nach oben

Erschienen in:

14.12.2018 | Neuro

Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors

verfasst von: Thomas Huber, Lukas Rotkopf, Benedikt Wiestler, Wolfgang G. Kunz, Stefanie Bette, Jens Gempt, Christine Preibisch, Jens Ricke, Claus Zimmer, Jan S. Kirschke, Wieland H. Sommer, Kolja M. Thierfelder

Erschienen in: European Radiology | Ausgabe 5/2019

Einloggen, um Zugang zu erhalten

Abstract

Objectives

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).

Methods

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.

Results

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).

Conclusions

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.

Key Points

• 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.

Nur mit Berechtigung zugänglich

Guo L, Wang G, Feng Y et al (2016) Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors. Radiat Oncol 11:123. https://doi.org/10.1186/s13014-016-0702-y CrossRefPubMedPubMedCentral

Lee SK (2012) Diffusion tensor and perfusion imaging of brain tumors in high-field MR imaging. Neuroimaging Clin N Am 22:123–134. https://doi.org/10.1016/j.nic.2012.02.001 CrossRefPubMed

Aronen HJ, Gazit IE, Louis DN et al (1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191:41–51. https://doi.org/10.1148/radiology.191.1.8134596 CrossRefPubMed

Jain R, Griffith B, Alotaibi F et al (2015) Glioma angiogenesis and perfusion imaging: understanding the relationship between tumor blood volume and leakiness with increasing glioma grade. AJNR Am J Neuroradiol 36:2030–2035. https://doi.org/10.3174/ajnr.A4405 CrossRefPubMedPubMedCentral

Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1 CrossRefPubMed

Maeda M, Itoh S, Kimura H et al (1993) Tumor vascularity in the brain: evaluation with dynamic susceptibility-contrast MR imaging. Radiology 189:233–238. https://doi.org/10.1148/radiology.189.1.8372199 CrossRefPubMed

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

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

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 CrossRefPubMedPubMedCentral

10.

Ken S, Deviers A, Filleron T et al (2015) Voxel-based evidence of perfusion normalization in glioblastoma patients included in a phase I-II trial of radiotherapy/tipifarnib combination. J Neurooncol 124:465–473. https://doi.org/10.1007/s11060-015-1860-8 CrossRefPubMed

11.

Kudo K, Christensen S, Sasaki M et al (2013) Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom. Radiology 267:201–211. https://doi.org/10.1148/radiol.12112618 CrossRefPubMedPubMedCentral

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

14.

Kunz WG, Schuler F, Sommer WH et al (2017) Wavelet-based angiographic reconstruction of computed tomography perfusion data. Invest Radiol 52:302–309. https://doi.org/10.1097/RLI.0000000000000337 CrossRefPubMed

15.

Liu G, Sobering G, Duyn J, Moonen CT (1993) A functional MRI technique combining principles of echo-shifting with a train of observations (PRESTO). Magn Reson Med 30:764–768. Available: http://www.ncbi.nlm.nih.gov/pubmed/8139461. Accessed 24 Jul 2018CrossRefPubMed

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

19.

Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128. https://doi.org/10.1016/j.neuroimage.2006.01.015 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

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

23.

Hu LS, Kelm Z, Korfiatis P et al (2015) Impact of software modeling on the accuracy of perfusion MRI in glioma. AJNR Am J Neuroradiol 36:2242–2249. https://doi.org/10.3174/ajnr.A4451 CrossRefPubMedPubMedCentral

24.

Korfiatis P, Kline TL, Kelm ZS, Carter RE, Hu LS, Erickson BJ (2016) Dynamic susceptibility contrast-MRI quantification software tool: development and evaluation. Tomography 2:448–456. https://doi.org/10.18383/j.tom.2016.00172 CrossRefPubMedPubMedCentral

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

26.

Hardee ME, Zagzag D (2012) Mechanisms of glioma-associated neovascularization. Am J Pathol 181:1126–1141. https://doi.org/10.1016/j.ajpath.2012.06.030 CrossRefPubMedPubMedCentral

Titel: Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors
verfasst von: Thomas Huber
Lukas Rotkopf
Benedikt Wiestler
Wolfgang G. Kunz
Stefanie Bette
Jens Gempt
Christine Preibisch
Jens Ricke
Claus Zimmer
Jan S. Kirschke
Wieland H. Sommer
Kolja M. Thierfelder
Publikationsdatum: 14.12.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 5/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5892-2

Neu im Fachgebiet Radiologie

18.04.2024 | Pädiatrische Notfallmedizin | Nachrichten

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Neu im Fachgebiet Radiologie

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Studie bestätigt Potenzial von KI in der Radiologie

Quantitative Angiografie könnte IVUS-Alternative darstellen

Update Radiologie

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 5/2019

Multidetector CT findings differ between surgical grades of pancreatic fistula after pancreaticoduodenectomy

Diabetes risk assessment with imaging: a radiomics study of abdominal CT

Double-inversion recovery with synthetic magnetic resonance: a pilot study for assessing synovitis of the knee joint compared to contrast-enhanced magnetic resonance imaging

Evaluation of elevated left ventricular end diastolic pressure in patients with preserved ejection fraction using cardiac magnetic resonance

Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis

Forensic age estimation for pelvic X-ray images using deep learning

Neu im Fachgebiet Radiologie

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Studie bestätigt Potenzial von KI in der Radiologie

Quantitative Angiografie könnte IVUS-Alternative darstellen

Update Radiologie