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September 13, 2024Journal of Thoracic Imaging

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm

Wesley Bocquet R. Bouzerar Geraldine François Antoine Leleu C. Renard

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Abstract

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).

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Cite This Study

Bocquet et al. (2024) studied this question.

synapsesocial.com/papers/68e58a5ab6db643587526425 https://doi.org/10.1097/rti.0000000000000806

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT2024 · 13 citations
2Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT2024 · 7 citations
3Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT2024 · 2 citations
4Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach2024 · 2 citations
5Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT2024 · 9 citations

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Detection and Size Quantification of Pulmonary Nodules Using Ultra-Low-Dose CT with Tin Filtration: A Prospective Comparative Study2026

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