NMR in Biomedicine最新文献

The purpose of this study was to compare between compressed sensing (CS) and deep learning (DL) accelerated T_1ρ mapping in knee cartilage, a quantitative imaging technique that provides valuable information for disease diagnosis but requires long scan time. Both retrospectively and prospectively undersampled reconstruction were evaluated in nine volunteers including three with diagnosed pathology. For data collection, DESS images were collected for segmentation of six cartilage compartments. T_1ρ-weighted 3D MAPSS sequence was used to create T_1ρ maps. A 3T MRI scanner was used and GRAPPA 2 accelerated data were collected to provide 8-echo reference T_1ρ maps and was retrospectively undersampled for reconstruction with two sampling schemes: 4 TSLs with each echo image undersampled by 4 (UF4_4echo), and 8 TSLs with each echo image undersampled by 8 (UF8_8echo). Separate prospectively undersampled datasets were also collected for reconstruction. Volunteers were scanned and rescanned with repositioning for repeatability comparison. Reference, retrospectively undersampled reconstruction, and prospectively undersampled reconstruction were compared by voxel-wise median normalized absolute differences (MNADs), concordance correlation coefficient (CCC), and coefficient of variation (CV) using cartilage compartment-wise mean value. As a result, for retrospective undersampling, CS showed CCC 0.992, MNAD 10.0%, and CV 1.3% for UF4_4echo, and CCC 0.988, MNAD 9.9%, and CV 1.4% for UF8_8echo. DL showed CCC 0.971, MNAD 9.8%, and CV 1.7% for UF4_4echo, and CCC 0.968, MNAD 10.6%, and CV 1.7% for UF8_8echo. For prospective undersampling, CS showed CCC 0.853 and CV 3.3% for UF4_4echo, and CCC 0.754 and CV 3.9% for UF8_8echo. DL showed CCC 0.939 and CV 2.4% for UF4_4echo and CCC 0.845 and CV 2.8% for UF8_8echo. The maps had 2.57%, 3.80%, 2.79%, 2.29%, and 2.85% scan-rescan CV, respectively, for reference, CS UF4_4echo, CS UF8_8echo, DL UF4_4echo, and DL UF8_8echo reconstructions. As a conclusion, DL provided better results compared to CS in prospectively undersampled reconstruction.

Longitudinal (T₁) and transverse (T₂) relaxation times measured by MRI are promising markers for assessing biological processes and disease pathology. In this study, we characterized the T₁ and T₂ 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 T₁ 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 T₂ 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).

Deuterium metabolic imaging (DMI) is an innovative technique in which ²H magnetic resonance spectroscopic imaging (MRSI) is utilized to determine the metabolic activity of administered ²H-labeled substrates. As such it can be viewed as the ²H counterpart to more traditional ¹³C labeling methods that can be considered the gold standard for metabolic mapping in vivo. To ensure reliable findings from dynamic ²H MRSI experiments about absolute metabolic flux rates after administration of a ²H-labeled substrate it is essential to take into account ²H-specific aspects, namely ²H label losses and kinetic isotopy effects (KIEs). Here, a modified version of a ¹³C-based metabolic model for glucose metabolism in rat brain was developed to address these ²H-related effects, tested for ²H MRSI data acquired during infusion of [6,6'-²H₂]-glucose, and validated by comparison with indirect ¹H-[¹³C] MRSI data acquired during infusion of [1-¹³C]-glucose. The flux rates for glucose consumption (CMR_gl = 0.57 ± 0.08 μmol/min/g) and the TCA cycle (V_tca = 1.24 ± 0.14 μmol/min/g) derived from the ²H MRSI data and using the updated metabolic model were in excellent agreement with the estimates based on ¹³C data (CMR_gl = 0.59 ± 0.14 μmol/min/g and V_tca = 1.24 ± 0.32 μmol/min/g). The successful validation of dynamic ²H MRSI for absolute flux rate determination forms the basis for future quantitative study of metabolic disorders in vivo.

Quantitative MR-derived tissue parameters are typically measured one by one, which is time-consuming for clinical practice. MR fingerprinting (MRF) allows the efficient and simultaneous measurement of multiple tissue properties. The purpose of this study was to develop a novel, multiparametric MRF framework for the simultaneous measurement of quantitative bulk water, semisolid magnetization transfer (MT), myelin water fraction (MWF), and B₀ inhomogeneity (ΔB₀) and susceptibility-weighted imaging (SWI) and chemical exchange saturation transfer (CEST) imaging contrast. A motion-robust, rosette-accelerated MRF sequence was developed by integrating RF saturation and T₂-preparation modules. Optimized MRF acquisition parameters, including RF saturation strength, saturation duration, frequency offset, relaxation delay, T₂-prep TE, and readout TE, were varied during image acquisition. Quantitative tissue parameters were estimated from unique MRF signal evolutions in human brain scans of healthy volunteers at 3T and evaluated against the reference parameters calculated using conventional standalone sequences. Quantitative bulk water, MTC, myelin water parameters, SWI, ΔB₀, and semiqualitative CEST estimated from a single scan using the multiparametric rosette-MRF technique were in very good agreement with reference parameters. Overall, the semisolid macromolecular pool size ratio (relative to bulk water) and MWF were higher in the white matter (WM) compared to the gray matter (GM). Susceptibility-dependent tissue contrast was visible in the SWI. An accurate ΔB₀ map was derived from the rosette images themselves. Furthermore, multimolecular (MTC, APT, rNOE, and CEST at 3 ppm) images were synthesized by solving forward Bloch equations with the tissue parameter estimated from the MRF reconstruction. In conclusion, a rosette-accelerated, multiparametric MRF technique, combined with synthetic MRI analysis, has the potential to offer valuable insights into disease pathology and serve as an efficient tool for the evaluation of various MRI biomarkers in clinical settings within a short time frame.

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.

The aim of this study is to examine cervix-adapted versions of steady-state multi-parameter MRS (SMP MRS) and flip-angle-corrected multi-parameter MRS (CMP MRS), comparing estimated cervix T1_w and T2_w for the two sequences. CMP MRS and SMP MRS were adapted from liver versions of the sequences, adding long TR acquisitions to better estimate cervix T1_w. CMP MRS differs from SMP MRS by correcting for inaccurate B1 calibration. Both CMP MRS and SMP MRS were acquired at 3 T in 13 adult female subjects (10 healthy, 3 with cancer). Values of T1_w and T2_w were estimated from both sequences, and the relationship between the values was examined. While there was no significant difference in T1_w given by the two sequences (CMP T1_w = 1568 ms, SMP T1_w = 1571 ms, p = 0.95; SMP T1_w = 0.657 CMP T1_w + 541 ms, r = 0.36), there was a single case where SMP MRS underestimated T1_w by over 400 ms. A significant difference was observed in T2_w (CMP T2_w = 39.9 ms, SMP T2_w = 45.6 ms, p = 0.001; SMP T2_w = 0.812 CMP T2_w + 13.3 ms, r = 0.87). Cervix adapted CMP MRS and SMP MRS both successfully estimated values of T1_w and T2_w, though the single case where SMP MRS gave a non-physical T1_w suggests CMP MRS may be better suited for cervix T1_w estimation.

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.

To evaluate the feasibility of diffusion tensor imaging (DTI) using quantitative ultrashort echo time double-echo steady-state (qUTE-DESS) MRI in short T₂ 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 T₁, T₂, 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 T₁, T₂, or diffusivity and their concentrations (R² > 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 T₂ 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 T₂ 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 T₂ tissues, offering potential benefits in clinical diagnosis and research. Further studies should assess its diagnostic utility in larger cohorts with musculoskeletal pathologies.

The development of noninvasive MRI biomarkers as surrogates of histopathological features in kidney tissue requires detailed explorations of contrast. Therefore, we studied kidney diffusion kurtosis imaging (DKI) with a wide array of encodings, including flow compensation, variable directional sampling, and cardiac gating regimes. Twelve healthy volunteers underwent DKI at 5-10 diffusion weightings (b-values) ranging from 0 to 1200 smm^-2 with 12 or 30 directional samplings, bipolar or flow-compensated diffusion gradient waveforms, and at systolic or diastolic cardiac phases. DKI biomarkers, mean diffusivity (MD) and kurtosis (MK), were interrogated using a directionally robust fitting algorithm compared to conventional fits. The combination of flow compensation and cardiac triggering at the diastolic phase in the kidneys reduced flow effects on DKI. In systole, flow-compensated waveforms significantly reduced MD and MK for both cortex and medulla: cortex MD: 3.00 versus 2.55 μm² ms^-1, medulla MD: 2.80 versus 2.39 μm² ms^-1, cortex MK: 0.58 versus 0.45, and medulla MK: 0.60 versus 0.47 (all p < 0.05). Flow suppression alleviated requirements for processing the DKI at higher minimum b-values, as neither MD nor MK significantly differed at the diastolic phase for minimum b-values of 0 versus 200 smm^-2: cortex MD: 2.30 versus 2.28 μm² ms^-1, p = 0.278; medulla MD: 2.29 versus 2.28 μm² ms^-1, p = 0.437; cortex MK: 0.37 versus 0.36, p = 0.308; and medulla MK: 0.40 versus 0.40, p = 0.904. Flow-compensated waveforms mitigate cardiac and respiratory motion-related artifacts at higher diffusion encodings in addition to microcirculation effects. The robust fitting initially developed for brain DKI is highly applicable to the kidneys because it disentangles tissue-specific directional diffusion information from artifacts.