NMR in Biomedicine最新文献

Functional magnetic resonance spectroscopy (fMRS) measures dynamic changes in metabolite concentration in response to neural stimulation. The biophysical basis of these changes remains unclear. One hypothesis suggests that an increase or decrease in the glutamate signal detected by fMRS could be due to neurotransmitter movements between cellular compartments with different T₂ relaxation times. Previous studies reporting glutamate (Glu) T₂ values have generally sampled at echo times (TEs) within the range of 30-450 ms, which is not adequate to observe a component with short T₂ (<20 ms). Here, we acquire MRS measurements for Glu, (t) total creatine (tCr) and total N-acetylaspartate (tNAA) from the visual cortex in 14 healthy participants at a range of TE values between 9.3-280 ms during short blocks (64 s) of flickering checkerboards and rest to examine both the short- and long-T₂ components of the curve. We fit monoexponential and biexponential Glu, tCr and tNAA T₂ relaxation curves for rest and stimulation and use Akaike information criterion to assess best model fit. We also include power calculations for detection of a 2% shift of Glu between compartments for each TE. Using pooled data over all participants at rest, we observed a short Glu T₂-component with T₂ = 10 ms and volume fraction of 0.35, a short tCr T₂-component with T₂ = 26 ms and volume fraction of 0.25 and a short tNAA T₂-component around 15 ms with volume fraction of 0.34. No statistically significant change in Glu, tCr and tNAA signal during stimulation was detected at any TE. The volume fractions of short-T₂ component between rest and active conditions were not statistically different. This study provides evidence for a short T₂-component for Glu, tCr and tNAA but no evidence to support the hypothesis of task-related changes in glutamate distribution between short and long T₂ compartments.

This work aims to develop and implement a pulse-acquire sequence for three-dimensional (3D) single-voxel localized ¹³C MRS in humans at 7 T, in conjunction with bilevel broadband ¹H decoupling, and to test its feasibility in vitro and in vivo in human calf muscle with emphasis on the detection of glycogen C₁-C₆. A localization scheme suitable for measuring fast-relaxing ¹³C signals in humans at 7 T was developed and implemented using the outer volume suppression (OVS) and one-dimensional image selected in vivo spectroscopy (ISIS-1D) schemes, similar to that which was previously reported in humans at 4 T. The 3D ¹³C localization scheme was followed by uniform ¹³C adiabatic excitation, all complemented with an option for bilevel broadband ¹H decoupling to improve both ¹³C sensitivity and spectral resolution at 7 T. The performance of the pulse-acquire sequence was investigated in vitro on phantoms and in vivo in the human calf muscle of three healthy volunteers, while measuring glycogen C₁-C₆. In addition, T₁ and T₂ of glycogen C₁-C₆ were measured in vitro at 7 T, as well as T₁ of glycogen C₁ in vivo. The glycerol C₂ and C_1,3 lipid resonances were efficiently suppressed in vitro at 7 T using the OVS and ISIS-1D schemes, allowing distinct detection of glycogen C₂-C₆. While some glycerol remained in calf muscle in vivo, the intense lipid at 130 ppm was efficiently suppressed. The ¹³C sensitivity and spectral resolution of glycogen C₁-C₆ in vitro and glycogen C₁ in vivo were improved at 7 T using bilevel broadband ¹H decoupling. The T₁ and T₂ of glycogen C₁-C₆ in vitro at 7 T were consistent compared with those at 8.5 T, while the T₁ of glycogen C₁ in vivo at 7 T resulted similar to that in vitro. Localized ¹³C MRS is feasible in human calf muscle in vivo at 7 T, and this will allow further extension of this method for ¹³C MRS measurements such as in the brain.

Slice-to-volume registration and super-resolution reconstruction are commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion among all input stacks as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Here, we presented an MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Experiments on linearly and randomly simulated motion illustrated that CP demonstrated higher sensitivity in detecting small motion with a lower baseline bias, and achieved a higher assessment accuracy of 95.45% in identifying the minimum motion stack, compared to the SVD-based method with 58.18%. CP also showed superior motion assessment capabilities in real-data evaluations. Additionally, combining CP with the existing SRR-SVR pipeline significantly improved 3D volume reconstruction. The results indicated that our proposed CP showed superior performance compared to SVD-based methods with higher sensitivity to motion, assessment accuracy, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.

Leukemia is a group of blood cancers that are classified in four major classes. Within these four classes, many different subtypes exists with similar origin, genetic mutations, and level of maturity, which can make them difficult to distinguish. Despite their similarities, they might respond differently to treatment, and therefore distinguishing between them is of crucial importance. A deranged metabolic phenotype (Warburg effect) is often seen in cancer cells, leukemia cells included, and is increasingly a target for improved diagnosis and treatment. In this study, hyperpolarized ¹³C NMR spectroscopy was used to characterize the metabolic signatures of the six leukemia cell lines ML-1, CCRF-CEM, THP-1, MOLT-4, HL-60, and K562. This was done using [1-¹³C]pyruvate and [1-¹³C]alanine as bioprobes for downstream metabolite quantification and kinetic analysis on cultured cells with and without 2-deoxy-D-glucose treatment. The metabolic signatures of similar leukemia subtypes could be readily distinguished. This includes ML-1 and THP-1, which are of the similar M4 and M5 AML subtypes, CCRF-CEM and MOLT-4, which are of the similar T-ALL lineage at different maturation states, and HL-60 and K562, which are of the closely related M1 and M2 AML subtypes. The data presented here demonstrate the potential of hyperpolarized ¹³C NMR spectroscopy as a method to differentiate between leukemia subtypes of similar origin. Combining this method with bioreactor setups could potentially allow for better leukemia disease management as metabolic signatures could be acquired from a single biopsy through repeated experimentation and intervention.

Cerebrospinal fluid (CSF) circulation plays a key role in cerebral waste clearance via the glymphatic system. Although CSF flow velocity is an essential component of CSF dynamics, it has not been sufficiently characterized, and particularly, in studies of the glymphatic system in rat. To investigate the relationship between the flow velocity of CSF in the brain aqueduct and the glymphatic waste clearance rate, using phase-contrast MRI we performed the first measurements of CSF velocity in rats. Phase-contrast MRI was performed using a 7 T system to map mean velocity of CSF flow in the aqueduct in rat brain. The effects of age (3 months old versus 18 months old), gender, strain (Wistar, RNU, Dark Agouti), anesthetic agents (isoflurane versus dexmedetomidine), and neurodegenerative disorder (Alzheimer' disease in Fischer TgF344-AD rats, males and females) on CSF velocity were investigated in eight independent groups of rats (12 rats per group). Our results demonstrated that quantitative velocities of CSF flow in the aqueduct averaged 5.16 ± 0.86 mm/s in healthy young adult male Wistar rats. CSF flow velocity in the aqueduct was not altered by rat gender, strain, and the employed anesthetic agents in all rats, also age in the female rats. However, aged (18 months) Wistar male rats exhibited significantly reduced the CSF flow velocity in the aqueduct (4.31 ± 1.08 mm/s). In addition, Alzheimer's disease further reduced the CSF flow velocity in the aqueduct of male and female rats.

Understanding the effects of white matter (WM) axon fibre microstructure on T1 relaxation is important for neuroimaging. Here, we have studied the interrelationship between T1 and axon fibre configurations at 3T and 7T. T1 and S0 (=signal intensity at zero TI) were computed from MP2RAGE images acquired with six inversion recovery times. Multishell diffusion MRI images were analysed for fractional anisotropy (FA); MD; V1; the volume fractions for the first (f₁), second (f₂) and third (f₃) fibre configuration; and fibre density cross-section images for the first (fdc₁), second (fdc₂) and third (fdc₃) fibres. T1 values were plotted as a function of FA, f₁, f₂, f₃, fdc₁, fdc₂ and fdc₃ to examine interrelationships between the longitudinal relaxation and the diffusion MRI microstructural measures. T1 values decreased with increasing FA, f₁ and f₂ in a nonlinear fashion. At low FA values (from 0.2 to 0.4), a steep shortening of T1 was followed by a shallow shortening by 6%-10% at both fields. The steep shortening was associated with decreasing S0 and MD. T1 also decreased with increasing fdc₁ values in a nonlinear fashion. Instead, only a small T1 change as a function of either f₃ or fdc₃ was observed. In WM areas selected by fdc₁ only masks, T1 was shorter than in those with fdc₂/fdc₃. In WM areas with high single fibre populations, as delineated by f₁/fdc₁ masks, T1 was shorter than in tissue with high complex fibre configurations, as segmented by f₂/fdc₂ or f₃/fdc₃ masks. T1 differences between these WM areas are attributable to combined effects by T1 anisotropy and lowered FA. The current data show strong interrelationships between T1, axon fibre configuration and orientation in healthy WM. It is concluded that diffusion MRI microstructural measures are essential in the effort to interpret quantitative T1 images in terms of tissue state in health and disease.

Non-contrast enhanced ¹H magnetic resonance imaging (MRI) is promising for ventilation/perfusion (V/Q) assessment of the lung but the influence of the echo time (TE) on V/Q parameters is lacking. Therefore, the purpose of this study was to investigate the influence of different TEs on pulmonary V/Q parameters derived by phase-resolved functional lung (PREFUL) MRI using a multi-echo ultrashort TE (UTE) acquisition. A 2D multi-echo UTE sequence with radial center out readout and tiny golden angle increment was developed. Forty-eight participants were enrolled in this study: 25 healthy subjects, six patients with asthma, and 17 patients with pulmonary fibrosis. Participants underwent two acquisitions of 2D multi-echo UTE MRI with three TEs per acquisition (TE_1-6: 0.07, 0.82, 1.72, 2.47, 3.37, and 4.12 ms). Regional ventilation (RVent), flow-volume loop cross-correlation metric (FVL-CM), and normalized perfusion-weighted signal (QN) maps were calculated. V/Q defect percentages (VDP/QDP) were determined. To assess repeatability, the measurement was repeated in healthy subjects. Median and interquartile range of RVent, FVL-CM, QN, VDP, and QDP were calculated. To assess significant differences between parameters obtained at different TEs, Friedman's test and Dunnett's test were performed. Pearson correlation coefficients between RVent derived at TE₁ and the difference in RVent between TE_2,3 and TE₁ were calculated. For repeatability assessment, coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were determined. Significant differences were found comparing V/Q parameters obtained at TE_3-6 compared to TE₁. CoV increased with TE. For ICC, values between 0.35 (QDP at TE₁) and 0.83 (VDP_RVent at TE₂) were obtained for T_1,2. Statistically significant differences for ventilation and perfusion parameters derived by PREFUL were found for TE_3-6 compared to TE₁. All V/Q parameters were well repeatable for TE_1-2. With increasing TE and respiratory volume, RVent shows a T2*-dependency leading to biased ventilation assessment compared to TE₁.

Low-field strength scanners present an opportunity for more inclusive imaging exams and bring several challenges including lower signal-to-noise ratio (SNR) and longer scan times. Magnetic resonance fingerprinting (MRF) is a rapid quantitative multiparametric method that can enable multiple quantitative maps simultaneously. To demonstrate the feasibility of an MRF sequence for knee cartilage evaluation in a 0.55T system we performed repeatability and accuracy experiments with agar-gel phantoms. Additionally, five healthy volunteers (age 32 ± 4 years old, 2 females) were scanned at 3T and 0.55T. The MRI acquisition protocols include a stack-of-stars T_1ρ-enabled MRF sequence, a VIBE sequence with variable flip angles (VFA) for T₁ mapping, and fat-suppressed turbo flash (TFL) sequences for T₂ and T_1ρ mappings. Double-Echo steady-state (DESS) sequence was also used for cartilage segmentation. Acquisitions were performed at two different field strengths, 0.55T and 3T, with the same sequences but protocols were slightly different to accommodate differences in signal-to-noise ratio and relaxation times. Cartilage segmentation was done using five compartments. T₁, T₂, and T_1ρ values were measured in the knee cartilage using both MRF and conventional relaxometry sequences. The MRF sequence demonstrated excellent repeatability in a test-retest experiment with model agar-gel phantoms, as demonstrated with correlation and Bland-Altman plots. Underestimation of T₁ values was observed on both field strengths, with the average global difference between reference values and the MRF being 151 ms at 0.55T and 337 ms at 3T. At 0.55T, MRF measurements presented significant biases but strong correlations with the reference measurements. Although a larger error was present in T₁ measurements, MRF measurements trended similarly to the conventional measurements for human subjects and model agar-gel phantoms.

In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN-WHOA-BTD) is proposed. Here, the input imageries are gathered using BraTS, RemBRANDT, and Figshare datasets. Initially, the images are preprocessed to increase the quality of images and eliminate the unwanted noises. The preprocessing is performed with dual-tree complex wavelet transform (DTCWT). The image features like geodesic data and texture features like contrasts, energy, correlations, homogeneity, and entropy are extracted using multilayer dense net methods. Then, the extracted images are given to attentive dual residual generative adversarial network (ADRGAN) classifier for classifying the brain imageries. The ADRGAN weight parameters are tuned based on wild horse optimization algorithm (WHOA). The proposed method is executed in MATLAB. For the BraTS dataset, the ADRGAN-WHOA-BTD method achieved accuracy, sensitivity, specificity, F-measure, precision, and error rates of 99.85%, 99.82%, 98.92%, 99.76%, 99.45%, and 0.15%, respectively. Then, the proposed technique demonstrated a runtime of 13 s, significantly outperforming existing methods.

Spectroscopic imaging, rooted in Dixon's two-echo spin sequence to distinguish water and fat, has evolved significantly in acquisition and processing. Yet precise fat quantification remains a persistent challenge in ongoing research. With adequate phase characterization and correction, the fat composition models will impact measurements of fatty tissue. However, the effect of the used fat model in low-fat regions such as healthy muscle is unknown. In this study, we investigate the effect of assumed fat composition, in terms of chain length and double bond count, on fat fraction quantification in healthy muscle, while addressing phase and relaxometry confounders. For this purpose, we acquired bilateral thigh datasets from 38 healthy volunteers. Fat fractions were estimated using the IDEAL algorithm employing three different fat models fitted with and without the initial phase constrained. After data processing and model fitting, we used a convolutional neural net to automatically segment all thigh muscles and subcutaneous fat to evaluate the fitted parameters. The fat composition was compared with those reported in the literature. Overall, all the observed estimated fat composition values fall within the range of previously reported fatty acid composition based on gas chromatography measurements. All methods and models revealed different estimates of the muscle fat fractions in various evaluated muscle groups. Lateral differences changed from 0.5% to 5.3% in the hamstring muscle groups depending on the chosen method. The lowest observed left-right differences in each muscle group were all for the fat model estimating the number of double bonds with the initial phase unconstrained. With this model, the left-right differences were 0.64% ± 0.31%, 0.50% ± 0.27%, and 0.50% ± 0.40% for the quadriceps, hamstrings, and adductors muscle groups, respectively. Our findings suggest that a fat model estimating double bond numbers while allowing separate phases for each chemical species, given some assumptions, yields the best fat fraction estimate for our dataset.