ArticleMedical physics2022
Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.
Article in Medical physics, 2022. 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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29 citing papers in PubMed, 46 citations in OpenAlex.
- Evaluation of deep-learning iterative reconstruction combined with high-frequency kernels in CT: a task-based image quality study.European radiology experimental · 2026Article
- Brain cone-beam CT image quality improvement using a deep-learning-based denoising method: a multicenter retrospective study.Neuroradiology · 2026Article
- Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.European radiology · 2026Article
- Evaluation of image quality between hybrid iterative reconstruction and deep learning reconstruction in low dose abdominopelvic CT in low body mass index individuals.Scientific reports · 2026Article
- Myocardial extracellular volume fraction in cardiac CT: comparison of deep learning reconstruction and iterative reconstruction.Journal of thoracic disease · 2026Article
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Assessing the influence of kernel selection on chest computed tomography image quality across varying dose levels using TrueFidelity reconstruction.Radiation protection dosimetry · 2026Article
- Application of Deep Learning Algorithm-Based Image Reconstruction in Improving Abdominal CT Image Quality in Adrenal and Renal Lesions Detection.Radiology research and practice · 2026Article
- Deep learning image reconstruction technique for improving image quality and radiation dose reduction compared to iterative reconstruction technique in non-contrast CT head imaging.Frontiers in radiology · 2026Article
- Impact of a prototype, dedicated AI-based spectral image reconstruction algorithm on the quality of low-keV virtual monoenergetic images and iodine maps: A phantom study.Medical physics · 2025Article
- Thin-Slice Brain CT Image Quality and Lesion Detection Evaluation in Deep Learning Reconstruction Algorithm.Clinical neuroradiology · 2025Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- The application of deep learning-based artificial intelligence algorithms combined with low-dose scanning protocols in chest CT.Quantitative imaging in medicine and surgery · 2025Article
- Performance of a Deep Learning Reconstruction Method on Clinical Chest-Abdomen-Pelvis Scans from a Dual-Layer Detector CT System.Tomography (Ann Arbor, Mich.) · 2025Article
- Automated Task-Transfer Function Measurement for CT Image Quality Assessment Based on AAPM TG 233.Journal of imaging · 2025Article
- Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions.Research in diagnostic and interventional imaging · 2025Article
- Detectability of Iodine in Mediastinal Lesions on Photon Counting CT: A Phantom Study.Diagnostics (Basel, Switzerland) · 2025Article
- Radiation Detectors and Sensors in Medical Imaging.Sensors (Basel, Switzerland) · 2024Review
- Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions.Quantitative imaging in medicine and surgery · 2024Review
- Dose robustness of deep learning models for anatomic segmentation of computed tomography images.Journal of medical imaging (Bellingham, Wash.) · 2024Article
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7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels. PURPOSE: To assess the impact of an artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications.
methodsAcquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU-41 phantoms were performed at five dose levels (CTDI
resultsFrom Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: -66.3% ± 0.5% for mediastinal images and -63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: -35.3% ± 2.2% for mediastinal images and -13.3% ± 2.2% for parenchymal images), and the detectability (d') of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low-contrast polyethylene insert and the opposite for the high-contrast air insert. Compared to the i4 used in clinical practice, d' values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required.
conclusionWith AI-DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.
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