The precision of pharmacokinetic parameters in dynamic contrast-enhanced magnetic resonance imaging: the effect of sampling frequency and duration

2011 ◽  
Vol 56 (17) ◽  
pp. 5665-5678 ◽  
Author(s):  
Hugo J W L Aerts ◽  
K Jaspers ◽  
Walter H Backes
2016 ◽  
Vol 16 (4) ◽  
pp. 446-460 ◽  
Author(s):  
Chunhao Wang ◽  
Fang-Fang Yin ◽  
John P. Kirkpatrick ◽  
Zheng Chang

Purpose: To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. Methods: Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio–optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant Ktrans in the extended Tofts model and blood flow FB and blood volume VB from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter’s accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. Results: The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data. Conclusion: With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.


Head & Neck ◽  
2021 ◽  
Author(s):  
Soumya Ranjan Malla ◽  
Ashu Seith Bhalla ◽  
Smita Manchanda ◽  
Devasenathipathy Kandasamy ◽  
Rakesh Kumar ◽  
...  

Author(s):  
L. A. R. Righesso ◽  
M. Terekhov ◽  
H. Götz ◽  
M. Ackermann ◽  
T. Emrich ◽  
...  

Abstract Objectives Micro-computed tomography (μ-CT) and histology, the current gold standard methods for assessing the formation of new bone and blood vessels, are invasive and/or destructive. With that in mind, a more conservative tool, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), was tested for its accuracy and reproducibility in monitoring neovascularization during bone regeneration. Additionally, the suitability of blood perfusion as a surrogate of the efficacy of osteoplastic materials was evaluated. Materials and methods Sixteen rabbits were used and equally divided into four groups, according to the time of euthanasia (2, 3, 4, and 6 weeks after surgery). The animals were submitted to two 8-mm craniotomies that were filled with blood or autogenous bone. Neovascularization was assessed in vivo through DCE-MRI, and bone regeneration, ex vivo, through μ-CT and histology. Results The defects could be consistently identified, and their blood perfusion measured through DCE-MRI, there being statistically significant differences within the blood clot group between 3 and 6 weeks (p = 0.029), and between the former and autogenous bone at six weeks (p = 0.017). Nonetheless, no significant correlations between DCE-MRI findings on neovascularization and μ-CT (r =−0.101, 95% CI [−0.445; 0.268]) or histology (r = 0.305, 95% CI [−0.133; 0.644]) findings on bone regeneration were observed. Conclusions These results support the hypothesis that DCE-MRI can be used to monitor neovascularization but contradict the premise that it could predict bone regeneration as well.


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