Longitudinal (T) and transverse (T) relaxation times measured by MRI are promising markers for assessing biological processes and disease pathology. In this study, we characterized the T and T relaxation times in the Tg2576 mouse model of Alzheimer's disease (N = 10) across ten time points, ranging from 3 to 14 months of age, using an 11.7 T MRI scanner. Genotype-dependent changes over time were observed in the thalamus, hypothalamus, and piriform cortex, suggesting that the rates of change in relaxation times within these regions may serve as potential markers for distinguishing Tg2576 mice from their wildtype (WT) counterparts. In addition, significant genotype differences were detected in the isocortex and hippocampus. These observations likely reflect the interplay between changes in tissue water content and the accumulation of amyloid plaques. To provide a reference for future MRI studies, we also calculated the average relaxation times over time points for WT mice. The mean T values were 2036.3 ± 26.8 ms (isocortex), 2046.5 ± 28.7 ms (hippocampus), 1861.7 ± 22.2 ms (thalamus), 1897.8 ± 57.0 ms (hypothalamus), and 2099.7 ± 30.5 ms (piriform cortex). Corresponding T values were 38.3 ± 0.5 ms (isocortex), 39.0 ± 0.2 ms (hippocampus), 35.4 ± 0.3 ms (thalamus), 36.9 ± 0.4 ms (hypothalamus), and 40.3 ± 0.3 ms (piriform cortex).

This study aimed to design and implement an optimized gradient scheme for PRESS-localized edited magnetic resonance spectroscopy (MRS) to enhance suppression of out-of-voxel (OOV) artifacts. These artifacts, which originate from insufficient crushing of unwanted coherence transfer pathways (CTPs), are particularly challenging in editing schemes for metabolites like gamma-aminobutyric acid and glutathione. To address this, a volume-based likelihood model was developed to guide gradient scheme optimization, prioritizing suppression of CTPs based on likelihood. The volume-based likelihood model for CTP weighting was integrated into a Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient optimization. Using a genetic algorithm with a weighted dual-penalty cost function, gradient schemes were optimized to maximize pathway-specific suppression. Hardware and sequence constraints, maximum gradient amplitudes and delay durations respectively, informed the optimization. Validation of the optimized scheme was performed with simulations by calculating the k-space crushing efficiency analytically with k-space trajectory and in vivo using an edited MRS sequence in three brain regions (posterior cingulate cortex PCC, thalamus, and medial prefrontal cortex [mPFC]), with particular focus on OOV artifact reduction and spectral quality improvements. A three-way Analysis of Variance was used to assess the significance level of OOV artifact reduction. The optimized gradient scheme demonstrated improved k-space crushing efficiency (by an average of 197%). OOV artifacts were reduced in all brain regions, particularly in highly OOV-susceptible regions (thalamus and mPFC). Improvements were most notable around 4.3 ppm with significant OOV artifact amplitude reductions (p < 0.001). By using a volume-based likelihood model for CTP prioritization, the optimized DOTCOPS scheme ensures robust and region-agnostic performance in reducing OOV artifacts.

MRSI is a non-invasive tool for mapping metabolic distributions in multi-focal or other diseases where the location of abnormalities may be uncertain. High-concentration metabolites can be investigated at 3T using non-edited MRSI, whereas lower-concentration metabolites, such as GABA, typically require specialized editing techniques because of spectral overlap. This study reports on the reproducibility of a protocol containing both co-localized short-TE and GABA-edited multi-slice spin-echo 2D MRSI. Multi-slice, short-TE (TE 20 ms) and GABA-edited (TE 68 ms) MRSI at a nominal spatial resolution of 2.2 cm was performed twice (7 to 14 days apart) at 3T on 11 healthy volunteers (age range 7 to 43 years). Data analysis was performed in the "Osprey" software package, including retrospective motion compensation, consensus-recommended processing, and linear-combination modeling. Metabolite estimates for six metabolites were quantified relative to total creatine (tCr) and water in 14 regions of interest. Reproducibility was assessed using intra- and inter-subject coefficients of variation. Short-TE MRSI metabolite estimates for total N-acetylaspartate (tNAA), tCr, total choline (tCho), myo-inositol (mI), and the sum of glutamate and glutamine (Glx) were found to be highly reproducible for both creatine- and water-referenced concentration estimates, with 77% of the regions of interest meeting the quality-control criteria for both visits and 96% for at least one visit. Average intra-subject CVs were 5.8% and 4.8%, and inter-subject CVs were 11.1% and 9.7% for water-referenced and tCr-referenced estimates, respectively. For GABA+ (GABA + macromolecules) estimates, 46% of the voxels of interest met quality-control criteria for both visits, and 82% for at least one visit. In the remaining datasets, the average intra-subject CVs were 13.5% for both quantification methods, and the inter-subject CVs were 13.5% and 16.9% for water-referenced and creatine-referenced estimates, respectively. 3T-MRSI sequences can achieve reproducible mapping with extended brain coverage of five major metabolites (tNAA, tCr, tCho, mI, and Glx). Reproducibility assessment for GABA+ mapping remains challenging, with 18% of the data being rejected in at least one visit, but it yielded acceptable reproducibility in datasets that met quality control criteria in both visits (46%).

Cerebrovascular reactivity (CVR) is a crucial physiological marker of vascular health and has been linked to aging-related cerebrovascular decline. Resting-state BOLD MRI-based relative CVR mapping (RS-rCVR) offers a noninvasive and compliance-friendly alternative to gas-challenge methods, making it suitable for lifespan studies. This study aimed to examine age-related differences in RS-rCVR among healthy adults using voxel-wise and region-of-interest (ROI) analyses. We prospectively recruited 54 healthy adults, including 27 younger (20-28 years, mean = 23.3 ± 3.4; 16 females) and 27 older (57-75 years, mean = 66.5 ± 5.3; 16 females) participants. Resting-state fMRI data were acquired using a T2*-weighted gradient-echo EPI sequence at 3T. RS-rCVR maps were generated by linear regression of the voxel-wise BOLD time series against the global BOLD signal and normalized to cerebellar gray matter. Group-level analyses included voxel-wise comparisons, histogram analyses, and ROI-based statistical tests. The distribution of RS-rCVR values significantly differed between age groups (Kolmogorov-Smirnov test: KS = 0.039, p < 0.001). Voxel-wise comparisons revealed age-related reductions in RS-rCVR in the medial frontal cortex, precuneus, and cuneus in older adults. In contrast, ROI-averaged RS-rCVR values showed no statistically significant group differences across frontal, temporal, and occipital cortices (p > 0.05). Effect size analysis indicated small to moderate differences in specific regions (e.g., occipital cortex: d = 0.441; parietal cortex: d = 0.430; frontal middle gyrus: d = 0.275), but negligible effects in others (e.g., cingulate cortex: d = 0.006). While voxel-wise RS-rCVR mapping detects spatially localized age-related reductions in cerebrovascular reactivity, ROI-based analysis may obscure these effects due to anatomical averaging. These findings underscore the spatial heterogeneity of cerebrovascular aging and support the utility of voxel-level RS-rCVR approaches in lifespan research.

This prospective study aimed to perform a qualitative and quantitative comparison of deep learning (DL) and conventional T2-weighted Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) sequences for 3T MRI acquisition of the upper abdomen. From January 2024 to April 2024, 166 patients (60 ± 14 years) scheduled for MRI of the upper abdomen were prospectively enrolled. Each patient underwent two MRI examinations: one using a conventional T2-weighted HASTE sequence, followed by a fast T2-weighted HASTE sequence reconstructed with DL. Image quality, anatomical structure visualization, and diagnostic performance were independently assessed by three readers using a 5-point Likert scale. Quantitative analysis included measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for both sequences. Additionally, radiomic features were extracted and analyzed for significant variations. Interreader agreement was evaluated using Fleiss' Kappa. The DL HASTE sequence showed significantly superior overall image quality (p < 0.001), fewer artifacts (p < 0.001), and improved delineation of anatomical structures (p < 0.01) compared to the conventional T2-weighted HASTE sequence. DL sequences exhibited better SNR (p < 0.001), whereas CNR values did not show a difference between the two acquisition types. Radiomics feature analysis unveiled significant differences in contrast and gray-level characteristics (p ≤ 0.001). DL HASTE demonstrated a significant time reduction of 62.5% together with significant energy cost savings of 0.34 kW per scan compared to the conventional sequence acquisition. The DL HASTE sequence enhanced image quality and diagnostic confidence while minimizing artifacts, time, and energy costs, enabling a more accurate detection of pathologies than the conventional T2-weighted product solution with potential clinical impact.

Arterial spin labeling (ASL) MRI suffers from low signal-to-noise ratio. Current background suppression (BS) methods focus on suppressing tissue signal. The present work aims to test the hypothesis that BS schemes to suppress CSF signal can produce a greater benefit, given the recent observations that water in CSF has more pulsation as part of the neurofluid circulation. We developed a CSF BS scheme that maximally suppresses residual CSF signal using two inversion pulses. Its performance was compared with regular BS and enhanced BS, both of which primarily suppress gray and white matter signals. All schemes were evaluated in single-delay and multi-delay pseudo-continuous ASL (pCASL) MRI. The single-delay scans assessed cerebral blood flow (CBF), while the multi-delay scans measured both CBF and arterial transit time (ATT). Reproducibility was assessed using voxel-wise coefficient of variation (CoV) and spatial Spearman correlation coefficients (Rs). When using the CSF BS scheme, CSF signals were suppressed to less than 1% of the equilibrium magnetization, with gray and white matter signals around 5% in opposite magnetization signs. Complex control/label subtraction effectively accounted for magnetization signs and obtained the correct difference signal. In single-delay pCASL, CSF BS reduced visually apparent hyper- and hypo-intensity signals in CBF maps, which were present in the tissue-focused regular and enhanced BS schemes. These artifactual signal fluctuations were particularly pronounced in the brain-stem regions where CSF pulsation was the most severe, but also spread along the z-encoding direction. Quantitatively, CSF BS resulted in a lower voxel-wise CoV (8.8%) and higher R (0.89) in the CBF maps, compared with tissue-focused BS (CoV 18.8%, R 0.64). In multi-delay pCASL, CSF BS yielded a CoV of 5.9% and 11.3% for CBF and ATT maps, respectively, outperforming both enhanced and regular BS. These results demonstrate that CSF BS can reduce spurious signals and improve image quality in ASL perfusion MRI.

Codeletion of chromosome 1p and 19q arms in a subset of gliomas has been shown to associate with the accumulation of cystathionine in the tumor. Here, we report the analyses of cystathionine and 2-hydroxyglutarate (2HG) in 38 biopsy-proven glioma patients, as measured with TE 97-ms point-resolved spectroscopy (PRESS) at 3 T. Following the confirmation of in-house calculated cystathionine basis signals in a phantom solution, LCModel spectral fitting was performed to decompose metabolite signals, and the millimolar concentrations of metabolites were estimated with reference to water. Cystathionine was estimated to be significantly higher in IDH mutated 1p/19q codeleted gliomas than in noncodeleted gliomas (1.4 ± 1.1 vs. 0.3 ± 0.4 mM; n = 15 and 14; p = 0.002). 2HG estimation was significantly higher in IDH-mutant gliomas compared to IDH-wildtype gliomas (3.7 ± 3.3 vs. 0.1 ± 0.1 mM; n = 29 and 9; p = 0.002). Using cutoff values from receiver operating characteristic curve analysis, the sensitivity and specificity of the cystathionine measures with respect to 1p/19q status were estimated to be 0.8 and 0.79 (cutoff at 0.6 mM), and those of 2HG measures with respect to IDH status were both unity (cutoff at 0.5 mM). A TE 113-ms PRESS sequence was designed to improve detection of the cystathionine C4-proton resonance at 2.72 ppm. Data from two glioma patients showed that cystathionine can be detected with minimal interference from the overlapping aspartate signal. 2D chemical-shift imaging of cystathionine using the TE 113-ms PRESS is demonstrated. Our MRS data confirm elevation of cystathionine in 1p/19q codeleted gliomas, as demonstrated in prior studies, and suggest that relatively high estimates of cystathionine may provide a biomarker of 1p/19q codeleted gliomas.

Magnetic resonance spectroscopy (MRS) enables noninvasive assessment of brain metabolites and is commonly implemented using single-voxel spectroscopy (SVS) or magnetic resonance spectroscopic imaging (MRSI). This study directly compares the reproducibility of single-voxel sLASER and 3D-Concentric Ring Trajectory-based Free Induction Decay MRSI (3D-CRT-FID-MRSI) at 3 T and 7 T in the same cohort. Five healthy adults were each scanned twice on a 3 T PrismaFit (Siemens; 64-channel head coil) and a 7 T MAGNETOM 7 T Plus (Siemens; 32-channel head coil), with sessions 5-9 days apart. To explore MRSI's capabilities for regional metabolite quantification and reproducibility assessment, three masking strategies were applied. Additionally, two spatial averaging approaches for MRSI, averaging before vs. after spectral fitting, were evaluated. Coefficients of variation (CVs) and voxel-wise correlation analyses were used to assess intrasubject and intersession reproducibility. Results showed good-to-excellent reproducibility across both techniques, with SVS generally providing lower CVs at 7 T, while MRSI outperformed SVS in several metabolites at 3 T. MRSI allowed tissue-specific analysis, with lower CVs observed in WM compared to GM, especially at 7 T. Although MRSI reproducibility was slightly reduced at 7 T likely due to longer scan times and lack of prospective motion correction at 7 T (which was available at 3 T in this study), the spatial coverage and retrospective region analysis makes it an attractive alternative to SVS for many brain regions. This study demonstrates that both sLASER and CRT-FID-MRSI provide reproducible metabolite measurements at 3 T and 7 T. The findings highlight MRSI's advantages for retrospective multiregional and tissue-specific analysis, facilitating its integration into future clinical research.

To evaluate the feasibility of diffusion tensor imaging (DTI) using quantitative ultrashort echo time double-echo steady-state (qUTE-DESS) MRI in short T musculoskeletal tissues, we validated it in phantoms, an ex vivo porcine hoof, and an in vivo human knee. The qUTE-DESS sequence was implemented on a clinical 3 T MRI system, enabling simultaneous estimation of T, T, and diffusivity in tissues with rapid signal decay. Data were acquired with six diffusion-weighting orientations (x, y, z, xy, yz, xz) to obtain mean diffusivity (MD) and fractional anisotropy (FA). Sucrose and agarose phantoms demonstrated linear relationships between T, T, or diffusivity and their concentrations (R > 0.88). A celery phantom demonstrated anisotropic diffusion by revealing elevated FA in fibrous structures. In experiments with the porcine hoof and healthy volunteers' knees, qUTE-DESS generated high-resolution parameter maps of MD and FA across various tissues, including cartilage, meniscus, tendon, ligament, and muscle, effectively capturing short T components that conventional DTI could not visualize. By preserving ultrashort echo signals, qUTE-DESS appeared to overcome the limitations of spin-echo-based DTI, which suffers from longer echo times and subsequent signal loss in short T tissues. Therefore, this approach may serve as a valuable quantitative imaging tool for assessing microstructural features in the musculoskeletal system, facilitating detection and evaluation of joint abnormalities or degenerative changes. The results suggest qUTE-DESS can provide insight into both long and short T tissues, offering potential benefits in clinical diagnosis and research. Further studies should assess its diagnostic utility in larger cohorts with musculoskeletal pathologies.

Lung cancer (LC) and Alzheimer's disease (AD) are both age-associated diseases with high rates of mortality. Studies have reported a possible inverse relationship between LC and AD incidences; however, possible shared molecular mechanisms have not been well investigated. Better characterizations of both diseases and their potential molecular relationships may advance the development of successful therapies for both LC and AD. Metabolomics, as a holistic study of the entire measurable metabolome, has the potential to probe into their metabolic connections. Herein, we used high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy to study 36 human serum samples collected from primary lung adenocarcinoma patients with or without AD, or AD and related dementia (ADRD). We identified 88 metabolites with 66 metabolites differentiating LC patients from controls, and 80 metabolites discerning LC patients without ADRD from those with ADRD. Our results demonstrate the capability of metabolomics to reveal inversely dysregulated glycolysis, oxidative phosphorylation, and proline metabolism in LC and ADRD.

Cerebrovascular reactivity (CVR) mapping based on resting-state BOLD fMRI can be widely available for research of vascular health not only in clinical studies but also in open databases. However, as it utilizes spontaneous CO fluctuations of blood as endogenous stimuli, resting-state CVR may be prone to low SNR and reproducibility if the CO fluctuation of an individual is small. The automatic identification of such poor-quality CVR datasets is crucial for large-scale research. Thus, in this work, we developed an automatic quality control algorithm for resting-state CVR mapping. Utilizing a total of 51 resting-state CVR maps acquired with three scanning protocols in each healthy participant, quality control parameters reflecting common characteristics of poor-quality CVR, including pooled variance of different tissue types, proportion of negative voxels in gray matter, and the sensitivity of the BOLD signal to CVR, were extracted and then combined into one comprehensive quality evaluation index (QEI). We further evaluated its performance by leave-one-out cross-validation and correlation analyses with test-retest reproducibility. Leave-one-out cross-validation showed that QEI was significantly correlated with the reference standard of quality evaluation in all left-out cases (r = 0.766). Correlation analyses with test-retest reproducibility revealed significant positive correlations between the worse QEI and similarity index of CVR maps from two tests (r = 0.809, 0.890, 0.396, and 0.654 for data from four open databases). The proposed QEI performed not only in good agreement with visual inspection but can also adapt in resting-state CVR from multiple age groups and scanning protocols, paving the way for the clinical applications of resting-state CVR mapping technology.

1H-Magnetic resonance spectroscopy (1H-MRS) is a noninvasive technique for quantifying brain metabolites, including glutamate, glutathione (GSH), and γ-aminobutyric acid (GABA), which are essential for brain function and implicated in various neurodevelopmental conditions. As such, 1H-MRS methods that enable reliable and accurate measurement of these metabolites are of considerable clinical value. Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy (HERMES; echo time [TE] = 80 ms) is a spectral editing technique that allows for the simultaneous quantification of GABA and GSH, using subtraction approaches to resolve these metabolites in a difference spectrum. Additionally, glutamate plus glutamine resonances (Glx) can be resolved either from the HERMES GABA-edited difference spectrum (GABA-DIFF) or from the sum of all HERMES transients (SUM spectrum). However, the reliability of 80-ms HERMES for quantification of Glx has not been systematically assessed. Here, we evaluate the agreement between Glx obtained from HERMES GABA-DIFF and SUM spectra with Glx derived from short-TE PRESS (TE = 35 ms), which is conventionally used for Glx estimation and has demonstrated reproducibility. Data were acquired from 139 participants across two brain regions (ACC and Thalamus voxels), three scanners, two diagnostic groups (autism and neurotypical development) and two age groups (adolescent/adult and preschooler). Comparisons were made using both creatine-scaled and tissue-corrected Glx estimates. Our findings demonstrate significant systematic and proportional bias between Glx estimates from HERMES (SUM and GABA-DIFF) and short-TE PRESS, consistent across scanners, voxels, age groups and diagnostic categories. These findings indicate that Glx estimates derived from HERMES are not directly comparable to those from short-TE PRESS, and this discrepancy is consistent across a multisite study setting. This underscores the importance of sequence selection and careful methodological consideration when integrating and interpreting data from 1H-MRS across different acquisition protocols.

Altered pH is a hallmark of metabolic disruption in the tumor microenvironment. Chemical exchange saturation transfer (CEST) MRI has emerged as a valuable technique for pH-sensitive imaging. However, the pH specificity of conventional CEST measurements is usually compromised by T relaxation and labile proton concentration. Ratiometric CEST MRI can eliminate these contaminations. This study aimed to investigate the feasibility of radiofrequency (RF) amplitude-based ratiometric CEST MRI for tumor pH-sensitive imaging. Six adult rats implanted with C6 glioma cells underwent CEST MRI under four RF saturation (B) levels of 0.75, 1.0, 1.5, and 2.0 μT at an 11.7 T scanner. A quasi-steady-state (QUASS) algorithm was used to reconstruct equilibrium Z-spectra. Guanidinium CEST (GuanCEST) signal at 2 ppm was isolated from inverse Z-spectra using multi-pool Lorentzian fitting. The chemical exchange rate (k) of guanidinium protons was quantified with a spillover- and magnetization transfer-corrected omega plot model. The RF amplitude-based guanidinium CEST ratio (R) was quantified by ratioing GuanCEST signals under B of 1.5 μT over that under 0.75 μT and was correlated with k using Pearson correlation analysis. Paired Student's t tests were performed to evaluate differences between tumor and contralateral normal regions. p < 0.05 was considered statistically significant. Strong correlations between R and k were observed both at the pixel level (R = 0.863, p < 0.001) and across animals (R = 0.817, p = 0.001). Tumor regions exhibited significantly lower R (0.90 ± 0.16) compared with contralateral normal tissues (1.08 ± 0.21, p = 0.001), consistent with corresponding reductions in k (298.04 ± 61.71 s in tumors vs. 418.86 ± 79.45 s in contralateral normal tissues, p < 0.001), indicating an acidic microenvironment in gliomas. In conclusion, this study demonstrates the feasibility of endogenous GuanCEST ratiometric MRI for pH-sensitive imaging of gliomas, providing an effective way for evaluating tumor acidity.

The clinical application of MR-compatible ergometers for muscle contractile assessment is limited by a lack of validation against standard clinical dynamometers. Moreover, the impact of obesity on the reliability of MR ergometer-based muscle contractile assessments and the quality of phosphorus-31 magnetic resonance spectroscopy (P-MRS) data remains unclear. This study aimed to validate an MR-compatible ergometer against a clinical dynamometer and to evaluate the applicability of P-MRS in individuals with severe obesity. Twenty adults (35-60 years) were recruited and divided into groups of nonobesity (BMI 18.5-30 kg/m, n = 10) and severe obesity (BMI ≥ 35 kg/m, n = 10), matched for age, sex, and height. Ankle dorsiflexion was assessed using both a clinical dynamometer and an MR ergometer, measuring maximal voluntary isometric contraction (MVIC) and a 4-min isotonic fatiguing exercise. P-MRS was continuously acquired during the in-scanner exercise. Agreement between devices was assessed using Bland-Altman plots and intraclass correlation coefficients (ICCs). P-MRS data quality was evaluated based on signal-to-noise ratio (SNR), uncertainty of fit (CRLB), and phosphocreatine (PCr) recovery fit (R). Pearson's correlations examined relationships between muscle fatigue and metabolic parameters. All subjects successfully completed the protocol on both devices. The MR ergometer demonstrated moderate-to-excellent reliability (ICC ≥ 0.50) for most contractile parameters. While maximal torque, power, and work were underestimated on the MR ergometer (16-28%), this bias was consistent across BMI groups. P-MRS met preset quality thresholds (SNR ≥ 5, CRLB < 20%, R ≥ 0.70) in both groups. Dorsiflexion fatigue (reduction in power) correlated strongly (r ≥ 0.77) with metabolic changes, including PCr depletion (R = 0.68), pH drop (R = 0.59), PCr recovery time constant (R = 0.62), and inorganic phosphate accumulation (Pi/PCr) (R = 0.67). The MR ergometer demonstrated feasibility, acceptable reliability, and consistent P-MRS data quality across BMI groups. These findings support the use of the MR ergometer for in-scanner dorsiflexor assessments, even in individuals with severe obesity.

Motion artifacts in magnetic resonance imaging (MRI) are one of the frequently occurring artifacts due to patient movements during scanning. Motion is estimated to be present in approximately 30% of clinical MRI scans; however, motion has not been explicitly modeled within deep learning image reconstruction models. Deep learning (DL) algorithms have been demonstrated to be effective for both the image reconstruction task and the motion correction task, but the two tasks are considered separately. The image reconstruction task involves removing undersampling artifacts such as noise and aliasing artifacts, whereas motion correction involves removing artifacts including blurring, ghosting, and ringing. In this work, we propose a novel method to simultaneously accelerate imaging and correct motion. This is achieved by integrating a motion module into the DL-based MRI reconstruction process, enabling detection and correction of motion. We model motion as a tightly integrated auxiliary layer in the DL model during training, making the DL model "motion-informed". During inference, image reconstruction is performed from undersampled raw k-space data using a trained motion-informed DL model. Experimental results demonstrate that the proposed motion-informed DL image reconstruction network outperformed the conventional image reconstruction network for motion-degraded MRI datasets.

As a promising in vivo metabolic imaging method, chemical exchange saturation transfer (CEST) MRI requires collecting a series of images using varied saturation frequencies (ω). The low-rankness feature was utilized for acquisition acceleration and data denoising but primarily in the image domain. Herein, we aim to utilize such features in both k-space and the image domain, for achieving faster imaging and higher quality reconstruction. According to Parseval's theorem, CEST images at each ω have equal total energy to their k-space counterparts; that is, k-space signal along ω exhibited a similar valley shape as Z-spectra in the image domain, whereas the central k-space and peripheral regions reflect different spatial-frequency components, corresponding to varied low-rankness features. Because k-space series contain sparsity and redundancy complementary to the image domain, we proposed a hybrid k-space and image domain reconstruction for CEST MRI based on low-rank plus sparse (KILS). Both retrospective and prospective experiments were conducted at 3 T, on a BSA phantom, 11 healthy volunteers and 15 glioma patients. KILS was also retrospectively evaluated in human liver at 3 T and rat brains at 9.4 T. In retrospective experiments of phantoms and human brains, KILS demonstrated the best quantitative metrics among all reconstruction methods, with acceleration factors (AFs) ranging from 2 to 8. The ablation study demonstrated that KILS well preserved both CEST contrast and image anatomy. Prospectively, a 31-offset spectral scan of 2-mm isotropic whole-brain images was demonstrated, taking 5.7 min. Additionally, KILS proved effective in retrospective reconstruction for human liver at 3 T (AF = 6) and ischemic rat brain at 9.4 T (AF = 6). KILS demonstrated accurate and robust reconstruction of undersampled CEST MRI. The complementary nature of k-space and image domain may enable KILS to be readily applicable to multiple application scenarios.

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique with established clinical relevance in neuro-oncology. While CEST contrast differences between gray matter (GM) and white matter (WM) are documented, brain region-specific contrast variations remain underexplored. This study investigates the regional variability of CEST contrasts in healthy brains to provide a baseline reference, which could enhance the detection of subtle pathological changes in clinical settings. Ten healthy volunteers (five female, mean age 25 ± 3.1 years) underwent 3D CEST imaging on a 3-T Siemens Prisma scanner. Using a custom segmentation tool, GM and WM regions of interest (ROIs) were automatically selected in the frontal, parietotemporal, and occipital regions and the calcarine sulcus to analyze regional contrast changes for the relaxation-compensated MTR and asymmetry-based APTw CEST contrasts. Individual and grouped analyses showed significant regional differences in GM and WM for all CEST contrasts. Globally, significant GM-WM differences were also detected for the APTw, MTR AMIDE, and MTR ssMT, which demonstrated higher GM contrast values for APTw and MTR AMIDE and lower GM contrast values for the MTR ssMT. Regionally, all contrasts showed reduced GM signals in the frontal lobe and increased signals in the calcarine sulcus when compared to the occipital and parietotemporal lobe; however, these differences were less pronounced for MTR rNOE and MTR ssMT. Relaxation-compensated CEST and APTw CEST contrast values exhibit significant regional variation in the healthy brain, highlighting the importance of consistent ROI placement in clinical studies. At the same time, low intersubject variability was observed, providing robust normative values for future comparisons. These regional reference values can aid in the detection of subtle pathological changes in CEST MRI by offering a reliable baseline for interpreting deviations in patient data.

Accurate quantification of extracellular volume (ECV) and fractional myocardial blood volume (fMBV) in cardiac magnetic resonance (CMR) relies on precise alignment between precontrast and postcontrast images. Variable image contrast often undermines conventional motion correction, causing misalignment due to respiration or cardiac motion. Herein, we present a registration approach that accounts for varying image contrast levels and cardiac motion to achieve more precise and high-quality quantitative cardiac mapping. Patients with suspected myocardial diseases underwent cardiac MRI with Gadavist (0.1 mmol/kg, n = 11) and ferumoxytol (4.0 mg/kg cumulative, n = 9) enhancement for ECV and fMBV measurements, respectively. T1 maps were generated using the MOLLI sequence. To remove contrast variations across different inversion times and contrast doses, precontrast and postcontrast MOLLI images were grouped and processed using correlation-weighted representations based on the myocardium and blood pool signals. Groupwise registration is performed based on the maximization of mutual information. The image registration accuracy and mapping precision of the proposed method were assessed relative to those of conventional methods. ResultsCompared with the conventional groupwise registration approach, the proposed decontrasted approach showed superior alignment between images of different contrasts, as evidenced by the higher Dice scores (mean 0.77 vs. 0.69, p < 0.001). It also eliminated artifacts commonly observed owing to image misalignment (all 11 cases showed improvement). Improved myocardial mapping precision was observed for both ECV (median coefficient of variation, 0.14 vs. 0.27; p < 0.001) and fMBV (median coefficient of variation, 0.59 vs. 0.71; p < 0.001). It also reduced individual myocardial segmental variations in the ECV (5.8 to 3.58, p < 0.001) and fMBV maps (9.86 to 7.93, p < 0.001). Overall, decontrasted image registration improves the precision of contrast-enhanced myocardial parametric mapping by reducing the misalignment between multicontrast images. This framework may be extended to other postprocessing tasks in cardiac MRI that involve variable image contrasts.

In early kidney disease, tissue hypoxia occurs due to an imbalance between ATP supply and demand. Whole-organ renal metabolic rate of oxygen (rMRO) is therefore a potential biomarker for assessing renal function. This study evaluated the sensitivity of a quantitative MRI method to detect within-subject changes in metabolic parameters during hypoxic gas challenges. Ten healthy adults were imaged at 3 T (5 female, ages 23-53 years) while undergoing mild and moderate hypoxia (PO 62 and 52 mmHg, respectively). The utilized MRI sequence simultaneously quantified blood flow rate (BFR) and venous oxygen saturation (SvO) at the left renal vein, yielding, together with arterial oxygen saturation (SaO) obtained by pulse oximetry, whole-organ rMRO by invoking Fick's Principle. Repeated-measures ANOVA was used to test differences in metabolic parameters between baseline and hypoxic conditions. SaO at baseline was 99% ± 1%, while renal SvO was 92% ± 3%. During progressive hypoxemia, the drop in SvO (mild 83% ± 4%, moderate 76% ± 5%, p < 0.01) paralleled the drop in SaO (mild 90% ± 1%, moderate 84% ± 2%), such that the arteriovenous difference in oxygenation (AVDO) was constant when compared to baseline (p = 1). Renal BFR did not vary significantly between baseline (410 ± 65 mL/min) and hypoxemic conditions (mild, moderate of 430 ± 56 and 440 ± 48 mL/min, p > 0.34). Thus, rMRO did not significantly change during hypoxemia (baseline, mild, and moderate of 140 ± 50, 180 ± 80, and 170 ± 90 (μmol O/min)/100 g, respectively, p = 1). In conclusion, the results demonstrate the method's sensitivity in detecting within-subject changes in metabolic parameters in response to graded hypoxia. Quantitative MRI oximetry may be a feasible tool to assess and longitudinally monitor early metabolic changes in kidney disease.

Thanks to the rapid evolution of therapeutic strategies for muscular and neuromuscular diseases, the identification of quantitative biomarkers for disease identification and monitoring has become crucial. Magnetic resonance imaging (MRI) has been playing an important role by noninvasively assessing structural and functional muscular changes. This exploratory study investigated the potential of dynamic MRI during neuromuscular electrical stimulation (NMES) to detect differences between healthy controls (HCs) and patients with metabolic and myotonic myopathies. The study included 14 HCs and 10 patients with confirmed muscular diseases. All individuals were scanned with 3 T MRI with a protocol that included a multi-echo gradient echo sequence for fat fraction quantification, multi-echo spin-echo for water T2 relaxation time calculation, and 3D phase contrast sequences during NMES. The strain tensor, buildup, and release rates were calculated from velocity datasets. Results showed that strain and strain buildup rate were reduced in the soleus muscle of patients compared to HCs, suggesting these parameters could serve as biomarkers of muscle dysfunction. Notably, there were no significant differences in fat fraction or water T2 measurements between patients and HCs, indicating that the observed changes reflect alterations in muscle contractile properties that are not reflected by structural changes. The findings provide preliminary evidence that dynamic muscle MRI during NMES can detect abnormalities in muscle contraction in patients with myotonia and metabolic myopathies, warranting further research with larger, more homogeneous patient cohorts.