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.

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.

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.

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.

Magnetic resonance imaging (MRI) applications to the study of gastric function in humans have started to incorporate dynamic volumetric imaging, thus calling for specialized approaches for motion correction. A method for retrospective respiratory motion correction in free-breathing, four-dimensional (4D) abdominal MRI is presented. Our gastric low-rank tensor-based (GLOW) algorithm uses a low-rank tensor (LRT) model to separate the temporal components that correspond to breathing motion from those related to gut motion, which are preserved due to being uncorrelated and spatially localized. As a proof-of-concept, the GLOW algorithm is applied to a human 4D gastric MRI dataset that includes data collected during both a fasted and fed state using a food-based contrast meal. This approach allows for a more robust and accurate assessment of gastric peristalsis. The GLOW algorithm represents an important step toward the effective application of noninvasive, naturalistic approaches to robustly and accurately evaluate gastric function via MRI.

We analyzed the feasibility of using a UTE stack-of-spirals turbo FLASH (STFL) sequence to measure T_1ρ relaxation in the Achilles tendon. Six HS (25-31 years) and five AT patients (32-47 years) participated. The study evaluates the clinical utility of the STFL sequence to generate T_1ρ maps using mono-exponential (ME) and bi-exponential (BE) fitting models. In a phantom experiment, ME-T_1ρ values and SNR estimated from the STFL sequence are compared with those of the Cartesian turbo FLASH (CTFL) sequence. In human subjects, we evaluate differences in estimated ME (ME-T_1ρ) and BE parameters (short T_1ρ, long T_1ρ, and short fraction) between AT and HS groups along with repeatability of STFL. The agarose phantom demonstrates biases of 2.89% (3% agarose), -1.88% (5%), and -0.92% (7%) between ME-T_1ρ values from STFL and CTFL. In the bovine Achilles tendon, STFL shows a large bias of -58.6%, with a lower median ME-T_1ρ (2.9 ms) than CTFL (4.6 ms). SNR is higher in STFL (77.05-80.72 for 3%-7% agarose; 24.43 for bovine tendon) than CTFL (66.73-58.97 for agarose; 3.21 for bovine tendon). ME and BE parameters were averaged over the entire Achilles tendon, and none showed significant group differences (p > 0.05; effect size = 0.05-0.22). Subregional analysis showed that in the mid-Achilles tendon, short and long BE-T_1ρ components were 26% and 37% lower in AT than HS, though not statistically significant. The LDA-combined BE parameter showed significant group separation in the midtendon region (p = 0.016; effect size = 1.53). In HS, the long BE-T_1ρ component showed subregional variation (p = 0.006), increasing 58% from calcaneal to midtendon, and then decreasing 23% toward the intramuscular region. ME and BE fitting showed high repeatability with scan-rescan variations of 2.64% (T_1ρ), 3.38% (short T_1ρ), 3.0% (long T_1ρ), and 0.21% (short fraction). We demonstrated the feasibility of using STFL for T_1ρ quantification in the Achilles tendon.

An organism's metabolic profile provides vital information pertaining to its physiology or pathology. To monitor these biochemical changes, Nuclear Magnetic Resonance (NMR) spectroscopy has found success in non-invasively observing metabolite changes within intact samples in an untargeted manner. However, biological samples are chemically complex, comprised of many different constituents (amino acids, carbohydrates, and lipids) at varying concentrations depending on physiological and pathological conditions. Due to the narrow spectral window of proton NMR, compound resonance frequencies can often overlap, making the identification and monitoring of metabolites difficult and time consuming, particularly when dealing with large numbers of samples. Here, we introduce a Python program (ROIAL-NMR) to systematically identify potential metabolites from defined proton NMR spectral regions-of-interest (ROIs), which are identified from complex biological samples (i.e., human serum, saliva, sweat, urine, CSF, and tissues) using the Human Metabolome Database (HMDB) as a reference platform. Briefly, for disease-versus-control studies, the program considers disease types and utilizes study-defined ROIs together with their differing intensity levels, according to sample types, in differentiating disease from control to propose potential metabolites represented by these ROIs in an output table. In this report, we illustrate the utility of the program with one of our recent studies, where we measured proton NMR spectra of serum samples taken from lung cancer (LC) patients, with and without Alzheimer's disease and related dementia (ADRD). The program successfully identified 88 metabolites, with 66 differentiating LC from control patients, and 80 distinguishing LC patients with ADRD from those without ADRD to provide important information regarding pathophysiology in complex biological samples.

Objectives: Early diagnosis and timely treatment of renal fibrosis can improve the prognosis of patients with nephropathy. We aim to investigate the utility of multi-parametric MRI for evaluating early renal fibrosis and therapeutic efficacy in a rat model.

Methods: Eighty-four male SD rats receiving tail vein injection of adriamycin doxorubicin (ADR) to establish renal fibrosis models were utilized. Twelve rats underwent pilot experiments to identify successful renal fibrosis modeling timepoints. Seventy-two were assigned to treated (AA) and untreated (ADR) groups, which were subdivided into AA-1 and ADR-1 groups (N = 6 each, underwent continuous MRI scanning at 0, 14, 21, 28, 35, 42d), AA-2 and ADR-2 groups (N = 30 each, 6 underwent MRI scanning at 0, 14, 21, 28, 35d). Repeated measures ANOVA was used to evaluate changes in parameters over time within continuous MRI scanning groups (AA-1 and ADR-1). Independent samples t test or Wilcoxon rank sum test were used to compare the differences of parameters among groups and different time points. Pearson's correlation coefficients were used to investigate relationships between renal blood flow (RBF), cortical and medullary T1, mean kurtosis (MK) and mean diffusivity (MD) values and the laboratory results, α-smooth muscle actin (α-SMA), transforming growth factor-β1 (TGF-β1), Smad3, and Smad7.

Results: T1 and MK values increased over time in all groups, while RBF and MD values decreased. Significant differences in all MRI parameters except medullary MK were observed between AA and ADR groups. RBF, MK, MD, and T1 values were significantly correlated with renal interstitial collagen area, α-SMA, TGF-β1, Smad3, and Smad7 (|r| = 0.5882 to 0.9756, p < 0.0001).

Conclusion: Multi-parametric MRI can enable the detection of early microstructural and functional alterations in the kidney associated with renal fibrosis and provides a means to quantify the therapeutic efficacy of interventions.

To assess lower back pain using quantitative chemical exchange saturation transfer (qCEST) imaging in a porcine model by comparing exchange rate maps obtained from multitasking qCEST with conventional qCEST. Use a permuted random forest (PRF) model trained on CEST-derived magnetization transfer ratio (MTR) and exchange rate (k_sw) features to predict Glasgow pain scores. Six Yucatan minipigs were scanned at baseline and at four post-injury time points (weeks 4, 8, 12, and 16) following intervertebral disc injury. Conventional qCEST imaging was performed at four B1 powers using a two-dimensional reduced field of view turbo spin-echo (TSE) sequence, with a total acquisition time of 24 min per slice. Multitasking steady-state (SS) CEST imaging was performed with pulsed saturation to achieve a steady state, acquiring 32 slices at 59 offsets for 4 B1 powers in 36 min. Exchange rate maps were generated using omega plot analysis, and CEST images were analyzed using a multi-pool fitting model to produce MTR and k_sw maps. Permuted random forest (PRF) model was trained on MTR and k_sw values to predict pain scores. Modic changes were assessed using T2-weighted MR images. The Pearson correlation coefficient between exchange rate maps from multitasking qCEST and conventional qCEST was 0.82, demonstrating strong agreement. The 3D qCEST (SS-CEST) technique effectively differentiated between healthy and injured discs, with injured discs exhibiting significantly higher k_sw values. Using MTR and k_sw, the PRF model achieved 80% accuracy in predicting pain scores disc-by-disc, outperforming the correlation with Modic changes (r = 0.45, p < 0.05); with a Cohen's Kappa of 0.4. 3D steady-state qCEST with whole-spine coverage can be done at 3T within 32 min using MR Multitasking (acceleration factor of 22), and qCEST-derived biomarkers (MTR and k_sw) can predict pain scores with an accuracy of 80%.