An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors

• Main Authors: Rahimzadeh H., Fathi Kazerooni A., Deevband M. R., Saligheh Rad H.
• Format: Article
• Language: English
• Published: Shiraz University of Medical Sciences 2019-02-01
• Series: Journal of Biomedical Physics and Engineering
• Subjects: Perfusion; Dynamic Susceptibility Contrast Enhanced MRI; Arterial Input Function; Cluster Analysis
• Online Access: http://jbpe.ir/Journal_OJS/JBPE/index.php/jbpe/article/view/899/472
• ISSN: 2251-7200; 2251-7200

Introduction: Automatic and accurate arterial input function (AIF) selection has an essential role for quantification of cerebral perfusion hemodynamic parameters using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). The purpose of this study is to develop an optimal automatic method for arterial input function determination in DSC-MRI of glioma brain tumors by using a new preprocessing method. Material and Methods: For this study, DSC-MR images of 43 patients with glioma brain tumors were retrieved retrospectively. Our proposed AIF selection framework consisted an effcient pre-processing step, through which non-arterial curves such as tumorous, tissue, noisy and partial-volume affected curves were excluded, followed by AIF selection through agglomerative hierarchical (AH) clustering method. The performance of automatic AIF clustering was compared with manual AIF selection performed 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: Mean values of AIFs shape parameters were compared with those derived from manually selected AIFs by two-tailed paired t-test. The results showed statistically insignificant differences in MP, FWHM, and M parameters and lower RMSE, approving the resemblance of the selected AIF with the gold standard. The intraclass correlation coefficient and coefficients of variation percent showed a better agreement between manual AIF and our proposed AIF selection than previously proposed methods. Conclusion: The results of current work suggest that by using efficient preprocessing steps, the accuracy of automatic AIF selection could be improved and this method appears promising for efficient and accurate clinical applications.