Ke Li1,2, Charles M Strother2, Guang-Hong Chen1,2. 1. Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA. 2. Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI, 53792, USA.
Abstract
PURPOSE: The development and clinical employment of a computed tomography (CT) imaging system benefit from a thorough understanding of the statistical properties of the output images; cerebral CT perfusion (CTP) imaging system is no exception. A series of articles will present statistical properties of CTP systems and the dependence of these properties on system parameters. This Part I paper focuses on the signal and noise properties of cerebral blood volume (CBV) maps calculated using a nondeconvolution-based method. METHODS: The CBV imaging chain was decomposed into a cascade of subimaging stages, which facilitated the derivation of analytical models for the probability density function, mean value, and noise variance of CBV. These models directly take CTP source image acquisition, reconstruction, and postprocessing parameters as inputs. Both numerical simulations and in vivo canine experiments were performed to validate these models. RESULTS: The noise variance of CBV is linearly related to the noise variance of source images and is strongly influenced by the noise variance of the baseline images. Uniformly partitioning the total radiation dose budget across all time frames was found to be suboptimal, and an optimal dose partition method was derived to minimize CBV noise. Results of the numerical simulation and animal studies validated the derived statistical properties of CBV. CONCLUSIONS: The statistical properties of CBV imaging systems can be accurately modeled by extending the linear CT systems theory. Based on the statistical model, several key signal and noise characteristics of CBV were identified and an optimal dose partition method was developed to improve the image quality of CBV.
PURPOSE: The development and clinical employment of a computed tomography (CT) imaging system benefit from a thorough understanding of the statistical properties of the output images; cerebral CT perfusion (CTP) imaging system is no exception. A series of articles will present statistical properties of CTP systems and the dependence of these properties on system parameters. This Part I paper focuses on the signal and noise properties of cerebral blood volume (CBV) maps calculated using a nondeconvolution-based method. METHODS: The CBV imaging chain was decomposed into a cascade of subimaging stages, which facilitated the derivation of analytical models for the probability density function, mean value, and noise variance of CBV. These models directly take CTP source image acquisition, reconstruction, and postprocessing parameters as inputs. Both numerical simulations and in vivo canine experiments were performed to validate these models. RESULTS: The noise variance of CBV is linearly related to the noise variance of source images and is strongly influenced by the noise variance of the baseline images. Uniformly partitioning the total radiation dose budget across all time frames was found to be suboptimal, and an optimal dose partition method was derived to minimize CBV noise. Results of the numerical simulation and animal studies validated the derived statistical properties of CBV. CONCLUSIONS: The statistical properties of CBV imaging systems can be accurately modeled by extending the linear CT systems theory. Based on the statistical model, several key signal and noise characteristics of CBV were identified and an optimal dose partition method was developed to improve the image quality of CBV.
Authors: R Souillard-Scemama; M Tisserand; D Calvet; D Jumadilova; S Lion; G Turc; M Edjlali; C Mellerio; C Lamy; O Naggara; J-F Meder; C Oppenheim Journal: J Neuroradiol Date: 2015-01-31 Impact factor: 3.447
Authors: Jeffrey L Saver; Mayank Goyal; Alain Bonafe; Hans-Christoph Diener; Elad I Levy; Vitor M Pereira; Gregory W Albers; Christophe Cognard; David J Cohen; Werner Hacke; Olav Jansen; Tudor G Jovin; Heinrich P Mattle; Raul G Nogueira; Adnan H Siddiqui; Dileep R Yavagal; Blaise W Baxter; Thomas G Devlin; Demetrius K Lopes; Vivek K Reddy; Richard du Mesnil de Rochemont; Oliver C Singer; Reza Jahan Journal: N Engl J Med Date: 2015-04-17 Impact factor: 91.245
Authors: Bruce C V Campbell; Peter J Mitchell; Timothy J Kleinig; Helen M Dewey; Leonid Churilov; Nawaf Yassi; Bernard Yan; Richard J Dowling; Mark W Parsons; Thomas J Oxley; Teddy Y Wu; Mark Brooks; Marion A Simpson; Ferdinand Miteff; Christopher R Levi; Martin Krause; Timothy J Harrington; Kenneth C Faulder; Brendan S Steinfort; Miriam Priglinger; Timothy Ang; Rebecca Scroop; P Alan Barber; Ben McGuinness; Tissa Wijeratne; Thanh G Phan; Winston Chong; Ronil V Chandra; Christopher F Bladin; Monica Badve; Henry Rice; Laetitia de Villiers; Henry Ma; Patricia M Desmond; Geoffrey A Donnan; Stephen M Davis Journal: N Engl J Med Date: 2015-02-11 Impact factor: 91.245