Compartmental anisotropy of filtered exchange imaging (FEXI) in human white matter: What is happening in FEXI?

PURPOSE: To investigate the effects of compartmental anisotropy on filtered exchange imaging (FEXI) in white matter (WM). THEORY AND METHODS: FEXI signals were measured using multiple combinations of diffusion filter and detection directions in five healthy volunteers. Additional filters, including a trace-weighted diffusion filter with trapezoidal gradients, a spherical b-tensor encoded diffusion filter, and a T2 filter, were tested with trace-weighted diffusion detection. RESULTS: A large range of apparent exchange rates (AXR) and both positive and negative filter efficiencies (σ) were found depending on the mutual orientation of the filter and detection gradients relative to WM fiber orientation. The data demonstrated that the fast-diffusion compartment suppressed by diffusional filtering is not exclusively extra-cellular, but also intra-cellular. While not comprehensive, a simple two-compartment diffusion tensor model with water exchange was able to account qualitatively for the trends in positive and negative filtering efficiencies, while standard model imaging (SMI) without exchange could not. This two-compartment diffusion tensor model also demonstrated smaller AXR variances across subjects. When employing trace-weighted diffusion detection, AXR values were on the order of the R1 (=1/T1) of water at 3T for crossing fibers, while being less than R1 for parallel fibers. CONCLUSION: Orientation-dependent AXR and σ values were observed when using multi-orientation filter and detection gradients in FEXI, indicating that WM FEXI models need to account for compartmental anisotropy. When using trace-weighted detection, AXR values were on the order of or less than R1, complicating the interpretation of FEXI results in WM in terms of biological exchange properties. These findings may contribute toward better understanding of FEXI results in WM.

Advanced Imaging in the Diagnosis and Response Assessment of High-Grade Glioma: AJR Expert Panel Narrative Review.

This AJR Expert Panel Narrative explores the current status of advanced MRI and PET techniques for the post-therapeutic response assessment of high-grade adult-type gliomas, focusing on ongoing clinical controversies in current practice. Discussed techniques that complement conventional MRI and aid the differentiation of recurrent tumor from post-treatment effects include DWI and diffusion tensor imaging; perfusion MRI techniques including dynamic susceptibility contrast (DSC), dynamic contrast-enhanced MRI, and arterial spin labeling; MR spectroscopy including assessment of 2-hydroxyglutarate (2HG) concentration; glucose- and amino acid (AA)-based PET; and amide proton transfer imaging. Updated criteria for Response Assessment in Neuro-Oncology are presented. Given the abundant supporting clinical evidence, the panel supports a recommendation that routine response assessment after HGG treatment should include perfusion MRI, particularly given the development of a consensus recommended DSC-MRI protocol. Although published studies support 2HG MRS and AA PET, these techniques' widespread adoption will likely require increased availability (for 2HG MRS) or increased insurance funding in the United States (for AA PET). The article concludes with a series of consensus opinions from the author panel, centered on the clinical integration of the advanced imaging techniques into posttreatment surveillance protocols.

Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding.

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.

Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI.

PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone to errors because of the unavoidable noise in vivo. A deep learning-based single Lorentzian Fitting Network (sLoFNet) is proposed to overcome these shortcomings. METHODS: A neural network architecture was constructed and its hyperparameters optimized. Training was conducted on a simulated and in vivo-paired data sets of discrete signal values and their corresponding Lorentzian shape parameters. The sLoFNet performance was compared with LS on several WASSR data sets (both simulated and in vivo 3T brain scans). Prediction errors, robustness against noise, effects of sampling density, and time consumption were compared. RESULTS: LS and sLoFNet performed comparably in terms of RMS error and mean absolute error on all in vivo data with no statistically significant difference. Although the LS method fitted well on samples with low noise, its error increased rapidly when increasing sample noise up to 4.5%, whereas the error of sLoFNet increased only marginally. With the reduction of Z-spectral sampling density, prediction errors increased for both methods, but the increase occurred earlier (at 25 vs. 15 frequency points) and was more pronounced for LS. Furthermore, sLoFNet performed, on average, 70 times faster than the LS-method. CONCLUSION: Comparisons between LS and sLoFNet on simulated and in vivo WASSR MRI Z-spectra in terms of robustness against noise and decreased sample resolution, as well as time consumption, showed significant advantages for sLoFNet.

A numerical human brain phantom for dynamic glucose-enhanced (DGE) MRI: On the influence of head motion at 3T.

PURPOSE: 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 B0 -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. B0 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 B0 correction will be crucial for clinical implementation. The proposed phantom should be useful for evaluation and optimization of such techniques.

Imaging of sugar-based contrast agents using their hydroxyl proton exchange properties.

The ability of CEST MRI to detect the presence of millimolar concentrations of non-metallic contrast agents has made it possible to study, non-invasively, important biological molecules such as proteins and sugars, as well as drugs already approved for clinical use. Here, we review efforts to use sugar and sugar polymers as exogenous contrast agents, which is possible based on the exchange of their hydroxyl protons with water protons. While this capability has raised early enthusiasm, for instance about the possibility of imaging D-glucose metabolism with MRI in a way analogous to PET, experience over the past decade has shown that this is not trivial. On the other hand, many studies have confirmed the possibility of imaging a large variety of sugar analogues, each with potentially interesting applications to assess tissue physiology. Some promising applications are the study of (i) sugar delivery and transport to assess blood-brain barrier integrity and (ii) sugar uptake by cells for their characterization (e.g., cancer versus healthy), as well as (iii) clearance of sugars to assess tissue drainage-for instance, through the glymphatic system. To judge these opportunities and their challenges, especially in the clinic, it is necessary to understand the technical aspects of detecting the presence of rapidly exchanging protons through the water signal in MRI, especially as a function of magnetic field strength. We expect that novel approaches in terms of MRI detection (both saturation transfer and relaxation based), MRI data analysis, and sugar design will push this young field forward in the next decade.

Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange.

Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 µm and exchange rates in the simulated range of 0 to 20 s - 1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.

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 (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.

Concurrent measurement of perfusion parameters related to small blood vessels and cerebrospinal fluid circulation in the human brain using dynamic dual-spin-echo perfusion MRI.

Accumulating evidence from recent studies has indicated the importance of studying the interaction between the microvascular and lymphatic systems in the brain. To date, most imaging methods can only measure blood or lymphatic vessels separately, such as dynamic susceptibility contrast (DSC) MRI for blood vessels and DSC MRI-in-the-cerebrospinal fluid (CSF) (cDSC MRI) for lymphatic vessels. An approach that can measure both blood and lymphatic vessels in a single scan offers advantages such as a halved scan time and contrast dosage. This study attempts to develop one such approach by optimizing a dual-echo turbo-spin-echo sequence, termed "dynamic dual-spin-echo perfusion (DDSEP) MRI". Bloch simulations were performed to optimize the dual-echo sequence for the measurement of gadolinium (Gd)-induced blood and CSF signal changes using a short and a long echo time, respectively. The proposed method furnishes a T1-dominant contrast in CSF and a T2-dominant contrast in blood. MRI experiments were performed in healthy subjects to evaluate the dual-echo approach by comparing it with existing separate methods. Based on simulations, the short and long echo time were chosen around the time when blood signals show maximum difference between post- and pre-Gd scans, and the time when blood signals are completely suppressed, respectively. The proposed method showed consistent results in human brains as previous studies using separate methods. Signal changes from small blood vessels occurred faster than from lymphatic vessels after intravenous Gd injection. In conclusion, Gd-induced signal changes in blood and CSF can be detected simultaneously in healthy subjects with the proposed sequence. The temporal difference in Gd-induced signal changes from small blood and lymphatic vessels after intravenous Gd injection was confirmed using the proposed approach in the same human subjects. Results from this proof-of-concept study will be used to further optimize DDSEP MRI in subsequent studies.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors.

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Towards robust glucose chemical exchange saturation transfer imaging in humans at 3 T: Arterial input function measurements and the effects of infusion time.

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 D-glucose 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 experiments, because it can minimize the glucose infusion side effects without negatively affecting the DGE signal change.

Dynamic contrast-enhanced CEST MRI using a low molecular weight dextran.

Natural and synthetic sugars have great potential for developing highly biocompatible and translatable chemical exchange saturation transfer (CEST) MRI contrast agents. In this study, we aimed to develop the smallest clinically available form of dextran, Dex1 (molecular weight, MW ~ 1 kDa), as a new CEST agent. We first characterized the CEST properties of Dex1 in vitro at 11.7 T and showed that the Dex1 had a detectable CEST signal at ~1.2 ppm, attributed to hydroxyl protons. In vivo CEST MRI studies were then carried out on C57BL6 mice bearing orthotopic GL261 brain tumors (n = 5) using a Bruker BioSpec 11.7 T MRI scanner. Both steady-state full Z-spectral images and single offset (1.2 ppm) dynamic dextran-enhanced (DDE) images were acquired before and after the intravenous injection of Dex1 (2 g/kg). The steady-state Z-spectral analysis showed a significantly higher CEST contrast enhancement in the tumor than in contralateral brain (∆MTRasym1.2 ppm  = 0.010 ± 0.006 versus 0.002 ± 0.008, P = 0.0069) at 20 min after the injection of Dex1. Pharmacokinetic analyses of DDE were performed using the area under the curve (AUC) in the first 10 min after Dex1 injection, revealing a significantly higher uptake of Dex1 in the tumor than in brain tissue for tumor-bearing mice (AUC[0-10 min] = 21.9 ± 4.2 versus 5.3 ± 6.4%·min, P = 0.0294). In contrast, no Dex1 uptake was foundling in the brains of non-tumor-bearing mice (AUC[0-10 min] = -1.59 ± 2.43%·min). Importantly, the CEST MRI findings were consistent with the measurements obtained using DCE MRI and fluorescence microscopy, demonstrating the potential of Dex1 as a highly translatable CEST MRI contrast agent for assessing tumor hemodynamics.

Diffusion tensor imaging in glioblastoma patients treated with volumetric modulated arc radiotherapy: a longitudinal study.

BACKGROUND: Chemo- and radiotherapy (RT) is standard treatment for patients with high-grade glioma, but may cause side-effects on the patient's cognitive function. AIM: Use of diffusion tensor imaging (DTI) to investigate the longitudinal changes in normal-appearing brain tissue in glioblastoma patients undergoing modern arc-based RT with volumetric modulated arc therapy (VMAT) or helical tomotherapy. MATERIALS AND METHODS: The study included 27 patients newly diagnosed with glioblastoma and planned for VMAT or tomotherapy. All subjects underwent magnetic resonance imaging at the start of RT and at week 3, 6, 15, and 26. Fourteen subjects were additionally imaged at week 52. The DTI data were co-registered to the dose distribution maps. Longitudinal changes in fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were assessed in the corpus callosum, the centrum semiovale, the hippocampus, and the amygdala. RESULTS: Significant longitudinal changes in FA, MD, and RD were mainly found in the corpus callosum. In the other examined brain structures, only sparse and transient changes were seen. No consistent correlations were found between biodose, age, or gender and changes in DTI parameters. CONCLUSION: Longitudinal changes in MD, FA, and RD were observed but only in a limited number of brain structures and the changes were smaller than expected from literature. The results suggest that modern, arc-based RT may have less negative effect on normal-appearing parts of the brain tissue up to 12 months after radiotherapy.

Non-parametric deconvolution using Bézier curves for quantification of cerebral perfusion in dynamic susceptibility contrast MRI.

OBJECTIVE: Deconvolution is an ill-posed inverse problem that tends to yield non-physiological residue functions R(t) in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). In this study, the use of Bézier curves is proposed for obtaining physiologically reasonable residue functions in perfusion MRI. MATERIALS AND METHODS: Cubic Bézier curves were employed, ensuring R(0) = 1, bounded-input, bounded-output stability and a non-negative monotonically decreasing solution, resulting in 5 parameters to be optimized. Bézier deconvolution (BzD), implemented in a Bayesian framework, was tested by simulation under realistic conditions, including effects of arterial delay and dispersion. BzD was also applied to DSC-MRI data from a healthy volunteer. RESULTS: Bézier deconvolution showed robustness to different underlying residue function shapes. Accurate perfusion estimates were observed, except for boxcar residue functions at low signal-to-noise ratio. BzD involving corrections for delay, dispersion, and delay with dispersion generally returned accurate results, except for some degree of cerebral blood flow (CBF) overestimation at low levels of each effect. Maps of mean transit time and delay were markedly different between BzD and block-circulant singular value decomposition (oSVD) deconvolution. DISCUSSION: A novel DSC-MRI deconvolution method based on Bézier curves was implemented and evaluated. BzD produced physiologically plausible impulse response, without spurious oscillations, with generally less CBF underestimation than oSVD.

Pulseq-CEST: Towards multi-site multi-vendor compatibility and reproducibility of CEST experiments using an open-source sequence standard.

PURPOSE: As the field of CEST grows, various novel preparation periods using different parameters are being introduced. At the same time, large, multisite clinical studies require clearly defined protocols, especially across different vendors. Here, we propose a CEST definition standard using the open Pulseq format for a shareable, simple, and exact definition of CEST protocols. METHODS: We present the benefits of such a standard in three ways: (1) an open database on GitHub, where fully defined, human-readable CEST protocols can be shared; (2) an open-source Bloch-McConnell simulation to test and optimize CEST preparation periods in silico; and (3) a hybrid MR sequence that plays out the CEST preparation period and can be combined with any existing readout module. RESULTS: The exact definition of the CEST preparation period, in combination with the flexible simulation, leads to a good match between simulations and measurements. The standard allowed finding consensus on three amide proton transfer-weighted protocols that could be compared in healthy subjects and a tumor patient. In addition, we could show coherent multisite results for a sophisticated CEST method, highlighting the benefits regarding protocol sharing and reproducibility. CONCLUSION: With Pulseq-CEST, we provide a straightforward approach to standardize, share, simulate, and measure different CEST preparation schemes, which are inherently completely defined.

d-glucose weighted chemical exchange saturation transfer (glucoCEST)-based dynamic glucose enhanced (DGE) MRI at 3T: early experience in healthy volunteers and brain tumor patients.

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.

The role of cartilage glycosaminoglycan structure in gagCEST.

Glycosaminoglycan (GAG) chemical exchange saturation transfer (gagCEST) is a potential method for cartilage quality assessment. The aim of this study was to investigate how the gagCEST effect depends on the types and molecular organization of GAG typically found in articular cartilage. gagCEST was performed on different concentrations of GAG in various forms: free chains of chondroitin sulfate (CS) of different types (-A and -C) and GAG bound to protein in aggregated and nonaggregated aggrecan extracted from calf articular cartilage. The measured magnetization transfer ratio asymmetry (MTRasym ) was compared with known GAG concentrations or GAG concentrations determined through biochemical analysis. The gagCEST effect was assessed through the linear regression coefficient with 95% confidence interval of MTRasym per GAG concentration. We observed a lower gagCEST effect in phantoms containing a mixture of CS-A and CS-C compared with phantoms containing mainly CS-A. The difference in response corresponds well to the difference in CS-A concentration. GAG bound in aggrecan from calf articular cartilage, where CS-A is assumed to be the major type of GAG, produed a similar gagCEST effect as that observed for free CS-A. The effect was also similar for aggregated (ie, bound to hyaluronic acid) and nonaggregated aggrecan. In conclusion, our results indicate that the aggrecan structure in itself does not impact the gagCEST effect, but that the effect is strongly dependent on GAG type. In phantoms, the current implementation of gagCEST is sensitive to CS-A while for CS-C, the main GAG component in mature human articular cartilage, the sensitivity is limited. This difference in gagCEST sensitivity between GAG types detected in phantoms is a strong motivation to also explore the possibility of a similar effect in vivo.

Dynamic contrast-enhanced QSM for perfusion imaging: a systematic comparison of ΔR2*- and QSM-based contrast agent concentration time curves in blood and tissue.

OBJECTIVE: In dynamic susceptibility contrast MRI (DSC-MRI), an arterial input function (AIF) is required to quantify perfusion. However, estimation of the concentration of contrast agent (CA) from magnitude MRI signal data is challenging. A reasonable alternative would be to quantify CA concentration using quantitative susceptibility mapping (QSM), as the CA alters the magnetic susceptibility in proportion to its concentration. MATERIAL AND METHODS: AIFs with reasonable appearance, selected on the basis of conventional criteria related to timing, shape, and peak concentration, were registered from both ΔR2* and QSM images and mutually compared by visual inspection. Both ΔR2*- and QSM-based AIFs were used for perfusion calculations based on tissue concentration data from ΔR2*as well as QSM images. RESULTS: AIFs based on ΔR2* and QSM data showed very similar shapes and the estimated cerebral blood flow values and mean transit times were similar. Analysis of corresponding ΔR2* versus QSM-based concentration estimates yielded a transverse relaxivity estimate of 89 s-1 mM-1, for voxels identified as useful AIF candidate in ΔR2* images according to the conventional criteria. DISCUSSION: Interestingly, arterial concentration time curves based on ΔR2* versus QSM data, for a standard DSC-MRI experiment, were generally very similar in shape, and the relaxivity obtained in voxels representing blood was similar to tissue relaxivity obtained in previous studies.

The effect of the mTOR inhibitor rapamycin on glucoCEST signal in a preclinical model of glioblastoma.

PURPOSE: The mammalian target of rapamycin is an enzyme that regulates cell metabolism and proliferation. It is up-regulated in aggressive tumors, such as glioblastoma, leading to increased glucose uptake and consumption. It has been suggested that glucose CEST signals reflect the delivery and tumor uptake of glucose. The inhibitor rapamycin (sirolimus) has been applied as a glucose deprivation treatment; thus, glucose CEST MRI could potentially be useful for monitoring the tumor responses to inhibitor treatment. METHODS: A human U87-EGFRvIII xenograft model in mice was studied. The mice were treated with a mammalian target of Rapamycin inhibitor, rapamycin. The effect of the treatment was evaluated in vivo with dynamic glucose CEST MRI. RESULTS: Rapamycin treatment led to significant increases (P < 0.001) in dynamic glucose-enhanced signal in both the tumor and contralateral brain as compared to the no-treatment group, namely a maximum enhancement of 3.7% ± 2.3% (tumor, treatment) versus 1.9% ± 0.4% (tumor, no-treatment), 1.7% ± 1.1% (contralateral, treatment), and 1.0% ± 0.4% (contralateral, no treatment). Dynamic glucose-enhanced contrast remained consistently higher in treatment versus no-treatment groups for the duration of the experiment (17 min). This was confirmed with area-under-curve analysis. CONCLUSION: Increased glucose CEST signal was found after mammalian target of Rapamycin inhibition treatment, indicating potential for dynamic glucose-enhanced MRI to study tumor response to glucose deprivation treatment.

APT-weighted MRI: Techniques, current neuro applications, and challenging issues.

Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.