scholarly journals Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e100308 ◽  
Author(s):  
Jiandong Yin ◽  
Jiawen Yang ◽  
Qiyong Guo
Keyword(s):  
Clustering Algorithm ◽  
Arterial Input Function ◽  
Automatic Detection ◽  
Input Function ◽  
Dsc Mri

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.

Automatic selection of arterial input function in DSC-MRI measurements for calculation of brain perfusion parameters using parametric modelling

2018 ◽  
Vol 13 (6) ◽  
pp. 58
Author(s):  
Seweryn Lipiński ◽  
Renata Kalicka
Keyword(s):  
Arterial Input Function ◽  
Regression Function ◽  
Input Function ◽  
Model Parameters ◽  
Parametric Modelling ◽  
Automatic Selection ◽  
Dsc Mri ◽  
Mri Measurements ◽  
Selection Of

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.

scholarly journals Simultaneous acquisition of dynamic PET-MRI: arterial input function using DSC-MRI and [18F]-FET

2015 ◽  
Vol 2 (S1) ◽  
Author(s):  
Liliana Caldeira ◽  
Seong Dae Yun ◽  
Nuno da Silva ◽  
Christian Filss ◽  
Juergen Scheins ◽  
...  
Keyword(s):  
Arterial Input Function ◽  
Input Function ◽  
Dynamic Pet ◽  
Simultaneous Acquisition ◽  
Dsc Mri

scholarly journals Validation of an automatic reference region extraction for the quantification of [18F]DPA-714 in dynamic brain PET studies

2017 ◽  
Vol 38 (2) ◽  
pp. 333-346 ◽  
Author(s):  
Daniel García-Lorenzo ◽  
Sonia Lavisse ◽  
Claire Leroy ◽  
Catriona Wimberley ◽  
Benedetta Bodini ◽  
...  
Keyword(s):  
Gray Matter ◽  
Clustering Algorithm ◽  
Arterial Input Function ◽  
Neurological Diseases ◽  
Input Function ◽  
Translocator Protein ◽  
Reference Region ◽  
Supervised Clustering ◽  
Region Extraction ◽  
Highly Correlated

There is a great need for a non-invasive methodology enabling the quantification of translocator protein overexpression in PET clinical imaging. [18F]DPA-714 has emerged as a promising translocator protein radiotracer as it is fluorinated, highly specific and returned reliable quantification using arterial input function. Cerebellum gray matter was proposed as reference region for simplified quantification; however, this method cannot be used when inflammation involves cerebellum. Here we adapted and validated a supervised clustering (supervised clustering algorithm (SCA)) for [18F]DPA-714 analysis. Fourteen healthy subjects genotyped for translocator protein underwent an [18F]DPA-714 PET, including 10 with metabolite-corrected arterial input function and three for a test–retest assessment. Two-tissue compartmental modelling provided [Formula: see text] estimates that were compared to either [Formula: see text] or [Formula: see text] generated by Logan analysis (using supervised clustering algorithm extracted reference region or cerebellum gray matter). The supervised clustering algorithm successfully extracted a pseudo-reference region with similar reliability using classes that were defined using either all subjects, or separated into HAB and MAB subjects. [Formula: see text], [Formula: see text] and [Formula: see text] were highly correlated (ICC of 0.91 ± 0.05) but [Formula: see text] were ∼26% higher and less variable than [Formula: see text]. Reproducibility was good with 5% variability in the test–retest study. The clustering technique for [18F]DPA-714 provides a simple, robust and reproducible technique that can be used for all neurological diseases.

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