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Rahimzadeh, H, Fathi Kazerooni, A, Deevband, M R, Saligheh Rad, H. (1397). An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors. سامانه مدیریت نشریات علمی, 9(1), 69-80. doi: 10.31661/jbpe.v0i0.899
H Rahimzadeh; A Fathi Kazerooni; M R Deevband; H Saligheh Rad. "An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors". سامانه مدیریت نشریات علمی, 9, 1, 1397, 69-80. doi: 10.31661/jbpe.v0i0.899
Rahimzadeh, H, Fathi Kazerooni, A, Deevband, M R, Saligheh Rad, H. (1397). 'An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors', سامانه مدیریت نشریات علمی, 9(1), pp. 69-80. doi: 10.31661/jbpe.v0i0.899
Rahimzadeh, H, Fathi Kazerooni, A, Deevband, M R, Saligheh Rad, H. An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors. سامانه مدیریت نشریات علمی, 1397; 9(1): 69-80. doi: 10.31661/jbpe.v0i0.899

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

Journal of Biomedical Physics and Engineering
مقاله 8، دوره 9، شماره 1، اردیبهشت 2019، صفحه 69-80    اصل مقاله (1.18 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.899
نویسندگان
H Rahimzadeh1، 2؛ A Fathi Kazerooni1، 3؛ M R Deevband2؛ H Saligheh Rad* 1، 3
1Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
2Department of Bioengineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3Department of Biomedical Engineering and Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Perfusion؛ Dynamic Susceptibility Contrast Enhanced MRI؛ Arterial Input Function؛ Cluster Analysis
مراجع
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