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•Imaging of brain glucose metabolism using MR spectroscopic imaging and 2H labeling.•Direct (2H DMI at 7T) and indirect detection (1H QELT at 3T) were compared.•Comparable enrichment of labeled compounds was observed with both methods.•Reproduced 7T DMI results using 1H QELT at clinical 3T without additional hardware. Deuterium metabolic imaging (DMI) and quantitative exchange label turnover (QELT) are novel MR spectroscopy techniques for non-invasive imaging of human brain glucose and neurotransmitter metabolism with high clinical potential. Following oral or intravenous administration of non-ionizing [6,6′-2H2]-glucose, its uptake and synthesis of downstream metabolites can be mapped via direct or indirect detection of deuterium resonances using 2H MRSI (DMI) and 1H MRSI (QELT), respectively. The purpose of this study was to compare the dynamics of spatially resolved brain glucose metabolism, i.e., estimated concentration enrichment of deuterium labeled Glx (glutamate+glutamine) and Glc (glucose) acquired repeatedly in the same cohort of subjects using DMI at 7T and QELT at clinical 3T. Five volunteers (4 m/1f) were scanned in repeated sessions for 60 min after overnight fasting and 0.8 g/kg oral [6,6′-2H2]-glucose administration using time-resolved 3D 2H FID-MRSI with elliptical phase encoding at 7T and 3D 1H FID-MRSI with a non-Cartesian concentric ring trajectory readout at clinical 3T. One hour after oral tracer administration regionally averaged deuterium labeled Glx4 concentrations and the dynamics were not significantly different over all participants between 7T 2H DMI and 3T 1H QELT data for GM (1.29±0.15 vs. 1.38±0.26 mM, p=0.65 & 21±3 vs. 26±3 µM/min, p=0.22) and WM (1.10±0.13 vs. 0.91±0.24 mM, p=0.34 & 19±2 vs. 17±3 µM/min, p=0.48). Also, the observed time constants of dynamic Glc6 data in GM (24±14 vs. 19±7 min, p=0.65) and WM (28±19 vs. 18±9 min, p=0.43) dominated regions showed no significant differences. Between individual 2H and 1H data points a weak to moderate negative correlation was observed for Glx4 concentrations in GM (r=-0.52, p<0.001), and WM (r=-0.3, p<0.001) dominated regions, while a strong negative correlation was observed for Glc6 data GM (r=-0.61, p<0.001) and WM (r=-0.70, p<0.001). This study demonstrates that indirect detection of deuterium labeled compounds using 1H QELT MRSI at widely available clinical 3T without additional hardware is able to reproduce absolute concentration estimates of downstream glucose metabolites and the dynamics of glucose uptake compared to 2H DMI data acquired at 7T. This suggests significant potential for widespread application in clinical settings especially in environments with limited access to ultra-high field scanners and dedicated RF hardware.

Deuterium metabolic imaging (DMI) is a non-invasive magnetic resonance spectroscopic imaging technique enabling in vivo mapping of glucose metabolism. Dynamic DMI provides time-resolved metabolite maps and allows spatially resolved fitting of metabolic models to capture metabolite concentration dynamics. However, conventional fitting tools often require long processing times for high-resolution datasets, limiting their practical utility. To address this bottleneck, we propose a deep autoencoder (DAE) for rapid spectral–temporal fitting of dynamic DMI data, supporting arbitrary parametric model constraints to describe metabolite concentration dynamics. The DAE was benchmarked against spectral–temporal fitting using FSL-MRS and LCModel. Fitting accuracy was evaluated on in vivo and synthetic whole-brain dynamic DMI data acquired at 7T using Bland–Altman metrics, Pearson correlation coefficients, structural similarity index measures, and root mean squared errors for both metabolite concentrations and model constraint parameters. The DAE achieved processing times of 0.29 ms per voxel, corresponding to an acceleration of more than three orders of magnitude compared to LCModel/FSL-MRS (0.55/0.65 s per voxel). On in vivo data, it showed excellent agreement with LCModel, and on synthetic data, it consistently outperformed both reference methods across all evaluation metrics. The proposed DAE enables accurate spectral–temporal fitting for whole-brain dynamic DMI scans within less than a second, matching or exceeding the performance of conventional state-of-the-art fitting methods. This makes it a promising tool for integration into efficient post-processing pipelines for research and clinical DMI workflows. [Display omitted] •Deep Autoencoder approach for dynamic fitting of dynamic DMI data.•Proposed model shows strong agreement with reference standard on in vivo data.•Proposed model outperforms reference methods on synthetic fitting accuracy.•Proposed model fits whole-brain dynamic DMI datasets in under one second.•Proposed model achieves >1000× speedup over reference methods.

•Echo-less 3D-MRSI at 7T provides high-resolution metabolic maps in just 8 min.•Voxel-wise analysis reveals distinct metabolic patterns in multiple sclerosis.•Elevated myo-inositol primarily affects periventricular white matter beyond lesions.•NAA reductions exceed mI elevation, notably in prefrontal, motor, and sensory areas.•NAA reductions strongly correlate with MS disability score in motor/cognitive areas. To assess topographical patterns of metabolic abnormalities in the cerebrum of multiple sclerosis (MS) patients and their relationship to clinical disability using rapid echo-less 3D-MR spectroscopic imaging (MRSI) at 7T. This study included 26 MS patients (13 women; median age 34) and 13 age- and sex-matched healthy controls (7 women; median age 33). Metabolic maps were obtained using echo-less 3D-MRSI at 7T with a 64 × 64 × 33 matrix and a nominal voxel size of 3.4 × 3.4 × 4 mm³ in an 8-minute scan. After spatial normalization, voxel-wise comparisons between MS and controls were conducted to identify clusters of metabolic abnormalities, while correlations with clinical disability were analyzed using Expanded Disability Status Scale (EDSS) scores. Statistical mapping (FWE-corrected; P<.05) revealed elevated myo-inositol to total creatine (mI/tCr) ratios in the bilateral periventricular white matter and reduced N-acetylaspartate to total creatine (NAA/tCr) within and beyond lesions, notably near the lateral ventricles, cingulate gyrus, and superior frontal gyrus. Patients with sustained disability (EDSS≥2) showed additional reductions in the posterior parietal lobe. A strong negative association was found between NAA/tCr and EDSS in the precentral gyrus (Spearman's rank ρ=-0.58, P=.005), and a moderate positive association between mI/NAA and EDSS in the precentral and superior frontal gyri (ρ=0.47, P=.015). This study highlights the ability of 3D-MRSI at 7T to map widespread metabolic abnormalities in MS, with NAA reductions in prefrontal, motor, and sensory areas, linked to neuroaxonal damage and disability progression, and elevated mI in periventricular regions, reflecting gliosis.

Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps. Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm3 isotropic resolution with acquisition times between 4:11–9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to iterative compressed sensing Total Generalized Variation reconstruction using image and spectral quality metrics. Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial–spectral quality and metabolite quantification with 12%–45% (P<0.05) higher signal-to-noise and 8%–50% (P<0.05) smaller Cramer–Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Deep-ER generalizes reliably to unseen data. Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine. •Acceleration of 3D MRSI acquisition by compressed sense non-cartesian encoding.•Clinically feasible high-resolution metabolic imaging over the whole brain.•End-to-end automated processing pipeline that integrates Deep Learning image reconstruction.•Physics-based model provides high-quality metabolic maps with fast processing times.•High-throughput metabolic imaging that is compatible with radiological workflow.

•We achieved 3D-FID-MRSI via concentric ring trajectory readouts at 10.5 t with nominal 2.75 mm isotropic resolution within 25 min.•Mapping of up to 13 brain metabolites plus macromolecules including overlapping and low concentrated neurochemicals such as aspartate, GABA, glucose, glutamine and NAAG.•Dedicated parallel transmit and receive coil setup, with up to 80 coils, ensured high SNR and homogenous field distributions. Ultra-high-field magnetic resonance (MR) systems (7 T and 9.4 T) offer the ability to probe human brain metabolism with enhanced precision. Here, we present the preliminary findings from 3D MR spectroscopic imaging (MRSI) of the human brain conducted with the world's first 10.5 T whole-body MR system. Employing a custom-built 16-channel transmit and 80-channel receive MR coil at 10.5 T, we conducted MRSI acquisitions in six healthy volunteers to map metabolic compounds in the human cerebrum in vivo. Three MRSI protocols with different matrix sizes and scan times (4.4 × 4.4 × 4.4 mm³: 10 min, 3.4 × 3.4 × 3.4 mm³: 15 min, and 2.75×2.75×2.75 mm³: 25 min) were tested. Concentric ring trajectories were utilized for time-efficient encoding of a spherical 3D k-space with ∼4 kHz spectral bandwidth. B0/B1 shimming was performed based on respective field mapping sequences and anatomical T1-weighted MRI were obtained. By combining the benefits of an ultra-high-field system with the advantages of free-induction-decay (FID-)MRSI, we present the first metabolic maps acquired at 10.5 T in the healthy human brain at both high (voxel size of 4.4³ mm³) and ultra-high (voxel size of 2.75³ mm³) isotropic spatial resolutions. Maps of 13 metabolic compounds (aspartate, choline compounds and creatine + phosphocreatine, γ-aminobutyric acid (GABA), glucose, glutamine, glutamate, glutathione, myo-inositol, scyllo-inositol, N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), taurine) and macromolecules were obtained individually. The spectral quality was outstanding in the parietal and occipital lobes, but lower in other brain regions such as the temporal and frontal lobes. The average total NAA (tNAA = NAA + NAAG) signal-to-noise ratio over the whole volume of interest was 12.1± 8.9 and the full width at half maximum of tNAA was 24.7± 9.6 Hz for the 2.75 × 2.75 × 2.75 mm³ resolution. The need for an increased spectral bandwidth in combination with spatio-spectral encoding imposed significant challenges on the gradient system, but the FID approach proved very robust to field inhomogeneities of ∆B0 = 45 ± 38 Hz (frequency offset ± spatial STD) and B1+ = 65 ± 11° within the MRSI volume of interest. These preliminary findings highlight the potential of 10.5 T MRSI as a powerful imaging tool for probing cerebral metabolism. By providing unprecedented spatial and spectral resolution, this technology could offer a unique view into the metabolic intricacies of the human brain, but further technical developments will be necessary to optimize data quality and fully leverage the capabilities of 10.5 T MRSI. [Display omitted]

Background: Acute myeloid leukemia (AML) complicated by disseminated intravascular coagulation (DIC) poses major diagnostic and therapeutic challenges. While DIC is well documented in acute promyelocytic leukemia, its manifestations in non-APL AML remain underexplored, necessitating precise diagnostic strategies for effective management. Methods: AML patients with overt DIC were analyzed, including morphological, immunophenotypic, cytogenetic, and genetic evaluations. DIC was diagnosed using the ISTH scoring system, and AML subtypes were classified following WHO criteria. Results: Three diagnostic patterns were identified. (1) Acute promyelocytic leukemia: Leukemia characterized by PML::RARa rearrangements, FLT3 co-mutations, and frequent Auer rods and faggot bundles. Immunocytological analysis showed CD34 and HLA-DR negativity. (2) AML with FLT3 and/or NPM1 mutations: A high prevalence of cup-like blasts was found in 70% of cases. FLT3 mutations, often co-occurring with NPM1, dominated, while karyotypes were typically normal. Immunophenotyping revealed strong myeloid marker expression (MPO+, CD13+, and CD33+), with occasional CD34 negativity. (3) AML with monocytic differentiation: Leukemia defined by monoblastic/promonocytic morphology, DNMT3A mutations, and complex karyotypes or 11q23 rearrangements. Immunophenotyping demonstrated a dominance of monocytic markers (CD4+, CD14+, CD15+, and CD64+). Two patients presented unique profiles with no alignment to these patterns. Conclusions: This study highlights distinct hematopathological patterns of AML with overt DIC, providing a framework for early and precise diagnosis. Recognizing these patterns is critical for tailoring diagnostic and therapeutic approaches to improve outcomes in this high-risk population.

Background: Methods such as cytogenetics and immunocytology/immunohistology provide essential diagnostic insights but may be limited in ambiguous cases of mature B-cell lymphoma. Next-generation sequencing (NGS) has emerged as a potential tool to improve diagnostics. Methods: We validated the analytical performance of a lymphoid customized NGS panel. Clinical validation was conducted in 226 patients with suspected mature B-cell lymphoma with potential bone marrow involvement across multiple clinically relevant scenarios. Results: NGS (1) achieved 100% sensitivity and specificity with high reproducibility (r = 0.995), confirming its analytical performance. (2) It reliably detected WHO-classified markers, including BRAF mutations in all hairy cell leukemia cases, MYD88/CXCR4 mutations in lymphoplasmacytic lymphoma, and absence of BRAF mutations in splenic B-cell lymphoma with prominent nucleoli. (3) In lymphoma exclusion diagnostics, NGS identified mutations in previously undiagnosed cases, including a BCORL1 mutation leading to reclassification as marginal zone lymphoma. (4) Among 105 confirmed lymphomas, 65% harbored mutations, with detection rates highest in HCL and LPL (100%) and CLL (62%), while follicular lymphoma showed no detectable mutations. (5) In cases with non-interpretable cytogenetics, NGS detected pathogenic variants in 61% of patients, compensating for inconclusive findings. (6) In cases with limited morphological assessment, NGS identified relevant mutations in 70%, outperforming cytogenetics (30%; p = 0.0256, OR = 5.44). Conclusions: NGS enhances the diagnostic accuracy of mature B-cell lymphomas by complementing traditional methods, refining WHO-classified subtypes, and improving detection in cases with inconclusive cytogenetics or morphology. NGS may reduce the need for unnecessary bone marrow re-punctures by providing additional information in ambiguous cases.

Metabolic imaging of the human brain by in-vivo magnetic resonance spectroscopic imaging (MRSI) can non-invasively probe neurochemistry in healthy and disease conditions. MRSI at ultra-high field (≥ 7 T) provides increased sensitivity for fast high-resolution metabolic imaging, but comes with technical challenges due to non-uniform B 0 field. Here, we show that an integrated RF-receive/B 0 -shim (AC/DC) array coil can be used to mitigate 7 T B 0 inhomogeneity, which improves spectral quality and metabolite quantification over a whole-brain slab. Our results from simulations, phantoms, healthy and brain tumor human subjects indicate improvements of global B 0 homogeneity by 55%, narrower spectral linewidth by 29%, higher signal-to-noise ratio by 31%, more precise metabolite quantification by 22%, and an increase by 21% of the brain volume that can be reliably analyzed. AC/DC shimming provide the highest correlation (R 2  = 0.98, P = 0.001) with ground-truth values for metabolite concentration. Clinical translation of AC/DC and MRSI is demonstrated in a patient with mutant-IDH1 glioma where it enables imaging of D-2-hydroxyglutarate oncometabolite with a 2.8-fold increase in contrast-to-noise ratio at higher resolution and more brain coverage compared to previous 7 T studies. Hence, AC/DC technology may help ultra-high field MRSI become more feasible to take advantage of higher signal/contrast-to-noise in clinical applications.

Background: The enzyme A Disintegrin and metalloprotease with thrombospondin type 1 motif, member 13 (ADAMTS13) regulates hemostasis by cleaving von Willebrand factor (VWF) multimers. ADAMTS13–VWF axis dysregulation leads to different thrombotic conditions. This study investigated changes in ADAMTS13 activity during major cardiac procedures and their relationship to VWF changes and clinical complications. Methods: A total of 628 ADAMTS13 activity and inhibitor measurements were carried out in 168 patients who underwent cardiac surgery. ADAMTS13 activity was measured after the initiation of anesthesia and daily for up to 6 days postoperatively via Technozym chromogenic ELISA. The von Willebrand factor antigen (VWF:Ag) and collagen binding (VWF:CB) were also measured. Clinical complications and correlations with liver function biomarkers were also assessed. Results: ADAMTS13 activity significantly decreased during surgery, with mean values markedly decreasing from preoperative to postoperative measurements (p = 0.01). A clear inverse relationship between ADAMTS13 activity and the VWF:CB/VWF:AG ratio was observed, indicating that increased high-molecular-weight VWF multimers are associated with decreased ADAMTS13 activity. Correlation analyses (CHE, Spearman’s rho = 0.39) indicated that the reduction in ADAMTS13 activity was not attributable to impaired liver synthesis but likely resulted from peripheral consumption, potentially influenced by surgical stress. Conclusions: Perioperative reductions in ADAMTS13 activity are associated with an accumulation of high-molecular-weight VWF multimers and a higher incidence of postoperative complications. These results demonstrate that ADAMTS13 could be a useful perioperative risk biomarker for cardiac surgery patients.

(1) Background: Advanced MR imaging (MRI) of brain tumors is mainly based on qualitative contrast images. MR Fingerprinting (MRF) offers a novel approach. The purpose of this study was to use MRF-derived T1 and T2 relaxation maps to differentiate diffuse gliomas according to isocitrate dehydrogenase (IDH) mutation. (2) Methods: Twenty-four patients with histologically verified diffuse gliomas (14 IDH-mutant, four 1p/19q-codeleted, 10 IDH-wildtype) were enrolled. MRF T1 and T2 relaxation times were compared to apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV) within solid tumor, peritumoral edema, and normal-appearing white matter (NAWM), using contrast-enhanced MRI, diffusion-, perfusion-, and susceptibility-weighted imaging. For perfusion imaging, a T2* weighted perfusion sequence with leakage correction was used. Correlations of MRF T1 and T2 times with two established conventional sequences for T1 and T2 mapping were assessed (a fast double inversion recovery-based MR sequence (‘MP2RAGE’) for T1 quantification and a multi-contrast spin echo-based sequence for T2 quantification). (3) Results: MRF T1 and T2 relaxation times were significantly higher in the IDH-mutant than in IDH-wildtype gliomas within the solid part of the tumor (p = 0.024 for MRF T1, p = 0.041 for MRF T2). MRF T1 and T2 relaxation times were significantly higher in the IDH-wildtype than in IDH-mutant gliomas within peritumoral edema less than or equal to 1cm adjacent to the tumor (p = 0.038 for MRF T1 mean, p = 0.010 for MRF T2 mean). In the solid part of the tumor, there was a high correlation between MRF and conventionally measured T1 and T2 values (r = 0.913, p < 0.001 for T1, r = 0.775, p < 0.001 for T2), as well as between MRF and ADC values (r = 0.813, p < 0.001 for T2, r = 0.697, p < 0.001 for T1). The correlation was weak between the MRF and rCBV values (r = −0.374, p = 0.005 for T2, r = −0.181, p = 0.181 for T1). (4) Conclusions: MRF enables fast, single-sequence based, multi-parametric, quantitative tissue characterization of diffuse gliomas and may have the potential to differentiate IDH-mutant from IDH-wildtype gliomas.