Access the full text.
Sign up today, get DeepDyve free for 14 days.
K. Awai, M. Kanematsu, Tonsok Kim, T. Ichikawa, Yuko Nakamura, A. Nakamoto, K. Yoshioka, T. Mochizuki, N. Matsunaga, Y. Yamashita (2016)
The Optimal Body Size Index with Which to Determine Iodine Dose for Hepatic Dynamic CT: A Prospective Multicenter Study.Radiology, 278 3
K. Bae, J. Heiken (2005)
Scan and contrast administration principles of MDCTEuropean Radiology Supplements, 15
T. Higaki, T. Nakaura, M. Kidoh, Hideaki Yuki, Y. Yamashita, Yuko Nakamura, F. Tatsugami, Y. Baba, M. Iida, K. Awai (2018)
Effect of contrast material injection duration on arterial enhancement at CT in patients with various cardiac indices: Analysis using computer simulationPLoS ONE, 13
C. Jensen, Katherine Blair, N. Wagner-Bartak, Lan Vu, B. Carter, Jia Sun, T. Bathala, Shiva Gupta (2019)
Comparison of Abdominal Computed Tomographic Enhancement and Organ Lesion Depiction Between Weight-Based Scanner Software Contrast Dosing and a Fixed-Dose Protocol in a Tertiary Care Oncologic CenterJournal of Computer Assisted Tomography, 43
T. Masuda, Y. Funama, T. Nakaura, N. Imada, Tomoyasu Sato, T. Okimoto, K. Awai (2017)
CT Angiography of Suspected Peripheral Artery Disease: Comparison of Contrast Enhancement in the Lower Extremities of Patients Undergoing and Those Not Undergoing Hemodialysis.AJR. American journal of roentgenology, 208 5
Kyongtae Bae, J. Heiken, James Brink (1998)
Aortic and hepatic contrast medium enhancement at CT. Part II. Effect of reduced cardiac output in a porcine model.Radiology, 207 3
Y. Yanaga, K. Awai, T. Nakaura, T. Namimoto, S. Oda, Y. Funama, Y. Yamashita (2008)
Optimal contrast dose for depiction of hypervascular hepatocellular carcinoma at dynamic CT using 64-MDCT.AJR. American journal of roentgenology, 190 4
Arkadiusz Gertych, N. Ing, Zhaoxuan Ma, Thomas Fuchs, S. Salman, S. Mohanty, S. Bhele, Adriana Velásquez-Vacca, M. Amin, B. Knudsen (2015)
Machine learning approaches to analyze histological images of tissues from radical prostatectomiesComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 46 Pt 2
A. Fukuhara, M. Matsuda, M. Nishizawa, Katsumori Segawa, Masaki Tanaka, Kae Kishimoto, Yasushi Matsuki, M. Murakami, T. Ichisaka, H. Murakami, Eijiro Watanabe, T. Takagi, M. Akiyoshi, Tsuguteru Ohtsubo, S. Kihara, S. Yamashita, M. Makishima, T. Funahashi, S. Yamanaka, R. Hiramatsu, Y. Matsuzawa, I. Shimomura (2007)
RetractionScience, 318
J. Heiken, James Brink, Bruce McClennan, Stuart Sagel, Tamara Crowe, Mary Gaines (1995)
Dynamic incremental CT: effect of volume and concentration of contrast material and patient weight on hepatic enhancement.Radiology, 195 2
T. Masuda, T. Nakaura, Y. Funama, T. Higaki, M. Kiguchi, N. Imada, Tomoyasu Sato, K. Awai (2017)
Aortic and Hepatic Contrast Enhancement During Hepatic-Arterial and Portal Venous Phase Computed Tomography Scanning: Multivariate Linear Regression Analysis Using Age, Sex, Total Body Weight, Height, and Cardiac OutputJournal of Computer Assisted Tomography, 41
Xin Zhang, Lin-Feng Yan, Yu-Chuan Hu, Gang-feng Li, Yang Yang, Yu Han, Ying-Zhi Sun, Zhi-cheng Liu, Qiang Tian, Zi-Yang Han, Le-De Liu, Bingbing Hu, Zi-Yu Qiu, Wen Wang, Guang-Bin Cui (2017)
Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture featuresOncotarget, 8
Kyongtae Bae, J. Heiken, James Brink (1998)
Aortic and hepatic contrast medium enhancement at CT. Part I. Prediction with a computer model.Radiology, 207 3
M. Kidoh, T. Nakaura, S. Oda, T. Namimoto, K. Awai, I. Yoshinaka, K. Harada, Y. Yamashita (2013)
Contrast Enhancement During Hepatic Computed Tomography: Effect of Total Body Weight, Height, Body Mass Index, Blood Volume, Lean Body Weight, and Body Surface AreaJournal of Computer Assisted Tomography, 37
Kyongtae Bae, J. Heiken, James Brink, James Brink (1998)
Aortic and hepatic peak enhancement at CT: effect of contrast medium injection rate--pharmacokinetic analysis and experimental porcine model.Radiology, 206 2
Y. Yanaga, K. Awai, Y. Nakayama, T. Nakaura, Y. Tamura, Y. Funama, M. Aoyama, Naoki Asada, Y. Yamashita (2007)
Optimal dose and injection duration (injection rate) of contrast material for depiction of hypervascular hepatocellular carcinomas by multidetector CTRadiation Medicine, 25
Y. Matsumoto, T. Higaki, T. Masuda, Tomoyasu Sato, Yuko Nakamura, F. Tatsugami, K. Awai (2018)
Minimizing individual variations in arterial enhancement on coronary CT angiographs using “contrast enhancement optimizer”: a prospective randomized single-center studyEuropean Radiology, 29
Yasuyuki Yamashita, Y. Komohara, Mutsumasa Takahashi, M. Uchida, N. Hayabuchi, Tadafumi Shimizu, Isamu Narabayashi (2000)
Abdominal helical CT: evaluation of optimal doses of intravenous contrast material--a prospective randomized study.Radiology, 216 3
Joonnyung Heo, Jihoon Yoon, Hyungjong Park, Y. Kim, H. Nam, J. Heo (2019)
Machine Learning–Based Model for Prediction of Outcomes in Acute StrokeStroke, 50
T. Nakaura, K. Awai, Yumi Yauaga, Y. Nakayama, S. Oda, M. Hatemura, Y. Nagayoshi, H. Ogawa, Y. Yamashita (2008)
Contrast Injection Protocols for Coronary Computed Tomography Angiography Using a 64-Detector Scanner: Comparison Between Patient Weight-Adjusted- and Fixed Iodine-Dose ProtocolsInvestigative Radiology, 43
T. Masuda, T. Nakaura, Y. Funama, T. Okimoto, Tomoyasu Sato, T. Higaki, N. Noda, N. Imada, Y. Baba, K. Awai (2019)
Machine-learning integration of CT histogram analysis to evaluate the composition of atherosclerotic plaques: Validation with IB-IVUS.Journal of cardiovascular computed tomography, 13 2
Objectives The aim of this study was to compare prediction ability between ensemble machine learning (ML) methods and simulation software for aortic contrast enhancement on dynamic hepatic computed tomography. Methods We divided 339 human hepatic dynamic computed tomography scans into 2 groups. One group consisted of 279 scans used to create cross-validation data sets, the other group of 60 scans were used as test data sets. To evaluate the effect of the patient characteristics on enhancement, we calculated changes in the contrast medium dose per enhancement of the abdominal aorta in the hepatic arterial phase. The parameters for ML were the patient sex, age, height, body weight, body mass index, and cardiac output. We trained 9 ML regressors by applying 5-fold cross-validation, integrated the predictions of all ML regressors for ensemble learning and the simulations, and used the training and test data to compare their Pearson correlation coefficients. Results Comparison of different ML methods showed that the Pearson correlation coefficient for the real and predicted contrast medium dose per enhancement of the abdominal aorta was highest with ensemble ML (r = 0.786). It was higher than that obtained with the simulation software (r = 0.350). With ensemble ML, the Bland-Altman limit of agreement [mean difference, 5.26 Hounsfield units (HU); 95% limit of agreement, −112.88 to 123.40 HU] was narrower than that obtained with the simulation software (mean difference, 11.70 HU; 95% limit of agreement, −164.71 to 188.11 HU). Conclusion The performance for predicting contrast enhancement of the abdominal aorta in the hepatic arterial phase was higher with ensemble ML than with the simulation software.
Journal of Computer Assisted Tomography – Wolters Kluwer Health
Published: Mar 1, 2022
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.