scholarly journals Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Haesung Yoon ◽  
Jisoo Kim ◽  
Hyun Ji Lim ◽  
Mi-Jung Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Noise Reduction ◽  
Iterative Reconstruction ◽  
Ct Images ◽  
Chest Ct ◽  
Abdomen Ct ◽  
Contrast Enhanced ◽  
Pediatric Chest ◽  
Body Ct

Abstract Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images. Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests. Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture. Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

scholarly journals Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

2021 ◽  
Author(s):  
Haesung Yoon ◽  
Jisoo Kim ◽  
Hyun Ji Lim ◽  
Mi-Jung Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Noise Reduction ◽  
Iterative Reconstruction ◽  
Ct Images ◽  
Chest Ct ◽  
Abdomen Ct ◽  
Contrast Enhanced ◽  
Pediatric Chest ◽  
Body Ct

Abstract Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). Quantitative and qualitative parameters were compared using repeated measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8% and 53.1% respectively. The subjective assessment of overall image quality and noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods.Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

scholarly journals Model-based iterative reconstruction in pediatric chest CT: assessment of image quality in a prospective study of children with cystic fibrosis

2012 ◽  
Vol 43 (5) ◽  
pp. 558-567 ◽  
Author(s):  
Frédéric A. Miéville ◽  
Laureline Berteloot ◽  
Albane Grandjean ◽  
Paul Ayestaran ◽  
François Gudinchet ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Image Quality ◽  
Prospective Study ◽  
Iterative Reconstruction ◽  
Chest Ct ◽  
Model Based ◽  
Pediatric Chest ◽  
A Prospective Study

The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting

2021 ◽  
Vol 76 (2) ◽  
pp. 155.e15-155.e23
Author(s):  
A. Hata ◽  
M. Yanagawa ◽  
Y. Yoshida ◽  
T. Miyata ◽  
N. Kikuchi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Ct Images ◽  
Chest Ct

scholarly journals A dóziscsökkentés lehetőségei CT-képalkotás során az iteratív képrekonstrukció alkalmazásával

2019 ◽  
Vol 160 (35) ◽  
pp. 1387-1394
Author(s):  
Gábor Bajzik ◽  
Anett Tóth ◽  
Tamás Donkó ◽  
Péter Kovács ◽  
Dávid Sipos ◽  
...  
Keyword(s):  
Image Quality ◽  
Effective Dose ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Chest Ct ◽  
Ct Scans ◽  
Rank Test ◽  
Dose Length Product ◽  
Contrast Enhanced ◽  
Significant Difference

Abstract: Introduction and aim: In case of imaging modalities using ionizing radiation, radiation exposure of the patients is a vital issue. It is important to survey the various dose-reducing techniques to achieve optimal radiation protection while keeping image quality on an optimal level. Method: We reprocessed 105 patients’ data prospectively between February and April 2017. The determination of the radiation dose was based on the effective dose, calculated by multiplying the dose-length product (DLP) and dose-conversation coefficient. In case of image quality we used signal-to-noise ratio (SNR) based on manual segmentation of region of interest (ROI). For statistical analysis, one sample t-test and Wilcoxon signed rank test were used. Results: Using iterative reconstruction, the effective dose was significantly lower (p<0.001) in both native and contrast-enhanced abdominal, contrast-enhanced chest CT scans and in the case of the total effective dose. At native and contrast-enhanced abdominal CT scans, the noise content of the images showed significantly lower (p<0.001) values for iterative reconstruction images. At contrast-enhanced chest CT scans there was no significant difference between the noise content of the images (p>0.05). Conclusion: Using iterative reconstruction, it was possible to achieve significant dose reduction. Since the noise content of the images was not significantly higher using the iterative reconstruction compared to the filtered back projection, further dose reduction can be achievable while preserving the optimal quality of the images. Orv Hetil. 2019; 160(35): 1387–1394.

Radiation dose and image quality in pediatric chest CT: effects of iterative reconstruction in normal weight and overweight children

2014 ◽  
Vol 45 (3) ◽  
pp. 337-344 ◽  
Author(s):  
Haesung Yoon ◽  
Myung-Joon Kim ◽  
Choon-Sik Yoon ◽  
Jiin Choi ◽  
Hyun Joo Shin ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Normal Weight ◽  
Chest Ct ◽  
Overweight Children ◽  
Pediatric Chest

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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