2022
DOI: 10.1002/acm2.13759
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Applying a CT texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis

Abstract: Objective To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules. Materials and methods CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep‐learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction … Show more

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Cited by 7 publications
(2 citation statements)
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“…Pulmonary and aortic CTA scans were reconstructed using FBP, a routinely available HIR algorithm (Karl 3D, United Imaging Healthcare), and AIIR. Specifically, AIIR [10][11][12] is a CT reconstruction algorithm with the deep learning technique integrated into the model-based iterative reconstruction. 14 In developing this algorithm, the traditional regularization term in model-based iterative reconstruction for reducing noises and artifacts is replaced by a dedicated denoising convolutional neural network, which is trained with a large number of image pairs, ie, standard dose images (noise-free images) and the corresponding simulated low-dose images (noisy images).…”
Section: Image Reconstructionmentioning
confidence: 99%
See 1 more Smart Citation
“…Pulmonary and aortic CTA scans were reconstructed using FBP, a routinely available HIR algorithm (Karl 3D, United Imaging Healthcare), and AIIR. Specifically, AIIR [10][11][12] is a CT reconstruction algorithm with the deep learning technique integrated into the model-based iterative reconstruction. 14 In developing this algorithm, the traditional regularization term in model-based iterative reconstruction for reducing noises and artifacts is replaced by a dedicated denoising convolutional neural network, which is trained with a large number of image pairs, ie, standard dose images (noise-free images) and the corresponding simulated low-dose images (noisy images).…”
Section: Image Reconstructionmentioning
confidence: 99%
“…The purpose of this study is to investigate whether a newly introduced deep learning-based iterative reconstruction algorithm, ie, the artificial intelligence iterative reconstruction (AIIR; United Imaging Healthcare, Shanghai, China), [10][11][12] has a value in CTA image quality, especially on the visualization of vascular structures and associated lesions with routine dose settings. If it is so, how exactly is the improvement compared with the filtered back projection (FBP) 13 and hybrid iterative reconstruction (HIR).…”
mentioning
confidence: 99%