Focal liver lesion diagnosis with deep learning and multistage CT imaging.

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.

Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging.

Introduction: The hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature. Methods: We retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC). Results: In the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively. Conclusion: HBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.

Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma.

PURPOSE: To appraise the ability of the computed tomography (CT) radiomics signature for prediction of early recurrence (ER) in patients with hepatocellular carcinoma (HCC). METHODS: A set of 325 HCC patients were enrolled in this retrospective study and the whole dataset was divided into 2 cohorts, including "training set" (225 patients) and "test set" (100 patients). All patients who underwent partial hepatectomy were followed up at least within 1 year. 656 Radiomics features were extracted from arterial-phase and portal venous-phase CT images. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Univariate analysis was used to identify clinical and radiomics significant features. Models (radiomics signature, clinical model, and combined model) were evaluated by area under the curve (AUC) of receiver operating characteristic curve. The models' performances for prediction of ER were assessed. RESULTS: The radiomics signature was built by 14 selected radiomics features and was significantly associated with ER (P < 0.001); the AUCs of the "train set" and the "test set" were 0.818 (95% CI 0.760-0.865) and 0.719 (95% CI 0.621-0.805), respectively. The tumor size, tumor capsule, and γ-glutamyl transferase (GGT) were significantly associated with ER in the clinical model (P < 0.05). The combined model showed incremental prognostic value, with the AUCs of "training dataset" and "test dataset" were 0.846 (95% CI 0.792-0.890) and 0.737 (95% CI 0.640-0.820), respectively. The radiomics signature, tumor size, and the level of GGT were independent predictors of ER (P < 0.05). CONCLUSIONS: The CT radiomics signature can be conveniently used to predict the ER in patient with HCC. The combined model performed better for prediction of ER than radiomics signature or clinical model.

Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature.

PURPOSE: This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade. METHODS: Data from 170 patients confirmed to have HCC by surgical pathology were divided into a training group (n = 125) and a test group (n = 45). The radiomics features of tumours based on both T1-weighted imaging (WI) and T2WI were extracted by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The predicted values of pathological HCC grades using radiomics signatures, clinical factors (including age, sex, tumour size, alpha fetoprotein (AFP) level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) and the combined models were assessed. RESULTS: Radiomics signatures could successfully categorise high-grade and low-grade HCC cases (p < 0.05) in both the training and test datasets. Regarding the performances of clinical factors, radiomics signatures and the combined clinical and radiomics signature (from the combined T1WI and T2WI images) models for HCC grading prediction, the areas under the curve (AUCs) were 0.600, 0.742 and 0.800 in the test datasets, respectively. Both the AFP level and radiomics signature were independent predictors of HCC grade (p < 0.05). CONCLUSIONS: Radiomics signatures may be important for discriminating high-grade and low-grade HCC cases. The combination of the radiomics signatures with clinical factors may be helpful for the preoperative prediction of HCC grade. KEY POINTS: • The radiomics signature based on non-contrast-enhanced MR images was significantly associated with the pathological grade of HCC. • The radiomics signatures based on T1WI or T2WI images performed similarly at predicting the pathological grade of HCC. • Combining the radiomics signature and clinical factors (including age, sex, tumour size, AFP level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) may be helpful for the preoperative prediction of HCC grade.

Esophageal carcinoma: Intravoxel incoherent motion diffusion-weighted MRI parameters and histopathological correlations.

BACKGROUND: The pathological grade of esophageal carcinoma is highly determinant of patient prognosis, but it still cannot be adequately evaluated preoperatively. Compared with conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) diffusion-weighted MRI can separate true molecular diffusion and perfusion in tissues and has been shown to be useful in characterizing malignant tumors. There is no report that compared IVIM and conventional DWI in grading esophageal carcinoma. PURPOSE: To prospectively determine the diagnostic performance of conventional DWI and IVIM models in differentiating the pathological differentiated grade of esophageal carcinoma. STUDY TYPE: Prospective. POPULATION: A cohort comprising 81 patients with newly diagnosed esophageal squamous cell carcinoma (ESCC) between December 2015 and August 2017 were evaluated. FIELD STRENGTH/SEQUENCE: 3.0T, axial echo-planer imaging, fast spin echo (FSE) sequence, IVIM sequence (b = 0, 20, 50, 80, 100, 150, 200, 400, 600, 800, 1000, 1200). ASSESSMENT: Apparent diffusion coefficient (ADC), true ADC (ADCslow ), pseudo ADC (ADCfast ), and perfusion fraction (f) of each tumor were calculated by two independent radiologists. Histopathologic grade was used as the reference standard. STATISTICAL TESTS: Games-Howell test; diagnostic accuracy; Spearman correlation; intraclass correlation coefficient; and Bland-Altman analysis. Receiver operating characteristics (ROC) curves. RESULTS: ADCslow demonstrated the highest area under curve (AUC) with a value of 0.830 (95% confidence interval [CI]: 0.730-0.904) and 0.816 (95% CI: 0.714-0.893) by two radiologists, followed by ADC with a value of 0.754 (95% CI: 0.646-0.843) and 0.761 (95% CI: 0.653-0.848). Good correlation was obtained between the histologic grade and ADCslow (r(R1) = 0.748, r(R2) = 0.720) and ADC (r(R1) = 0.576, r(R2) = 0.571). DATA CONCLUSION: ADCslow and ADC had a significantly higher performance than the ADCfast and f, and ADCslow had a significantly higher performance than the ADC. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:253-261.

Evaluation of liver fibrosis with a monoexponential model of intravoxel incoherent motion magnetic resonance imaging.

To evaluate hepatic fibrosis with a monoexponential model of intravoxel incoherent motion magnetic resonance imaging, and assess the potential application value of intravoxel incoherent motion (IVIM) in diffusion-weighted imaging (IVIM-DWI) in determining staging of liver fibrosis. 28 patients with hepatic fibrosis and 25 volunteers with healthy livers had IVIM examination and conventional MRI. All standard apparent diffusion coefficient (ADC) values of IVIM raw data were post-processed off-line after completion of data collection. All regions of interest (ROIs) were manually positioned by two experienced radiologists. All values of the different fibrosis stages in the study group were compared using independent sample t tests. Using ROC analysis, both AUC values of ADCtotal and ADC0-400-600-800 from study and control group were found to be between 0.8 and 1 for staging fibrosis. The mean ADCtotal and ADC0-400-600-800 values of the liver in the study group were significantly lower than the values in the control group (P < 0.05). Spearman rho correlation analysis was used to determine the relationship among fibrosis stages and the ADCtotal and ADC0-400-600-800 in the study group. As the stage of the fibrosis increased, the values decreased. Significant differences between the two subgroups of liver fibrosis stages were found (P < 0.05). The monoexponential model of IVIM-DWI adopted multiple b values for quantitative analysis of the water molecules diffused in the tissue. It could be used as a noninvasive and valuable method for assessment of liver fibrosis.

Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for predicting histological grade of hepatocellular carcinoma: Comparison with conventional diffusion-weighted imaging.

AIM: To compare intravoxel incoherent motion (IVIM)-derived parameters with conventional diffusion-weighted imaging (DWI) parameters in predicting the histological grade of hepatocellular carcinoma (HCC) and to evaluate the correlation between the parameters and the histological grades. METHODS: A retrospective study was performed. Sixty-two patients with surgically confirmed HCCs underwent diffusion-weighted magnetic resonance imaging with twelve b values (10-1200 s/mm2). The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were calculated by two radiologists. The IVIM and conventional DWI parameters were compared among the different grades by using analysis of variance (ANOVA) and the Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic efficiency of distinguishing between low-grade (grade 1, G1) and high-grade (grades 2 and 3, G2 and G3) HCC. The correlation between the parameters and the histological grades was assessed by using the Spearman correlation test. Bland-Altman analysis was used to evaluate the reproducibility of the two radiologists' measurements. RESULTS: The differences in the ADC and D values among the groups with G1, G2, and G3 histological grades of HCCs were statistically significant (P < 0.001). The D* and f values had no significant differences among the different histological grades of HCC (P > 0.05). The ROC analyses demonstrated that the D and ADC values had better diagnostic performance in differentiating the low-grade HCC from the high-grade HCC, with areas under the curve (AUCs) of 0.909 and 0.843, respectively, measured by radiologist 1 and of 0.911 and 0.852, respectively, measured by radiologist 2. The following significant correlations were obtained between the ADC, D, and D* values and the histological grades: r = -0.619 (P < 0.001), r = -0.628 (P < 0.001), and r = -0.299 (P = 0.018), respectively, as measured by radiologist 1; r = -0.622 (P < 0.001), r = -0.633 (P < 0.001), and r = -0.303 (P = 0.017), respectively, as measured by radiologist 2. The intra-class correlation coefficient (ICC) values between the two observers were 0.996 for ADC, 0.997 for D, 0.996 for D*, and 0.992 for f values, which indicated excellent inter-observer agreement in the measurements between the two observers. CONCLUSION: The IVIM-derived D and ADC values show better diagnostic performance in differentiating high-grade HCC from low-grade HCC, and there is a moderate to good correlation between the ADC and D values and the histological grades.

Simultaneous segmentation and bias field estimation using local fitted images.

Level set methods often suffer from boundary leakage and inadequate segmentation when used to segment images with inhomogeneous intensities. To handle this issue, a novel region-based level set method was developed, in which two different local fitted images are used to construct a hybrid region intensity fitting energy functional. This novel method enables simultaneous segmentation of the regions of interest and estimation of the bias fields from inhomogeneous images. Our experiments on both synthetic images and a publicly available dataset demonstrate the feasibility and reliability of the proposed method.

Esophageal carcinoma: Ex vivo evaluation by high-spatial-resolution T2 -mapping MRI compared with histopathological findings at 3.0T.

PURPOSE: To prospectively determine the feasibility of T2 -mapping magnetic resonance imaging (MRI) to quantitatively describe the signal characteristics of the normal esophageal wall and assess the depth of esophageal wall invasion by carcinoma at 3.0T. MATERIALS AND METHODS: Thirty-two patient specimens, each having foci of carcinoma, were studied using 3.0T MR. Freehand regions of interest were placed to measure the T2 value of the normal esophageal layers and were compared with the regions of carcinoma. Three independent readers reviewed the MR images to evaluate the depth of carcinoma invasion; when the three radiologists could not fully agree with each other, the final stage was determined by consensus. The Games-Howell test was used to compare the difference between the normal esophageal layers and carcinoma. Spearman correlation coefficient analysis was used to compare the stage at MRI with that at histopathological analysis. The interobserver agreement was compared with Cohen's kappa. The sensitivity, specificity, and accuracy for detecting carcinoma invasion were calculated. RESULTS: The T2 values between the carcinoma and normal esophageal layers were different (all P < 0.01), except for the inner circular muscle (P = 0.511). The T2 value of each layer of the normal esophageal wall was also different from that of the adjacent layer (all P < 0.01). In 29 of 32 lesions, the depth of the esophageal wall invasion determined by MR was consistent with the histopathological stage (r = 0.969, P < 0.001). The sensitivity, specificity, and accuracy were 80%, 96.3%, and 93.8%, respectively, for invasion into the mucosa; 77.8%, 95.7%, and 90.6%, respectively, for invasion into submucosa; 100%, 95.8%, and 96.9%, respectively, for invasion into muscularis propria; and 100%, 100%, and 100%, respectively, for invasion into the adventitia. CONCLUSION: T2 -mapping MR images obtained using a 3.0T MR scanner can be used to depict the precise histopathological layers of the esophageal wall clearly and provide excellent diagnostic accuracy for assessing esophageal carcinoma invasion. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1609-1616.

Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging of Cervical Cancer With Different b-Values.

OBJECTIVE: The aims of this study were to evaluate the dependence of diffusion parameters on the b values adopted for intravoxel incoherent motion diffusion-weighted magnetic resonance imaging and to investigate the application value of multiple diffusion parameters obtained from monoexponential and biexponential models in subjects with a normal cervix and in cervical cancer patients. METHODS: A total of 120 female patients with cervical cancer and 21 female control subjects with a normal cervix underwent diffusion-weighted magnetic resonance imaging with 13 b values (0-2000 s/mm) at 3 T. The standard apparent diffusion coefficient (Dst), diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were calculated by fitting with monoexponential and biexponential models at 2 different ranges of b values: 0 to 1000 and 0 to 2000 s/mm. A univariate analysis was performed to identify factors that could distinguish cervical carcinoma from normal cervical tissue. Parameters that correlated with the pathological grade and stage of cervical cancer were also evaluated. Receiver operating characteristic curves were used to evaluate the diagnostic efficiency of every parameter. RESULTS: All the tested parameters, except the D* of the 2 different ranges of b value groups, significantly differed between the patients with cervical carcinoma and control subjects (P < 0.01). D2000, Dst2000, and D1000 showed comparable diagnostic value, with an area under the curve of 0.923, 0.909, and 0.907, respectively. Dst2000, D2000, Dst1000, and D1000 differed significantly among the 3 degrees of cervical stromal infiltration depth (P < 0.05). CONCLUSIONS: D2000 and Dst2000 tended to outperform D1000 in terms of diagnostic efficiency, but there was no significant difference in their ability to differentiate cervical carcinoma from normal cervix. Cervical cancers with lower Dst and D values tended to have greater infiltration depth.

Oesophageal carcinoma: comparison of ex vivo high-resolution 3.0 T MR imaging with histopathological findings.

High-resolution magnetic resonance (MR) images clearly depict the normal oesophageal wall as consisting of eight layers, which correlates well with histopathological findings. In 56 (91.8%) of 61 lesions, the depth of oesophageal wall invasion determined through MR imaging was consistent with histopathological staging (r = 0.975, P < 0.001). The sensitivity, specificity and accuracy for the mucosa were 71.4%, 98.1%, and 95.1%, respectively, and the corresponding values for the submucosa were 82.4%, 95.5%, and 91.8%; for the muscularis propria, the sensitivity, specificity and accuracy were 100%, 95.7%, and 96.7%, respectively, and for the adventitia, these values were 100%, 100%, and 100%. The Cohen k values for interobserver agreement were excellent: K = 0.839, P < 0.001 (observer 1 vs. observer 2); K = 0.908, P < 0.001 (observer 1 vs. observer 3); and K = 0.885, P < 0.01 (observer 2 vs. observer 3). High-resolution ex vivo MR images obtained with a 3.0 T scanner can be used to precisely evaluate oesophageal carcinoma invasion and provide good diagnostic sensitivity, specificity and accuracy.

Association of SPP1 promoter variants with hip osteoarthritis susceptibility in Chinese population.

Three genetic variants in the promoter of SPP1 (secreted phosphoprotein 1) gene have been reported to affect transcriptional activity of SPP1, thus conferring an increased risk for some diseases. To testify if these variants are associated with risk of hip osteoarthritis (OA) as well, we performed a case-control study including 389 hip OA patients and 315 healthy controls. Genotypes of SPP1 were determined by DNA sequencing, and differential expressions of SPP1 in relation with genotypes were evaluated by RT-PCR and ELISA. The results showed that rs17524488 (delG>insG) increased the risk of hip OA, with the adjusted OR 1.48 (95% CI 1.18-1.85, P<0.01) for risk allele insG, 1.90 (95% CI 1.35-2.66, P<0.01) for delG/insG and 2.04 (95% CI 1.20-3.49, P<0.01) for insG/insG respectively. However, as for rs11730582 (T>C), the adjusted ORs were 1.18 (95% CI 0.94-1.49, P=0.148) for allele C, 1.26 (95% CI 0.90-1.75, P=0.158) for TC, and 1.31 (95% CI 0.77-2.24, P=0.293) for CC, indicating no association of rs11730582 with hip OA risk. The variant rs28357094 was not observed in the tested subjects. Furthermore, the delG/insG and insG/insG genotypes of rs17524488 both correlated with higher levels of SPP1 expression in articular cartilage (P<0.01 for all comparisons) as well as in in synovial fluid (P<0.01 for all comparisons) compared with delG/delG, while rs11730582 had no effect on the SPP1 expression (P>0.05 for all comparisons). These results collectively indicate that the genetic variant rs17524488 in SPP1 promoter confers high risk for hip OA in a Chinese population, possibly through enhancing SPP1 expression.

Assessment of esophageal carcinoma undergoing concurrent chemoradiotherapy with quantitative dynamic contrast-enhanced magnetic resonance imaging.

The aim of the present study was to investigate whether quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict an early response in primary esophageal carcinoma patients undergoing concurrent chemoradiotherapy. A total of 25 patients with who were pathologically confirmed stage II-III esophageal carcinoma underwent quantitative DCE-MRI prior to chemoradiotherapy, and at 3 weeks post-treatment, the quantitative parameters [Ktrans (volume transfer constant; the rate at which contrast agent distributes from the plasma to the EES), Kep (rate contrast; the rate at which the contrast agent that has diffused to the EES returns to the plasma) and Ve (the contrast agent percentage in the space of the extracellular fluid)] were analyzed respectively. The 25 cases were categorized as a complete response (CR) or a partial response (PR). An independent samples Mann-Whitney U test was used to compare the quantitative parameters between CR and PR. A receiver operating characteristic curve (ROC) was used to determine the best predictor. In total, 17 patients were in the CR group and 8 patients were in the PR group. Pretreatment Ktrans, Kep and Ve values were 0.54±0.17/min, 1.12±0.46/min and 0.37±0.14, respectively, in the CR group, and 0.40±0.21/min, 1.07±0.37/min and 0.40±0.22, respectively, in the PR group. There was a significant difference between the two groups for Ktrans, but there were no significant differences between the two groups for Kep and Ve. The Ktrans, Kep and Ve values at 3 weeks post-treatment were 0.33±0.11/min, 0.86±0.31/min and 0.66±0.05, respectively, in the CR group, and 0.62±0.22/min, 1.19±0.39/min and 0.45±0.19, respectively, in the PR group. The corresponding U values were -3.319, -1.719 and -2.628, respectively, and the P-values were 0.006, 0.119 and 0.021, respectively. The areas under the ROC curve of Ktrans prior to chemoradiotherapy, and of Ktrans and Kep at 3 weeks post-treatment were 0.648, 0.741 and 0.796, respectively. In conclusion, DCE-MRI can predict an early response in primary esophageal carcinoma following 3 weeks of concurrent chemoradiotherapy. Ktrans prior to chemoradiotherapy, and Ktrans and Kep at 3 weeks post-treatment are sensitive prediction parameters.

Initial study of biexponential model of intravoxel incoherent motion magnetic resonance imaging in evaluation of the liver fibrosis.

BACKGROUND: The diagnosis of liver fibrosis is a difficult task at any time using conventional clinical imaging. Intravoxel incoherent motion (IVIM) can be used to investigate both diffusion and perfusion changes in tissues. This study was designed to determine the value of IVIM in the diagnosis and staging of liver fibrosis. METHODS: IVIM examinations were performed on a GE 3.0T MR scanner in 25 patients with liver fibrosis and 25 healthy volunteers as the control group. Patients with liver fibrosis diagnosis were confirmed by pathology and staged on a scale of F0-4. The standard ADC values and the values of a biexponential model (slow ADC (Dslow), fast ADC (Dfast) and fraction of fast ADC (FF)) were measured in three liver regions per person. The mean standard ADC values, Dslow values, Dfast values and FF values from the study group were compared among the right posterior hepatic lobe, right anterior hepatic lobe and medial segment of the left lobe. Receiver Operating Characteristic (ROC) curves and independent-samples t-tests were used to calculate the mean standard ADC values, Dslow values, Dfast values and FF values from the study group and the control group. Spearman rho correlation analysis was used for the stage of liver fibrosis. The liver fibrosis stages between the groups F0-1 and F2-4, the groups F0-2 and F3-4 were compared. RESULTS: Among the liver fibrosis, there was no significant difference in the mean standard ADC values, Dslow values, Dfast values, and FF values obtained from the right posterior hepatic lobe, right anterior hepatic lobe and medial segment of the left lobe. Using ROC analysis, the Area Under the Curve (AUC) values of standard ADC, Dslow, Dfast, FF were all between 0.7 to 0.9. The mean standard ADC values, Dslow values, Dfast values and FF values of the liver in the study group were significantly lower than the values in the control group (P < 0.05). As the stage of the fibrosis increased, the values decreased by Spearman rho correlation analysis. The mean values (standard ADC, Dslow, Dfast, and FF) of liver fibrosis stages between the groups F0-1 and F2-4, the groups F0-2 and F3-4 showed significant differences (P < 0.05). CONCLUSIONS: IVIM can reflect the conditions of perfusion and diffusion in liver fibrosis and thus distinguish between normal liver and liver fibrosis. The IVIM technique may serve as a valuable tool for detecting and characterizing liver fibrosis, and monitoring its progression in a noninvasive manner.

X-ray dose reduction in abdominal computed tomography using advanced iterative reconstruction algorithms.

OBJECTIVE: This work aims to explore the effects of adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) algorithms in reducing computed tomography (CT) radiation dosages in abdominal imaging. METHODS: CT scans on a standard male phantom were performed at different tube currents. Images at the different tube currents were reconstructed with the filtered back-projection (FBP), 50% ASiR and MBIR algorithms and compared. The CT value, image noise and contrast-to-noise ratios (CNRs) of the reconstructed abdominal images were measured. Volumetric CT dose indexes (CTDIvol) were recorded. RESULTS: At different tube currents, 50% ASiR and MBIR significantly reduced image noise and increased the CNR when compared with FBP. The minimal tube current values required by FBP, 50% ASiR, and MBIR to achieve acceptable image quality using this phantom were 200, 140, and 80 mA, respectively. At the identical image quality, 50% ASiR and MBIR reduced the radiation dose by 35.9% and 59.9% respectively when compared with FBP. CONCLUSIONS: Advanced iterative reconstruction techniques are able to reduce image noise and increase image CNRs. Compared with FBP, 50% ASiR and MBIR reduced radiation doses by 35.9% and 59.9%, respectively.

Illustration of the obstacles in computerized lung segmentation using examples.

PURPOSE: Automated lung volume segmentation is often a preprocessing step in quantitative lung computed tomography (CT) image analysis. The objective of this study is to identify the obstacles in computerized lung volume segmentation and illustrate those explicitly using real examples. Awareness of these "difficult" cases may be helpful for the development of a robust and consistent lung segmentation algorithm. METHODS: We collected a large diverse dataset consisting of 2768 chest CT examinations acquired on 2292 subjects from various sources. These examinations cover a wide range of diseases, including lung cancer, chronic obstructive pulmonary disease, human immunodeficiency virus, pulmonary embolism, pneumonia, asthma, and interstitial lung disease (ILD). The CT acquisition protocols, including dose, scanners, and reconstruction kernels, vary significantly. After the application of a "neutral" thresholding-based approach to the collected CT examinations in a batch manner, the failed cases were subjectively identified and classified into different subgroups. RESULTS: Totally, 121 failed examinations are identified, corresponding to a failure ratio of 4.4%. These failed cases are summarized as 11 different subgroups, which is further classified into 3 broad categories: (1) failure caused by diseases, (2) failure caused by anatomy variability, and (3) failure caused by external factors. The failure percentages in these categories are 62.0%, 32.2%, and 5.8%, respectively. CONCLUSIONS: The presence of specific lung diseases (e.g., pulmonary nodules, ILD, and pneumonia) is the primary issue in computerized lung segmentation. The segmentation failures caused by external factors and anatomy variety are relatively low but unavoidable in practice. It is desirable to develop robust schemes to handle these issues in a single pass when a large number of CT examinations need to be analyzed.

CT based computerized identification and analysis of human airways: a review.

As one of the most prevalent chronic disorders, airway disease is a major cause of morbidity and mortality worldwide. In order to understand its underlying mechanisms and to enable assessment of therapeutic efficacy of a variety of possible interventions, noninvasive investigation of the airways in a large number of subjects is of great research interest. Due to its high resolution in temporal and spatial domains, computed tomography (CT) has been widely used in clinical practices for studying the normal and abnormal manifestations of lung diseases, albeit there is a need to clearly demonstrate the benefits in light of the cost and radiation dose associated with CT examinations performed for the purpose of airway analysis. Whereas a single CT examination consists of a large number of images, manually identifying airway morphological characteristics and computing features to enable thorough investigations of airway and other lung diseases is very time-consuming and susceptible to errors. Hence, automated and semiautomated computerized analysis of human airways is becoming an important research area in medical imaging. A number of computerized techniques have been developed to date for the analysis of lung airways. In this review, we present a summary of the primary methods developed for computerized analysis of human airways, including airway segmentation, airway labeling, and airway morphometry, as well as a number of computer-aided clinical applications, such as virtual bronchoscopy. Both successes and underlying limitations of these approaches are discussed, while highlighting areas that may require additional work.

Human patellar cartilage: echo planar diffusion-weighted MR imaging findings at 3.0 T.

PURPOSE: To explore the findings of diffusion-weighted imaging (DWI) and the diffusion characteristics on patellar cartilage in healthy adults. MATERIALS AND METHODS: Thirty healthy subjects were studied on SIEMENS 3.0-T Trio Tim magnetic resonance (MR) scanner. The apparent diffusion coefficient (ADC) values of the patellar cartilage were measured in different areas. RESULTS: The patellar cartilage demonstrated homogeneously high signal intensity on the images of DWI and maps of ADC. The ADC values displayed a spatial dependency, approximately (1.17 ± 0.31)×10(-3) mm(2)/s of the entire cartilage. CONCLUSIONS: Diffusion-weighted MR imaging may display articular cartilage structure. There is a consistent pattern of spatial variation of the ADC values.

Non-enhanced MR angiography of renal artery using inflow-sensitive inversion recovery pulse sequence: a prospective comparison with enhanced CT angiography.

PURPOSE: To prospectively evaluate the diagnostic value of non-enhanced inflow-sensitive inversion recovery (IFIR) MR angiography for the detection of renal artery stenosis (RAS), with enhanced CT angiography performed as the reference standard. MATERIALS AND METHODS: Sixty consecutive patients suspected of RAS underwent both of IFIR MR and enhanced CT angiography. Subjective image quality, renal artery depiction and renal artery grading were all evaluated on artery-by-artery basis. Spearman rank correlation analysis was used to assess agreement between the two techniques. The diagnostic sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for RAS detection at IFIR MR angiography were calculated. RESULTS: One hundred twenty-six main renal arteries were visualized on enhanced CT and non-enhanced MR angiographic images, respectively. The Spearman rank correlation was 0.773 (P<.001) for renal artery depiction, 0.998 (P<.001) for renal arteries grading and 0.833 (P<.001) for RAS detection between the two modalities. The sensitivity, specificity, PPV and NPV of IFIR MR angiography for RAS detection demonstrated 100%, 99.0%, 92.0% and 100%, respectively. CONCLUSION: Non-enhanced IFIR MR angiography had high sensitivity, specificity, PPV and NPV for RAS detection. It could be the first choice of renal artery imaging methods to avoid ionizing irradiation and renal toxicity from contrast media.

Epiphyseal and physeal injury: comparison of conventional radiography and magnetic resonance imaging.

PURPOSE: To compare conventional radiography and magnetic resonance imaging (MRI) in the assessment of epiphyseal and physeal injury. MATERIALS AND METHODS: Seventeen cases with epiphyseal and physeal injury were examined with conventional radiography and MRI. Two blinded experienced radiologists separately evaluated the results retrospectively. RESULTS: Two Salter-Harris II fractures showed displacement of epiphysis and metaphyseal fragment, as well as T1 hypointensity and T2 hyperintensity in the physis on MRI. Three Salter-Harris IV fractures showed a fracture line in the epiphysis and metaphysis on two modalities, as well as linear T2 hyperintensity in the physis. All five slipped epiphysis showed signal intensity abnormality in the physis on MRI aside from the physeal widening and epiphyseal displacement seen on radiographs. Seven patients with bony bridge formation showed premature closure of the physis, shortening, and remodeling of the end of bones involved. CONCLUSIONS: Conventional radiography and MRI are both useful in the diagnosis of epiphyseal and physeal injury. MRI can provide more detailed information, which could direct treatment planning and prognosis predicting.