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Quantitative assessment of multi-phase contrast-enhanced CT features in hepatic epithelioid hemangioendothelioma. [PDF]
J Gastrointest OncolChen H, Zhou W, Lin J, Xu Y, Li S.
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Retrospective Analysis of Incidental Myocardial Perfusion Defects on Non-ECG-Gated Contrast-Enhanced CT in Emergency Settings. [PDF]
Medicina (Kaunas)Zhou JH, Wu MY, Hsiao JK.
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Deep learning-based system to predict hepatocellular carcinoma resection volume using contrast-enhanced CT. [PDF]
Sci RepWang X +9 more
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Intratumoral and Peritumoral Radiomics based on Contrast-enhanced CT Images: Biomarkers to Predict the Risk-Grade of Gastrointestinal Stromal Tumors [PDF]
Curr Med ImagingLiu Y, Wu Y, Cao X, Zhang G, Yin L.
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Neural Contrast Enhancement of CT Image
2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021Contrast materials are often injected into body to contrast specific tissues in Computed Tomography (CT) images. Contrast Enhanced CT (CECT) images obtained in this way are more useful than Non-Enhanced CT (NECT) images for medical diagnosis, but not available for everyone due to side effects of the contrast materials.
Minkyo Seo +6 more
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Contrast-enhanced CT colonography
Seminars in Ultrasound, CT and MRI, 2001Computed tomography (CT) colonography provides both a multiplanar and endoluminal perspective of the air-filled, distended, cleansed colon, offering an alternative to traditional approaches to colorectal cancer screening. However, there are still limitations associated with CT colonography that need to be resolved, including inadequate bowel distention
M M, Morrin, V, Raptopoulos
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2023
Medical imaging is a dynamic domain where new acquisition protocols are regularly developed and employed to meet changing clinical needs. Deep learning models for medical image segmentation have proven to be a valuable tool for medical image processing. Creating such a model from scratch requires a lot of effort in terms of annotating new types of data
Maryna Kvasnytsia +3 more
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Medical imaging is a dynamic domain where new acquisition protocols are regularly developed and employed to meet changing clinical needs. Deep learning models for medical image segmentation have proven to be a valuable tool for medical image processing. Creating such a model from scratch requires a lot of effort in terms of annotating new types of data
Maryna Kvasnytsia +3 more
openaire +2 more sources
CT contrast enhancement in cerebral infarction
American Journal of Roentgenology, 1978Computed tomography was used to study 100 patients with ischemic cerebral infarcts. All cases were documented by autopsy, radionuclide imaging, cerebral angiography, or clinical course. Vascular distribution of infarcts was varied and included infarcts of cerebral hemispheres, basal ganglia, and cerebellum.
G A, Norton, P R, Kishore, J, Lin
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2017
Contrast-enhanced CT studies are highly diagnostic and involve little physiological stress. To obtain the best results, protocols for contrast enhancement must take into consideration the patient factors, CM factors, and scan factors for contrast enhancement.
Kazuo Awai +2 more
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Contrast-enhanced CT studies are highly diagnostic and involve little physiological stress. To obtain the best results, protocols for contrast enhancement must take into consideration the patient factors, CM factors, and scan factors for contrast enhancement.
Kazuo Awai +2 more
openaire +1 more source