Tissue response curve-shape analysis of dynamic glucose-enhanced and dynamic contrast-enhanced magnetic resonance imaging in patients with brain tumor.
Anina SeidemoRonnie WirestamGunther HelmsKarin Markenroth BlochXiang XuJohan BengzonPia C Maly-SundgrenPeter C M van ZijlLinda KnutssonPublished in: NMR in biomedicine (2022)
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 < 0.007). Furthermore, the distribution of curve types differed between lesions and normal tissue for both DGE and DCE. The DGE and DCE response curves in a 6-min postinjection time interval were classified as the same curve type in 20% of the lesion voxels, which increased to 29% when a 12-min DGE time interval was considered. While both DGE and DCE tissue response curve-shape analysis enabled differentiation of lesions from normal brain tissue in humans, their enhancements were neither temporally identical nor confined entirely to the same regions. Curve maps can provide accessible and intuitive information about the shape of DGE response curves, which is expected to be useful in the continued work towards the interpretation of DGE uptake curves in terms of D-glucose delivery, transport, and metabolism.
Keyphrases
- label free
- contrast enhanced
- magnetic resonance imaging
- computed tomography
- magnetic resonance
- deep learning
- blood glucose
- blood pressure
- type diabetes
- metabolic syndrome
- drug delivery
- climate change
- ultrasound guided
- blood brain barrier
- functional connectivity
- brain injury
- resting state
- high grade
- weight loss
- glycemic control