ArticleThe British journal of radiology2018
Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT.
Article in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 47 citations in OpenAlex.
- Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy.European radiology · 2020Trial
- Impact of Combined Deep Learning Image Reconstruction and Metal Artifact Reduction Algorithm on CT Image Quality in Different Scanning Conditions for Maxillofacial Region with Metal Implants: A Phantom Study.Journal of imaging informatics in medicine · 2025Article
- Computed tomography dose index determination in dose modulation prospectively involving the third-generation iterative reconstruction and noise index.Journal of applied clinical medical physics · 2024Article
- Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.European radiology · 2024Article
- Iodine density mapping for the diagnosis of acute bowel ischemia using fast kV-switching dual-energy CT.Abdominal radiology (New York) · 2024Article
- The influence of image reconstruction methods on the diagnosis of pulmonary emphysema with convolutional neural network.Radiological physics and technology · 2023Article
- Effect of Low Tube Voltage (100 kV) Combined with ASIR-V on the Visualization and Image Quality of the Adamkiewicz Artery: A Comparison with 120 kV Protocol.Diagnostics (Basel, Switzerland) · 2023Article
- Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses.Quantitative imaging in medicine and surgery · 2023Article
- Evaluation of thin-slice abdominal DECT using deep-learning image reconstruction in 74 keV virtual monoenergetic images: an image quality comparison.Abdominal radiology (New York) · 2023Article
- Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice.Advanced biomedical research · 2023Article
- Pseudo dual-energy CT-derived iodine mapping using single-energy CT data based on a convolution neural network.BJR open · 2023Article
- Iterative reconstructionThe British journal of radiology · 2022Article
- Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction.Japanese journal of radiology · 2022Article
- Application of deep learning image reconstruction in low-dose chest CT scan.The British journal of radiology · 2022Article
- Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction.Korean journal of radiology · 2022Article
- Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography.Journal of X-ray science and technology · 2022Article
- Low keV portal venous phase as a surrogate for pancreatic phase in a pancreatic protocol dual-energy CT: feasibility, image quality, and lesion conspicuity.European radiology · 2021Article
- Article
- A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions.The British journal of radiology · 2021Article
- Impact of preset and postset adaptive statistical iterative reconstruction-V on image quality in nonenhanced abdominal-pelvic CT on wide-detector revolution CT.Quantitative imaging in medicine and surgery · 2021Article
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8 authors at 2 institutions in 1 country.
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Abstract
objectiveTo compare image quality of two adaptive statistical iterative reconstruction (ASiR and ASiR-V) algorithms using objective and subjective metrics for routine liver CT, with the conventional filtered back projection (FBP) reconstructions as reference standards.
methodsThis institutional review board-approved study included 52 patients with clinically suspected hepatic metastases. Patients were divided equally into ASiR and ASiR-V groups with same scan parameters. Images were reconstructed with ASiR and ASiR-V from 0 (FBP) to 100% blending percentages at 10% interval in its respective group. Mean and standard deviation of CT numbers for liver parenchyma were recorded. Two experienced radiologists reviewed all images for image quality blindly and independently. Data were statistically analyzed.
resultsThere was no difference in CT dose index between ASiR and ASiR-V groups. As the percentage of ASiR and ASiR-V increased from 10 to 100% , image noise reduced by 8.6 -57.9% and 8.9-81.6%, respectively, compared with FBP. There was substantial interobserver agreement in image quality assessment for ASiR and ASiR-V images. Compared with FBP reconstruction, subjective image quality scores of ASiR and ASiR-V improved significantly as percentage increased from 10 to 80% for ASiR (peaked at 50% with 32.2% noise reduction) and from 10 to 90% (peaked at 60% with 51.5% noise reduction) for ASiR-V.
conclusionBoth ASiR and ASiR-V improved the objective and subjective image quality for routine liver CT compared with FBP. ASiR-V provided further image quality improvement with higher acceptable percentage than ASiR, and ASiR-V60% had the highest image quality score. Advances in knowledge: (1) Both ASiR and ASiR-V significantly reduce image noise compared with conventional FBP reconstruction. (2) ASiR-V with 60 blending percentage provides the highest image quality score in routine liver CT.
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