The study aimed to investigate the feasibility of dynamic glucose-enhanced (DGE) MRI technology in the clinical application of glioma. Twenty patients with glioma were examined using a preoperative DGE-MRI protocol before clinical intervention. A brief hyperglycemic state was achieved by injecting 50 mL of 50% w/w D-glucose intravenously during the DGE imaging. The total acquisition time for the DGE was 15 min. Area-under-the-curve (AUC) images were calculated using the DGE images. AUC values of the glioma core, margin area, edema area, and contralateral brain parenchyma were compared using Mann-Whitney U tests. Overall, gray and white matter areas in the AUC images showed relatively low DGE signal change and bilateral symmetry. However, the tumor cores displayed a significant hyperintensity. A high DGE signal change was also seen in the necrotic, cystic, and cerebrospinal areas. These results show that DGE MRI is a feasible technique for the study of brain tumors as part of a clinical exam. Importantly, DGE MRI showed enhancement in areas confirmed histopathologically as tumors, whereas Gd T1w MRI did not show any enhancement in this area. Since the D-glucose molecule is smaller than Gd-based contrast agents, DGE MRI may be more sensitive to subtle blood-brain barrier disruptions, thus potentially providing early information about possible malignancy. These findings provide a new perspective for the further exploration and analysis of D-glucose uptake in brain tumors.

Articular cartilage (AC) is a specialized connective tissue that covers the ends of long bones and facilitates the load-bearing of joints. It consists of chondrocytes distributed throughout an extracellular matrix and organized into three zones: superficial, middle, and deep. Nuclear magnetic resonance (NMR) techniques can be used to characterize this layered structure. In this study, devoted to a better understanding of the NMR response of this complex tissue, 20 specimens excised from femoral and tibial cartilage of bovine samples were analyzed by the low-field single-sided NMR-MOUSE-PM10. A multiparametric depth-wise analysis was performed to characterize the laminar structure of AC and investigate the origin of the NMR dependence on depth. The depth dependence of the single parameters T, T, and D has been described in literature, but their simultaneous measurement has not been fully exploited yet, as well as the extent of their variability. A novel parameter, α, evaluated by applying a double-quantum-like sequence, has been measured. The significant decrease in T, T, and D from the middle to the deep zone is consistent with depth-dependent composition and structure changes of the complex matrix of fibers confining and interacting with water. The α parameter appears to be a robust marker of the layered structure with a well-reproducible monotonic trend across the zones. The discrimination of cartilage zones was reinforced by a multivariate principal component analysis statistical analysis. The large number of samples allowed for the identification of the smallest number of parameters or their combination able to classify samples. The first two components clustered the data according to the different zones, highlighting the sensitivity of the NMR parameters to the structural and compositional variations of AC. Using two parameters, the best result was obtained by considering T and α. Single-sided NMR devices, portable and low-cost, provide information on NMR parameters related to tissue composition and structure.

The conventional gradient-echo steady-state signal model is the basis of various spoiled gradient-echo (SPGR) based quantitative MRI models, including variable flip angle (VFA) MRI and dynamic contrast-enhanced MRI (DCE). However, including preparation pulses, such as fat suppression or saturation bands, disrupts the steady-state and leads to a bias in T and DCE parameter estimates. This work introduces a signal model that improves the accuracy of VFA T-mapping and DCE for interrupted spoiled gradient-echo (I-SPGR) acquisitions. The proposed model was applied to a VFA T-mapping I-SPGR sequence in the Gold Standard T-phantom (3 T), in the brain of four healthy volunteers (3 T), and to an abdominal DCE examination (1.5 T). T-values obtained with the proposed and conventional model were compared to reference T-values. Bland-Altman analysis (phantom) and analysis of variance (in vivo) were used to test whether bias from both methods was significantly different (p = 0.05). The proposed model outperformed the conventional model by decreasing the bias in the phantom with respect to the phantom reference values (mean bias -2 vs. -35% at 3 T) and in vivo with respect to the conventional SPGR (-6 vs. -37% bias in T, p < 0.01). The proposed signal model estimated approximately 48% (depending on baseline T) higher contrast concentrations in vivo, which resulted in decreased DCE pharmacokinetic parameter estimates of up to 35%. The proposed signal model improves the accuracy of quantitative parameter estimation from disrupted steady-state I-SPGR sequences. It therefore provides a flexible method for applying fat suppression, saturation bands, and other preparation pulses in VFA T-mapping and DCE.

This study investigated the association between the fatty acid composition of abdominal adipose tissue in NAFLD patients using chemical shift-encoded MRI and the development of insulin resistance and T2DM. We enrolled 231 subjects with NAFLD who underwent both abdominal magnetic resonance spectroscopy and chemical shift-encoded MRI: comprising of 49 T2DM patients and 182 subjects without. MRI- and MRS-based liver fat fraction was measured from a circular region of interest on the right lobe of the liver. The abdominal fatty acid compositions were measured at the umbilical level with chemical shift-encoded MRI. Bland-Altman analysis, Student's t test, Mann-Whitney U test, and Spearman correlation analysis were performed. The logistic regression was applied to identify the independent factors for T2DM. Then, the predictive performance was assessed by Receiver operating characteristic curve analyses. An excellent agreement was found between liver fat fraction measured by MRS and MRI. (slope = 0.8; bias =-0.92%). In, patients with T2DM revealed lower fractions of mono-unsaturated fatty acid (F) (33.68 ± 10.62 vs 38.62 ± 12.21, P =.0089) and higher fractions of saturated fatty acid (F) (34.11 ± 9.746 vs 31.25 ± 8.66, P =.0351) of visceral fat tissue compared with patients without. BMI, HDL-c, F and F of visceral fat were independent factors for T2DM. Furthermore, F-S% was positively correlated with liver enzyme levels (P =.003 and 0.04). However, F-V% was negatively correlated with fasting blood glucose, HbA1c and HOMA-IR (P =.004, P =.001 and P =.03 respectively). Hence, the evaluation of fatty acid compositions of abdominal fat tissue using chemical shift-encoded MRI may have a predictive value for T2DM in patients with NAFLD.

Adequate perfusion is critical for maintaining normal brain function and aberrations thereof are hallmarks of many diseases. Pseudo-Continuous Arterial Spin Labeling (pCASL) MRI enables noninvasive quantitative perfusion mapping without contrast agent injection and with a higher signal-to-noise ratio (SNR) than alternative methods. Despite its great potential, pCASL remains challenging, unstable, and relatively low-resolution in rodents - especially in mice - thereby limiting the investigation of perfusion properties in many transgenic or other relevant rodent models of disease. Here, we address this gap by developing a novel experimental setup for high-resolution pCASL imaging in mice and combining it with the enhanced SNR of cryogenic probes. We show that our new experimental setup allows for optimal positioning of the carotids within the cryogenic coil, rendering labeling reproducible. With the proposed methodology, we managed to increase the spatial resolution of pCASL perfusion images by a factor of 15 in mice; a factor of 6 in rats is gained compared to the state of the art just by virtue of the cryogenic coil. We also show that the improved pCASL perfusion imaging allows much better delineation of specific brain areas, both in healthy animals as well as in rat and mouse models of stroke. Our results bode well for future high-definition pCASL perfusion imaging in rodents.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex-valued convolutional neural network (CNN) for MRI denoising. A complex-valued CNN incorporating the noise level map (non-blind DnCNN) was trained with ground truth and simulated noise-corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low-field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real-valued CNN and several other algorithms. For the simulated noise-corrupted testing dataset, the complex-valued models had superior normalized root-mean-square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non-blind DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low-field data, the non-blind DnCNN significantly improved the SNR and visual quality of the image. The proposed non-blind DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non-blind DnCNN to medical imaging. The method holds the potential to enable improved low-field MRI, facilitating enhanced diagnostic imaging in under-resourced areas.

Baluns are crucial in MRI RF coils, essential for minimizing common-mode currents, maintaining signal-to-noise ratio, and ensuring patient safety. This paper introduces the innovative float solenoid balun, based on the renowned solenoid cable trap, and conducts a comparative analysis with the widely used float bazooka balun. Leveraging robust inductive coupling between the cable shield and float resonator, the float solenoid balun offers compact dimensions and post-installation adjustability. Through electromagnetic simulations and bench testing across static fields (1.5, 3, and 7 T), the float solenoid balun demonstrates superior common-mode rejection ratios compared to the float bazooka balun. Notably, its float design facilitates easy post-installation adjustment and eliminates the need for soldering on the cable shield, enhancing usability and reducing risks. Furthermore, the solenoid balun's compact footprint addresses the increasing demand for smaller baluns in modern MRI scanners with denser coil arrays. The float solenoid balun offers a promising solution by conserving valuable space within the RF coil, simplifying practical hardware implementation and cable routing, and accommodating more elements in RF arrays, with great potential for enhancing MRI performance.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful imaging technique sensitive to tissue molecular composition, pH, and metabolic processes in situ. CEST MRI uniquely probes the physical exchange of protons between water and specific molecules within tissues, providing a window into physiological phenomena that remain invisible to standard MRI. However, given the very low concentration (millimolar range) of CEST compounds, the effects measured are generally only on the order of a few percent of the water signal. Consequently, a few critical challenges, including correction of motion artifacts and magnetic field (B and B ) inhomogeneities, have to be addressed in order to unlock the full potential of CEST MRI. Motion, whether from patient movement or inherent physiological pulsations, can distort the CEST signal, hindering accurate quantification. B and B inhomogeneities, arising from scanner hardware imperfections, further complicate data interpretation by introducing spurious variations in the signal intensity. Without proper correction of these confounding factors, reliable analysis and clinical translation of CEST MRI remain challenging. Motion correction methods aim to compensate for patient movement during (prospective) or after (retrospective) image acquisition, reducing artifacts and preserving data quality. Similarly, B and B inhomogeneity correction techniques enhance the spatial and spectral accuracy of CEST MRI. This paper aims to provide a comprehensive review of the current landscape of motion and magnetic field inhomogeneity correction methods in CEST MRI. The methods discussed apply to saturation transfer (ST) MRI in general, including semisolid magnetization transfer contrast (MTC) and relayed nuclear Overhauser enhancement (rNOE) studies.

Quantitative magnetization transfer (MT) imaging enables noninvasive characterization of the macromolecular environment of tissues. However, recent work has highlighted that the quantification of MT parameters using saturation radiofrequency (RF) pulses exhibits orientation dependence in ordered tissue structures, potentially confounding its clinical applications. Notably, in tissues with ordered structures, such as articular cartilage and myelin, the residual dipolar coupling (RDC) effect can arise owing to incomplete averaging of dipolar-dipolar interactions of water protons. In this study, we demonstrated the confounding effect of RDC on quantitative MT imaging in ordered tissues can be suppressed by using an emerging technique known as macromolecular proton fraction mapping based on spin-lock (MPF-SL). The off-resonance spin-lock RF pulse in MPF-SL could be designed to generate a strong effective spin-lock field to suppress RDC without violating the specific absorption rate and hardware limitations in clinical scans. Furthermore, suppressing the water pool contribution in MPF-SL enabled the application of a strong effective spin-lock field without confounding effects from direct water saturation. Our findings were experimentally validated using human knee specimens and healthy human cartilage. The results demonstrated that MPF-SL exhibits lower sensitivity to tissue orientation compared with , , and saturation-pulse-based MT imaging. Consequently, MPF-SL could serve as a valuable orientation-independent technique for the quantification of MPF.

Tissue-mimicking reference phantoms are indispensable for the development and optimization of magnetic resonance (MR) measurement sequences. Phantoms have greatest utility when they mimic the MR signals arising from tissue physiology; however, many of the properties underlying these signals, including tissue relaxation characteristics, can vary as a function of magnetic field strength. There has been renewed interest in magnetic resonance imaging (MRI) at field strengths less than 1 T, and phantoms developed for higher field strengths may not be physiologically relevant at these lower fields. This work focuses on developing materials with specific relaxation properties for lower magnetic field strengths. Specifically, we developed recipes that can be used to create synthetic samples for target nuclear magnetic resonance relaxation values for fields between 0.0065 and 0.55 T. and mixing models for agarose-based gels doped with a paramagnetic salt (one of CuSO, GdCl, MnCl, or NiCl) were created using relaxation measurements of synthetic gel samples at 0.0065, 0.064, and 0.55 T. Measurements were evaluated for variability with respect to measurement repeatability and changing synthesis protocol or laboratory temperature. The mixing models were used to identify formulations of agarose and salt composition to approximately mimic the relaxation times of five neurological tissues (blood, cerebrospinal fluid, fat, gray matter, and white matter) at 0.0065, 0.0475, 0.05, 0.064, and 0.55 T. These mimic sample formulations were measured at each field strength. Of these samples, the GdCl and NiCl measurements were closest to the target tissue relaxation times. The GdCl or NiCl mixing model recipes are recommended for creating target relaxation samples below 0.55 T. This work can help development of MRI methods and applications for low-field systems and applications.

Spin-lock (SL) pulses have been proposed to directly detect neuronal activity otherwise inaccessible through standard functional magnetic resonance imaging. However, the practical limits of this technique remain unexplored. Key challenges in SL-based detection include ultra-weak signal variations, sensitivity to magnetic field inhomogeneities, and potential contamination from blood oxygen level-dependent effects, all of which hinder the reliable isolation of neuronal signals. This pilot study evaluates the performance of the stimulus-induced rotary saturation (SIRS) technique to map visual stimulation response in the human cortex. A rotary echo spin-lock (RESL) preparation followed by a 2D echo planar imaging readout was used to investigate 12 healthy subjects at rest and during continuous exposure to 8 Hz flickering light. The SL amplitude was fixed to the target neuroelectric oscillations at that frequency. The signal variance was used as contrast metric, and two alternative post-processing pipelines (regression-filtering-rectification and normalized subtraction) were statistically evaluated. Higher variance in the SL signal was detected in four of the 12 subjects. Although group-level analysis indicated activation in the occipital pole, analysis of variance revealed that this difference was not statistically significant, highlighting the need for comparable control measures and more robust preparations. Further optimization in sensitivity and robustness is required to noninvasively detect physiological neuroelectric activity in the human brain.

Cerebral glucose and oxygen metabolism and blood perfusion play key roles in neuroenergetics and oxidative phosphorylation to produce adenosine triphosphate (ATP) energy molecules in supporting cellular activity and brain function. Their impairments have been linked to numerous brain disorders. This study aimed to develop an in vivo magnetic resonance spectroscopy (MRS) method capable of simultaneously assessing and quantifying the major cerebral metabolic rates of glucose (CMR) and oxygen (CMRO) consumption, lactate formation (CMR), and tricarboxylic acid (TCA) cycle (V); cerebral blood flow (CBF); and oxygen extraction fraction (OEF) via a single dynamic MRS measurement using an interleaved deuterium (H) and oxygen-17 (O) MRS approach. We introduced a single-loop multifrequency radio-frequency (RF) surface coil that can be used to acquire proton (H) magnetic resonance imaging (MRI) or interleaved low-γ X-nuclei H and O MRS. By combining this RF coil with a modified MRS pulse sequence, O-isotope-labeled oxygen gas inhalation, and intravenous H-isotope-labeled glucose administration, we demonstrate for the first time the feasibility of simultaneously and quantitatively measuring six important physiological parameters, CMR, CMRO, CMR, V, CBF, and OEF, in rat brains at 16.4 T. The interleaved H-O MRS technique should be readily adapted to image and study cerebral energy metabolism and perfusion in healthy and diseased brains.

Advances in gene therapy, especially for brain diseases, have created new imaging demands for noninvasive monitoring of gene expression. While reporter gene imaging using co-expression of fluorescent protein-encoding gene has been widely developed, these conventional methods face significant limitations in longitudinal in vivo applications. Magnetic resonance imaging (MRI), specifically chemical exchange saturation transfer (CEST) MRI, provides a robust noninvasive alternative that offers unlimited depth penetration, reliable spatial resolution, and specificity toward particular molecules. In this study, we explore the potential of CEST-MRI for monitoring gene expression in neurons. We designed a CEST polypeptide reporter expressing 150 arginine residues and evaluated its expression in the living brain after viral vector delivery. A longitudinal study performed at one and 2 months postinjection showed that specific CEST signal was observable. In particular, the CEST contrast exhibited distinct peaks at 0.75 and 1.75 ppm, consistent with the expected hydroxyl and guanidyl protons resonance frequencies. Histological study confirmed the specific neuronal expression of the transgene evidenced by the fluorescence signal from the td-Tomato fluorophore fused to the polypeptide. The ability to image noninvasively a neuron-specific CEST-MRI reporter gene could offer valuable insights for further developments of gene therapy for neurological disorders.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time.

Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning (DL) model to detect rigid motion in T-weighted brain images. We leveraged a 2D convolutional neural network (CNN) trained on motion-synthesized data for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed 93% agreement with the labeling of a radiologist a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is aimed at inline automatic detection of motion artifacts, accelerating part of the time-consuming quality assessment (QA) process and augmenting expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.

Quantitative MRI and MRS have become important tools for the assessment and management of patients with neuromuscular disorders (NMDs). Despite significant progress, there is a need for new objective measures with improved specificity to the underlying pathophysiological alteration. This would enhance our ability to characterize disease evolution and improve therapeutic development. In this study, qMRI methods that are commonly used in clinical studies involving NMDs, like water T2 (T2) and T1 and fat-fraction (FF) mapping, were employed to evaluate disease activity and progression in the skeletal muscle of golden retriever muscular dystrophy (GRMD) dogs. Additionally, extracellular volume (ECV) fraction and single-voxel bicomponent water T2 relaxometry were included as potential markers of specific histopathological changes within the tissue. Apart from FF, which was not significantly different between GRMD and control dogs and showed no trend with age, T2, T1, ECV, and the relative fraction of the long-T2 component, A, were significantly elevated in GRMD dogs across all age ranges. Moreover, longitudinal assessment starting at 2 months of age revealed significant decreases in T2, T1, ECV, A, and the T2 of the shorter-T2 component, T2, in both control and GRMD dogs during their first year of life. Notably, insights from ECV and bicomponent water T2 indicate that (I) the elevated T2 and T1 values observed in dystrophic muscle are primarily driven by an expansion of the extracellular space, likely driven by the edematous component of inflammatory responses to tissue injury and (II) the significant decrease of T2 and T1 with age in control and GRMD dogs reflects primarily the progressive increase in fiber diameter and protein content during tissue development. Our study underscores the potential of multicomponent water T2 relaxometry and ECV to provide valuable insights into muscle pathology in NMDs.

Diffusion tensor imaging (DTI) provides insight into the skeletal muscle microstructure and can be acquired using a stimulated echo acquisition mode (STEAM)-based approach to quantify time-dependent tissue diffusion. This study examined diffusion metrics and signal-to-noise ratio (SNR) in the supraspinatus muscle obtained with a STEAM-DTI sequence with different diffusion encoding times (Δ) and compared them to measures from a spin echo (SE) sequence. Ten healthy subjects (mean age 31.5 ± 4.7 years; five females) underwent 3-Tesla STEAM and SE-DTI of the shoulder in three sessions. STEAM was acquired with Δ of 100/200/400/600 ms. The diffusion encoding time in SE scans was 19 ms (b = 500 s/mm). Region of interest-based measurement of fractional anisotropy (FA), mean diffusivity (MD), and SNR was performed. Intraclass correlation coefficients (ICCs) were computed to assess test-retest reliability. ANOVA with post-hoc pairwise tests was used to compare measures between different Δ of STEAM as well as STEAM and SE, respectively. FA was significantly higher (FA: 0.38-0.46 vs. FA: 0.26) and MD significantly lower (MD: 1.20-1.33 vs. MD: 1.62 × 10 mm/s) in STEAM compared to SE (p < 0.001, respectively). SNR was significantly higher for SE (72.3 ± 8.7) than for STEAM (p < 0.001). ICCs were excellent for FA in STEAM (≥0.911) and SE (0.960). For MD, ICCs were good for STEAM (≥0.759) and SE (0.752). STEAM and SE exhibited excellent reliability for FA and good reliability for MD in the supraspinatus muscle. SNR was significantly higher in SE compared to STEAM.

Ultra-high field (7T) MRI allows scans at sub-millimetre resolution with exquisite signal-to-noise ratio (SNR). As 7T MRI becomes more widely used clinically, the challenge of patient motion must be overcome. Retrospective motion correction is used successfully for some protocols, but for acquisitions such as slice-by-slice scans only prospective motion correction can deliver the full potential of 7T MRI. We report the first implementation of prospective 3D Fat Navigator ("FatNav") motion correction for the Siemens 7T Terra MRI. We implemented a modular Sequence Building Block for FatNav and embedded it into the vendor's gradient-recalled echo (GRE) sequence. We modified the reconstruction pipeline to reconstruct FatNav images online, coregistering them and sending motion updates to the host sequence online. We tested five registration algorithms for performance and accuracy on synthetic FatNav data. We implemented the best three of these in our sequence and tested them online. We acquired T and T* weighted brain images of healthy volunteers correcting every other image for motion to visualise the effectiveness of online motion correction. Data were acquired with and without head immobilisation. We also tested performance while correcting every measurement for motion. Our implementation uses a 1.23 s 3D FatNav acquisition module and delivers motion updates in less than 3 s, which is sufficient for motion updates every few k-space lines in typical scans. Corrected images are crisper with fewer visible motion artefacts. This improved sharpness is reflected quantitatively by an increase in the variance of the image Laplacian which is 1.59 x better for corrected vs uncorrected images. Profiles across the cerebral falx are 33% steeper for corrected vs uncorrected images. Prospective FatNav improves GRE image quality in the brain. Our modular Sequence Building Block provides a simple method to integrate motion correction in 7T MRI pulse sequences.

Measuring the intracellular pH (pHi) is of interest for brain tumor diagnostics. Common metrics of CEST imaging like the amide proton transfer-weighted (APTw) MTR are pHi sensitive and allow differentiating malignant tumor from healthy tissue. Yet, the image contrast also depends on additional magnetization transfer effects and T1. In contrast, the apparent exchange-dependent relaxation (AREX) provides a T1 corrected exchange rate of the amide protons. As AREX still depends on amide proton density, its pHi sensitivity remains ambiguous. Hence, we conducted this study to assess the influence of pathologic tissue changes on the pHi sensitivity of AREX in vivo. Patients with newly diagnosed intra-axial brain tumors were prospectively recruited and underwent conventional MRI, quantitative T1 relaxometry, APT-CEST and P-MRS on a 3T MRI scanner. Tumors were segmented into contrast-enhancing tumor (CE), surrounding T2 hyperintensity (T2-H) and contralateral normal appearing white matter (CNAWM). T1 mapping and APT-CEST metrics were correlated with P-MRS-derived pHi maps (Pearson's correlation). Without differentiating tissue subtypes, pHi did not only correlate significantly with MTR (r = 0.46) but also with T1 (r = 0.49). Conversely, AREX only correlated poorly with pHi (r = 0.17). Analyzing different tissue subtypes separately revealed a tissue dependency of the pHi sensitivity of AREX with a significant correlation (r = 0.6) in CNAWM and no correlation in T2-H or CE (r = -0.11/-0.24). CE showed significantly increased MTR, pHi, and T1 compared with CNAWM (p < 0.001). In our study, the pHi sensitivity of AREX was limited to CNAWM. The lack of sensitivity in CE and T2-H is probably attributable to altered amide and water proton concentrations in these tissues. Conversely, the correlation of pHi with MTR may be explained by the coincidental contrast increase through increased T1 and amide proton density. Therefore, limited structural deviations from CNAWM might be a perquisite for the use of CEST contrasts as pHi-marker.

Diabetes affects metabolism and metabolite concentrations in multiple organs. Previous preclinical studies have shown that receptor for advanced glycation end products (RAGE, gene symbol Ager) and its cytoplasmic domain binding partner, Diaphanous-1 (DIAPH1), are key mediators of diabetic micro- and macro-vascular complications. In this study, we used H-Magnetic Resonance Spectroscopy (MRS) and chemical shift encoded (CSE) Magnetic Resonance Imaging (MRI) to investigate the metabolite and water-fat fraction in the heart and hind limb muscle in a murine model of type 1 diabetes (T1D) and to determine if the metabolite changes in the heart and hind limb are influenced by (a) deletion of Ager or Diaph1 and (b) pharmacological blockade of RAGE-DIAPH1 interaction in mice. Nine cohorts of male mice, with six mice per cohort, were used: wild type non-diabetic control mice (WT-NDM), WT-diabetic (WT-DM) mice, Ager knockout non-diabetic (RKO-NDM) and diabetic mice (RKO-DM), Diaph1 knockout non-diabetic (DKO-NDM), and diabetic mice (DKO-DM), WT-NDM mice treated with vehicle, WT-DM mice treated with vehicle, and WT-DM mice treated with RAGE229 (antagonist of RAGE-DIAPH1 interaction). A Point Resolved Spectroscopy (PRESS) sequence for H-MRS, and multi-echo gradient recalled echo (GRE) for CSE were employed. Triglycerides, and free fatty acids in the heart and hind limb obtained from MRS and MRI were compared to those measured using biochemical assays. Two-sided t-test, non-parametric Kruskal-Wallis Test, and one-way ANOVA were employed for statistical analysis. We report that the results were well-correlated with significant differences using MRI and biochemical assays between WT-NDM and WT-DM, as well as within the non-diabetic groups, and within the diabetic groups. Deletion of Ager or Diaph1, or treatment with RAGE229 attenuated diabetes-associated increases in triglycerides in the heart and hind limb in mice. These results suggest that the employment of H-MRS/MRI is a feasible non-invasive modality to monitor metabolic dysfunction in T1D and the metabolic consequences of interventions that block RAGE and DIAPH1.

The focus of this work was to identify the optimal magnetic resonance imaging (MRI) contrast between orthotopic U-87 MG tumours and normal appearing brain with the eventual goal of treatment response monitoring. U-87 MG human glioblastoma cells were injected into the brain of RNU nude rats (n = 9). The rats were imaged at 7 T at three timepoints for all animals: 3-5, 7-9, and 11-13 days after implantation. Whole-brain T-weighted (before and after gadolinium contrast agent injection), diffusion, and fluid-attenuated inversion recovery scans were performed. In addition, single-slice saturation-transfer-weighted chemical exchange saturation transfer (CEST), magnetization transfer (MT), and water saturation shift referencing (WASSR) contrast Z-spectra and T and T maps were also acquired. The MT and WASSR Z-spectra and T map were fitted to a two-pool quantitative MT model to estimate the T of the free and macromolecular-bound water molecules, the relative macromolecular pool size (M), and the magnetization exchange rate from the macromolecular pool to the free pool (R). The T-corrected apparent exchange-dependent relaxation (AREX) metric to isolate the CEST contributions was also calculated. The lesion on M and AREX maps with a B of 2 μT best matched the hyperintensity on the post-contrast T-weighted image. There was also good separation in Z-spectra between the lesion and contralateral cortex in the 2-μT CEST and 3- and 5-μT MT Z-spectra at all time points. A pairwise Wilcoxon signed-rank tests with Holm-Bonferroni adjustment on MRI parameters was performed and the differences between enhancing lesion and contralateral cortex for the MT ratio with 2 μT saturation at 3.6 ppm frequency offset (corresponding to the amide chemical group) and M were both strongly significant (p < 0.001) at all time points. This work has identified that differences between enhancing lesion and contralateral cortex are strongest in MTR with B = 2 μT at 3.6 ppm and relative macromolecular pool size (M) images over entire period of 3-13 days after cancer cell implantation.