Hepatic Perfusion CT Imaging Analyzed by the Dual-input One-compartment Model

Funabasama, Shintaro; Tsushima, Yoshito; Sanada, Shigeru; Inoue, Keiji

doi:10.6009/jjrt.kj00000921686

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Recently, dual-input, one-compartment model analysis was adopted for dynamic CT perfusion analysis in liver disease. [9][10][11] This method may be more reliable than single-input, one-compartment model analysis. However, in either case, it is very di‹cult to apply an optimal model for the target organ or disease.…”

Section: Discussionmentioning

confidence: 99%

Hemodynamic Changes with Liver Fibrosis Measured by Dynamic Contrast-Enhanced MRI in the Rat

et al. 2006

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Purpose: To evaluate the hemodynamic changes of liver cirrhosis in the rat and investigate the relationship between hemodynamic changes and properties ofˆbrotic change in the liver.Materials and Methods: Three rats with cirrhosis induced by thioacetamide (TAA), three with disease induced by carbon tetrachloride (CCl 4 ), and three with no treatment were measured on dynamic MRI using a 1.5T scanner. Compartment and moment analysis were used to quantitate hemodynamic changes.Results: Compartment model analysis showed that increased transition speed from vessels to the liver correlated with grade of liverˆbrosis. Moment analysis demonstrated that decrease of area under the curve (AUC), mean residence time (MRT), variance of residence time (VRT), half life (T1 W 2) and increased total clearance (CL) correlated with grade of liverˆbrosis.Conclusions: Hemodynamic changes in injuredˆbrotic liver may be in‰uenced by the grade ofˆbrosis. Compartment model and moment analysis may be useful for evaluating hemodynamic changes in injured liver.

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Section: Discussionmentioning

confidence: 99%

Hemodynamic Changes with Liver Fibrosis Measured by Dynamic Contrast-Enhanced MRI in the Rat

et al. 2006

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“…A detailed description of these methods is available elsewhere. 1,2,[12][13][14][15][16][17] Liver ROIs on the perfusion maps for perfusion measurements were made as large as possible to cover the entire hepatic normal parenchyma on the slice while avoiding large vessels and focal liver lesions if they were present. Arrival time of contrast material at the abdominal aorta (arrival time), transit time, and compensation for respiratory misregistration were recorded as extrahepatic factors, which could affect estimated hepatic perfusion values.…”

Section: Image Analysismentioning

confidence: 99%

“…Subsequently, hepatic arterial and portal perfusions (HAP and HPP, ml/min/dl) and arterial perfusion fraction (APF, %) were calculated with the above-mentioned two methods using free software (Basama Perfusion Version 3.1.0.4). 14 Regions of interest (ROIs) were placed at the abdominal aorta at the level of the celiac axis, the main portal vein, and the liver to generate time-density curves (TDCs). Based on these TDCs, with the MS method, HAP was determined by dividing the peak gradient of the hepatic TDC before the peak splenic enhancement (arterial-dominant phase) by the peak aortic enhancement.…”

Section: Image Analysismentioning

confidence: 99%

Hepatic computed tomography perfusion: comparison of maximum slope and dual-input single-compartment methods

et al. 2010

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Purpose. The aim of the study was to compare two analytical methods-maximum slope (MS) and the dualinput single-compartment model (CM)-in computed tomography (CT) measurements of hepatic perfusion and to assess the effects of extrahepatic systemic factors. Materials and methods. A total of 109 patients underwent hepatic CT perfusion. The scans were conducted at the hepatic hilum 7-77 s after administration of contrast material. Hepatic arterial perfusion (HAP) and portal perfusion (HPP) (ml/min/100 ml) and the arterial perfusion fraction (APF, %) were calculated with the two methods, followed by correlation assessment. Partial correlation analysis was used to assess the effects on hepatic perfusion values by various factors, including age, sex, risk of cardiovascular disease, compensation for respiratory misregistration, arrival time of contrast material at the abdominal aorta, transit time from abdominal aorta to hepatic parenchyma, and liver dysfunction. Results. The mean HAPs, HPPs, and APFs were, respectively, 31.4, 104.2, and 23.9 for MS and 27.1, 141.3, and 22.1 for CM. HAP and APF showed signifi cant (P < 0.0001) and moderate correlation (γ = 0.417 and 0.548) and HPP showed poor correlation (γ = 0.172) between the two methods. While MS showed weak correlations (γ = −0.39 to 0.34; P < 0.001 to <0.02) between multiple extrahepatic factors and perfusion values, CM showed weak correlation only between the patients' sex and HAP (γ = 0.31, P = 0.001). Conclusion. Hepatic perfusion values estimated by the two methods are not interchangeable. CM is less susceptible to extrahepatic systemic factors.

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“…Under the imaging condition generally used in clinical settings (Funabasama et al 2003, Miyazaki et al 2008, the signal-to-noise ratio (SNR) for a voxel was approximately 10-20. Therefore, we considered two cases with SNR of 10 and 20 in this study.…”

Section: Computer Simulationmentioning

confidence: 99%

“…First, noise-free C a (t) was generated by interpolating and fitting actually measured data using gamma-variate functions. The data were acquired using a CT scanner (HiSpeed Nx/i, GE-Yokogawa Medical Systems, Tokyo, Japan) with a tube voltage of 120 kV and a tube current of 100 mA (Funabasama et al 2003). A total of 20 scans (12 scans with a duration of 2 s and 8 scans with a duration of 7 s) were acquired with a gantry rotation speed of 1 s/rotation, a slice thickness of 10 mm, and a matrix size of 512 × 512.…”

Section: Appendix Generation Of C a (T) And C P (T)mentioning

confidence: 99%

Error analysis of the quantification of hepatic perfusion using a dual-input single-compartment model

Miyazaki

Yamazaki²,

Murase³

2008

Phys. Med. Biol.

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We performed an error analysis of the quantification of liver perfusion from dynamic contrast-enhanced computed tomography (DCE-CT) data using a dual-input single-compartment model for various disease severities, based on computer simulations. In the simulations, the time-density curves (TDCs) in the liver were generated from an actually measured arterial input function using a theoretical equation describing the kinetic behavior of the contrast agent (CA) in the liver. The rate constants for the transfer of CA from the hepatic artery to the liver (K(1a)), from the portal vein to the liver (K(1p)), and from the liver to the plasma (k(2)) were estimated from simulated TDCs with various plasma volumes (V(0)s). To investigate the effect of the shapes of input functions, the original arterial and portal-venous input functions were stretched in the time direction by factors of 2, 3 and 4 (stretching factors). The above parameters were estimated with the linear least-squares (LLSQ) and nonlinear least-squares (NLSQ) methods, and the root mean square errors (RMSEs) between the true and estimated values were calculated. Sensitivity and identifiability analyses were also performed. The RMSE of V(0) was the smallest, followed by those of K(1a), k(2) and K(1p) in an increasing order. The RMSEs of K(1a), K(1p) and k(2) increased with increasing V(0), while that of V(0) tended to decrease. The stretching factor also affected parameter estimation in both methods. The LLSQ method estimated the above parameters faster and with smaller variations than the NLSQ method. Sensitivity analysis showed that the magnitude of the sensitivity function of V(0) was the greatest, followed by those of K(1a), K(1p) and k(2) in a decreasing order, while the variance of V(0) obtained from the covariance matrices was the smallest, followed by those of K(1a), K(1p) and k(2) in an increasing order. The magnitude of the sensitivity function and the variance increased and decreased, respectively, with increasing disease severity and decreased and increased, respectively, with increasing stretching factor except for V(0). Identifiability analysis showed that the identifiability between K(1)(p) and k(2) was lower than that between K(1)(a) and k(2) or between K(1a) and K(1p). In conclusion, this study will be useful for understanding the accuracy and reliability of the quantitative measurement of liver perfusion using a dual-input single-compartment model and DCE-CT data.

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Hepatic Perfusion CT Imaging Analyzed by the Dual-input One-compartment Model

Abstract: The dual-input one-compartmental model makes it possible to obtain more detailed information on liver hemodynamics.

Cited by 9 publications

References 12 publications

Hemodynamic Changes with Liver Fibrosis Measured by Dynamic Contrast-Enhanced MRI in the Rat

Hemodynamic Changes with Liver Fibrosis Measured by Dynamic Contrast-Enhanced MRI in the Rat

Hepatic computed tomography perfusion: comparison of maximum slope and dual-input single-compartment methods

Error analysis of the quantification of hepatic perfusion using a dual-input single-compartment model

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