine · 2024Article
- Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?Abdominal radiology (New York) · 2023Article
- Virtual monoenergetic images by spectral detector computed tomography may improve image quality and diagnostic ability for ischemic lesions in acute ischemic stroke.Acta radiologica (Stockholm, Sweden : 1987) · 2023Article
- Evaluation of Apparent Noise on CT Images Using Moving Average Filters.Journal of digital imaging · 2022Article
- 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
- Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations.The neuroradiology journal · 2021Article
- Application of low-dose CT combined with model-based iterative reconstruction algorithm in oncologic patients during follow-up: dose reduction and image quality.The British journal of radiology · 2021Observational
- Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke.Emergency radiology · 2021Article
- CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts.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
- Brain computed tomography using iterative reconstruction to diagnose acute middle cerebral artery stroke: usefulness in combination of narrow window setting and thin slice reconstruction.Neuroradiology · 2018Article
- Could new reconstruction CT techniques challenge MRI for the detection of brain metastases in the context of initial lung cancer staging?European radiology · 2018Article
- Article
- Diagnosis of small posterior fossa stroke on brain CT: effect of iterative reconstruction designed for brain CT on detection performance.European radiology · 2017Article
- Image Quality Required for the Diagnosis of Skull Fractures Using Head CT: A Comparison of Conventional and Improved Reconstruction Kernels.AJNR. American journal of neuroradiology · 2016Article
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8 authors.
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Abstract
introductionThe purpose of this study was to evaluate the utility of iterative model reconstruction (IMR) in brain CT especially with thin-slice images.
methodsThis prospective study received institutional review board approval, and prior informed consent to participate was obtained from all patients. We enrolled 34 patients who underwent brain CT and reconstructed axial images with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and IMR with 1 and 5 mm slice thicknesses. The CT number, image noise, contrast, and contrast noise ratio (CNR) between the thalamus and internal capsule, and the rate of increase of image noise in 1 and 5 mm thickness images between the reconstruction methods, were assessed. Two independent radiologists assessed image contrast, image noise, image sharpness, and overall image quality on a 4-point scale.
resultsThe CNRs in 1 and 5 mm slice thickness were significantly higher with IMR (1.2 ± 0.6 and 2.2 ± 0.8, respectively) than with FBP (0.4 ± 0.3 and 1.0 ± 0.4, respectively) and HIR (0.5 ± 0.3 and 1.2 ± 0.4, respectively) (p < 0.01). The mean rate of increasing noise from 5 to 1 mm thickness images was significantly lower with IMR (1.7 ± 0.3) than with FBP (2.3 ± 0.3) and HIR (2.3 ± 0.4) (p < 0.01). There were no significant differences in qualitative analysis of unfamiliar image texture between the reconstruction techniques.
conclusionIMR offers significant noise reduction and higher contrast and CNR in brain CT, especially for thin-slice images, when compared to FBP and HIR.
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