ArticleFrontiers in artificial intelligence2021
Kernel Conversion for Robust Quantitative Measurements of Archived Chest Computed Tomography Using Deep Learning-Based Image-to-Image Translation.
Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- The Use of Computed Tomography Densitometry for the Assessment of Emphysema in Clinical Trials: A Position Paper from the Fleischner Society.American journal of respiratory and critical care medicine · 2025Pooled it
- Precise Lung Density Quantification with a Physics-based CT Harmonizer.Radiology. Cardiothoracic imaging · 2026Observational
- Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation.PloS one · 2026Article
- Identification of imaging-based pulmonary and extrapulmonary treatable traits in COPD: a review.Frontiers in medicine · 2026Review
- Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels.Medical physics · 2025Article
- Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.Korean journal of radiology · 2025Article
- Establishing the effect of computed tomography reconstruction kernels on the measure of bone mineral density in opportunistic osteoporosis screening.Scientific reports · 2025Article
- Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A Physics-Informed Deep Neural Network for Harmonization of CT Images.IEEE transactions on bio-medical engineering · 2024Article
- Spatial lung imaging in clinical and translational settings.Breathe (Sheffield, England) · 2024Review
- Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.Medical physics · 2024Article
- Centrilobular emphysema and airway dysanapsis: factors associated with low respiratory function in younger smokers.ERJ open research · 2024Article
- Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Harmonizing CT Images via Physics-based Deep Neural Networks.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Centrilobular Emphysema Is Associated with Pectoralis Muscle Reduction in Current Smokers without Airflow Limitation.Respiration; international review of thoracic diseases · 2023Article
- The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest computed tomography (CT) is used to screen for lung cancer and evaluate pulmonary and extra-pulmonary abnormalities such as emphysema and coronary artery calcification, particularly in smokers. In real-world practice, lung abnormalities are visually assessed using high-contrast thin-slice images which are generated from raw scan data using sharp reconstruction kernels with the sacrifice of increased image noise. In contrast, accurate CT quantification requires low-contrast thin-slice images with low noise, which are generated using soft reconstruction kernels. However, only sharp-kernel thin-slice images are archived in many medical facilities due to limited data storage space. This study aimed to establish deep neural network (DNN) models to convert sharp-kernel images to soft-kernel-like images with a final goal to reuse historical chest CT images for robust quantitative measurements, particularly in completed previous longitudinal studies. By using pairs of sharp-kernel (input) and soft-kernel (ground-truth) images from 30 patients with chronic obstructive pulmonary disease (COPD), DNN models were trained. Then, the accuracy of kernel conversion based on the established DNN models was evaluated using CT from independent 30 smokers with and without COPD. Consequently, differences in CT values between new images converted from sharp-kernel images using the established DNN models and ground-truth soft-kernel images were comparable with the inter-scans variability derived from repeated phantom scans (6 times), showing that the conversion error was the same level as the measurement error of the CT device. Moreover, the Dice coefficients to quantify the similarity between low attenuation voxels on given images and the ground-truth soft-kernel images were significantly higher on the DNN-converted images than the Gaussian-filtered, median-filtered, and sharp-kernel images (
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