Segmentation of dynamic contrast enhanced micro-CT images for fluorescence molecular tomography reconstruction

Objectives: To evaluate the diagnostic accuracy using sodium iodide (NaI) and dimethyl sulfoxide (DMSO) as contrast agent in cone beam computed tomography (CBCT) scanning, and compare this with micro-CT. Methods: 18 teeth were cracked artificially by soaking them cyclically in liquid nitrogen and hot water. After pre-treatment with artificial saliva, the teeth were scanned in four modes: CBCT routine scanning without contrast agent (RS); CBCT with meglumine diatrizoate (MD) as contrast agent (ES1); CBCT with NaI + DMSO as contrast agent (ES2); and micro-CT (mCT). The number of crack lines was evaluated in all four modes. Depth of crack lines and number of cracks presented from the occlusal surface to the pulp cavity (Np) in ES2 and micro-CT images were evaluated. Results: There were 63 crack lines in all 18 teeth. 45 crack lines were visible on ES2 images as against four on the RS and ES1 images (p<0.05) and 37 on micro-CT images (p>0.05). Further, 34 crack lines could be observed on both ES2 and micro-CT images, and the average depth presented on ES2 images was 4.56 ± 0.88 mm and 3.89 ± 1.08 mm on micro-CT images (p<0.05). More crack lines could be detected from the occlusal surface to the pulp cavity on ES2 images than on micro-CT images (22 vs 11). Conclusion: CBCT with NaI +DMSO as the contrast agent was equivalent to micro-CT for number of crack lines and better for depth of crack lines. NaI + DMSO could be a potential CBCT contrast agent to improve diagnostic accuracy for cracked tooth.

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Quantitative Imaging Parameters of Contrast-Enhanced Micro-Computed Tomography Correlate with Angiogenesis and Necrosis in a Subcutaneous C6 Glioma Model

Cancers ◽

10.3390/cancers12113417 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3417

Author(s):

Lízbeth Ayala-Domínguez ◽

Enrique Pérez-Cárdenas ◽

Alejandro Avilés-Salas ◽

Luis Alberto Medina ◽

Marcela Lizano ◽

...

Keyword(s):

Computed Tomography ◽

Quantitative Imaging ◽

C6 Glioma ◽

Micro Ct ◽

Micro Computed Tomography ◽

Imaging Parameters ◽

Dynamic Contrast Enhanced ◽

Tumor Periphery ◽

Contrast Enhanced ◽

Glioma Model

The aim of this work was to systematically obtain quantitative imaging parameters with static and dynamic contrast-enhanced (CE) X-ray imaging techniques and to evaluate their correlation with histological biomarkers of angiogenesis in a subcutaneous C6 glioma model. Enhancement (E), iodine concentration (CI), and relative blood volume (rBV) were quantified from single- and dual-energy (SE and DE, respectively) micro-computed tomography (micro-CT) images, while rBV and volume transfer constant (Ktrans) were quantified from dynamic contrast-enhanced (DCE) planar images. CI and rBV allowed a better discernment of tumor regions from muscle than E in SE and DE images, while no significant differences were found for rBV and Ktrans in DCE images. An agreement was found in rBV for muscle quantified with the different imaging protocols, and in CI and E quantified with SE and DE protocols. Significant strong correlations (Pearson r > 0.7, p < 0.05) were found between a set of imaging parameters in SE images and histological biomarkers: E and CI in tumor periphery were associated with microvessel density (MVD) and necrosis, E and CI in the complete tumor with MVD, and rBV in the tumor periphery with MVD. In conclusion, quantitative imaging parameters obtained in SE micro-CT images could be used to characterize angiogenesis and necrosis in the subcutaneous C6 glioma model.

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Abstract 2071A: Anti-VEGF treatment in orthotopic breast cancer xenografts: Dynamic contrast-enhanced micro-CT correlates with 3D multispectral fluorescence histology

10.1158/1538-7445.am2014-2071a ◽

2014 ◽

Author(s):

Thomas Pöschinger ◽

Anja Renner ◽

Fabian Eisa ◽

Michael Dobosz ◽

Robert Brauweiler ◽

...

Keyword(s):

Breast Cancer ◽

Micro Ct ◽

Dynamic Contrast Enhanced ◽

Anti Vegf ◽

Contrast Enhanced ◽

Breast Cancer Xenografts

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A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images

2016 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2016.7532990 ◽

2016 ◽

Cited By ~ 4

Author(s):

Fahmi Khalifa ◽

Ahmed Soliman ◽

Amy C. Dwyer ◽

Georgy Gimel'farb ◽

Ayman El-Baz

Keyword(s):

Random Forest ◽

Ct Images ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Kidney Segmentation ◽

Dynamic Contrast Enhanced Ct

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Dynamic contrast-enhanced micro-CT on mice with mammary carcinoma for the assessment of antiangiogenic therapy response

European Radiology ◽

10.1007/s00330-011-2318-9 ◽

2011 ◽

Vol 22 (4) ◽

pp. 900-907 ◽

Cited By ~ 15

Author(s):

Fabian Eisa ◽

Robert Brauweiler ◽

Martin Hupfer ◽

Tristan Nowak ◽

Laura Lotz ◽

...

Keyword(s):

Mammary Carcinoma ◽

Antiangiogenic Therapy ◽

Therapy Response ◽

Micro Ct ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced

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Segmentation and Registration of the Liver in Dynamic Contrast-Enhanced Computed Tomography Images

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3327 ◽

2021 ◽

Vol 11 (3) ◽

pp. 773-780

Author(s):

Shuai Ren ◽

Ling Zhan ◽

Shuchao Chen ◽

Haitao Dai ◽

Guangying Ruan ◽

...

Keyword(s):

Liver Diseases ◽

Big Data Analysis ◽

Ct Images ◽

Liver Segmentation ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Enhanced Computed Tomography ◽

Contrast Enhanced Computed Tomography ◽

Ct Slices ◽

Dominant Phase

Dynamic contrast-enhanced computed tomography (DCE-CT) is the main auxiliary diagnostic tool for liver diseases. Liver segmentation and registration in all stages of DCE-CT images are the key technology for big data analysis of liver disease diagnosis. The change of imaging conditions in different stages of DCE-CT brings enormous challenges to the segmentation of liver CT images. This study proposes an automatic model for liver segmentation from abdominal CT images in different stages of DCE on the basis of U-Net. The skip connection in U-Net can improve the ability of complex feature recognition. A total of 4863 CT slices from 16 patients with hepatocellular carcinoma (HCC) were selected as the training set, and 1754 CT slices from 6 patients with HCC were selected as the test set. The training and test sets included plain scan, hepatic arterial-dominant phase, and portal venous-dominant phase CT scans. Results showed that the Dice value of the proposed method was significantly higher than those of the full convolutional network and region-growing method. Then, 3D reconstruction and registration were performed on the segmentation results of the liver region of DCE-CT images. The proposed method obtained the best performance, which can provide technical support for the big data analysis of liver diseases.

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