ArticleEuropean radiology2019
Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance.
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 29 papers.
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Who cites it
29 citing papers in PubMed.
- Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.European radiology · 2023Trial
- Computed Tomography Imaging under Artificial Intelligence Reconstruction Algorithm Used in Recovery of Sports Injury of the Knee Anterior Cruciate Ligament.Contrast media & molecular imaging · 2022Trial
- Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy.European radiology · 2020Trial
- Artificial intelligence iterative imaging reconstruction with low-dose CT scans for hepatic metastases.Scientific reports · 2026Article
- AI-based denoising improves image quality in HCC volume perfusion CT without affecting Milan classification.BMC medical imaging · 2026Article
- Application of AIIR algorithm for quality improvement and noise reduction in pediatric abdominal contrast-enhanced CT.Frontiers in radiology · 2026Article
- Image quality, diagnostic performance of reduced-dose abdominal CT with artificial intelligence model-based iterative reconstruction for colorectal liver metastasis: a prospective cohort study.Quantitative imaging in medicine and surgery · 2025Article
- The Impact of Weighting Factors on Dual-Energy Computed Tomography Image Quality in Non-Contrast Head Examinations: Phantom and Patient Study.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.Journal of applied clinical medical physics · 2024Article
- Comparison of different iterative reconstruction algorithms with contrast-enhancement boost technique on the image quality of CT pulmonary angiography for obese patients.BMC medical imaging · 2024Article
- Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.Physical and engineering sciences in medicine · 2024Article
- A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.BMC medical imaging · 2024Article
- Article
- Application of double low-dose mode in left atrial-pulmonary venous computed tomography angiography.Scientific reports · 2023Article
- Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.Journal of digital imaging · 2023Article
- Deep learning-based reconstruction can improve the image quality of low radiation dose head CT.European radiology · 2023Article
- Review
- Physical and visual evaluations of CT image quality of large low-contrast objects with visual model-based iterative reconstruction technique: a phantom study.Physical and engineering sciences in medicine · 2023Article
- Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction.BMC medical imaging · 2023Article
- Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study.Acta radiologica open · 2022Article
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Abstract
objectiveEvaluate and compare the image quality and acceptance of a full MBIR algorithm to that of an earlier full IR hybrid algorithm and filtered back projection (FBP).
methodsAcquisitions were performed with a 320 detector-row CT scanner with seven different dose levels. Images were reconstructed with three algorithms: FBP, full hybrid iterative reconstruction (HIR), and a full model-based iterative reconstruction algorithm (full MBIR). The sensitometry, spatial resolution, image texture, and low-contrast detectability of these algorithms were compared. Subjective analysis of low-contrast detectability was performed. Ten radiologists answered a questionnaire on image quality and confidence in full MBIR images in clinical practice.
resultsThe contrast-to-noise ratio of full MBIR was significantly higher than in the other algorithms (p < 0.0015). The spatial resolution was also higher with full MBIR at high frequencies (> 0.3 lp/mm). Full MBIR at low dose levels led to better low-contrast detectability and more inserts being identified with a higher confidence (p < 0.0001). Full MBIR was associated with a change in image texture compared to HIR and FBP. Eighty percent of radiologists judged general appearance and texture of full MBIR images worse than HIR. Moreover, compared with HIR, for 50% of radiologists, the diagnostic confidence on full MBIR images was worse. Questionnaire reliability was considered acceptable (Cronbach alpha 0.7).
conclusionCompared to conventional iterative reconstruction algorithms, full MBMIR presented a higher image quality and low-contrast detectability and a worse acceptance among radiologists. KEY POINTS: • Full MBIR used led to an overall improvement in image quality compared with FBP and HIR. • Full MBIR leads to image texture change which reduces the confidence in these images among radiologists. • Awareness of the image texture change and improved quality of full MBIR reconstructed images could improve the acceptance of this technique in clinical practice.
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