SU-E-T-545: Dose Comparison between Intravenous Contrast-Enhanced CT and Non Contrast CT in Treatment Planning

2012 ◽  
Vol 39 (6Part18) ◽  
pp. 3831-3831
Author(s):  
W Xiong ◽  
D Huang ◽  
R Gewanter ◽  
C Burman
Keyword(s):  
Treatment Planning ◽  
Intravenous Contrast ◽  
Dose Comparison ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Contrast Ct

A deep learning approach to generate contrast-enhanced computerised tomography angiograms without the use of intravenous contrast agents

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Chandrashekar ◽  
N Shivakumar ◽  
P Lapolla ◽  
A Handa ◽  
V Grau ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Contrast Agents ◽  
Ct Images ◽  
Learning Methods ◽  
Intravenous Contrast ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Contrast Ct

Abstract Introduction Contrast-enhanced computerised tomographic (CT) angiograms are widely used in cardiovascular imaging to obtain a non-invasive view of arterial structures. In aortic aneurysmal disease (AAA), CT angiograms are required prior to surgical intervention to differentiate between blood and the intra-luminal thrombus, which is present in 95% of cases. However, contrast agents are associated with complications at the injection site as well as renal toxicity leading to contrast-induced nephropathy (CIN) and renal failure. Purpose We hypothesised that the raw data acquired from a non-contrast CT contains sufficient information to differentiate blood and other soft tissue components. Therefore, we utilised deep learning methods to define the subtleties between the various components of soft tissue in order to simulate contrast enhanced CT images without the need of contrast agents. Methods Twenty-six AAA patients with paired non-contrast and contrast-enhanced CT images were randomly selected from an ethically approved ongoing study (Ethics Ref 13/SC/0250) and used for model training and evaluation (13/13). Non-contrast axial slices within the aneurysmal region from 10 patients (n=100) were sampled for the underlying Hounsfield unit (HU) distribution at the lumen, intra-luminal thrombus and interface locations, identified from their paired contrast axial slices. Subsequently, paired axial slices within the training cohort were augmented in a ratio of 10:1 to produce a total of 23,551 2-D images. We trained a 2-D Cycle Generative Adversarial Network (cycleGAN) for this non-contrast to contrast transformation task. Model output was assessed by comparison to the contrast image, which serves as a gold standard, using image similarity metrics (ex. SSIM Index). Results Sampling HUs within the non-contrast CT scan across multiple axial slices (Figure 1A) revealed significant differences between the blood flow lumen (yellow), blood/thrombus interface (red), and thrombus (blue) regions (p<0.001 for all comparisons). This highlighted the intrinsic differences between the regions and established the foundation for subsequent deep learning methods. The Non-Contrast-to-Contrast (NC2C)-cycleGAN was trained with a learning rate of 0.0002 for 200 epochs on 256 x 256 images centred around the aorta. Figure 1B depicts “contrast-enhanced” images generated from non-contrast CT images across the aortic length from the testing cohort. This preliminary model is able to differentiate between the lumen and intra-luminal thrombus of aneurysmal sections with reasonable resemblance to the ground truth. Conclusion This study describes, for the first time, the ability to differentiate between visually incoherent soft tissue regions in non-contrast CT images using deep learning methods. Ultimately, refinement of this methodology may negate the use of intravenous contrast and prevent related complications. CTA Generation from Non-Contrast CTs Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Clarendon

scholarly journals Comparative diagnostic value of contrast-enhanced ultrasonography, computed tomography, and magnetic resonance imaging in diagnosis of hepatic hemangiomas

Medicina ◽  
2010 ◽  
Vol 46 (5) ◽  
pp. 329 ◽  
Author(s):  
Kristina Žvinienė ◽  
Inga Zaborienė ◽  
Algidas Basevičius ◽  
Nemira Jurkienė ◽  
Giedrius Barauskas ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prognostic Value ◽  
Hepatic Hemangioma ◽  
Resonance Imaging ◽  
Intravenous Contrast ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Contrast Enhanced Ultrasonography ◽  
Hepatic Hemangiomas

Aim. To compare the value of intravenous contrast-enhanced ultrasonography (US), intravenous contrast-enhanced computed tomography (CT), and magnetic resonance imaging (MRI) in the diagnosis of hepatic hemangiomas. Material and methods. The study enrolled 48 patients, aged between 20 and 79 years (35 [72.9%] women, 13 [27.1%] men; mean age, 53.5±12.855 years), who were examined and treated in the Departments of Gastroenterology, Surgery, and Oncology, Hospital of Kaunas University of Medicine, in the year 2007. All patients underwent intravenous contrast-enhanced US, intravenous contrast-enhanced CT, and MRI and were diagnosed with hepatic hemangioma according to the findings of these examinations. Results. The size of hemangiomas was ≤2.0 cm in 20 cases (41.7%) and >2.0 cm in 28 (58.3%). No association between hepatic hemangioma and patient’s age was found (χ2=0.547, df=2, P=0.761). Nearly one-third of hemangiomas were located in the segment IV of the left hepatic lobe. There were a few complicated hemangiomas in the study sample: 2 with calcification and 1 with necrosis. The sensitivity of CT in the diagnosis of hepatic hemangioma was 76.92%; specificity, 33.3%; positive prognostic value, 83.3%; and negative prognostic value, 25.0%. The sensitivity of intravenous contrast-enhanced US in the diagnosis of hepatic hemangioma was 77.8%; specificity, 100%; positive prognostic value, 100%; and negative prognostic value, 23.1%. Conclusions. Intravenous contrast-enhanced US is more specific than intravenous contrast-enhanced CT in the diagnosis of hepatic hemangioma (P=0.0005) and has a higher positive prognostic value (P=0.001).

Hepatic leiomyomas in two adult patients with AIDS: intravenous contrast-enhanced CT and MR imaging

2002 ◽  
Vol 9 (3) ◽  
pp. 175-177 ◽  
Author(s):  
Michael Sadler ◽  
Warren L. Mays ◽  
Pradeep Albert ◽  
Bruce Javors
Keyword(s):  
Mr Imaging ◽  
Adult Patients ◽  
Intravenous Contrast ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

scholarly journals Contrast-enhanced computed tomography assessment of aortic stenosis

Heart ◽  
2021 ◽  
pp. heartjnl-2020-318556
Author(s):  
Timothy RG Cartlidge ◽  
Rong Bing ◽  
Jacek Kwiecinski ◽  
Ezequiel Guzzetti ◽  
Tania A Pawade ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Aortic Stenosis ◽  
Ex Vivo ◽  
Agatston Score ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Stenosis Severity ◽  
Jet Velocity ◽  
Contrast Ct

ObjectivesNon-contrast CT aortic valve calcium scoring ignores the contribution of valvular fibrosis in aortic stenosis. We assessed aortic valve calcific and non-calcific disease using contrast-enhanced CT.MethodsThis was a post hoc analysis of 164 patients (median age 71 (IQR 66–77) years, 78% male) with aortic stenosis (41 mild, 89 moderate, 34 severe; 7% bicuspid) who underwent echocardiography and contrast-enhanced CT as part of imaging studies. Calcific and non-calcific (fibrosis) valve tissue volumes were quantified and indexed to annulus area, using Hounsfield unit thresholds calibrated against blood pool radiodensity. The fibrocalcific ratio assessed the relative contributions of valve fibrosis and calcification. The fibrocalcific volume (sum of indexed non-calcific and calcific volumes) was compared with aortic valve peak velocity and, in a subgroup, histology and valve weight.ResultsContrast-enhanced CT calcium volumes correlated with CT calcium score (r=0.80, p<0.001) and peak aortic jet velocity (r=0.55, p<0.001). The fibrocalcific ratio decreased with increasing aortic stenosis severity (mild: 1.29 (0.98–2.38), moderate: 0.87 (1.48–1.72), severe: 0.47 (0.33–0.78), p<0.001) while the fibrocalcific volume increased (mild: 109 (75–150), moderate: 191 (117–253), severe: 274 (213–344) mm3/cm2). Fibrocalcific volume correlated with ex vivo valve weight (r=0.72, p<0.001). Compared with the Agatston score, fibrocalcific volume demonstrated a better correlation with peak aortic jet velocity (r=0.59 and r=0.67, respectively), particularly in females (r=0.38 and r=0.72, respectively).ConclusionsContrast-enhanced CT assessment of aortic valve calcific and non-calcific volumes correlates with aortic stenosis severity and may be preferable to non-contrast CT when fibrosis is a significant contributor to valve obstruction.

scholarly journals No increase in Kidney Injury Molecule-1 and Neutrophil Gelatinase-Associated Lipocalin excretion following intravenous contrast enhanced-CT

2015 ◽  
Vol 25 (7) ◽  
pp. 1926-1934 ◽  
Author(s):  
Judith Kooiman ◽  
Wilke R. van de Peppel ◽  
Yvo W. J. Sijpkens ◽  
Harald F. H. Brulez ◽  
P. M. de Vries ◽  
...  
Keyword(s):  
Kidney Injury ◽  
Intravenous Contrast ◽  
Neutrophil Gelatinase Associated Lipocalin ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Kidney Injury Molecule 1 ◽  
Neutrophil Gelatinase
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