Background:
Arterial input function (AIF) accurate extraction is an important step in the quantification of cerebral perfusion hemodynamic parameters using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI).
IJ Radiology
An Innovative Journal in the Field of Radiology
An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors
Author(s):
1Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
Published online:Dec 10, 2019
Article type:Abstract
Received:Oct 26, 2019
Accepted:Dec 10, 2019
How to Cite:Rahimzadeh H, Rezaie Molood S, Fathi Kazerooni A, Saligheh Rad H. An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors. I J Radiol. 2019;16(Special Issue):e99136. doi: https://doi.org/10.5812/iranjradiol.99136
Abstract
Objectives:
In this study, using machine learning methods, an optimal automatic algorithm was developed to accurately detect AIF in DSC-MRI of glioma brain tumors with a new pre-processing method.
Methods:
DSC-MR images of 43 patients with glioma brain tumors were retrieved retrospectively. Our proposed method consisted of a pre-processing step to remove non-arterial curves such as tumorous, tissue, noisy, and partial-volume affected curves and a clustering step through agglomerative hierarchical (AH) clustering method to cluster the remaining curves. The performance of automatic AIF clustering was compared with the performance of manual AIF selection by an experienced radiologist, based on curve shape parameters, i.e., maximum peak (MP), full-width-at-half-maximum (FWHM), M (= MP / (TTP × FWHM)), and root mean square error (RMSE).
Results:
The mean values of AIFs shape parameters were compared with those derived from manually selected AIFs by a two-tailed Paired t-test. The results showed statistically insignificant differences in MP, FWHM, and M parameters and lower RMSE, confirming the resemblance of the selected AIF with the gold standard. The intraclass correlation coefficient and percentage coefficient of variation showed a better agreement between manual AIF and our proposed AIF selection method rather than previously proposed methods.
Conclusion:
The results of the current work suggest that by using efficient preprocessing steps, the accuracy of automatic AIF selection could be improved and this method appears to be promising for efficient and accurate clinical applications.
Copyright
Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
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