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Background: The time to maximum of the tissue residue function (Tmax) is commonly used parameter to determine hypoperfused cerebral parenchyma .However, the post-processing software to calculate Tmax is problematic since it is optional, expensive and requires a powerful workstation.

Methods: The purpose of this study was to present the development of a perfusion imaging analysis workflow that creates reliable and reproducible Tmax maps. The quantification procedure begins by estimating the perfusion signal from the data provided by the MRI system. The curve is then converted into a concentration curve. The next step is the determination of the Arterial Input Function (AIF) by an automatic method followed by deconvolution using the Singular Value Decomposition (SVD). Finally, we calculated the parameter Tmax and generated the mapping of this parameter by applying thresholds.

The clinical utility was tested in healthy subjects, in patients with confirmed brain infarction and two patients with no mismatch. Performance of the Tmax parameter was also assessed by comparison with the usual semi-quantitative analysis (time-to-peak (TTP) parameter).

Results and Conclusion: The obtained results show that the Tmax map offers a rapid and accurate estimation of the hypoperfused cerebral parenchyma. It provides a much better solution with clearer edges and more detailed information achieved by the TTP parameter.

Ischemic penumbra, time to maximum, arterial input function, dsc-mri, perfusion analysis, thrombectomy.

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INES, B. A., & CYRINE, D. (2020). THE ROLE OF TIME TO MAXIMUM PARAMETER IN THE QUANTIFICATION OF THE ISCHEMIC PENUMBRA FOR DSC-MRI IMAGING. Journal of International Research in Medical and Pharmaceutical Sciences, 15(2), 32-41. Retrieved from
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Warach S. Thrombolysis in stroke beyond three hours: targeting patients with diffusion and perfusion MRI. Ann Neurol. 2002;51:11–13.

Kiselev VG. On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI. Magn Reson Med. 2001;46:1113–1122.

Simonsen CZ. Comparison of gradient- and spin-echo imaging: CBF, CBV, and MTT Measurements by Bolus Tracking. J Magn Reson Imaging. 2000;12:411-416.

Fiehler J, Knudsen K, Thomalla G, et al. Vascular occlusion sites determine differences in lesion growth from early apparent diffusion coefficient lesion to final infarct. AJNR Am J Neuroradiol. 2005;26: 1056–61

Kudo K, Sasaki M, Momoshima S. et al. Development of perfusion mismatch analyzer (PMA): fully automated, operator-independent and standardized PWI-DWI analysis software. In: Proceedings of the 93rd Annual Meetings of RSNA, Educational Exhibit, LL-NR3212; 2007.

Straka M, Albers GW, Bammer R. Real-time diffusion-perfusion mismatch analysis in acute stroke. J Magn Reson Imaging. 2010;32:1024–37.

Kosior JC, Frayne R. PerfTool: a software platform for investigating bolus-tracking perfusion imaging quantification strategies. J Magn Reson Imaging. 2007;25:653–659.

Calamante F. The physiological significance of the Time-to-Maximum (Tmax) Parameter in Perfusion MRI Stroke. 2007;41:1169-1174.

Muir KW, Buchan A, Von Kummer R et al. Imaging of acute stroke. The Lancet Neurology. 2006;5:755–768.

Albers GW, Thijs VN, Wechsler L, et al. Magnetic resonance imaging profiles predict clinical response to early reperfusion: the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) study. Ann Neurol. 2006;60:508–517.

Zhang J, Hu J. Image segmentation based on 2D Otsu Method with Histogram Analysis. International Conference on Computer Science and Software Engineering; 2008.

Østergaard L. Principles of cerebral perfusion imaging by bolus tracking. J Magn Reson Imaging. 2005;22;710-717.

Rosen BR, Belliveau JW, Buchbinder BR., et al. Contrast agent and cerebral hemodynamics. Magn Reson Med. 1991;19;285–292.

Calamante F. Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc. 2013;74:1–32.

Ho-Ling L, Pu Y, Liu Y, et al. Cerebral Blood Flow Measurement by Dynamic Contrast MRI Using Singular Value Decomposition With an Adaptive Threshold. Magn Reson Med. 1999;42:167-172.

Christensen S, Campbell B, Perez de la Ossa N. et al. Optimal perfusion thresholds for prediction of tissue destined for infarction in the combined EPITHET and DEFUSE dataset. Stroke. 2010;41:e297.

Zaro‐Weber O, Fleischer H, Reiblich L, et al. Penumbra detection in acute stroke with perfusion magnetic resonance imaging: Validation with 15O‐positron emission tomography. Ann Neurol. 2019;85(6):875–886.

Heiss WD, Zaro Weber O. Validation of MRI Determination of the Penumbra by PET Measurements in Ischemic Stroke. J Nucl Med. 2017;(58):187-193.