A deep learning approach for dual-energy CT imaging using a single-energy CT data

2019 ◽  
Author(s):  
Wei Zhao ◽  
Tianling Lv ◽  
Peng Gao ◽  
Liyue Shen ◽  
Xianjin Dai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dual Energy ◽  
Ct Imaging ◽  
Dual Energy Ct ◽  
Learning Approach ◽  
Ct Data

scholarly journals Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Brent van der Heyden ◽  
Patrick Wohlfahrt ◽  
Daniëlle B. P. Eekers ◽  
Christian Richter ◽  
Karin Terhaag ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Brain Tumor ◽  
Organs At Risk ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Learning Approach ◽  
Tumor Patients

Iterative dynamic dual-energy CT algorithm in reducing statistical noise in multi-energy CT imaging

2021 ◽  
Author(s):  
Yidi Yao ◽  
Liang Li ◽  
Zhiqiang Chen
Keyword(s):  
Noise Level ◽  
Photon Counting ◽  
Reconstruction Algorithm ◽  
Dual Energy ◽  
Ct Imaging ◽  
Dual Energy Ct ◽  
Spectral Ct ◽  
Photon Counting Detectors ◽  
Ct Data ◽  
Statistical Noise

Abstract Multi-energy spectral CT has a broader range of applications with the recent development of photon-counting detectors. However, the photons counted in each energy bin decrease when the number of energy bins increases, which causes a higher statistical noise level of the CT image. In this work, we propose a novel iterative dynamic dual-energy CT algorithm to reduce the statistical noise. In the proposed algorithm, the multi-energy projections are estimated from the dynamic dual-energy CT data during the iterative process. The proposed algorithm is verified on sufficient numerical simulations and a laboratory two-energy-threshold PCD system. By applying the same reconstruction algorithm, the dynamic dual-energy CT's final reconstruction results have a much lower statistical noise level than the conventional multi-energy CT. Moreover, based on the analysis of the simulation results, we explain why the dynamic dual-energy CT has a lower statistical noise level than the conventional multi-energy CT. The reason is that: the statistical noise level of multi-energy projection estimated with the proposed algorithm is much lower than that of the conventional multi-energy CT, which leads to less statistical noise of the dynamic dual-energy CT imaging.

scholarly journals Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network

2021 ◽  
Vol 70 ◽  
pp. 102001
Author(s):  
Tianling Lyu ◽  
Wei Zhao ◽  
Yinsu Zhu ◽  
Zhan Wu ◽  
Yikun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Dual Energy ◽  
Ct Imaging ◽  
Dual Energy Ct ◽  
Material Decomposition ◽  
Ct Data

Abstract P327: Utilizing Iodine Contrast Extravasation Post Reperfusion and Dual Energy Ct Imaging to Predict Risk of Hemorrhagic Conversion in Acute Stroke Patients

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Tri Huynh* ◽  
Niran Vijayaraghavan* ◽  
Hannah Branstetter ◽  
Natalie Buchwald ◽  
Justin De Prey ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Infarct Volume ◽  
Reperfusion Therapy ◽  
Dual Energy ◽  
Ct Imaging ◽  
Dual Energy Ct ◽  
Stroke Patients ◽  
Post Intervention ◽  
Contrast Extravasation ◽  
Post Contrast

Introduction: Hyperintense acute reperfusion marker (HARM) has been identified on post-contrast magnetic resonance imaging (MRI) to be a marker of hemorrhagic conversion (HC) post reperfusion therapy in acute stroke patients. We have previously described a case where MRI HARM was mimicked on post contrast computed topography (CT) imaging in an acute stroke patient post reperfusion. Dual-Energy (DECT) allows for differentiation between acute blood and iodine contrast extravasation (ICE), and thus can have utility when ICE is present. Here we sought to validate whether post-intervention ICE/CT hyperdensity reperfusion maker (CT HARM), and contrast subtracted on DECT is associated with HC in acute stroke patients. Method: Data was obtained from our Institutional Review Board approved stroke admission database from January 2017 to November 2019, including ischemic stroke patients that received thrombolysis or thrombectomy, had evaluable images within 24 hours of admission, and received a DECT. Ischemic volumes of the stroke was measured on diffusion-weighted image (DWI). ICE was measured on CT head and DECT using the freehand 3D region of interest tool on the Visage Imaging PACS System. Susceptibility weighted MRI sequences were used to grade HC. Data analysis was conducted with regression modeling. Results: A total of 82 patients were included, 49% women, median age 73 (interquartile range (IQR), 61- 77), admission NIHSS 12 (IQR, 7 - 21), 24 hour change in NIHSS 4 (IQR, 0 -13), glucose 125 (IQR, 106 -158), creatinine 1.0 (IQR, 0.8 - 1.2), infarct volume 50.6 ± 7.1 mL, 48% treated with thrombectomy, 7% with PH-1 or PH-2 identified on MRI, and 56% with MCA infarcts. ICE volume was 2.6 ± 1.0 mL and DECT volume was 2.2 ± 1.1mL. ICE increased the likelihood of MRI confirmed PH-1 or PH-2 hemorrhagic conversion (odds ratio (OR) 14.34, 95% confidence interval (CI) 5.74 - 22.94) and decreased likelihood of increase in NIHSS at 24 hours (OR 0.20, 95% CI 0.01 to 0.40). There were no other significant associations with ICE or DECT volumes. Conclusion: Our results are supportive of our proposed association between CT HARM and risk of HC. More studies are needed to study whether quantitative of DECT can be predictive of stroke outcomes post reperfusion therapy.

scholarly journals Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography

Radiographics ◽  
2018 ◽  
Vol 38 (7) ◽  
pp. 2034-2050 ◽  
Author(s):  
Rie Tachibana ◽  
Janne J. Näppi ◽  
Junko Ota ◽  
Nadja Kohlhase ◽  
Toru Hironaka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Colonography ◽  
Dual Energy ◽  
Dual Energy Ct
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