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.

Automatic selection of arterial input function on dynamic contrast-enhanced MR images

2011 ◽  
Vol 104 (3) ◽  
pp. e148-e157 ◽  
Author(s):  
Denis Peruzzo ◽  
Alessandra Bertoldo ◽  
Francesca Zanderigo ◽  
Claudio Cobelli
Keyword(s):  
Arterial Input Function ◽  
Input Function ◽  
Mr Images ◽  
Dynamic Contrast Enhanced ◽  
Automatic Selection ◽  
Contrast Enhanced ◽  
Selection Of

scholarly journals Automatic selection of arterial input function using cluster analysis

2006 ◽  
Vol 55 (3) ◽  
pp. 524-531 ◽  
Author(s):  
Kim Mouridsen ◽  
Søren Christensen ◽  
Louise Gyldensted ◽  
Leif Østergaard
Keyword(s):  
Cluster Analysis ◽  
Arterial Input Function ◽  
Input Function ◽  
Automatic Selection ◽  
Selection Of

scholarly journals New criterion to aid manual and automatic selection of the arterial input function in dynamic susceptibility contrast MRI

2010 ◽  
Vol 65 (2) ◽  
pp. 448-456 ◽  
Author(s):  
Egbert J. W. Bleeker ◽  
Matthias J. P. van Osch ◽  
Alan Connelly ◽  
Mark A. van Buchem ◽  
Andrew G. Webb ◽  
...  
Keyword(s):  
Arterial Input Function ◽  
Input Function ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Automatic Selection ◽  
Dynamic Susceptibility Contrast Mri ◽  
Susceptibility Contrast ◽  
New Criterion ◽  
Selection Of

Automatic selection of arterial input function using tri-exponential models

2009 ◽  
Author(s):  
Jianhua Yao ◽  
Jeremy Chen ◽  
Marcelo Castro ◽  
David Thomasson
Keyword(s):  
Arterial Input Function ◽  
Input Function ◽  
Exponential Models ◽  
Automatic Selection ◽  
Selection Of

scholarly journals Selection of catchment descriptors for the physical similarity approach. Part II: Application

2013 ◽  
Vol 8 (No. 4) ◽  
pp. 186-194
Author(s):  
M. Heřmanovský ◽  
P. Pech
Keyword(s):  
Land Cover ◽  
A Priori ◽  
Model Parameters ◽  
Physical Similarity ◽  
Model Parameter ◽  
Soil Moisture Accounting ◽  
Estimated Parameters ◽  
Selection Of

This paper demonstrates an application of the previously published method for selection of optimal catchment descriptors, according to which similar catchments can be identified for the purpose of estimation of the Sacramento – Soil Moisture Accounting (SAC-SMA) model parameters for a set of tested catchments, based on the physical similarity approach. For the purpose of the analysis, the following data from the Model Parameter Estimation Experiment (MOPEX) project were taken: a priori model parameter sets used as reference values for comparison with the newly estimated parameters, and catchment descriptors of four categories (climatic descriptors, soil properties, land cover and catchment morphology). The inverse clustering method, with Andrews’ curves for a homogeneity check, was used for the catchment grouping process. The optimal catchment descriptors were selected on the basis of two criteria, one comparing different subsets of catchment descriptors of the same size (MIN), the other one evaluating the improvement after addition of another catchment descriptor (MAX). The results suggest that the proposed method and the two criteria used may lead to the selection of a subset of conditionally optimal catchment descriptors from a broader set of them. As expected, the quality of the resulting subset of optimal catchment descriptors is mainly dependent on the number and type of the descriptors in the broader set. In the presented case study, six to seven catchment descriptors (two climatic, two soil and at least two land-cover descriptors) were identified as optimal for regionalisation of the SAC-SMA model parameters for a set of MOPEX catchments.

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.

SU-GG-I-134: Applications of the Levenberg-Marquardt Nonlinear Least Square Fitting to Obtain the Optimum Arterial Input Function for DSC-MRI

2008 ◽  
Vol 35 (6Part5) ◽  
pp. 2672-2672
Author(s):  
S Yoon ◽  
G Jahng ◽  
H Khang ◽  
Y Kim ◽  
J Kim ◽  
...  
Keyword(s):  
Arterial Input Function ◽  
Input Function ◽  
Least Square ◽  
Least Square Fitting ◽  
Dsc Mri ◽  
Levenberg Marquardt

Quality of the ICD-11 Beta Draft from the German Perspective: Evaluation Based on the Alphabet of ICD-10-GM 2017

2018 ◽  
Vol 57 (04) ◽  
pp. 168-176
Author(s):  
Jürgen Stausberg
Keyword(s):  
Medical Informatics ◽  
Field Test ◽  
High Reliability ◽  
Post Processing ◽  
Automatic Selection ◽  
Starting Point ◽  
Icd 10 ◽  
German Association ◽  
Selection Of

Summary Objectives: The German Association for Medical Informatics, Biometry and Epidemiology implemented a field test for the ICD-11 Beta Draft. Aim was to analyze completeness and appropriateness of the ICD-11 Beta Draft in its entire breadth. Methods: Starting point was the synonym thesaurus (“Alphabet”) of the German modification of ICD-10. The Alphabet included a list of diagnoses terms that supports the coding of diagnoses with ICD-10. A sample of 60,328 diagnosis terms was drawn to be mapped to the ICD-11 Beta Draft. A subsample of 13,975 diagnosis terms was prepared for assessing reliability. First, the coders had to assign a diagnosis term from the sample to an appropriate English one. This included the automatic selection of the respective code from the ICD-11 Beta Draft. Secondly, the coders had to answer questions regarding completeness, appropriateness, and other issues. Results: Finally, 49,184 results from 36 coders were available for the analysis. Problems with completeness were indicated in 4.7% of the results, problems with appropriateness in 5.3%. On the level of chapters, Cohen’s kappa reached grade “fair” at a maximum. The coders agreed in 31.4% of the terms. Conclusions: Problems with the ICD-11 Beta Draft appeared to be moderate. Completeness was high, reliability was low as it is known for ICD-10. Concerns with the structure of the ICD-11 Beta Draft were noted, e. g. for neoplasms. A post processing of the ICD-11 Beta Draft seems to be sufficient with regard to the content. Methodologically, a thorough review of the structure might be advisable.

ANTONIA Perfusion and Stroke

2014 ◽  
Vol 53 (06) ◽  
pp. 469-481 ◽  
Author(s):  
B. Cheng ◽  
A. Kemmling ◽  
G. Thomalla ◽  
J. Fiehler ◽  
N. D. Forkert
Keyword(s):  
Ischemic Stroke ◽  
Quantitative Analysis ◽  
Acute Ischemic Stroke ◽  
Arterial Input Function ◽  
Software Tool ◽  
Input Function ◽  
Lesion Volume ◽  
Acute Ischemic Stroke Patient ◽  
Stroke Research ◽  
Selection Of

SummaryObjectives: The objective of this work is to present the software tool ANTONIA, which has been developed to facilitate a quantitative analysis of perfusion-weighted MRI (PWI) datasets in general as well as the subsequent multi-parametric analysis of additional datasets for the specific purpose of acute ischemic stroke patient dataset evaluation.Methods: Three different methods for the analysis of DSC or DCE PWI datasets are currently implemented in ANTONIA, which can be case-specifically selected based on the study protocol. These methods comprise a curve fitting method as well as a deconvolution-based and deconvolution-free method integrating a previously defined arterial input function. The perfusion analysis is extended for the purpose of acute ischemic stroke analysis by additional methods that enable an automatic atlas-based selection of the arterial input function, an analysis of the perfusion-diffusion and DWI-FLAIR mismatch as well as segmentation-based volumetric analyses.Results: For reliability evaluation, the de-scribed software tool was used by two ob-servers for quantitative analysis of 15 data-sets from acute ischemic stroke patients to extract the acute lesion core volume, FLAIR ratio, perfusion-diffusion mismatch volume with manually as well as automatically selected arterial input functions, and follow-up lesion volume. The results of this evaluation revealed that the described software tool leads to highly reproducible results for all parameters if the automatic arterial input function selection method is used.Conclusion: Due to the broad selection of processing methods that are available in the software tool, ANTONIA is especially helpful to support image-based perfusion and acute ischemic stroke research projects.

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