Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI

Author(s):  
Koung Mi Kang ◽  
Seung Hong Choi ◽  
Park Chul-Kee ◽  
Tae Min Kim ◽  
Sung-Hye Park ◽  
...  
2020 ◽  
Vol 33 (5) ◽  
pp. 663-676
Author(s):  
Emelie Lind ◽  
Linda Knutsson ◽  
Freddy Ståhlberg ◽  
Ronnie Wirestam

Abstract Objective In dynamic susceptibility contrast MRI (DSC-MRI), an arterial input function (AIF) is required to quantify perfusion. However, estimation of the concentration of contrast agent (CA) from magnitude MRI signal data is challenging. A reasonable alternative would be to quantify CA concentration using quantitative susceptibility mapping (QSM), as the CA alters the magnetic susceptibility in proportion to its concentration. Material and methods AIFs with reasonable appearance, selected on the basis of conventional criteria related to timing, shape, and peak concentration, were registered from both ΔR2* and QSM images and mutually compared by visual inspection. Both ΔR2*- and QSM-based AIFs were used for perfusion calculations based on tissue concentration data from ΔR2*as well as QSM images. Results AIFs based on ΔR2* and QSM data showed very similar shapes and the estimated cerebral blood flow values and mean transit times were similar. Analysis of corresponding ΔR2* versus QSM-based concentration estimates yielded a transverse relaxivity estimate of 89 s−1 mM−1, for voxels identified as useful AIF candidate in ΔR2* images according to the conventional criteria. Discussion Interestingly, arterial concentration time curves based on ΔR2* versus QSM data, for a standard DSC-MRI experiment, were generally very similar in shape, and the relaxivity obtained in voxels representing blood was similar to tissue relaxivity obtained in previous studies.


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 ◽  
...  

2018 ◽  
Vol 13 (6) ◽  
pp. 58
Author(s):  
Seweryn Lipiński ◽  
Renata Kalicka

A novel method and algorithm of automatic selection of arterial input function (AIF) is presented and its efficiency is proved using exemplary DSC-MRI measurements. The method chooses AIF devoted to a particular purpose, which is calculation of perfusion parameters with the use of parametric modelling of DSC-MRI data. The quality of medical diagnosis made on the basis of perfusion parameters depends on the quality of these parameters, which in turn is determined by the quality of the AIF signal. The proposed algorithm combines physiological requirements for AIF with mathematical criteria. The choice of parametric approach, instead of black-box modelling, allows better understanding of the investigated system functioning, as model parameters may be credited with physical interpretation. Furthermore, using multi-compartmental model of the DSC-MRI data with AIF regression function in an exponential form, gives direct, analytic results concerning the basic descriptors of AIF. The method chooses candidates for AIF on the basis of the descriptors quality. This step allows rejecting measurements which do not fulfil fundamental requirements concerning AIF from the physiological point of view. As these requirements are met, the next criterion can be adopted, that is the quality of fitting the regression function to measurements. The final step is choosing the AIF for calculating perfusion parameters with the best accuracy, which is attainable thanks to implementing the AIF devoted particularly to parametric modelling.


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