Feasibility and measurement precision of 3D quantitative blood flow mapping of the prostate using dynamic contrast-enhanced multi-slice CT

2006 ◽  
Vol 51 (17) ◽  
pp. 4329-4343 ◽  
Author(s):  
Cécile R L P N Jeukens ◽  
Cornelis A T van den Berg ◽  
Remco Donker ◽  
Marco van Vulpen ◽  
Chris J G Bakker ◽  
...  
Radiology ◽  
1990 ◽  
Vol 176 (1) ◽  
pp. 211-220 ◽  
Author(s):  
R R Edelman ◽  
H P Mattle ◽  
D J Atkinson ◽  
T Hill ◽  
J P Finn ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Anika Sauerbrey ◽  
Stefan Hindel ◽  
Marc Maaß ◽  
Christine Krüger ◽  
Andreas Wissmann ◽  
...  

The aim of the study was to develop a suitable animal model for validating dynamic contrast-enhanced magnetic resonance imaging perfusion measurements. A total of 8 pigs were investigated by DCE-MRI. Perfusion was determined on the hind leg musculature. An ultrasound flow probe placed around the femoral artery provided flow measurements independent of MRI and served as the standard of reference. Images were acquired on a 1.5 T MRI scanner using a 3D T1-weighted gradient-echo sequence. An arterial catheter for local injection was implanted in the femoral artery. Continuous injection of adenosine for vasodilation resulted in steady blood flow levels up to four times the baseline level. In this way, three different stable perfusion levels were induced and measured. A central venous catheter was used for injection of two different types of contrast media. A low-molecular weight contrast medium and a blood pool contrast medium were used. A total of 6 perfusion measurements were performed with a time interval of about 20–25 min without significant differences in the arterial input functions. In conclusion the accuracy of DCE-MRI-based perfusion measurement can be validated by comparison of the integrated perfusion signal of the hind leg musculature with the blood flow values measured with the ultrasound flow probe around the femoral artery.


2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zongfang Li ◽  
Wei Zhao ◽  
Bo He ◽  
Tong San Koh ◽  
Yanxi Li ◽  
...  

Previous studies using contrast-enhanced imaging for glioma isocitrate dehydrogenase (IDH) mutation assessment showed promising yet inconsistent results, and this study attempts to explore this problem by using an advanced tracer kinetic model, the distributed parameter model (DP). Fifty-five patients with glioma examined using dynamic contrast-enhanced imaging sequence at a 3.0 T scanner were retrospectively reviewed. The imaging data were processed using DP, yielding the following parameters: blood flow F, permeability-surface area product PS, fractional volume of interstitial space Ve, fractional volume of intravascular space Vp, and extraction ratio E. The results were compared with the Tofts model. The Wilcoxon test and boxplot were utilized for assessment of differences of model parameters between IDH-mutant and IDH-wildtype gliomas. Spearman correlation r was employed to investigate the relationship between DP and Tofts parameters. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and quantified using the area under the ROC curve (AUC). Results showed that IDH-mutant gliomas were significantly lower in F ( P  = 0.018), PS ( P < 0.001 ), Vp ( P < 0.001 ), E ( P < 0.001 ), and Ve ( P  = 0.002) than IDH-wildtype gliomas. In differentiating IDH-mutant and IDH-wildtype gliomas, Vp had the best performance (AUC = 0.92), and the AUCs of PS and E were 0.82 and 0.80, respectively. In comparison, Tofts parameters were lower in Ktrans ( P  = 0.013) and Ve ( P < 0.001 ) for IDH-mutant gliomas. No significant difference was observed in Kep ( P  = 0.525). The AUCs of Ktrans, Ve, and Kep were 0.69, 0.79, and 0.55, respectively. Tofts-derived Ve showed a strong correlation with DP-derived Ve (r > 0.9, P < 0.001 ). Ktrans showed a weak correlation with F (r < 0.3, P  > 0.16) and a very weak correlation with PS (r < 0.06, P  > 0.8), both of which were not statistically significant. The findings by DP revealed a tissue environment with lower vascularity, lower vessel permeability, and lower blood flow in IDH-mutant than in IDH-wildtype gliomas, being hostile to cellular differentiation of oncogenic effects in IDH-mutated gliomas, which might help to explain the better outcomes in IDH-mutated glioma patients than in glioma patients of IDH-wildtype. The advantage of DP over Tofts in glioma DCE data analysis was demonstrated in terms of clearer elucidation of tissue microenvironment and better performance in IDH mutation assessment.


2018 ◽  
Vol 63 (3) ◽  
pp. 035008 ◽  
Author(s):  
Cristian Borrazzo ◽  
Nicola Galea ◽  
Massimiliano Pacilio ◽  
Luisa Altabella ◽  
Enrico Preziosi ◽  
...  

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 14109-14109 ◽  
Author(s):  
C. H. Thng ◽  
T. S. Koh ◽  
H. Rumpel ◽  
J. B. Khoo ◽  
A. B. Ong ◽  
...  

14109 Background: Transfer constant (Ktrans) and IAUC60 normalized with arterial input function are commonly used dynamic contrast enhanced magnetic resonance imaging (DCE MRI) parameters. The distributed parameters model (DP) is a DCE MRI model that enables derivation of blood flow and capillary permeability-surface area product (PS). We aim to study the distributed parameters model as an alternative method of angiogenesis assessment and correlate the above parameters to drug exposure and patient outcome in a Phase I anti- angiogenic trial. Methods: Fifteen evaluable patients from an on-going Phase I trial (ABT 869) with 3 dose escalations formed the study population. Pharmacokinetic study was performed on Day 1 and the area under the concentration time curve extrapolated to infinity (AUCinfinity) was used as an indicator of drug exposure. All patients underwent DCE MRI at baseline, Day 3 and Day 15 with temporal resolution of 4 seconds. Gadolinium concentrations were estimated using a dual flip angle method. Patients demonstrating progressive disease in first 2 evaluation scans (cycle 2 or 4) based on RECIST criteria were considered progressors and all other patients non-progressors. Receiver operating curve (ROC) analysis was performed. Correlation with AUCinfinity was analyzed. Results: There is good correlation (Spearman’s coefficient -0.67, p = 0.008) between AUCinfinity and DP derived PS and less strong correlation with normalized IAUC60 (Spearman’s coefficient -0.57, p = 0.03). There is no correlation for Ktrans (Spearman’s coefficient 0.04). ROC analysis for predicting progressors versus non-progressors showed a higher ROC area for PS compared to Ktrans (0.83 versus 0.47, p = 0.037). Normalized IAUC60 showed a slightly lower area compared to PS (0.77 versus 0.83) but the difference is not significant (p = 0.58). Conclusions: PS derived from DP model shows better correlation with drug exposure and may predict patient outcome better than Ktrans. [Table: see text]


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