Purpose: To present an application of the anisotropic diffusion (AD) method to improve the accuracy of the functional images of perfusion parameters such as cerebral blood flow (CBF), cerebral blood volume (CBV) and m...Purpose: To present an application of the anisotropic diffusion (AD) method to improve the accuracy of the functional images of perfusion parameters such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) generated from cerebral CT perfusion studies using multi-detector row CT (MDCT). Materials and Methods: Continuous scans (1 sec/rotation ×60 sec) consisting of four 5-mm-thick contiguous slices were acquired after an intravenous injection of iodinated contrast material in 6 patients with cerebrovascular disease using an MDCT scanner with a tube voltage of 80 kVp and a tube current of 200 mA. New image data were generated by thinning out the above original images at an interval of 2 sec or 3 sec. The thinned-out images were then interpolated by linear interpolation to generate the same number of images as originally acquired. The CBF, CBV and MTT images were generated using deconvolution analysis based on singular value decomposition. Results: When using the AD method, the correlation coefficient between the MTT values obtained from the original and thinned-out images was significantly improved. Furthermore, the coefficients of variation of the CBF, CBV and MTT values in the white matter significantly decreased as compared to not using the AD method. Conclusion: Our results suggest that the AD method is useful for improving the accuracy of the functional images of perfusion parameters and for reducing radiation exposure in cerebral CT perfusion studies using MDCT.展开更多
Estimated LBW could be used to determine the contrast material dose and rate during MDCT. The aim of this study is to test the accuracy of a technique for estimation of lean body weight (LBW) from a single multi-detec...Estimated LBW could be used to determine the contrast material dose and rate during MDCT. The aim of this study is to test the accuracy of a technique for estimation of lean body weight (LBW) from a single multi-detector row computed tomographic (MDCT) abdominal image, using a bioelectrical body composition analyzer scale as the reference standard. CT images of 21 patients with previously measured LBW (mLBW) were processed using computer-assisted, vendor-specific software (Advantage Windows 4.2;GE Healthcare, Inc). For each transverse image, a fat-fraction was automatically measured as the number of fat pixels (-200 to -50 HU) divided by the total number of pixels having an attenuation value ≥-200 HU. Estimated LBW (eLBW) of five single contiguous sections was calculated in each of three abdominal regions (upper abdomen, mid abdomen and pelvis) by multiplying TBW by (1 – fat-fraction). Bland-Altman plot with limits of agreement was used to assess agreement between mLBW and eLBW. The mean mLBW for all patients was 56 kg (range, 39 - 75 kg). Mean differences and limits of agreement between mLBW and eLBW measurements for the upper abdomen, mid abdomen and pelvis reported were -8.9 kg (-25.6 kg, +7.5 kg), -10.6 kg (-27.7 kg, +6.4 kg), and +0.5 kg (-12.8 kg, +13.8 kg) respectively. eLBW deriving directly from a transverse CT image of the pelvis can accurately predict mLBW.展开更多
文摘Purpose: To present an application of the anisotropic diffusion (AD) method to improve the accuracy of the functional images of perfusion parameters such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) generated from cerebral CT perfusion studies using multi-detector row CT (MDCT). Materials and Methods: Continuous scans (1 sec/rotation ×60 sec) consisting of four 5-mm-thick contiguous slices were acquired after an intravenous injection of iodinated contrast material in 6 patients with cerebrovascular disease using an MDCT scanner with a tube voltage of 80 kVp and a tube current of 200 mA. New image data were generated by thinning out the above original images at an interval of 2 sec or 3 sec. The thinned-out images were then interpolated by linear interpolation to generate the same number of images as originally acquired. The CBF, CBV and MTT images were generated using deconvolution analysis based on singular value decomposition. Results: When using the AD method, the correlation coefficient between the MTT values obtained from the original and thinned-out images was significantly improved. Furthermore, the coefficients of variation of the CBF, CBV and MTT values in the white matter significantly decreased as compared to not using the AD method. Conclusion: Our results suggest that the AD method is useful for improving the accuracy of the functional images of perfusion parameters and for reducing radiation exposure in cerebral CT perfusion studies using MDCT.
文摘Estimated LBW could be used to determine the contrast material dose and rate during MDCT. The aim of this study is to test the accuracy of a technique for estimation of lean body weight (LBW) from a single multi-detector row computed tomographic (MDCT) abdominal image, using a bioelectrical body composition analyzer scale as the reference standard. CT images of 21 patients with previously measured LBW (mLBW) were processed using computer-assisted, vendor-specific software (Advantage Windows 4.2;GE Healthcare, Inc). For each transverse image, a fat-fraction was automatically measured as the number of fat pixels (-200 to -50 HU) divided by the total number of pixels having an attenuation value ≥-200 HU. Estimated LBW (eLBW) of five single contiguous sections was calculated in each of three abdominal regions (upper abdomen, mid abdomen and pelvis) by multiplying TBW by (1 – fat-fraction). Bland-Altman plot with limits of agreement was used to assess agreement between mLBW and eLBW. The mean mLBW for all patients was 56 kg (range, 39 - 75 kg). Mean differences and limits of agreement between mLBW and eLBW measurements for the upper abdomen, mid abdomen and pelvis reported were -8.9 kg (-25.6 kg, +7.5 kg), -10.6 kg (-27.7 kg, +6.4 kg), and +0.5 kg (-12.8 kg, +13.8 kg) respectively. eLBW deriving directly from a transverse CT image of the pelvis can accurately predict mLBW.
