Purpose Dynamic glucose enhanced (DGE) MRI has shown potential for imaging glucose delivery and blood–brain barrier permeability at fields of 7T and higher. Here, we evaluated issues involved with translating d‐glucose weighted chemical exchange saturation transfer (glucoCEST) experiments to the clinical field strength of 3T. Methods Exchange rates of the different hydroxyl proton pools and the field‐dependent T2 relaxivity of water in d‐glucose solution were used to simulate the water saturation spectra (Z‐spectra) and DGE signal differences as a function of static field strength B0, radiofrequency field strength B1, and saturation time tsat. Multislice DGE experiments were performed at 3T on 5 healthy volunteers and 3 glioma patients. Results Simulations showed that DGE signal decreases with B0, because of decreased contributions of glucoCEST and transverse relaxivity, as well as coalescence of the hydroxyl and water proton signals in the Z‐spectrum. At 3T, because of this coalescence and increased interference of direct water saturation and magnetization transfer contrast, the DGE effect can be assessed over a broad range of saturation frequencies. Multislice DGE experiments were performed in vivo using a B1 of 1.6 µT and a tsat of 1 second, leading to a small glucoCEST DGE effect at an offset frequency of 2 ppm from the water resonance. Motion correction was essential to detect DGE effects reliably. Conclusion Multislice glucoCEST‐based DGE experiments can be performed at 3T with sufficient temporal resolution. However, the effects are small and prone to motion influence. Therefore, motion correction should be used when performing DGE experiments at clinical field strengths.

Dynamic glucose-enhanced (DGE) imaging uses chemical exchange saturation transfer magnetic resonance imaging to retrieve information about the microcirculation using infusion of a natural sugar (D-glucose). However, this new approach is not yet well understood with respect to the dynamic tissue response. DGE time curves for arteries, normal brain tissue, and cerebrospinal fluid (CSF) were analyzed in healthy volunteers and compared with the time dependence of sampled venous plasma blood glucose levels. The arterial response curves (arterial input function [AIF]) compared reasonably well in shape with the time curves of the sampled glucose levels but could also differ substantially. The brain tissue response curves showed mainly negative responses with a peak intensity that was of the order of 10 times smaller than the AIF peak and a shape that was susceptible to both noise and partial volume effects with CSF, attributed to the low contrast-to-noise ratio. The CSF response curves showed a rather large and steady increase of the glucose uptake during the scan, due to the rapid uptake of D-glucose in CSF. Importantly, and contrary to gadolinium studies, the curves differed substantially among volunteers, which was interpreted to be caused by variations in insulin response. In conclusion, while AIFs and tissue response curves can be measured in DGE experiments, partial volume effects, low concentration of D-glucose in tissue, and osmolality effects between tissue and blood may prohibit quantification of normal tissue perfusion parameters. However, separation of tumor responses from normal tissue responses would most likely be feasible.

Dynamic glucose-enhanced (DGE) magnetic resonance imaging (MRI) has shown potential for tumor imaging using D-glucose as a biodegradable contrast agent. The DGE signal change is small at 3 T (around 1%) and accurate detection is hampered by motion. The intravenous D-glucose injection is associated with transient side effects that can indirectly generate subject movements. In this study, the aim was to study DGE arterial input functions (AIFs) in healthy volunteers at 3 T for different scanning protocols, as a step towards making the glucose chemical exchange saturation transfer (glucoCEST) protocol more robust. Two different infusion durations (1.5 and 4.0 min) and saturation frequency offsets (1.2 and 2.0 ppm) were used. The effect of subject motion on the DGE signal was studied by using motion estimates retrieved from standard retrospective motion correction to create pseudo-DGE maps, where the apparent DGE signal changes were entirely caused by motion. Furthermore, the DGE AIFs were compared with venous blood glucose levels. A significant difference (p = 0.03) between arterial baseline and postinfusion DGE signal was found after Dglucose infusion. The results indicate that the measured DGE AIF signal change depends on both motion and blood glucose concentration change, emphasizing the need for sufficient motion correction in glucoCEST imaging. Finally, we conclude that a longer infusion duration (e.g. 3-4 min) should preferably be used in glucoCEST

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.

Dynamic glucose-enhanced (DGE) MRI relates to a group of exchange-based MRI techniques where the uptake of glucose analogues is studied dynamically. However, motion artifacts can be mistaken for true DGE effects, while motion correction may alter true signal effects. The aim was to design a numerical human brain phantom to simulate a realistic DGE MRI protocol at 3T that can be used to assess the influence of head movement on the signal before and after retrospective motion correction.Methods: MPRAGE data from a tumor patient were used to simulate dynamic Z-spectra under the influence of motion. The DGE responses for different tissue types were simulated, creating a ground truth. Rigid head movement patterns were applied as well as physiological dilatation and pulsation of the lateral ventricles and head-motion-induced B 0 -changes in presence of first-order shimming. The effect of retrospective motion correction was evaluated.Results: Motion artifacts similar to those previously reported for in vivo DGE data could be reproduced. Head movement of 1 mm translation and 1.5 degrees rotation led to a pseudo-DGE effect on the order of 1% signal change. B 0 effects due to head motion altered DGE changes due to a shift in the water saturation spectrum. Pseudo DGE effects were partly reduced or enhanced by rigid motion correction depending on tissue location. Conclusion:DGE MRI studies can be corrupted by motion artifacts. Designing post-processing methods using retrospective motion correction including B 0 correction will be crucial for clinical implementation. The proposed phantom should be useful for evaluation and optimization of such techniques.