Cargando…

Applying a CT texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis

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 o...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Qingle, Xu, Shijie, Zhang, Guozhi, Zhang, Xingwei, Gu, Junying, Yang, Shuyi, Zeng, Mengsu, Zhang, Zhiyong
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley and Sons Inc. 2022
Materias:	Medical Imaging
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680564/ https://www.ncbi.nlm.nih.gov/pubmed/35998185 http://dx.doi.org/10.1002/acm2.13759

Ejemplares similares

Joint application of hybrid iterative reconstruction and adaptive filters on neck‐and‐shoulder CT imaging: A clinical evaluation
por: Jin, Wenfeng, et al.
Publicado: (2022)

A stopping criterion for iterative proton CT image reconstruction based on correlated noise properties
por: DeJongh, Ethan A., et al.
Publicado: (2023)

A pitfall of using the circular‐edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images
por: Narita, Akihiro, et al.
Publicado: (2020)

Physical characterization of a new CT iterative reconstruction method operating in sinogram space
por: Ghetti, Caterina, et al.
Publicado: (2013)

Image texture and radiation dose properties in CT
por: Mozejko, Dawid, et al.
Publicado: (2016)

Ultra‐low dose CT colonography with automatic tube current modulation and sinogram‐affirmed iterative reconstruction: Effects on radiation exposure and image quality
por: Cianci, Roberta, et al.
Publicado: (2018)

A qualitative and quantitative analysis of radiation dose and image quality of computed tomography images using adaptive statistical iterative reconstruction
por: Hussain, Fahad Ahmed, et al.
Publicado: (2016)

Dose reduction in CT urography and vasculature phantom studies using model‐based iterative reconstruction
por: Page, Leland, et al.
Publicado: (2016)

Image quality improvement in low‐dose chest CT with deep learning image reconstruction
por: Tian, Qian, et al.
Publicado: (2022)

CARE Dose 4D combined with sinogram‐affirmed iterative reconstruction improved the image quality and reduced the radiation dose in low dose CT of the small intestine
por: Wang, Lin, et al.
Publicado: (2018)

Effects of voxel size and iterative reconstruction parameters on the spatial resolution of [Formula: see text] SPECT/CT
por: Kappadath, S. Cheenu
Publicado: (2011)

Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction — a phantom study
por: Dodge, Cristina T., et al.
Publicado: (2016)

Adaptive Statistical Iterative Reconstruction-Applied Ultra-Low-Dose CT with Radiography-Comparable Radiation Dose: Usefulness for Lung Nodule Detection
por: Yoon, Hyun Jung, et al.
Publicado: (2015)

Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT
por: Zhou, Yifang
Publicado: (2020)

Accuracy of Model-Based Iterative Reconstruction for CT Volumetry of Part-Solid Nodules and Solid Nodules in Comparison with Filtered Back Projection and Hybrid Iterative Reconstruction at Various Dose Settings: An Anthropomorphic Chest Phantom Study
por: Kim, Seung Kwan, et al.
Publicado: (2019)

Quality evaluation of image‐based iterative reconstruction for CT: Comparison with hybrid iterative reconstruction
por: Kawashima, Hiroki, et al.
Publicado: (2019)

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features
por: Lo, P., et al.
Publicado: (2016)

Evaluation of deep‐learning image reconstruction for chest CT examinations at two different dose levels
por: Svalkvist, Angelica, et al.
Publicado: (2022)

Image Quality and Radiation Dose in CT Venography Using Model-Based Iterative Reconstruction at 80 kVp versus Adaptive Statistical Iterative Reconstruction-V at 70 kVp
por: Park, Chankue, et al.
Publicado: (2019)

Increasing iterative reconstruction strength at low tube voltage in coronary CT angiography protocols using 3D‐printed and Catphan(®) 500 phantoms
por: Abdullah, Kamarul A., et al.
Publicado: (2020)

Effect of Hybrid Kernel and Iterative Reconstruction on Objective and Subjective Analysis of Lung Nodule Calcification in Low-Dose Chest CT
por: Hong, Seul Gi, et al.
Publicado: (2018)

A comparative study based on image quality and clinical task performance for CT reconstruction algorithms in radiotherapy
por: Li, Hua, et al.
Publicado: (2016)

Inter‐observer variability in library plan selection on iterative CBCT and synthetic CT images of cervical cancer patients
por: de Hond, Yvonne J. M., et al.
Publicado: (2023)

Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
por: Shirasaka, Takashi, et al.
Publicado: (2021)

Iterative reconstruction of CT images via Score Function
por: Cong, Wenxiang, et al.
Publicado: (2023)

Comparison of Filtered Back Projection, Hybrid Iterative Reconstruction, Model-Based Iterative Reconstruction, and Virtual Monoenergetic Reconstruction Images at Both Low- and Standard-Dose Settings in Measurement of Emphysema Volume and Airway Wall Thickness: A CT Phantom Study
por: Kim, Cherry, et al.
Publicado: (2018)

Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction
por: Hong, Jung Hee, et al.
Publicado: (2020)

Preclinical validation of a novel deep learning‐based metal artifact correction algorithm for orthopedic CT imaging
por: Guo, Rui, et al.
Publicado: (2023)

Image Quality in Children with Low-Radiation Chest CT Using Adaptive Statistical Iterative Reconstruction and Model-Based Iterative Reconstruction
por: Sun, Jihang, et al.
Publicado: (2014)

New adaptive statistical iterative reconstruction ASiR‐V: Assessment of noise performance in comparison to ASiR
por: De Marco, Paolo, et al.
Publicado: (2018)

Quantitative and qualitative evaluation of hybrid iterative reconstruction, with and without noise power spectrum models: A phantom study
por: Minamishima, Kazuya, et al.
Publicado: (2018)

Effect of radiation dose reduction on image quality in adult head CT with noise‐suppressing reconstruction system with a 256 slice MDCT
por: Baskan, Ozdil, et al.
Publicado: (2015)

Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image
por: Guo, Jingyu, et al.
Publicado: (2016)

A noise power spectrum study of a new model‐based iterative reconstruction system: Veo 3.0
por: Li, Guang, et al.
Publicado: (2016)

Reliability of CT‐based texture features: Phantom study
por: Varghese, Bino A., et al.
Publicado: (2019)

Evaluation of the Impact of Iterative Reconstruction Algorithms on Computed Tomography Texture Features of the Liver Parenchyma Using the Filtration-Histogram Method
por: Sung, Pamela, et al.
Publicado: (2019)

Iterative reconstruction does not substantially delay CT imaging in an emergency setting
por: Willemink, Martin J., et al.
Publicado: (2013)

Combined Use of Automatic Tube Voltage Selection and Current Modulation with Iterative Reconstruction for CT Evaluation of Small Hypervascular Hepatocellular Carcinomas: Effect on Lesion Conspicuity and Image Quality
por: Lv, Peijie, et al.
Publicado: (2015)

Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT
por: Son, Wookon, et al.
Publicado: (2022)

Attenuation-Based Automatic Kilovoltage Selection and Sinogram-Affirmed Iterative Reconstruction: Effects on Radiation Exposure and Image Quality of Portal-Phase Liver CT
por: Song, Ji Soo, et al.
Publicado: (2015)

Cannot write session to /tmp/vufind_sessions/sess_24p0j0p10o1ns0mbfu066pr6d9