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BACKGROUND@#Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology.@*METHODS@#The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted.@*RESULTS@#The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037).@*CONCLUSIONS@#We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
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Humans , Lymphocytes, Tumor-Infiltrating , Colorectal Neoplasms , Artificial Intelligence , Reproducibility of Results , Prognosis , CD8-Positive T-Lymphocytes , Tumor MicroenvironmentABSTRACT
Objective:To investigate the current funding landscape of medical artificial intelligence (AI) projects in National Natural Science Foundation of China (NSFC) from 2015 to 2019.Methods:From 2015 to 2019, AI-related projects in NSFC Medical Science Department were collected. Comprehensive analysis was performed in the projects information including year, title, supporting institution, fund type, research findings, etc.Results:NSFC has funded a total of 278 projects related to artificial intelligence, with the total funding amount of 139 million yuan. The number of projects and the funding amount were increasing year by year. Among these, 90% (249/278) were general programs and young scientist funds; 53% (148/278) of the projects were regionally distributed in Beijing, Shanghai and Guangdong; 66% (184/278) of the projects were imaging-related researches; the projects mainly focused on diseases with high incidence in China, including neoplastic diseases, cardiovascular and nervous system diseases.Conclusion:The AI-related projects funded by NSFC are characterized by rapid growth in number and fund amounts, wide coverage of disciplines, and diverse types of research diseases. However, the unbalanced distribution of regions, research fields, and supporting institutions demands more attention in future.
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Objective:To evaluate the effect of height (HT), total body weight (TBW), body mass index (BMI), lean body weight (LBW), body surface area (BSA) and blood volume (BV) on aortic and liver contrast enhancement during upper abdominal contrast-enhanced CT scans.Methods:One hundred and thirteen enrolled patients underwent upper abdominal multiphase contrast-enhanced CT scans. The enhancement (ΔHU) of aorta in hepatic arterial phase and liver parenchyma in portal venous phase were measured and calculated. The ΔHU values difference of aorta and liver parenchyma in subgroups between males and females, TBW<60 kg and TBW≥60 kg, BMI<25 kg/m 2 and BMI≥25 kg/m 2 were compared. To evaluate the effect of the patient′s body parameters on aortic and hepatic enhancement, we performed simple linear regression analyses between the change in CT numbers per gram of iodine (ΔHU/gI) at aorta and liver and each of the following: HT, TBW, BMI, LBW, BSA, and BV. Pearson and t test were used. Results:The mean ΔHU values of aorta and liver were significantly lower at males than that of at females ( P<0.05). They were higher at TBW<60 kg patients than at TBW≥60 kg patients( P<0.05), and the mean ΔHU values of the liver at BMI<25 kg/m 2 patients were significantly higher than at BMI≥25 kg/m 2 patients( P<0.05). The proportion of the ΔHU values at liver less than 50 HU was higher at males (18.3%, 11/60) than at females (7.5%, 4/53). The most obvious negative correlation coefficients were found between the ΔHU/gI of aorta and LBW at hepatic arterial phase ( r=-0.559, P<0.01), and between the ΔHU/gI of liver and BSA at portal venous phase ( r=-0.680, P<0.01). Conclusion:LBW or BSA could be the alternative body index to TBW for the calculation of personalized iodine dose protocol at aortic and liver enhanced CT scans.
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Objective:To explore the value of a radiomics nomogram based on T 1WI for prediction of the relapse of osteosarcoma after surgery within 1 year from multicenter data. Methods:The imaging and clinical data of 107 patients with pathologica1ly confirmed osteosarcoma who received neoadjuvant chemotherapy before surgery from 6 hospitals from January 2009 to October 2017 were retrospectively analyzed. A training cohort consisted of 75 patients from firstly enrolled 4 hospitals and an independent validation cohort of 32 patients from other 2 hospitals. Pretreatment T 1WI was used to extract radiomics features. Least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimension and then the radiomics signature was constructed to predict the relapse of osteosarcoma after surgery within 1 year in training cohort. Independent clinical risk factors were screened using one-way logistic regression, and then a radiomics nomogram incorporated the radiomics signature and MRI characteristics was developed by multivariate logistic regression. The predictive nomogram was evaluated using receiver operating characteristic (ROC) curve in the training cohort, and validated in the independent validation cohort. The calibration curve was used to evaluate the agreement between prediction and actual observation and the decision curve was used to demonstrate the clinical usefulness. Results:Based on T 1WI from multicenter institutions, the radiomics signature was built using 2 valuable selected features that were significantly associated with relapse within 1 year. Two selected features included 1 gray-level co-occurrence matrices (GLCM) feature (L_G_1.0_GLCM_homogeneity1, LASSO coefficient 3.122) and 1 gray-level run length matrix (GLRLM) feature (GLRLM_RP, LASSO coefficient -2.474). The prediction nomogram including radiomics signature and MRI characteristics (joint invasion and perivascular involvement) showed good discrimination with the area under the ROC curve of 0.884 and 0.821 in the training and validation cohorts, respectively. The calibration curve showed that the nomogram achieved good agreement between prediction and actual observation. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful when the threshold probability was greater than 21%. Conclusion:The radiomics nomogram based on T 1WI can be used as a non-invasive quantitative tool to predict relapse of osteosarcoma within 1 year before treatment, which provides support for clinical decision-making in osteosarcoma.
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Objective@#To evaluate the effect of height (HT), total body weight (TBW), body mass index (BMI), lean body weight (LBW), body surface area (BSA) and blood volume (BV) on aortic and liver contrast enhancement during upper abdominal contrast-enhanced CT scans.@*Methods@#One hundred and thirteen enrolled patients underwent upper abdominal multiphase contrast-enhanced CT scans. The enhancement (ΔHU) of aorta in hepatic arterial phase and liver parenchyma in portal venous phase were measured and calculated. The ΔHU values difference of aorta and liver parenchyma in subgroups between males and females, TBW<60 kg and TBW≥60 kg, BMI<25 kg/m2 and BMI≥25 kg/m2 were compared. To evaluate the effect of the patient′s body parameters on aortic and hepatic enhancement, we performed simple linear regression analyses between the change in CT numbers per gram of iodine (ΔHU/gI) at aorta and liver and each of the following: HT, TBW, BMI, LBW, BSA, and BV. Pearson and t test were used.@*Results@#The mean ΔHU values of aorta and liver were significantly lower at males than that of at females (P<0.05). They were higher at TBW<60 kg patients than at TBW≥60 kg patients(P<0.05), and the mean ΔHU values of the liver at BMI<25 kg/m2 patients were significantly higher than at BMI≥25 kg/m2 patients(P<0.05). The proportion of the ΔHU values at liver less than 50 HU was higher at males (18.3%, 11/60) than at females (7.5%, 4/53). The most obvious negative correlation coefficients were found between the ΔHU/gI of aorta and LBW at hepatic arterial phase (r=-0.559, P<0.01), and between the ΔHU/gI of liver and BSA at portal venous phase (r=-0.680, P<0.01).@*Conclusion@#LBW or BSA could be the alternative body index to TBW for the calculation of personalized iodine dose protocol at aortic and liver enhanced CT scans.
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Objective To explore the optimal body size index for the calculation of iodine contrast agent dose required for multiphase liver enhanced CT scans based on the total body weight (TBW),lean body weight (LBW)and body surface area (BSA).Methods Two hundred and twenty enrolled patients were randomly divided into three groups,TBW-group (n=75),LBW-group (n=72)and BSA-group (n=73),and administrated iodine doses were 600 mg I/TBW(kg),780 mg I/LBW(kg)and 22 g I/BSA(m2 ),respectively.All patients had taken upper abdominal plain scans and triple-phase enhanced CT scans.The enhanced values (ΔHU)of the aorta at hepatic arterial phase (HAP),the portal vein and liver parenchyma at portal venous phase (PVP)were compared.The correlation coefficients of adjusted maximal hepatic enhancement(aMHE)with TBW,LBW and BSA in three groups were evaluated,respectively.Results There were no statistical differences in the ΔHU values of the aorta at HAP and the portal vein and liver parenchyma at PVP in the three groups respectively.The smallest variances of the aorta at HAP,the portal vein and liver parenchyma at PVP were found in the LBW group. The aMHE showed mildly positive correlation with TBW (r=0.230)with a P value of 0.047,but it was consistent with LBW (r=0.158)and BSA (r=-0.1 54)with corresponding P values of 0.1 85 and 0.1 9 2 ,respectively.Conclusion Compared with TBW and BSA,iodine contrast agent dose calculated based on the patient’s LBW can improve the patient-to-patient uniformities on aorta,portal vein and liver enhancement during the liver multiphase enhanced CT scans.The LBW is the best body index for the calculation of iodine dose on liver enhanced CT scans.
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To develop and validate a fat-suppressed (T2 weighted-magnetic resonance imaging, T2W-MRI) based radiomics signature to preoperatively evaluate the histologic grade (grade I/II VS. grade III) of invasive breast cancer. Methods: A total of 202 patients with MRI examination and pathologically confirmed invasive breast cancer from June 2011 to February 2017 were retrospectively enrolled. After retrieving fat-suppressed T2W images and tumor segmentation, radiomics features were extracted and valuable features were selected to build a radiomic signature with the least absolute shrinkage and selection operator (LASSO) method. Mann-Whitney U test was used to explore the correlation between radiomics signature and histologic grade. Receiver operating characteristics (ROC) curve was applied to determine the discriminative performance of the radiomics signature [area under curre (AUC), sensitivity, specificity, and accuracy]. An independent validation dataset was used to confirm the discriminatory power of radiomics signature. Results: Eight radiomics features were selected to build a radiomics signature, which showed good performance for preoperatively evaluating histologic grade of invasive breast cancer, with an AUC of 0.802 (95% CI 0.729 to 0.875), sensitivity of 78.7%, specificity of 70.3% and accuracy of 73.7% in training dataset and AUC of 0.812 (95% CI 0.686 to 0.938), sensitivity of 80.0%, specificity of 73.3% and accuracy of 76.0% in the validation dataset. Conclusion: The fat-suppressed T2W-MRI based radiomics signature can be used to preoperatively evaluate the histologic grade of invasive breast cancer, which may assist clinical decision-maker.
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Humans , Breast Neoplasms , Diagnostic Imaging , Magnetic Resonance Imaging , Preoperative Care , ROC Curve , Retrospective StudiesABSTRACT
To build a CT-based radiomics predictive mode to evaluate the differentiation degree of the esophageal squamous carcinoma. Methods: A total of 160 patients with surgical pathology, complete clinical data and chest CT scanning before operation were retrospectively collected from January 2008 to August 2016. All patients were assigned randomly to a primary data set and an independent validation. Texture analysis was performed on CT images, while the carcinomas were performed by manual segmentation to extract the radiomics features. Radiomics features were extracted and 9 radiomics signatures were finally selected after dimension reduction. Radiomics features were extracted and established via Matlab. Multivariable logistic regression analysis was performed to build the predictive model. A 10-fold cross-validation was used for selecting parameters in the least absolute shrinkage and selection operator (LASSO) model by minimum criteria. The receiver operating characteristic (ROC) curves and areas under ROC curve (AUC) were used to compare the model performance in the primary validation and the independent validation for evaluating the differentiation degree of esophageal squamous carcinoma. Results: Radiomics signature showed great effect in discriminating primary data set and independent validation. The predictive model had a good performance in primary data set. The AUC was 0.791, the sensitivity was 81.6%, and specificity was 72.3%. In the independent validation, the AUC was 0.757, the sensitivity was 70.0%, and the specificity was 73.0%. Conclusion: The predictive model can be used for evaluating the differentiation degree of esophageal squamous carcinoma efficiently, which can be helpful to clinicians in diagnosis and choice of treatment for esophageal squamous carcinoma.
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Humans , Carcinoma, Squamous Cell , Esophageal Neoplasms , ROC Curve , Retrospective Studies , Tomography, X-Ray ComputedABSTRACT
To investigate the effects of different wavelet filters on correlation and diagnostic performance of radiomics features. Methods: A total of 143 colorectal cancer (CRC) patients (64 positive in lymph node metastasis and 79 negative) with contrast-enhanced CT examination were recruited. After labeling the tumor area by experienced radiologists, radiomics wavelets features based on 48 different wavelets were extracted using in-house software coded by Matlab. The correlation coefficients of the features with same names between different wavelets were calculated and got the distribution of high-correlation features between each wavelet. The least absolute shrinkage and selection operator (LASSO) was used to build signatures between lymph node metastasis and wavelet features data set based on different wavelets. The numbers of features in signatures and diagnostic performance were compared using Delong's test. Results: With the difference of wavelet order increased, the number of high-correlation features between two wavelets decreased. Some features were prone to high correlation between different wavelets. When building radiomics signature based on single wavelet, signatures built from 'rbio2.2', 'sym7' and 'db7' did well in predicting lymph node metastasis. The signature based on Daubechies wavelet feature set had the highest performance in predicting lymph node metastasis, while the signature from Biorthogonal wavelet features was worst. Improvement was significant in diagnostic performance after excluding the high-correlation features in the whole features set (P=0.004). Conclusion: In order to reduce the data redundancy of features, it is recommended to select wavelets with large differences in wavelet orders when calculating radiomics wavelet features. It is necessary to remove high correlation features for improving the diagnostic performance of radiomics signature.
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Humans , Colorectal Neoplasms , Lymphatic Metastasis , Retrospective StudiesABSTRACT
Purpose Lymph-vascular invasion (LVI) is a risk factor for the prognosis of colorectal cancer, and it is of great value to evaluate the status of lymphatic vessels before treatment. This study aims to predict colorectal cancer LVI preoperatively based on radiomics. Materials and Methods Radiomics features were extracted from preoperative CT images of colorectal cancer retrospectively collected and radiomics labels were constructed. The predictive efficacy of radiomics labels were assessed and internally verified. Joint predictive factors were established by combining clinical factors with independent predictive efficacy and radiomics labels, and their predictive efficacy was evaluated. Results Radiomics labels consisted of 58 radiomics features were correlated with LVI status (P<0.0001)with the former showing good discrimination ability[C-index 0.719,95% CI:0.715-0.723]and classification ability(sensitivity 0.726, specificity 0.628) with internal validation (C-index 0.720). Joint predictive factors containing radiomics labels and carcino-embryonic antigen further enhanced the predictability of radiomics labels (C-index 0.751, sensitivity 0.788, specificity 0.667). Conclusion The radiomics labels built in this study can provide individualized prediction of LVI status of patients with colorectal cancer before surgery. Joint predictive factors in combination with clinical risk factors further improved predictive efficacy.
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Objective To explore the relationship between radiomics signatures based on DWI and dynamic contrast-enhanced MRI (DCE-MRI) and molecular subtypes of breast cancer.Methods A retrospective analysis of 79 female breast cancer patients, with single mass, clear molecular subtypes and preoperative breast MRI scanning (obtaining DCE-MRI and ADC images), of Guangdong General Hospital from June 2015 to June 2016,were performed.Traditional quantitative parameters,including ADC value and initial enhancement rate(IER),were recorded.Texture analysis were performed on ADC map and DCE map, with manual segmentation and extraction of radiomic features,and Manual segmentation was performed on ADC map and DCE map, radiomics features were extracted and 10 radiomics signatures were finally selected after dimension reduction. Four molecular subtypes of breast cancer were classified by immunohistochemical detection of pathological specimens, including Luminal A, Luminal B, human epidermal growth factor receptor 2 (HER2) overexpression and triple negative (TN). Univariate logistic regression analysis was used for assessing the performance of ADC values, IER values and radiomics signatures to independently predict molecular subtypes groups.Multivariate logistic regression analysis was performed to establish predicting models, then receiver operating characteristic curves (ROC) were drawn and areas under ROC curve were calculated to compare the diagnostic performance of each model. The Hosmer-Lemeshow test was performed to test the goodness of model fitness. Results There were 29 cases of Luminal A, 39 cases of Luminal B, 5 cases of HER2 overexpression and 6 cases of TN breast cancer patients.Univariate logistic regression analysis was used to assess the ability of traditonal MRI parameters of ADC and IER values and ten of the radiomics siganitures in classifying molecular subtypes,results showed that the AUC values of ADC and IER values, were both less than 0.70 (range 0.516 to 0.605), which indicated valueless;at least one radiomic signature had AUC greater than 0.70 when identifying each molecular subtype, and AUC of DCE_L_G_2.5_autocorrelation achieved the highest value of 0.941 in identifying TN and non-TN subtypes.Multivariate logistic regression analysis were performed to obtain the best model, results showed that the AUCs for classifying Luminal A and non-Luminal A, Luminal B and non-Luminal B, TN and non-TN subtypes were 0.786 and 0.733 And 0.941, respectively. The Hosmer-Lemeshow test showed that the P values of all models were larger than 0.10 (0.156, 0.204 and 0.820,respectively),indicating that there was no significant difference between the predicted and observed values of each model established, these models were all fitted good. Conclusion The radiomics features based on ADC map and DCE map can help to identify the molecular subtypes of breast cancer,especially for the identification of TN type breast cancer.
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Accurate detection of breast cancer region is essential for treatment. X-ray computed tomography (CT) is an effective diagnostic method of breast cancer besides MRI and ultrasound. In this paper, a semi-automated breast cancer segmentation method was proposed to CT images. First, maximum region searching was used to find the rough boundary of the lesion. Then, a modified Histogram Equalization with Iterative-Filling was adopted to enhance the lesion and avoid the unbalanced intensity in the target region. Finally, a four-seeds Random Walk was used for accurate segmentation. The method was validated on a clinical dataset with 50 cases containing 630 slices in total. The experiments showed that the Dice Coefficient of our method was 88.6%, which was higher than that of Random Walk (76.9%) and Graph-Cut (79.8%).
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Breast Neoplasms , Algorithms , Humans , Magnetic Resonance Imaging , Tomography, X-Ray ComputedABSTRACT
As one the research hotspots in recent years,radiomics,allowing a high-throughput extraction of quantitative imaging features from medical imaging with computer software,has the promising potential in assisting individualized treatment for tumor patients.Though radiomics could quantify the heterogeneity of tumor and show potential values in clinical decision making to promote precision medicine for tumor patients,there are still lots of challenges in clinically translational research of radiomics.Thus,a joint effort should be made by researchers to promote the clinical application of radiomics to assist personalized management for patients.
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Objective To develop and validate a CT-based radiomics predictive model for preoperative predicting the stage of non-small cell lung cancer (NSCLC). Methods In this retrospective study, 657 patients with histologically confirmed was collected from October 2007 to December 2014.The primary dataset consisted of patients with histologically confirmed NSCLC from October 2007 to April 2012, while independent validation was conducted from May 2012 to December 2014.All the patients underwent non-enhanced and contrast-enhanced CT images scan with a standard protocol. The pathological stage (PTNM) of patients with NSCLC were determined by the intraoperative and postoperative pathological findings,and were divided into early stage(Ⅰ,Ⅱstage)and advanced stage(Ⅲ,Ⅳstage).A list of radiomics features were extracted using the software Matlab 2014a and the corresponding radiomics signature was constructed. Multivariable logistic regression analysis was performed with radiomics signature and clinical variables for developing the prediction model. The model performance was assessed with respect to discrimination using the area under the curve (AUC) of receiver operating characteristic(ROC) analysis. Results The discrimination performance of radiomics signature yielded a AUC of 0.715[95% confidence interval (CI):0.709 to 0.721] in the primary dataset and a AUC of 0.724(95% CI:0.717 to 0.731) in the validation dataset. On multivariable logistic regression, radiomics signature, tumor diameter, carcinoembryonic antigen (CEA) level, and cytokeratin 19 fragment (CYFRA21-1) level were showed independently associated with the stage ( Ⅰ,Ⅱ stage vs. Ⅲ, Ⅳ stage) of NSCLC. The prediction model showed good discrimination in both primary dataset (AUC=0.787, 95%CI:0.781 to 0.793;sensitivity=73.4%, specificity=72.2% ,positive predictive value=0.707,negative predictive value=0.868) and independent validation dataset (AUC=0.777, 95% CI:0.771 to 0.783,sensitivity=91.3% ,specificity=67.3% ,positive predictive value=0.607, negative predictive value=0.946). Conclusion The radiomics predictive model, which integrated with the radiomics signature and clinical characteristics can be used as a promising and applicable adjunct approach for preoperatively predicting the clinical stage (Ⅰ,Ⅱ stage vs. Ⅲ,Ⅳ stage) of patients with NSCLC.
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For non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, current staging methods do not accurately predict the risk of disease recurrence after tyrosine kinase inhibitors (TKI) therapy. Developing a noninvasive method to predict whether individual could benefit from TKI therapy has great clinical significance. In this research, a radiomics approach was proposed to determine whether the tumor heterogeneity of NSCLC, which was measured by the texture on computed tomography (CT), could make an independent prediction of progression-free survival (PFS). A primary dataset contained 80 patients (median PFS, 9.5 months) with positive EGFR mutations and a validation dataset contained 72 NSCLC (median PFS, 10.2 months) patients were used for prognosis trial. The experiment results indicated that the features: "Cluster Prominence of Gray Level Co-occurrence" (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: (1.33, 3.40), P = 0.010) and "Short Run High Gray Level Emphasis of Run Length" (HR: 2.43, 95%CI: (1.46, 4.05), P = 0.005) were significantly associated with PFS in the primary dataset, and these two texture features also make a consistent performance on the validation cohort. Our study further supported that the quantitative measurement of tumor heterogeneity can be associated with prognosis of NSCLC patients with EGFR mutation.
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Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Mutation , Carcinoma, Non-Small-Cell Lung/drug therapy , Disease Progression , Disease-Free Survival , ErbB Receptors , Humans , Lung Neoplasms/drug therapy , Neoplasm Recurrence, Local/drug therapy , Protein Kinase Inhibitors/therapeutic useABSTRACT
Objective To investigate the effect of image registration on quantitative measurements of free breathing diffusion kurtosis imaging (DKI) in normal human kidney. Methods Twenty healthy volunteers were prospectively enrolled to undergo DKI imaging with a 3.0 T MR scanner. Three b values (0, 500, and 1 000 s/mm2) were adopted,with image registration performed after image acquisition. Acquired images were fitted using the DKI fitting model to generate the DKI metric maps,which were performed on both the pre-registration images and post-registration images. Image quality of the derived metric maps (before and after image registration,respectively) was assessed by two radiologists. Measurements of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (D|), axial diffusivity (D⊥), mean kurtosis (MK), radial kurtosis (K|) and axial kurtosis (K⊥) were conducted. The inter-observer reproducibility of the image quality assessment was analyzed using intra-class correlation coefficient(ICC). Wilcoxon signed-rank test was used to evaluate the difference in the subjective scores of the metric maps between those obtained before registration and those after registration. While paired t test or Wilcoxon signed-rank test was performed to analyze the difference in the quantitative measurements of DKI metrics of the renal cortex and medulla between those obtained before registration and those after registration.Results For the inter-observer reproducibility, satisfactory ICCs were obtained for the quantitative metric measurements (pre-registration:0.784 to 0.821;post-registration:0.836 to 0.934). Significant difference was notice between subjective scores for the quality of metric maps (P<0.05 for each comparison). In both the renal cortex and medulla, significant difference was noticed between each metric value obtained with pre-registration images and that with post-registration images (P<0.05 for each comparison). Conclusion Image registration can not only offer higher quality DKI metric maps,but also has effect on the quantitative measurements of obtained metric maps.
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Objective To evaluate the effects of different reconstruction settings of knowledge-based iterative reconstruction technique (IMR) on noise reduction and image quality in hepatic contrast-enhanced CT(CECT) at different radiation dose levels.Methods Patients who underwent hepatic CECT because of their diseases were enrolled in this prospective study.According to random number table,patients were randomly assigned to two groups (standard dose group,SD-group,n=29; low dose group,LD-group,n=25).All patients underwent both non-enhanced CT and triphasic CECT scan including hepatic arterial phase (HAP),portal-venous phase (PVP) and delayed phase.PVP images of SD-group were acquired with tube voltage of 120 kVp and tube current-time products of 250 mAs,and 80 kVp and 500 mAs for LD-group.PVP images were reconstructed with both filtered back projection (FBP) and IMR techniques.Settings applied in IMR reconstructions consisted of body routine (BR) and body soft tissue (BST) with three levels (L1 to L3),and image series included:L1BR,L2BR,L3BR,L1BST,L2BST and L3BST.Subjective assessment of image quality including low contrast detectability (LCD),image distortion (ID) and diagnostic confidence (DC) as well as objective image quality including image noise,signal to noise ratio (SNR) and contrast to noise ratio (CNR) were compared between groups.Effective radiation dose was recorded.Objective image quality and subjective image quality were compared by using Friedman H test and ANOVA,respectively.In addition,Student t test was used to compare effective radiation doses between groups.Results In SD-group,IMR settings of L2BST,L3BST scored highest in LCD with median score of 4;L3BST and L2BST scored lowest in ID with median score of 3,while FBP and L1BR scored highest with median score of 4; L1BR,L2BR and L1BST scored highest in DC with median score of 4,while L3BST scored lowest with median score of 3.In LD-group,the distribution of all reconstruction settings scores in LCD and ID were similar to those in SD-group; however,L1BST scored highest with median score of 4,and FBP scored lowest with median score of 2 in DC.There were statistical differences in LCD,ID and DC among all the reconstruction settings for both groups (P<0.05).In terms of FBP technique,the image noise,SNR and CNR were (9.8±2.0)HU,13.3±3.3 and 6.0±1.9 in SD-group,and (16.2±4.1)HU,12.9±3.3 and 6.6± 2.3 in LD-group,respectively.In SD-group,image noise exhibited a trend of decrease from L1BR to L3BST [from (4.5±0.9) HU to (3.2±0.2)HU],while SNR,CNR trended to increase (SNR:from 21.4±1.4 to 24.6±4.6; CNR:from 9.4±2.2 to 10.9±2.9,respectively).The image noise,SNR and CNR in LD-group showed the same trends as SD-group [image noise:from (5.8±0.2)HU to (3.9±0.6)HU; SNR:from 26.3±4.5 to 33.1±6.9; CNR:from 13.6±4.0 to17.4±5.1,respectively].In both groups,there were statistically differences in image noise,SNR and CNR among all IMR settings and FBP (F values were 15.50 to 131.39,P<0.01).The effective radiation dose of the LD-group was significantly lower than that of the SD-group [(4.19±0.22) versus (7.32±0.58) mSy,t=15.27,P<0.01).Conclusions IMR can reduce image noise and improve image quality in hepatic CECT at both standard and low dose levels.L1BR,L2BR and L1BST are the most optimized reconstruction settings for the standard dose protocol,while L1BST performs best for the low dose protocol.
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Objective To investigate the effect of tube voltage and iodine concentration of contrast medium (CM) on abdominal dynamic enhanced CT image quality.Methods Six miniature pigs underwent repeated upper abdomen dynamic contrast-enhanced CT scans in 4 scanning protocols with different concentration of CM and tube voltage,namely,protocol 1,CM with iodine concentration of 270 milligrams iodine per milliliter (mg/ml) and 80 kV tube voltage;protocol 2,270 mg/ml and 120 kV;protocol 3,370 mg/ml and 80 kV and protocol 4,370 mg/ml and 120 kV.The same iodine dose (600 mg/ml) and iodine delivery rate (IDR) (920 mg/s) were used in all protocols.The CM with iodine concentration of 270 mg/ml were injected at a flow rate of 3.4 ml/s,and 370 mg/ml CM injected at 2.5 ml/s.Image reconstruction was performed with iterative reconstruction (iDose4) in protocol 1 and 3,filtered back projection (FBP) was used in protocol 2 and 4.A subjective scoring system for image quality,image noise and sharpness was conducted by 2 radiologists independently.The measured values (peak of enhanced CT values,image noise of aorta,inferior vena cava,portal vein,hepatic vein and liver parenchyma) as well as the calculated values [their time-to-peak,signal-to-noise (SNR) and contrast-to-noise (CNR) ratios] were compared between among 4 protocols.The CT volume dose index (CDTIvol) and dose length product (DLP) were recorded from the CT console after each scanning.Factorial designed ANOVA was used for comparison of enhanced CT values of vessels and liver parenchyma,noise,SNR and CNR.The Kruskal-Wallis test was used for comparison of values among the 4 protocols,including the time-to-peak enhancement of vessels and liver parenchyma,the subjective scores of image quality indices.Result There was no significant differences in subjective scores of the image quality,image noise and image sharpness (P>0.05).The scored were more than 3,and the images with 4 scanning protocols were all acceptable for diagnosis.There was no significant differences between protocol 1 and 3,protocol 2 and 4 in the peak enhancement CT values of aorta [(729±46) HU vs.(707±59)HU,(515±84)HU vs.(513±53)HU],inferior vena cava [(366±95)HU vs.(368±92)HU,(282±39)HU vs.(262 ± 67)HU],portal vein [(213± 18)HU vs.(201 ±29)HU,(180±21)HU vs.(176±27)HU],hepatic vein [(207±18)HU vs.(193±10)HU,(179±24)HU vs.(170±14)HU] and liver parenchyma [(128±7) HU vs.(127±4) HU,(135±5)HU vs.(135±6)HU] (P>0.05).But the CT values of vessels (aorta,inferior vena cava,portal vein and hepatic vein) in protocol 1 and 3 were significantly higher than those in protocol 2 and 4 (P<0.05),the CT values of liver parenchyma in protocol 1 and 3 were significantly lower than values in protocol 2 and 4 (P<0.05).The image noises of vessels were higher in protocol 1 and 3 than noises in other protocols (P<0.05),but there was no significant difference in liver parenchyma noise among protocols (P>0.05).No significant differences were observed on the peak times,SNR and CNR in aorta,inferior vena cava,portal vein,hepatic vein and liver parenchyma among 4 protocols (P>0.05).The CDTIvol and DLP were 199.67 mGy,1 597.4 mGy· cm respectively in protocol 1 and 3,585.12 mGy and 4 680.9 mGy· cm in protocol 2 and 4 (scanning with 120 kV).Conclusions CM with different iodinated concentration could achieve the same enhancement in the abdominal vessels and liver parenchyma by using the proper scan protocols,which have the same IDR and iodine dose per kilogram body weight.Higher vessel enhanced peak values were achieved when using the protocols with 80 kV tube voltage than 120 kV.By using a low dose protocol of 80 kV tube voltage with the iterative reconstruction algorithm,the quality of image can be warranted.
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PurposeTo prospectively investigate the effect of gadolinium contrast media on the quality of intravoxel incoherent motion (IVIM) MRI images and the quantification of corresponding parameters for abdominal solid organs, and to explore the appropriate time for abdominal IVIM scan.Materials and MethodsTwenty patients underwent the same abdominal IVIM scan before and after gadolinium contrast administration. The signal-to-noise ratio (SNR) of liver, spleen, pancreas and kidneys were measured on diffusion-weighted imaging (DWI) images at b=50 s/mm2, 300 s/mm2 and 800 s/mm2. SNR and contrast-to-noise ratio (CNR) of hepatocellular carcinoma (HCC) (n=16) were also calculated. Apparent diffusion coefficient (ADC) value and IVIM parameters including pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f) of liver, spleen, kidneys parenchyma and HCC were measured and compared. ResultsSNR of liver on post-contrast DWI at b=50 s/mm2 were significantly lower than that of pre-contrast (t=2.397,P0.05). As for IVIM parameters, only ADC and D values of kidneys demonstrated a significant reduction after contrast (ADC:t=3.569,P0.05). ConclusionAdministration of gadolinium contrast media may result in decrease of SNR of liver on low b value DWI and SNR of kidneys at any b value, but it increases CNR of hepatic lesions on medium b value DWI. Administration of gadolinium contrast media does not make a significant effect on SNR of spleen, pancreas and hepatic lesions. Furthermore, ADC and D values of kidneys decrease after administration of gadolinium contrast media. There is no significant influence of contrast medium on DWI and IVIM parameters of liver, hepatic lesions and spleen.
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Purpose To compare the image quality of contrast-enhanced hepatic CT using iterative reconstruction technique (IMR) at different radiation doses, and to explore the value of IMR in contrast-enhanced hepatic CT under different radiation doses. Materials and Methods Fifty-four cases undergoing contrast-enhanced hepatic CT were divided into two groups using different portal-venous phase protocols:29 cases in group A (120 kV, 250 mAs), 25 cases in group B (80 kV, 500 mAs). Portal venous phase CT images were reconstructed using IMR and filtered back projection to obtain 4 data sets:group A1 (120 kV, FBP), group A2 (120 kV, IMR), group B1 (80 kV, FBP) and group B2 (80 kV, IMR). Images were evaluated for noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) as well as low contrast detectability (LCD), image distortion (ID) and diagnostic confidence (DC). Effective radiation dose was recorded. Results The effective radiation dose in group B was 42.7%, lower than that in group A (t=15.27, PB2>A1>B1 (χ2=58.21, PB1>A2>B2 (χ2=12.94, PA1>B2>B1 (χ2=34.06, P0.05). Conclusion Compared with FBP, IMR technique can reduce image noise and improve image quality at low and high radiation doses, with better effect on low dose (80 kV, 500 mAs) hepatic CT.