In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the co...In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.展开更多
Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazard...Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.展开更多
In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and trans...In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.展开更多
Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung canc...Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung cancer mortality and to investigate the factors that associate with the screening effect.Methods: A decision tree model with three scenarios(low-dose CT screening, chest X-ray screening, and no screening) was developed to compare screening results in a simulated Chinese urban cohort(100,000 smokers aged45-80 years). Data of participant characteristics were obtained from national registries and epidemiological surveys for estimating lung cancer prevalence. The selection of other tree variables such as sensitivities and specificities of low-dose CT and chest X-ray screening were based on literature research. Differences in lung cancer mortality(primary outcome), false diagnoses, and deaths due to false diagnosis were calculated. Sensitivity analyses were performed to identify the factors that associate with the screening results and to ascertain worst and optimal screening effects considering possible ranges of the variables.Results: Among the 100,000 subjects, there were 448,541, and 591 lung cancer deaths in the low-dose CT, chest X-ray, and no screening scenarios, respectively(17.2% reduction in low-dose CT screening over chest X-ray screening and 24.2% over no screening). The costs of the two screening scenarios were 9387 and 2497 false diagnoses and 7and 2 deaths due to false diagnosis among the 100,000 persons, respectively. The factors that most influenced death reduction with low-dose CT screening over no screening were lung cancer prevalence in the screened cohort, lowdose CT sensitivity, and proportion of early-stage cancers among low-dose CT detected lung cancers. Considering all possibilities, reduction in deaths(relative numbers) with low-dose CT screening in the worst and optimal cases were16(5.4%) and 288(40.2%) over no screening, respectively.Conclusions: In terms of mortality outcomes, our findings favor conducting low-dose CT screening in urban China.However, approaches to reducing false diagnoses and optimizing important screening conditions such as enrollment criteria for screening are highly needed.展开更多
Objective: To assess influential factors of CT on image quality of the lung in children. Materials and methods: Retrospective evaluation of 82 consecutive chest-CT-scans in 50 children (1-16 years, 17 females and 33 m...Objective: To assess influential factors of CT on image quality of the lung in children. Materials and methods: Retrospective evaluation of 82 consecutive chest-CT-scans in 50 children (1-16 years, 17 females and 33 males). Two pediatric radiologists evaluated in consensus the subjective image quality on lung windows using a 4-point scale (1 = very good, 2 = good, 3 = moderate, 4 = poor). Ventilation, motion artifacts and beam hardening artifact were included in this score. The effects of the following factors were evaluated: 1) CT-settings (tube energy, tube current);2) Patient’s age, weight, chest size, ventilation;3) Artifacts of devices, tubes and lines;4) Combination MRI of the abdomen prior to CT of the chest with the same sedation/anesthesia in oncological patients. Results: The odds of having a better image quality increase with patient’s age, weight and chest diameter in a multiple-factor model. There was no difference between tube current protocols. In infants (15 kg) subjective image quality was good in only 1 (8%), moderate in 8 (67%) and poor in 3 (25%) scans. In childhood and adolescence (15 - 90 kg) 25 (36%) scans were very good, 28 (40%) good, 15 (21%) moderate and 2 (3%) poor. Artifacts of tubes and lines have no statistical significant influence on image quality. Lower lung densities were related to better ventilation and older children. Conclusion: Increasing dose parameters may not increase necessarily subjective image quality in infants (15 kg), rather than good ventilation, optimal preparation and avoiding artifacts. A possible explanation of the rather moderate image quality in infants may be the alveolar stage of the lung. Up to two years of age the lung has a high specific lung volume per kg and a low total lung volume with a low alveolar surface.展开更多
Objective: To optimize scan time and X-ray dose with no loss of image quality for retrospectively gated micro-CT scans of free-breathing rats. Methods: Five free-breathing rats were scanned using a dynamic micro-CT sc...Objective: To optimize scan time and X-ray dose with no loss of image quality for retrospectively gated micro-CT scans of free-breathing rats. Methods: Five free-breathing rats were scanned using a dynamic micro-CT scanner over 10 continuous gantry rotations (50 seconds and entrance dose of 0.28 Gy). The in-phase projection views were selected and reconstructed, representing peak inspiration and end expiration from all 10 rotations and progressively fewer rotations. A least error method was also used to ensure that all angular positions were filled. Image quality and reproducibility for physiological measurements were compared for the two techniques. Results: The least error approach underestimated the lung volume, air content in the lung at peak inspiration, and tidal volume. Other measurements showed no differences between the projection-sorting techniques. Conclusions: Seven gantry rotations (35 seconds and 0.2 Gy dose) proved to be the optimal protocol for both the in-phase images and the least error images.展开更多
The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images.The captured images may present with low contrast and low visibility,which migh...The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images.The captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis process.To overcome this problem,this paper presents a new fractional integral entropy(FITE)that estimates the unforeseeable probabilities of image pixels,posing as the main contribution of the paper.The proposed model dynamically enhances the image based on the image contents.The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’probability.Initially,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image.Next,the contrast of the image is then adjusted to enhance the regions with low visibility.Finally,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image.Tests were conducted on brain MRI,lungs CT,and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality.The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores.Overall,this model improves the details of brain MRI,lungs CT,and kidney MRI scans,and could therefore potentially help the medical staff during the diagnosis process.展开更多
Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined...Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for classifying a dataset of 448 lung CT images into 4 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal. The key difference between the commonly-used CNNs and the presented method is that in this method, we adopt the use of redundant wavelet coefficients at level 1 as inputs to the CNN, instead of using original images. One of the main advantages of the proposed method is that it is not necessary to extract regions of interest from original images. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method outperforms the other two methods and achieve the highest overall accuracy of 91.9%. It demonstrates the potential for use in classification of lung diseases in CT images.展开更多
目的分析放射性荧光脱氧葡萄糖(18F-FDG)正电子发射断层扫描(PET)/计算机断层扫描(CT)显像在肺癌术前分期诊断及复发转移预测中的应用价值。方法 回顾性分析2022年9月至2023年6月期间80例初诊肺癌患者的临床和影像学数据,所有患者均在术...目的分析放射性荧光脱氧葡萄糖(18F-FDG)正电子发射断层扫描(PET)/计算机断层扫描(CT)显像在肺癌术前分期诊断及复发转移预测中的应用价值。方法 回顾性分析2022年9月至2023年6月期间80例初诊肺癌患者的临床和影像学数据,所有患者均在术前1周内进行了18F-F DG P ET/CT显像检查,并在术后3~6个月内进行了复查,监测复发或转移情况。术前的TNM分期和术后的复发转移情况均以手术病理结果或临床随访结果为金标准进行评估。结果术前分期诊断中,18F-FD(G P ET/CT显像的T分期、N分期和M分期的符合率分别为86.59%、81.93%和100%,一致性检验Kappa值分别为0.834、0.793和1.000。术后的复发转移检测中,18F-FDG PET/CT显像在术后6个月内成功检出了22例(88.00%)的复发转移病例,其诊断灵敏度为88.00%,特异度为100.00%。结论18F-FDG PET/CT显像在肺癌术前分期以及术后复发转移的预测中具有较高的准确性和可靠性,该方法可以为临床提供有效的参考信息,有助于医生制定更准确的治疗方案和更有效的随访策略。展开更多
基金supported by National Natural Science Foundation ofChina (61672279)Project of “Six Talents Peak” in Jiangsu (2012-WLW-023)OpenFoundation of State Key Laboratory of Hydrology-Water Resources and HydraulicEngineering, Nanjing Hydraulic Research Institute, China (2016491411).
文摘In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.
基金supported in part by Science and Technology Program of Guangdong (No. 2018B030333001)the State’s Key Project of Research and Development Plan (Nos. 2017YFC0109202,2017YFA0104302 and 2017YFC0107900)the National Natural Science Foundation (Nos. 81530060 and 61871117)
文摘Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.
基金supported by the National Natural Science Foundation of China(Nos.11975292,12222512)the CAS"Light of West Chin"Program+1 种基金the CAS Pioneer Hundred Talent Programthe Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030008)。
文摘In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.
基金supported by Peking Union Medical College Youth Fund and the Fundamental Research Funds for the Central Universities(No.2017310049)
文摘Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung cancer mortality and to investigate the factors that associate with the screening effect.Methods: A decision tree model with three scenarios(low-dose CT screening, chest X-ray screening, and no screening) was developed to compare screening results in a simulated Chinese urban cohort(100,000 smokers aged45-80 years). Data of participant characteristics were obtained from national registries and epidemiological surveys for estimating lung cancer prevalence. The selection of other tree variables such as sensitivities and specificities of low-dose CT and chest X-ray screening were based on literature research. Differences in lung cancer mortality(primary outcome), false diagnoses, and deaths due to false diagnosis were calculated. Sensitivity analyses were performed to identify the factors that associate with the screening results and to ascertain worst and optimal screening effects considering possible ranges of the variables.Results: Among the 100,000 subjects, there were 448,541, and 591 lung cancer deaths in the low-dose CT, chest X-ray, and no screening scenarios, respectively(17.2% reduction in low-dose CT screening over chest X-ray screening and 24.2% over no screening). The costs of the two screening scenarios were 9387 and 2497 false diagnoses and 7and 2 deaths due to false diagnosis among the 100,000 persons, respectively. The factors that most influenced death reduction with low-dose CT screening over no screening were lung cancer prevalence in the screened cohort, lowdose CT sensitivity, and proportion of early-stage cancers among low-dose CT detected lung cancers. Considering all possibilities, reduction in deaths(relative numbers) with low-dose CT screening in the worst and optimal cases were16(5.4%) and 288(40.2%) over no screening, respectively.Conclusions: In terms of mortality outcomes, our findings favor conducting low-dose CT screening in urban China.However, approaches to reducing false diagnoses and optimizing important screening conditions such as enrollment criteria for screening are highly needed.
文摘Objective: To assess influential factors of CT on image quality of the lung in children. Materials and methods: Retrospective evaluation of 82 consecutive chest-CT-scans in 50 children (1-16 years, 17 females and 33 males). Two pediatric radiologists evaluated in consensus the subjective image quality on lung windows using a 4-point scale (1 = very good, 2 = good, 3 = moderate, 4 = poor). Ventilation, motion artifacts and beam hardening artifact were included in this score. The effects of the following factors were evaluated: 1) CT-settings (tube energy, tube current);2) Patient’s age, weight, chest size, ventilation;3) Artifacts of devices, tubes and lines;4) Combination MRI of the abdomen prior to CT of the chest with the same sedation/anesthesia in oncological patients. Results: The odds of having a better image quality increase with patient’s age, weight and chest diameter in a multiple-factor model. There was no difference between tube current protocols. In infants (15 kg) subjective image quality was good in only 1 (8%), moderate in 8 (67%) and poor in 3 (25%) scans. In childhood and adolescence (15 - 90 kg) 25 (36%) scans were very good, 28 (40%) good, 15 (21%) moderate and 2 (3%) poor. Artifacts of tubes and lines have no statistical significant influence on image quality. Lower lung densities were related to better ventilation and older children. Conclusion: Increasing dose parameters may not increase necessarily subjective image quality in infants (15 kg), rather than good ventilation, optimal preparation and avoiding artifacts. A possible explanation of the rather moderate image quality in infants may be the alveolar stage of the lung. Up to two years of age the lung has a high specific lung volume per kg and a low total lung volume with a low alveolar surface.
文摘Objective: To optimize scan time and X-ray dose with no loss of image quality for retrospectively gated micro-CT scans of free-breathing rats. Methods: Five free-breathing rats were scanned using a dynamic micro-CT scanner over 10 continuous gantry rotations (50 seconds and entrance dose of 0.28 Gy). The in-phase projection views were selected and reconstructed, representing peak inspiration and end expiration from all 10 rotations and progressively fewer rotations. A least error method was also used to ensure that all angular positions were filled. Image quality and reproducibility for physiological measurements were compared for the two techniques. Results: The least error approach underestimated the lung volume, air content in the lung at peak inspiration, and tidal volume. Other measurements showed no differences between the projection-sorting techniques. Conclusions: Seven gantry rotations (35 seconds and 0.2 Gy dose) proved to be the optimal protocol for both the in-phase images and the least error images.
基金funded by the Deanship of Scientic Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Progr。
文摘The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images.The captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis process.To overcome this problem,this paper presents a new fractional integral entropy(FITE)that estimates the unforeseeable probabilities of image pixels,posing as the main contribution of the paper.The proposed model dynamically enhances the image based on the image contents.The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’probability.Initially,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image.Next,the contrast of the image is then adjusted to enhance the regions with low visibility.Finally,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image.Tests were conducted on brain MRI,lungs CT,and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality.The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores.Overall,this model improves the details of brain MRI,lungs CT,and kidney MRI scans,and could therefore potentially help the medical staff during the diagnosis process.
文摘Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for classifying a dataset of 448 lung CT images into 4 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal. The key difference between the commonly-used CNNs and the presented method is that in this method, we adopt the use of redundant wavelet coefficients at level 1 as inputs to the CNN, instead of using original images. One of the main advantages of the proposed method is that it is not necessary to extract regions of interest from original images. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method outperforms the other two methods and achieve the highest overall accuracy of 91.9%. It demonstrates the potential for use in classification of lung diseases in CT images.
文摘目的分析放射性荧光脱氧葡萄糖(18F-FDG)正电子发射断层扫描(PET)/计算机断层扫描(CT)显像在肺癌术前分期诊断及复发转移预测中的应用价值。方法 回顾性分析2022年9月至2023年6月期间80例初诊肺癌患者的临床和影像学数据,所有患者均在术前1周内进行了18F-F DG P ET/CT显像检查,并在术后3~6个月内进行了复查,监测复发或转移情况。术前的TNM分期和术后的复发转移情况均以手术病理结果或临床随访结果为金标准进行评估。结果术前分期诊断中,18F-FD(G P ET/CT显像的T分期、N分期和M分期的符合率分别为86.59%、81.93%和100%,一致性检验Kappa值分别为0.834、0.793和1.000。术后的复发转移检测中,18F-FDG PET/CT显像在术后6个月内成功检出了22例(88.00%)的复发转移病例,其诊断灵敏度为88.00%,特异度为100.00%。结论18F-FDG PET/CT显像在肺癌术前分期以及术后复发转移的预测中具有较高的准确性和可靠性,该方法可以为临床提供有效的参考信息,有助于医生制定更准确的治疗方案和更有效的随访策略。