scholarly journals Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi-Jui Liu ◽  
Hou-Ting Yang ◽  
Melissa Min-Szu Yao ◽  
Shao-Chieh Lin ◽  
Der-Yang Cho ◽  
...  

AbstractThe purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters Ktrans, ve, and vp, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal–Wallis test with a Dunn–Bonferroni post hoc analysis were performed. In simulation, Ktrans and ve increased as the peak in the AIF decreased, but vp increased when delay time in the AIF increased. In humans, the estimated Ktrans and ve were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

2016 ◽  
Vol 269 ◽  
pp. 104-112 ◽  
Author(s):  
Xin Li ◽  
Yu Cai ◽  
Brendan Moloney ◽  
Yiyi Chen ◽  
Wei Huang ◽  
...  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mercy I. Akerele ◽  
Sara A. Zein ◽  
Sneha Pandya ◽  
Anastasia Nikolopoulou ◽  
Susan A. Gauthier ◽  
...  

Abstract Introduction Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and methods Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. Results Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is −10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%). Conclusions PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.


2020 ◽  
Vol 61 (11) ◽  
pp. 1512-1519 ◽  
Author(s):  
Wen Chen ◽  
Hao Hu ◽  
Huan-Huan Chen ◽  
Guo-Yi Su ◽  
Tao Yang ◽  
...  

Background Discriminating the stage of thyroid-associated ophthalmopathy (TAO) is crucial for the treatment strategy and prognosis prediction. Utility of conventional magnetic resonance imaging in the disease staging is limited. Purpose To investigate the performance of T2 mapping based on different region of interest (ROI) selection methods in the staging of TAO. Material and Methods Thirty-two patients with TAO were retrospectively enrolled. Two radiologists independently measured the T2 relaxation time (T2RT) of extraocular muscles using two different ROIs (hotspot [ROIHS]: T2RT-hot; single-slice [ROISS]: T2RT-mean, T2RT-max, T2RT-min). Independent-samples t test, Wilcoxon signed rank test, Spearman correlation analysis, receiver operating characteristic (ROC) curves analyses, multiple ROC comparisons, and intra-class correlation coefficient (ICC) were used for statistical analyses. Results No significant difference was found in the measuring time between ROIHS and ROISS methods ( P = 0.066). T2RT-mean demonstrated the highest ICC for measurement, followed by T2RT-max and T2RT-min, and T2RT-hot showed the poorest reproducibility. Active TAOs showed significantly higher values for all the T2RTs than inactive mimics (all P < 0.001). Significant positive correlations were found between T2RTs and CAS (all P < 0.005). T2RT-hot and T2RT-max showed significantly higher areas under the curve than that of T2RT-mean ( P = 0.013 and 0.024, respectively), while the difference between T2RT-hot and T2RT-max was not significant ( P = 0.970). Conclusion The T2RTs derived from both ROI selection methods could be useful for the staging of TAO. The results of measuring time, reproducibility, and diagnostic performance suggest that T2RT-max would be the optimal indicator for staging.


2010 ◽  
Vol 55 (16) ◽  
pp. 4871-4883 ◽  
Author(s):  
M Heisen ◽  
X Fan ◽  
J Buurman ◽  
N A W van Riel ◽  
G S Karczmar ◽  
...  

2016 ◽  
Vol 43 (6Part25) ◽  
pp. 3644-3644
Author(s):  
N Majtenyi ◽  
H Gabrani-Juma ◽  
R Klein ◽  
RA deKemp ◽  
G Cron ◽  
...  

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Parastou Eslami ◽  
Jung-Hee Seo ◽  
Albert C. Lardo ◽  
Marcus Y. Chen ◽  
Rajat Mittal

The arterial input function (AIF)—time-density curve (TDC) of contrast at the coronary ostia—plays a central role in contrast enhanced computed tomography angiography (CTA). This study employs computational modeling in a patient-specific aorta to investigate mixing and dispersion of contrast in the aortic arch (AA) and to compare the TDCs in the coronary ostium and the descending aorta. Here, we examine the validity of the use of TDC in the descending aorta as a surrogate for the AIF. Computational fluid dynamics (CFD) was used to study hemodynamics and contrast dispersion in a CTA-based patient model of the aorta. Variations in TDC between the aortic root, through the AA and at the descending aorta and the effect of flow patterns on contrast dispersion was studied via postprocessing of the results. Simulations showed complex unsteady patterns of contrast mixing and dispersion in the AA that are driven by the pulsatile flow. However, despite the relatively long intra-aortic distance between the coronary ostia and the descending aorta, the TDCs at these two locations were similar in terms of rise-time and up-slope, and the time lag between the two TDCs was 0.19 s. TDC in the descending aorta is an accurate analog of the AIF. Methods that use quantitative metrics such as rise-time and slope of the AIF to estimate coronary flowrate and myocardial ischemia can continue with the current practice of using the TDC at the descending aorta as a surrogate for the AIF.


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