Cerebral blood flow (CBF) quantification using dynamic-susceptibility contrast MRI can be achieved via model-independent deconvolution, with local arterial input function (AIF) deconvolution methods identifying multiple arterial regions with unique corresponding arterial input functions. The clinical application of local AIF methods necessitates an efficient and fully automated solution. To date, such local AIF methods have relied on the computation of a singular surrogate measure of bolus arrival time or custom arterial scoring functions to infer vascular supply origins. This paper aims to introduce a new local AIF method that alternatively utilises a multi-stage approach to perform AIF selection.
Material and methods
A fully automated, multi-stage local AIF method is proposed, leveraging both signal-based cluster analysis and priority flooding to define arterial regions and their corresponding vascular supply origins. The introduced method was applied to data from four patients with cerebrovascular disease who showed significant artefacts when using a prevailing automated local AIF method.
The immediately apparent image artefacts found using the pre-existing method due to poor AIF selection were found to be absent when using the proposed method.
The results suggest the proposed solution provides a more robust approach to perfusion quantification than currently available fully automated local AIF methods.
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Note that in practice, the deconvolution approach leads to CBF values in relative (arbitrary) units, and a further scaling is required for converting CBF to absolute units in ml/100 g/min .
Calamante F, Thomas DL, Pell GS, Wiersma J, Turner R (1999) Measuring cerebral blood flow using magnetic resonance imaging techniques. J Cereb Blood Flow Metab 19(7):701–735
Willats L, Calamante F (2012) The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI. NMR Biomed 26(8):913–931
Calamante F (2013) Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc 74:1–32
Schellinger PD, Fiebach JB, Hacke W (2003) Imaging-based decision making in thrombolytic therapy for ischemic stroke. Stroke 34(2):575–583
Østergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36(5):715–725
Wu O, Østergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG (2003) Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 50(1):164–174
Østergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results. Magn Reson Med 36(5):726–736
Liu H-L, Pu Y, Liu Y, Nickerson L, Andrews T, Fox PT, Gao J-H (1999) Cerebral blood flow measurement by dynamic contrast MRI using singular value decomposition with an adaptive threshold. Magn Reson Med 42(1):167–172
Sourbron S, Dujardin M, Makkat S, Luypaert R (2006) Pixel-by-pixel deconvolution of bolus-tracking data: optimization and implementation. Phys Med Biol 52(2):429–447
Calamante F, Gadian DG, Connelly A (2000) Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med 44(3):466–473
Willats L, Connelly A, Calamante F (2006) Improved deconvolution of perfusion MRI data in the presence of bolus delay and dispersion. Magn Reson Med 56(1):146–156
Calamante F, Yim PJ, Cebral JR (2003) Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics. NeuroImage. 19(2):341–353
Calamante F (2005) Bolus dispersion issues related to the quantification of perfusion MRI data. J Magn Reson Imaging 22(6):718–722
Calamante F, Willats L, Gadian DG, Connelly A (2006) Bolus delay and dispersion in perfusion MRI: implications for tissue predictor models in stroke. Magn Reson Med 55(5):1180–1185
Calamante F, Mørup M, Hansen LK (2004) Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med 52(4):789–797
Lorenz C, Benner T, Chen PJ et al (2006) Automated perfusion-weighted MRI using localized arterial input functions. J Magn Reson Imaging 24:1133–1139 (J Magn Reson Imaging 2007;25 (3):666–666)
Willats L, Christensen S, Ma HK, Donnan GA, Connelly A, Calamante F (2011) Validating a local arterial input function method for improved perfusion quantification in stroke. J Cereb Blood Flow Metab 31(11):2189–2198
Christensen S, Calamante F, Hjort N, Wu O, Blankholm AD, Desmond P, Davis S, Østergaard L (2008) Inferring origin of vascular supply from tracer arrival timing patterns using bolus tracking MRI. J Magn Reson Imaging 27(6):1371–1381
Mouridsen K, Christensen S, Gyldensted L, Østergaard L (2006) Automatic selection of arterial input function using cluster analysis. Magn Reson Med 55(3):524–531
Barnes R, Lehman C, Mulla D (2014) Priority-flood: an optimal depression-filling and watershed-labeling algorithm for digital elevation models. Comput Geosci 62:117–127
Tabbara R, Connelly A, Calamante F (2018) Automatic selection of local arterial input functions in perfusion MRI using cluster analysis and priority-flooding. Proc Intl Soc Magn Reson Med 26:2179
Patil V, Johnson G (2011) An improved model for describing the contrast bolus in perfusion MRI. Med Phys 38(12):6380–6383
Calamante F, Ganesan V, Kirkham F, Jan W, Chong W, Gadian D, Connelly A (2001) MR perfusion imaging in Moyamoya syndrome. Stroke 32(12):2810–2816
Bleeker EJ, van Buchem MA, van Osch MJ (2009) Optimal location for arterial input function measurements near the middle cerebral artery in first-pass perfusion MRI. J Cereb Blood Flow Metab 29(4):840–852
We are grateful to Siemens Healthineers (Erlangen, Germany), the National Health and Medical Research Council (NHMRC) of Australia, and the Victorian Government's Operational Infrastructure Support Program for their support. We thank Prof. Fenella Kirkham and Dr. Vijeya Ganesan (UCL GOS Institute of Child Health, London, UK) for their contribution to patient selection.
This study was funded by the Australian National Health and Medical Research Council (NHMRC; APP1091593), National Health and Medical Research Council (APP1117724).
Conflict of interest
Author Tabbara declares he has no conflict of interest. Author Connelly has received research funding from Siemens Healthcare Pty Ltd, Australia. Author Calamante has received research funding from Siemens Healthcare Pty Ltd, Australia.
This article does not contain any studies with animals performed by any of the authors. All procedures performed in studies involving human participants were in accordance with the ethical standards of our institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
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Tabbara, R., Connelly, A. & Calamante, F. Multi-stage automated local arterial input function selection in perfusion MRI. Magn Reson Mater Phy 33, 357–365 (2020). https://doi.org/10.1007/s10334-019-00798-4
- Perfusion MRI
- Arterial input function
- Bolus dispersion
- Cerebral blood flow