ArticleMedicine2021
The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom.
Article in Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Efficacy Evaluation of 64-Slice Spiral Computed Tomography Images in Laparoscopic-Assisted Distal Gastrectomy for Gastric Cancer under the Reconstruction Algorithm.Contrast media & molecular imaging · 2022Trial
- Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions.Scientific reports · 2024Article
- Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.Radiology · 2024Review
- A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results.Journal of digital imaging · 2023Article
- Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes.Journal of clinical medicine · 2023Review
- Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.Radiology · 2023Review
- Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- Radiation Dose Reduction Opportunities in Vascular Imaging.Tomography (Ann Arbor, Mich.) · 2022Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
abstractThe purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms.A total of 15 CT scans of whole body phantoms were taken with 3 different tube voltages and 5 different tube currents. The images were reconstructed with FBP, 30% and 50% hybrid IR adaptive statistical iterative reconstruction (ASIR-V), and low, medium and high strength DLIR algorithms. The image scanned with tube voltage/tube current of 120 kV/ 200 mA and reconstructed with FBP algorithm was chosen as the reference image. Five radiologists independently analyzed the images individually and also compared it with the reference image in overall, using the visual grading analysis. The mean score of each image was calculated and compared.Using DLIR algorithms, the radiation dose was reduced by 65.5% to 68.1% compared with the dose used in the reference image, while maintaining comparable image quality. Using the DLIR algorithm of medium strength, the image quality was even better than the reference image with a reduced radiation dose up to 36.2% to 50.0%. The DLIR algorithms generated better quality images than ASIR-V algorithms in all the data sets. In addition, among the data sets reconstructed with DLIR algorithms, image quality was the best at the medium strength level, followed by low and high.This phantom study suggests that DLIR algorithms may be considered as a new reconstruction technique by reducing radiation dose while maintaining the image quality of abdominal CTs.
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