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(a) A DCE-MRI image showed hepatic carcinoma on the right lobe (arrow); (b) K trans map showed higher permeability values of the lesion (arrow) compared with surrounding parenchyma, indicating increased vascularity; (c) V p map was also shown (arrow).
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Objective: 3D dynamic contrast enhanced (DCE) MRI with parallel imaging, a novel method to understand tumor vascularization in vivo, has been applied to liver in this work. Pharmacokinetics analysis could be performed with the help of motion correction by non-rigid registration using the first pass data. The purpose of this study was to assess the...
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... results revealed 51 lesions in 48 patients, which included 15 benign (hepatic hemangioma) and 36 ma- lignant (14 liver metastasis and 22 liver carcinoma). Their corresponding pharmacokinetic values were shown in Table 1 and the value distribution was shown in Fig. 3. Color maps were created that demonstrated the values of K trans and V p for each tumor type (HCC in Fig. 4, hemagioma in Fig. 5 and metastasis in Fig. 6). ANOVA test results showed that there was statistically significance of K trans among liver carcinoma, liver metastasis and hepatic hemangioma (F ¼ 21.3, p ¼ 2.46e-007 < 0.01). The student t-test was performed and found that there was statistical significance between benign and malignant tumor (t ¼ 6.586, p < 0.01), while there was no statistical difference of K trans between liver carcinoma and liver metastasis (t ¼ 0.0388, p ¼ 0.969). For V p value, sta- tistical results showed that there was no significant difference between any two kinds among these three types of tumors (F ¼ 0.24, p ¼ 0.788 > 0.05), ( p value between benign and malignant tumor is 0.488, between hepatic carcinoma and liver metastasis is 0.972). Tables 1 and 2 showed the complete statistical results of different tumor types using ANOVA and student ...
Citations
... The findings concerning the quantitative evaluation of K t in liver metastases reported in this study are in good agreement with other DCE-MR motion correction analyses carried out using DCE-VIBE (Zheng et al 2015). Zheng et al, report average K t values of 0.25 ± 0.08 min −1 , after the application of motion correction through non-rigid registration. ...
Simultaneous PET-MR imaging is a hybrid technique in oncological hepatic imaging combining soft-tissue and functional contrast of dynamic contrast enhanced MR (DCE-MR) with metabolic information from PET. In this context, respiratory motion represents a major challenge by introducing blurring, artifacts and misregistration in the liver. In this work, we propose a free-breathing 3D non-rigid respiratory motion correction framework for simultaneously acquired DCE-MR and PET data, which makes use of higher spatial resolution MR data to derive motion information used directly during image reconstruction to minimize image blurring and motion artifacts. The main aim was to increase contrast of hepatic metastases to improve their detection and characterization. DCE-MR data were acquired at 3T through a Golden Radial Phase Encoding (GRPE) scheme, enabling derivation of motion fields (MF). These were used in the motion compensated image reconstruction of DCE-MR time-series (48 time-points, 6 s temporal resolution, 1.5 mm isotropic spatial resolution) and 3D PET activity map, which was subsequently interpolated to the DCE-MR resolution. The Extended Tofts Model was fitted to DCE-MR data, obtaining functional parametric maps related to perfusion such as the endothelial permeability (Kt). Fifty-seven hepatic metastases were identified and analyzed. Quantitative evaluations of motion correction in PET images demonstrated average percentage increases of 16% ± 5% (mean ± SD) in Contrast (C), 18% ± 6% in SUVmean and 14% ± 2% in SUVmax, while DCE-MR and Kt scored contrast-to-noise-ratio (CNR) increases of 64% ± 3% and 90% ± 6%, respectively. Motion-corrected data visually showed improved image contrast of hepatic metastases and effectively reduced blurring and motion artefacts. Scatter plots of SUVmean vs Kt suggested that the proposed framework improved differentiation of Kt measurements. The presented motion correction framework for simultaneously acquired PET-DCE-MR data provides accurately aligned images with increased contrast of hepatic lesions allowing for improved detection and characterization.
... In recent years, pharmacokinetic parameters have been adopted to explore the penetration and perfusion changes inside the tumors (27)(28)(29). In the present study, the semi-quantitative parameters (e.g. ...
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has become a powerful tool for the diagnosis of breast cancer in the clinical setting due to its high sensitivity and specificity. Pharmacokinetic parameters, including Ktrans and area under the curve (AUC), and texture features derived from DCE-MRI have been used to specify the characteristics inside tumors. In the present study, 56 patients (average age 45.3±11.1; range 25-69 years) with histopathologically proved breast tumors were analyzed using the pharmacokinetic parameters and texture features. Malignant tumors displayed higher Ktrans and AUC values than the benign, Ktrans exhibited a significantly difference between the malignant and benign tumors (P=0.001) compared with the AUC values (P=0.029); texture features from DCE-MRI images and pharmacokinetic parameter maps also showed a good diagnostic ability. Alongside the routine method, principal components analysis (PCA) and Fisher discriminant analysis (FDA) were employed on these texture features to differentiate the breast lesions automatically. The Factor-1 scores of PCA were used to divide the patients into two groups, and the diagnosing accuracies of the FDA method on the texture features from DCE-MRI images, Ktrans maps, AUC maps were 93, 98 and 98%, with a cross validation accuracies of 82, 77 and 77%, respectively. To conclude, pharmacokinetic parameters, texture features and the combined computer-assisted classification method were discussed. All method involved in this study may be a potential assisted tool for radiological analysis on breast.
