Tissue-type mapping of gliomas
| Autor: | Raschke, Felix, Barrick, Thomas R., Jones, Timothy L., Yang, Guang, Ye, Xujiong, Howe, Franklyn A. |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
NAA
N-acetyl aspartate Male Magnetic Resonance Spectroscopy ROI region of interest CSF cerebrospinal fluid GII grade II lcsh:RC346-429 Glx glutamate & glutamine Ne necrosis Image Processing Computer-Assisted tCho total cholines GIII grade III Brain Mapping MET metastasis Brain Neoplasms Brain Glioma Middle Aged Magnetic Resonance Imaging PDD probability density distribution FLAIR fluid attenuated inversion recovery WM White matter lcsh:R858-859.7 Female Algorithms Nosologic imaging Adult mMRI multimodal MRI RGB red green blue GIV grade IV Oligodendroglioma lcsh:Computer applications to medicine. Medical informatics Article Magnetic resonance spectroscopy (MRS) Pattern recognition Humans PDw proton density weighted VO vasogenic oedema lcsh:Neurology. Diseases of the nervous system Aged Retrospective Studies Bayes Theorem GM grey matter MRS magnetic resonance spectroscopy PRESS point resolved spectroscopy T2n T2 normalised T2w T2 weighted MRSI magnetic resonance spectroscopic imaging Multimodal MRI tCr total creatines PDn proton density normalised Neoplasm Grading |
| Zdroj: | NeuroImage: Clinical, Vol 21, Iss, Pp-(2019) NeuroImage Clinical 21(2019), 101648 NeuroImage : Clinical |
| ISSN: | 2213-1582 |
| Popis: | Purpose To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p 2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics. Conclusions 1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours. Highlights • Non-Gaussian multimodal MRI characteristics of high and low grade glioma tissue. • Bayesian inference of multimodal MRI derives whole brain tumour tissue-type maps. • Automated segmentation of normal and tumour tissue volumes. • Visualisation of glioma heterogeneity, infiltration, necrosis and vasogenic oedema. |
| Databáze: | OpenAIRE |
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