Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors
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Parameter maps based on wavelet-transform post-processing of dynamic perfusion data offer an innovative way of visualizing blood vessels in a fully automated, user-independent way. The aims of this study were (i) a proof of concept regarding wavelet-based analysis of dynamic susceptibility contrast (DSC) MRI data and (ii) to demonstrate advantages of wavelet-based measures compared to standard cerebral blood volume (CBV) maps in patients with the initial diagnosis of glioblastoma (GBM).
Consecutive 3-T DSC MRI datasets of 46 subjects with GBM (mean age 63.0 ± 13.1 years, 28 m) were retrospectively included in this feasibility study. Vessel-specific wavelet magnetic resonance perfusion (wavelet-MRP) maps were calculated using the wavelet transform (Paul wavelet, order 1) of each voxel time course. Five different aspects of image quality and tumor delineation were each qualitatively rated on a 5-point Likert scale. Quantitative analysis included image contrast and contrast-to-noise ratio.
Vessel-specific wavelet-MRP maps could be calculated within a mean time of 2:27 min. Wavelet-MRP achieved higher scores compared to CBV in all qualitative ratings: tumor depiction (4.02 vs. 2.33), contrast enhancement (3.93 vs. 2.23), central necrosis (3.86 vs. 2.40), morphologic correlation (3.87 vs. 2.24), and overall impression (4.00 vs. 2.41); all p < .001. Quantitative image analysis showed a better image contrast and higher contrast-to-noise ratios for wavelet-MRP compared to conventional perfusion maps (all p < .001).
wavelet-MRP is a fast and fully automated post-processing technique that yields reproducible perfusion maps with a clearer vascular depiction of GBM compared to standard CBV maps.
• Wavelet-MRP offers high-contrast perfusion maps with a clear delineation of focal perfusion alterations.
• Both image contrast and visual image quality were beneficial for wavelet-MRP compared to standard perfusion maps like CBV.
• Wavelet-MRP can be automatically calculated from existing dynamic susceptibility contrast (DSC) perfusion data.
KeywordsBrain neoplasms Cerebral blood volume Glioblastoma Perfusion imaging Wavelet analysis
Arterial input function
Cerebral blood flow
Cerebral blood volume
Dynamic contrast enhanced
Dynamic susceptibility contrast
Fluid-attenuated inversion recovery
Magnetization prepared rapid gradient echo
Mean transit time
Principles of echo-shifting with a train of observations sequence
Time to peak
Wavelet magnetic resonance perfusion
The authors state that this work has not received any specific funding.
Compliance with ethical standards
The scientific guarantor of this publication is Dr. Thomas Huber.
Conflict of interest
The authors of this manuscript declare relationships with the following companies: TH and LR consultancy for Smart Reporting GmbH (Munich, Germany); BW speaker honoraria from Bayer; TH, JG and SB consultancy for Brainlab AG (Feldkirchen, Germany); JSK research support by DFG, ERC, Acandis—travel support by Kaneka Europe—speaker honoraria by Philips; WHS founder and CEO of Smart Reporting GmbH (Munich, Germany) and founder of Planerio GmbH (Munich, Germany); JR research grant Sirtex Medical Ltd. and research grant Bayer AG; CZ has served on scientific advisory boards for Philips and Bayer Schering; serves as co-editor on the Advisory Board of Clinical Neuroradiology; has received speaker honoraria from Bayer-Schering and Philips and has received research support and investigator fees for clinical studies from Biogen Idec, Quintiles, M.S.D. Sharp & Dome, Boehringer Ingelheim, Inventive Health Clinical U.K. Ltd., Advance Cor, Brainsgate, Pfizer, BayerSchering, Novartis, Roche, Servier, Penumbra, W.C.T. GmbH, Syngis, S.S.S. International Clinical Research, P.P.D. Germany GmbH, Worldwide Clinical Trials Ltd., Phenox, Covidien, Actelion, Medivation, Medtronic, Harrison Clinical Research, Concentric, Penumbra, Pharmtrace, Reverse Medical Corp., Premier Research Germany Ltd., Surpass Medical Ltd., and GlaxoSmithKline. All listed conflicts of interest are unrelated to the present study.
Statistics and biometry
One of the authors has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• Performed at one institution
- 7.Hojjati M, Badve C, Garg V et al (2017) Role of FDG-PET/MRI, FDG-PET/CT, and dynamic susceptibility contrast perfusion MRI in differentiating radiation necrosis from tumor recurrence in glioblastomas. J Neuroimaging. https://doi.org/10.1111/jon.12460
- 8.Chuang MT, Liu YS, Tsai YS, Chen YC, Wang CK (2016) Differentiating radiation-induced necrosis from recurrent brain tumor using MR perfusion and spectroscopy: a meta-analysis. PLoS One 11:e0141438. https://doi.org/10.1371/journal.pone.0141438
- 9.Singh R, Kesavabhotla K, Kishore SA et al (2016) Dynamic susceptibility contrast-enhanced MR perfusion imaging in assessing recurrent glioblastoma response to superselective intra-arterial bevacizumab therapy. AJNR Am J Neuroradiol 37:1838–1843. https://doi.org/10.3174/ajnr.A4823 CrossRefGoogle Scholar
- 12.Campbell BC, Christensen S, Foster SJ et al (2010) Visual assessment of perfusion-diffusion mismatch is inadequate to select patients for thrombolysis. Cerebrovasc Dis 29:592–596. https://doi.org/10.1159/000311080
- 13.Havla L, Thierfelder KM, Beyer SE, Sommer WH, Dietrich O (2015) Wavelet-based calculation of cerebral angiographic data from time-resolved CT perfusion acquisitions. Eur Radiol. https://doi.org/10.1007/s00330-015-3651-1
- 16.Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Amer Meteor Soc 79:61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
- 17.Marstal K, Berendsen FF, Staring M, Klein S (2016) SimpleElastix: a user-friendly, multi-lingual library for medical image registration. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, pp 574–582. https://doi.org/10.1109/CVPRW.2016.78
- 18.Kluge A, Lukas M, Toth V, Pyka T, Zimmer C, Preibisch C (2016) Analysis of three leakage-correction methods for DSC-based measurement of relative cerebral blood volume with respect to heterogeneity in human gliomas. Magn Reson Imaging. https://doi.org/10.1016/j.mri.2015.12.015
- 20.McGraw KO, Wong SP (1996) Forming inferences about some intraclass correlation coefficients. Psychol Methods 30–46. https://doi.org/10.1037/1082-989X.1.1.30
- 21.Hallgren KA (2012) Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol 8:23–34. https://doi.org/10.1016/j.biotechadv.2011.08.021.Secreted CrossRefGoogle Scholar
- 22.Kelm ZS, Korfiatis PD, Lingineni RK et al (2015) Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression. J Med Imaging (Bellingham) 2:026001. https://doi.org/10.1117/1.JMI.2.2.026001
- 25.Maia AC Jr, Malheiros SM, da Rocha AJ et al (2004) Stereotactic biopsy guidance in adults with supratentorial nonenhancing gliomas: role of perfusion-weighted magnetic resonance imaging. J Neurosurg 101:970–976. https://doi.org/10.3171/jns.2004.101.6.0970