Tissue response curve‐shape analysis of dynamic glucose‐enhanced and dynamic contrast‐enhanced magnetic resonance imaging in patients with brain tumor

Dynamic glucose‐enhanced (DGE) MRI is used to study the signal intensity time course (tissue response curve) after D‐glucose injection. D‐glucose has potential as a biodegradable alternative or complement to gadolinium‐based contrast agents, with DGE being comparable with dynamic contrast‐enhanced (...

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Bibliographic Details
Published in:NMR in biomedicine 2023-06, Vol.36 (6), p.e4863-n/a
Main Authors: Seidemo, Anina, Wirestam, Ronnie, Helms, Gunther, Markenroth Bloch, Karin, Xu, Xiang, Bengzon, Johan, Sundgren, Pia C., Zijl, Peter C. M., Knutsson, Linda
Format: Article
Language:English
Subjects:
Biodegradation
Biological products
Brain
Brain - diagnostic imaging
Brain cancer
Brain Neoplasms - diagnostic imaging
brain tumor
Brain tumors
Contrast agents
Contrast Media
curve‐shape analysis
Deep learning
dynamic contrast‐enhanced MRI
dynamic glucose‐enhanced MRI
D‐glucose
Gadolinium
glucoCEST
Glucose
Glucose metabolism
Glucose transport
Humans
Lesions
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Neuroimaging
Spatial distribution
Tissue analysis
Tissues
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Summary:Dynamic glucose‐enhanced (DGE) MRI is used to study the signal intensity time course (tissue response curve) after D‐glucose injection. D‐glucose has potential as a biodegradable alternative or complement to gadolinium‐based contrast agents, with DGE being comparable with dynamic contrast‐enhanced (DCE) MRI. However, the tissue uptake kinetics as well as the detection methods of DGE differ from DCE MRI, and it is relevant to compare these techniques in terms of spatiotemporal enhancement patterns. This study aims to develop a DGE analysis method based on tissue response curve shapes, and to investigate whether DGE MRI provides similar or complementary information to DCE MRI. Eleven patients with suspected gliomas were studied. Tissue response curves were measured for DGE and DCE MRI at 7 T and the area under the curve (AUC) was assessed. Seven types of response curve shapes were postulated and subsequently identified by deep learning to create color‐coded “curve maps” showing the spatial distribution of different curve types. DGE AUC values were significantly higher in lesions than in normal tissue (p 
ISSN:0952-3480
1099-1492
DOI:10.1002/nbm.4863