scholarly journals Modeling tissues contrast agent concentration: A solution to the tissue homogeneity model using a simulated arterial input function

2001 ◽  
Vol 46 (4) ◽  
pp. 837-837 ◽  
Author(s):  
Jyrki T. Kuikka
Keyword(s):  
Contrast Agent ◽  
Arterial Input Function ◽  
Input Function ◽  
Contrast Agent Concentration

scholarly journals Dynamic contrast-enhanced QSM for perfusion imaging: a systematic comparison of ΔR2*- and QSM-based contrast agent concentration time curves in blood and tissue

2020 ◽  
Vol 33 (5) ◽  
pp. 663-676
Author(s):  
Emelie Lind ◽  
Linda Knutsson ◽  
Freddy Ståhlberg ◽  
Ronnie Wirestam
Keyword(s):  
Contrast Agent ◽  
Arterial Input Function ◽  
Tissue Concentration ◽  
Input Function ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Concentration Data ◽  
Concentration Time ◽  
Dynamic Susceptibility Contrast Mri ◽  
Dsc Mri

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.

scholarly journals Multiple reference tissue method for contrast agent arterial input function estimation

2007 ◽  
Vol 58 (6) ◽  
pp. 1266-1275 ◽  
Author(s):  
Cheng Yang ◽  
Gregory S. Karczmar ◽  
Milica Medved ◽  
Walter M. Stadler
Keyword(s):  
Contrast Agent ◽  
Arterial Input Function ◽  
Input Function ◽  
Function Estimation ◽  
Reference Tissue ◽  
Multiple Reference

scholarly journals Automated Determination of Arterial Input Function for Dynamic Susceptibility Contrast MRI from Regions around Arteries Using Independent Component Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Sharon Chen ◽  
Yu-Chang Tyan ◽  
Jui-Jen Lai ◽  
Chin-Ching Chang
Keyword(s):  
Independent Component Analysis ◽  
Contrast Agent ◽  
Arterial Input Function ◽  
Input Function ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Independent Component ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Susceptibility Contrast

Purpose.Quantitative cerebral blood flow (CBF) measurement using dynamic susceptibility contrast- (DSC-) MRI requires accurate estimation of the arterial input function (AIF). The present work utilized the independent component analysis (ICA) method to determine the AIF in the regions adjacent to the middle cerebral artery (MCA) by the alleviated confounding of partial volume effect.Materials and Methods.A series of spin-echo EPI MR scans were performed in 10 normal subjects. All subjects received 0.2 mmol/kg Gd-DTPA contrast agent. AIFs were calculated by two methods:(1)the region of interest (ROI) selected manually and(2)weighted average of each component selected by ICA (weighted-ICA). The singular value decomposition (SVD) method was then employed to deconvolve the AIF from the tissue concentration time curve to obtain quantitative CBF values.Results. The CBF values calculated by the weighted-ICA method were 41.1 ± 4.9 and 22.1 ± 2.3 mL/100 g/min for cortical gray matter (GM) and deep white matter (WM) regions, respectively. The CBF values obtained based on the manual ROIs were 53.6 ± 12.0 and 27.9 ± 5.9 mL/100 g/min for the same two regions, respectively.Conclusion.The weighted-ICA method allowed semiautomatic and straightforward extraction of the ROI adjacent to MCA. Through eliminating the partial volume effect to minimum, the CBF thus determined may reflect more accurate physical characteristics of theT2⁎signal changes induced by the contrast agent.

Rapid-Steady-State-T1 signal modeling during contrast agent extravasation: Toward tumor blood volume quantification without requiring the arterial input function

2014 ◽  
Vol 73 (3) ◽  
pp. 1005-1014 ◽  
Author(s):  
Michel Sarraf ◽  
Adriana Teodora Perles-Barbacaru ◽  
Marie France Nissou ◽  
Boudewijn van der Sanden ◽  
François Berger ◽  
...  
Keyword(s):  
Steady State ◽  
Contrast Agent ◽  
Blood Volume ◽  
Arterial Input Function ◽  
Input Function ◽  
Signal Modeling

scholarly journals Computational Fluid Dynamics Simulations of Contrast Agent Bolus Dispersion in a Coronary Bifurcation: Impact on MRI-Based Quantification of Myocardial Perfusion

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Regine Schmidt ◽  
Dirk Graafen ◽  
Stefan Weber ◽  
Laura M. Schreiber
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Myocardial Perfusion ◽  
Contrast Agent ◽  
Arterial Input Function ◽  
Myocardial Perfusion Reserve ◽  
Input Function ◽  
Coronary Bifurcation ◽  
Perfusion Reserve ◽  
Contrast Agent Bolus

Contrast-enhanced first-pass magnetic resonance imaging (MRI) in combination with a tracer kinetic model, for example, MMID4, can be used to determine myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Typically, the arterial input function (AIF) required for this methodology is estimated from the left ventricle (LV). Dispersion of the contrast agent bolus might occur between the LV and the myocardial tissue. Negligence of bolus dispersion could cause an error in MBF determination. The aim of this study was to investigate the influence of bolus dispersion in a simplified coronary bifurcation geometry including one healthy and one stenotic branch on the quantification of MBF and MPR. Computational fluid dynamics (CFD) simulations were combined with MMID4. Different inlet boundary conditions describing pulsatile and constant flows for rest and hyperemia and differing outflow conditions have been investigated. In the bifurcation region, the increase of the dispersion was smaller than inside the straight vessels. A systematic underestimation of MBF values up to −16.1% for pulsatile flow and an overestimation of MPR up to 7.5% were found. It was shown that, under the conditions considered in this study, bolus dispersion can significantly influence the results of quantitative myocardial MR-perfusion measurements.

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