NMR in Biomedicine最新文献

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 ¹H-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 ¹H-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 ¹H-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.

Quantitative susceptibility mapping (QSM) is a tool for mapping tissue susceptibility. Using QSM for functional brain mapping, it is possible to directly quantify blood-oxygen-level-dependent (BOLD) susceptibility changes. This study presents a submillimeter functional QSM (fQSM) approach compared to BOLD fMRI from data acquired with 3D gradient-echo echo planar imaging (EPI) at ultra-high field. Complex EPI data were acquired in nine healthy subjects with varying temporal and spatial resolutions and used for BOLD fMRI and for fQSM. Right-hand finger tapping experiments were performed as well as one measurement with intentional subject movement. Susceptibility maps were computed using 3D path-based unwrapping, the variable-kernel sophisticated harmonic artifact reduction for phase data, and the streaking artifact reduction for QSM algorithm. Functional data analysis included general linear modeling and computation of z-scores. Submillimeter data were denoised using NOise reduction with DIstribution Corrected (NORDIC), which improved z-scores in the motor cortex for fQSM and fMRI. An expected increase in BOLD fMRI signal and corresponding decrease in magnetic susceptibility was observed in sensorimotor areas during active periods. For all experiments, fQSM showed smaller activation regions compared with fMRI. The percentage of high negative t-values localized in the cortex was higher for fQSM (52%) than for positive or negative t-values for fMRI (45%). For the scans with intentional motion, movement exceeded the size of a voxel, but paradigm dependent signal evolution could be recovered using motion correction. In conclusion, this study demonstrates the feasibility of submillimeter whole-brain fQSM with voxel volume of 0.53 μL. In comparison to traditional BOLD fMRI, fQSM provided improved localization of brain activation within the cortex, especially in submillimeter 3D EPI sequences.

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 T₁ and T₂* 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_asym 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 ³¹P-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 ³¹P-MRS-derived pHi maps (Pearson's correlation). Without differentiating tissue subtypes, pHi did not only correlate significantly with MTR_asym (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_asym, 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_asym 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.

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 $R_2$ , $R_{1\rho }$ , and saturation-pulse-based MT imaging. Consequently, MPF-SL could serve as a valuable orientation-independent technique for the quantification of MPF.

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.

Low magnetic field magnetic resonance imaging (MRI) ( $B_0$  < 1 T) is regaining interest in the magnetic resonance (MR) community as a complementary, more flexible, and cost-effective approach to MRI diagnosis. Yet, the impaired signal-to-noise ratio (SNR) per square root of time, or SNR efficiency, leading in turn to prolonged acquisition times, still challenges its relevance at the clinical level. To address this, researchers investigate various hardware and software solutions to improve SNR efficiency at low field, including the leveraging of latest advances in computing hardware. However, there may not be a single recipe for improving SNR at low field, and it is key to embrace the challenges and limitations of each proposed solution. In other words, suitable solutions depend on the final objective or application envisioned for a low-field scanner and, more importantly, on the characteristics of a specific low $B_0$ field. In this review, we aim to provide an overview on software solutions to improve SNR efficiency at low field. First, we cover techniques for efficient k-space sampling and reconstruction. Then, we present post-acquisition techniques that enhance MR images such as denoising and super-resolution. In addition, we summarize recently introduced electromagnetic interference cancellation approaches showing great promises when operating in shielding-free environments. Finally, we discuss the advantages and limitations of these approaches that could provide directions for future applications.

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 T₁-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.

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 T₁, T₂, 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 T₁, T₂, 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 T₁ and α. Single-sided NMR devices, portable and low-cost, provide information on NMR parameters related to tissue composition and structure.