Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistoch...Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistochemical subtypes of breast cancers.Methods A retrospective analysis of DCE-MRI was performed in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
Objective: to study the characteristics and application of spiral CT in early lung cancer. Methods: from January 2020 to November 2021, 40 patients of suspected early lung cancer, all received 64 spiral CT scan and pa...Objective: to study the characteristics and application of spiral CT in early lung cancer. Methods: from January 2020 to November 2021, 40 patients of suspected early lung cancer, all received 64 spiral CT scan and pathological examination, referring to the diagnostic value of 64 layers of spiral CT, and the characteristics of 64 spiral CT imaging and CT of early lung cancer cases with CT perfusion parameters. Results: the pathological examination results of 40 patients suspected early lung cancer were 30 malignant, 10 benign, 64 spiral CT showed malignant, 11 benign, 64 layers of spiral CT was 97.50%, 96.67%, specificity was 100.00%;64 spiral CT confirmed malignant cases, deep segmentation, fine spur, spike, the detection rate of vascular tract collection and vacuolar signs was higher than that in benign cases. Blood flow, permeability, blood volume, and mean passage time CT perfusion parameters were higher than benign cases, and the difference was all statistically significant (P <0.05). Conclusion: the 64-layer spiral CT imaging features, fine burr features and spike features are common in the initial diagnosis of early lung cancer patients, and the blood flow, permeability and blood volume are at high levels, which can provide a practical basis for the differentiation between disease diagnosis and benign and malignant.展开更多
Computed tomography perfusion(CTP)plays a crucial role in guiding reperfusion therapy and patient selection for acute ischemic stroke(AIS)through perfusion parameter maps of the brain;however,its widespread use is lim...Computed tomography perfusion(CTP)plays a crucial role in guiding reperfusion therapy and patient selection for acute ischemic stroke(AIS)through perfusion parameter maps of the brain;however,its widespread use is limited by the complexity of acquisition protocols and high radiation dose.Previous studies have attempted to reduce radiation exposure by equally lowering the temporal sampling rate;however,it may miss the peak of arterial enhancement,leading to underestimation of blood flow parameter.Here,we investigate the feasibility of using a generative adversarial network(GAN)to generate perfusion maps from 3 phases of CTP(mCTP).The three phases were chosen based on the multiphase computed tomography angiography scanning protocol:the peak arterial input function phase,the peak venous output function phase,and the delayed venous output function phase.The findings demonstrate that the GAN model achieved high visual overlap and performance for cerebral blood flow and time-to-maximum maps,with a mean structural similarity index measure of 0.921 to 0.971 and 0.817 to 0.883,a mean normalized root mean squared error of 0.019 to 0.108 and 0.058 to 0.064,and a mean learned perceptual image patch similarity of 0.039 to 0.088 and 0.141 to 0.146,respectively.For the 2 external datasets,the volume agreement between the model-and CTP-derived infarct and hypoperfusion areas was the intraclass correlation coefficient of 0.731 to 0.883 and 0.499 to 0.635,and the Spearman correlation coefficient of 0.720 to 0.808 and 0.533 to 0.6540,respectively.Qualitative assessments of diagnostic quality further confirmed that the mCTP-derived maps were comparable to those obtained from traditional CTP.In conclusion,the GAN-based model is effective in generating perfusion maps from mCTP,which could serve as a viable alternative to traditional CTP in the diagnostic evaluation of AIS.展开更多
文摘Objective To investigate whether correlation existsbetween quantitative perfusion parameters obtained from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and different prognostic factors or immunohistochemical subtypes of breast cancers.Methods A retrospective analysis of DCE-MRI was performed in
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.
文摘Objective: to study the characteristics and application of spiral CT in early lung cancer. Methods: from January 2020 to November 2021, 40 patients of suspected early lung cancer, all received 64 spiral CT scan and pathological examination, referring to the diagnostic value of 64 layers of spiral CT, and the characteristics of 64 spiral CT imaging and CT of early lung cancer cases with CT perfusion parameters. Results: the pathological examination results of 40 patients suspected early lung cancer were 30 malignant, 10 benign, 64 spiral CT showed malignant, 11 benign, 64 layers of spiral CT was 97.50%, 96.67%, specificity was 100.00%;64 spiral CT confirmed malignant cases, deep segmentation, fine spur, spike, the detection rate of vascular tract collection and vacuolar signs was higher than that in benign cases. Blood flow, permeability, blood volume, and mean passage time CT perfusion parameters were higher than benign cases, and the difference was all statistically significant (P <0.05). Conclusion: the 64-layer spiral CT imaging features, fine burr features and spike features are common in the initial diagnosis of early lung cancer patients, and the blood flow, permeability and blood volume are at high levels, which can provide a practical basis for the differentiation between disease diagnosis and benign and malignant.
基金supported in part by the National Key R&D Program of China under Grants 2024YFA1012002 and 2024YFC2417804the National Natural Science Foundation of China under Grants U21A6005 and 12226004.
文摘Computed tomography perfusion(CTP)plays a crucial role in guiding reperfusion therapy and patient selection for acute ischemic stroke(AIS)through perfusion parameter maps of the brain;however,its widespread use is limited by the complexity of acquisition protocols and high radiation dose.Previous studies have attempted to reduce radiation exposure by equally lowering the temporal sampling rate;however,it may miss the peak of arterial enhancement,leading to underestimation of blood flow parameter.Here,we investigate the feasibility of using a generative adversarial network(GAN)to generate perfusion maps from 3 phases of CTP(mCTP).The three phases were chosen based on the multiphase computed tomography angiography scanning protocol:the peak arterial input function phase,the peak venous output function phase,and the delayed venous output function phase.The findings demonstrate that the GAN model achieved high visual overlap and performance for cerebral blood flow and time-to-maximum maps,with a mean structural similarity index measure of 0.921 to 0.971 and 0.817 to 0.883,a mean normalized root mean squared error of 0.019 to 0.108 and 0.058 to 0.064,and a mean learned perceptual image patch similarity of 0.039 to 0.088 and 0.141 to 0.146,respectively.For the 2 external datasets,the volume agreement between the model-and CTP-derived infarct and hypoperfusion areas was the intraclass correlation coefficient of 0.731 to 0.883 and 0.499 to 0.635,and the Spearman correlation coefficient of 0.720 to 0.808 and 0.533 to 0.6540,respectively.Qualitative assessments of diagnostic quality further confirmed that the mCTP-derived maps were comparable to those obtained from traditional CTP.In conclusion,the GAN-based model is effective in generating perfusion maps from mCTP,which could serve as a viable alternative to traditional CTP in the diagnostic evaluation of AIS.
