Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors.
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 susceptibili...
| Publicado en: | European Radiology Vol. 29; no. 5; pp. 2669 - 2677 |
|---|---|
| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135662851&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135662851 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: May2019 vid: 29 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135662851 135662851 NLM30552476 135662851 10.1007/s00330-018-5892-2 NLM30552476 135662851 ppf: 2669 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors. aug: au: Huber, Thomas Rotkopf, Lukas Wiestler, Benedikt Kunz, Wolfgang G. Bette, Stefanie Gempt, Jens Preibisch, Christine Ricke, Jens Zimmer, Claus Kirschke, Jan S. Sommer, Wieland H. Thierfelder, Kolja M. affil: Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany sug: subj: Brain Neoplasms Pathology Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Perfusion Methods Glioma Pathology Female Male Retrospective Design Middle Age Human Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|