Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.

PURPOSE: To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. METHOD: A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. RESULTS: The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. CONCLUSION: The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.

Boosting quantification accuracy of chemical exchange saturation transfer MRI with a spatial-spectral redundancy-based denoising method.

Chemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal-to-noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial-spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw-rank constraint and Spectral local-smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low-dimensional subspace by leveraging the global spectral low-rank prior. Subsequently, a spatial nonlocal self-similarity prior is applied to the subspace-based images. Simultaneously, the spectral local-smoothness property of Z-spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state-of-the-art algorithms in terms of noise elimination. As a cost-effective and widely available post-processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.

Changes in aspartate metabolism in the medial-prefrontal cortex of nicotine addicts based on J-edited magnetic resonance spectroscopy.

This study aims to explore the changes of the aspartate (Asp) level in the medial-prefrontal cortex (mPFC) of subjects with nicotine addiction (nicotine addicts [NAs]) using the J-edited 1 H MR spectroscopy (MRS), which may provide a positive imaging evidence for intervention of NA. From March to August 2022, 45 males aged 40-60 years old were recruited from Henan Province, including 21 in NA and 24 in nonsmoker groups. All subjects underwent routine magnetic resonance imaging (MRI) and J-edited MRS scans on a 3.0 T MRI scanner. The Asp level in mPFC was quantified with reference to the total creatine (Asp/Cr) and water (Aspwater-corr , with correction of the brain tissue composition) signals, respectively. Two-tailed independent samples t-test was used to analyze the differences in levels of Asp and other coquantified metabolites (including total N-acetylaspartate [tNAA], total cholinine [tCho], total creatine [tCr], and myo-Inositol [mI]) between the two groups. Finally, the correlations of the Asp level with clinical characteristic assessment scales were performed using the Spearman criteria. Compared with the control group (n = 22), NAs (n = 18) had higher levels of Asp (Asp/Cr: p = .005; Aspwater-corr : p = .004) in the mPFC, and the level of Asp was positively correlated with the daily smoking amount (Asp/Cr: p < .001; Aspwater-corr : p = .004). No significant correlation was found between the level of Asp and the years of nicotine use, Fagerstrom Nicotine Dependence (FTND), Russell Reason for Smoking Questionnaire (RRSQ), or Barratt Impulsivity Scale (BIS-11) score. The elevated Asp level was observed in mPFC of NAs in contrast to nonsmokers, and the Asp level was positively correlated with the amount of daily smoking, which suggests that nicotine addiction may result in elevated Asp metabolism in the human brain.

Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network.

PURPOSE: To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising. METHODS: DECENT is composed of two parallel pathways with different convolution kernel sizes aiming to extract the global and spectral features embedded in CEST images. Each pathway consists of a modified U-Net with residual Encoder-Decoder network and 3D convolution. Fusion pathway with 1 × 1 × 1 convolution kernel is utilized to concatenate two parallel pathways, and the output of DECENT is noise-reduced CEST images. The performance of DECENT was validated in numerical simulations, egg white phantom experiments, and ischemic mouse brain and human skeletal muscle experiments in comparison with existing state-of-the-art denoising methods. RESULTS: Rician noise was added to CEST images to mimic a low SNR situation for numerical simulation, egg white phantom experiment, and mouse brain experiments, while human skeletal muscle experiments were of inherently low SNR. From the denoising results evaluated by peak SNR (PSNR) and structural similarity index (SSIM), the proposed deep learning-based denoising method (DECENT) can achieve better performance compared to existing CEST denoising methods such as NLmCED, MLSVD, and BM4D, avoiding complicated parameter tuning or time-consuming iterative processes. CONCLUSIONS: DECENT can well exploit the prior spatiotemporal correlation knowledge of CEST images and restore the noise-free images from their noisy observations, outperforming state-of-the-art denoising methods.

IMPULSED model based cytological feature estimation with U-Net: Application to human brain tumor at 3T.

PURPOSE: This work introduces and validates a deep-learning-based fitting method, which can rapidly provide accurate and robust estimation of cytological features of brain tumor based on the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting with diffusion-weighted MRI data. METHODS: The U-Net was applied to rapidly quantify extracellular diffusion coefficient (Dex ), cell size (d), and intracellular volume fraction (vin ) of brain tumor. At the training stage, the image-based training data, synthesized by randomizing quantifiable microstructural parameters within specific ranges, was used to train U-Net. At the test stage, the pre-trained U-Net was applied to estimate the microstructural parameters from simulated data and the in vivo data acquired on patients at 3T. The U-Net was compared with conventional non-linear least-squares (NLLS) fitting in simulations in terms of estimation accuracy and precision. RESULTS: Our results confirm that the proposed method yields better fidelity in simulations and is more robust to noise than the NLLS fitting. For in vivo data, the U-Net yields obvious quality improvement in parameter maps, and the estimations of all parameters are in good agreement with the NLLS fitting. Moreover, our method is several orders of magnitude faster than the NLLS fitting (from about 5 min to <1 s). CONCLUSION: The image-based training scheme proposed herein helps to improve the quality of the estimated parameters. Our deep-learning-based fitting method can estimate the cell microstructural parameters fast and accurately.

MR-Nucleomics: The study of pathological cellular processes with multinuclear magnetic resonance spectroscopy and imaging in vivo.

Clinical medicine has experienced a rapid development in recent decades, during which therapies targeting specific cellular signaling pathways, or specific cell surface receptors, have been increasingly adopted. While these developments in clinical medicine call for improved precision in diagnosis and treatment monitoring, modern medical imaging methods are restricted mainly to anatomical imaging, lagging behind the requirements of precision medicine. Although positron emission tomography and single photon emission computed tomography have been used clinically for studies of metabolism, their applications have been limited by the exposure risk to ionizing radiation, the subsequent limitation in repeated and longitudinal studies, and the incapability in assessing downstream metabolism. Magnetic resonance spectroscopy (MRS) or spectroscopic imaging (MRSI) are, in theory, capable of assessing molecular activities in vivo, although they are often limited by sensitivity. Here, we review some recent developments in MRS and MRSI of multiple nuclei that have potential as molecular imaging tools in the clinic.

Quantitative Analysis of Whole-Body MRI for Accessing the Degree of Diffuse Infiltration Patterns and Identifying High Risk Cases of Newly Diagnosed Multiple Myeloma.

BACKGROUND: Accurate identification of high-risk multiple myeloma (HRMM) is important for prognostication. The degree of diffuse infiltration patterns on magnetic resonance imaging (MRI) is associated with patient prognosis in multiple myeloma. However, objective indexes to determine the degree of diffuse infiltration patterns are unavailable. PURPOSE: To investigate whether qualitative and quantitative evaluations of diffuse infiltration patterns on MRI could identify HRMM. STUDY TYPE: Retrospective. SUBJECTS: Totally, 180 patients (79 HRMM and 101 standard-risk MM) were assessed. The presence of del(17p), t(4;14), t(14;16), t(14;20), gain 1q, and/or p53 mutations was considered to indicate HRMM. FIELD STRENGTH/SEQUENCE: 3.0 T/diffusion-weighted whole-body imaging with background body signal suppression (DWIBS), modified Dixon chemical-shift imaging Quant (mDIXON Quant), and short TI inversion recovery (STIR). ASSESSMENT: Qualitative analysis involved assessing the degree of diffuse marrow infiltration (mild, moderate, or severe), and quantitative analysis involved evaluating apparent diffusion coefficient (ADC), fat fraction (FF), and T2* values. Clinical data such as sex, age, hemoglobin, serum albumin, serum calcium, serum creatinine, serum lactate dehydrogenase, ß2-microglobulin, and bone marrow plasma cells (BMPCs) were also included. STATISTICAL TESTS: Univariate and multivariate analyses, receiver operating characteristic (ROC) curve. P < 0.05 was considered statistically significant. RESULTS: The high-risk group had significantly higher ADC and T2* and lower FF compared with the standard-risk group. Multivariate analysis indicated BMPCs as a significant independent risk factor for HRMM (odds ratio (OR) = 1.019, 95% CI 1.004-1.033), while FF was a significant independent protective factor associated with HRMM (OR = 0.972, 95% CI 0.946-0.999). The combination of BMPCs and FF achieved the highest areas under the curve (AUC) of 0.732, with sensitivity and specificity of 70.9% and 68.3%, respectively. DATA CONCLUSION: Compared with qualitative analysis, FF value was independently associated with HRMM. The quantitative features of diffuse marrow infiltration on MRI scans are more effective in detecting HRMM. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Amide Proton Transfer-Weighted Imaging Combined With Intravoxel Incoherent Motion for Evaluating Microsatellite Instability in Endometrial Cancer.

BACKGROUND: Microsatellite instability (MSI), caused by mismatch repair (MMR) protein defects that lead to uncorrectable mismatch bases, results in the accumulation of gene mutations and ultimately to tumors. Preoperative prediction of MSI can provide a basis for personalized and precise treatment of endometrial cancer (EC) patients. PURPOSE: To investigate amide proton transfer weighting (APTw) imaging combined with intravoxel incoherent motion (IVIM) in the assessment of MSI in EC. STUDY TYPE: Retrospective. POPULATION: A total of 71 patients with EC (12 classified as the MSI group and 22 as the microsatellite stabilization [MSS] group after entering and leaving the group standard). FIELD STRENGTH/SEQUENCE: A 3.0 T/IVIM, diffusion-weighted imaging (DWI) and APTw. ASSESSMENT: Amide proton transfer (APT) value, apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) were calculated and compared between MSI and MSS groups. STATISTICAL TESTS: The Kendall's W test; Mann-Whitney U-test; Chi-square test or Fisher's exact test; logistic regression analysis; Area under the receiver operating characteristic (ROC) curve (AUC); The Delong test; Pearson or Spearman correlation coefficients. The significance threshold was set at P < 0.05. RESULTS: APT and D* values of the MSI group were significantly higher than those of the MSS group. While ADC, D, and f values in the MSI group were significantly lower than those in the MSS group. The multivariate analysis revealed that only APT and D* values were independent predictors to evaluate the MSI status. And the ROC curves indicated that the combination of APT and D* values could distinguish the MSI status of EC with the highest diagnostic efficacy (AUC = 0.973), even without significant difference to those by APT (AUC = 0.894) or D* (AUC = 0.920) value separately (P = 0.149 and 0.078, respectively). CONCLUSION: Combination of APTw and IVIM imaging may serve as an effective noninvasive method for clinical assessment of MSI in EC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Transcriptomic Analysis of Metarhizium anisopliae-Induced Immune-Related Long Non-Coding RNAs in Polymorphic Worker Castes of Solenopsis invicta.

Long non-coding RNAs (lncRNAs) represent a class of RNA molecules that do not encode proteins. Generally studied for their regulatory potential in model insects, relatively little is known about their immunoregulatory functions in different castes of eusocial insects, including Solenopsis invicta, a notoriously invasive insect pest. In the current study, we used Metarhizium anisopliae, an entomopathogenic fungus, to infect the polymorphic worker castes (Major and Minor Workers) and subjected them to RNA sequencing at different intervals (6, 24, and 48 h post-infection (hpi)). Comprehensive bioinformatic analysis identified 5719 (1869 known and 3850 novel) lncRNAs in all libraries. Genomic characteristics analysis showed that S. invicta lncRNAs exhibited structural similarities with lncRNAs from other eusocial insects, including lower exon numbers, shorter intron and exon lengths, and a lower expression profile. A comparison of lncRNAs in major and minor worker ants revealed that several lncRNAs were exclusively expressed in one worker caste and remained absent in the other. LncRNAs such as MSTRG.12029.1, XR_005575440.1 (6 h), MSTRG.16728.1, XR_005575440.1 (24 h), MSTRG.20263.41, and MSTRG.11994.5 (48 h) were only present in major worker ants, while lncRNAs such as MSTRG.8896.1, XR_005574239.1 (6 h), MSTRG.20289.8, XR_005575051.1 (24 h), MSTRG.20289.8, and MSTRG.6682.1 (48 h) were only detected in minor workers. Additionally, we performed real-time quantitative PCR and experimentally validated these findings. Functional annotation of cis-acting lncRNAs in major worker ants showed that lncRNAs targeted genes such as serine protease, trypsin, melanization protease-1, spaetzle-3, etc. In contrast, apoptosis and autophagy-related genes were identified as targets of lncRNAs in minor ants. Lastly, we identified several lncRNAs as precursors of microRNAs (miRNAs), such as miR-8, miR-14, miR-210, miR-6038, etc., indicating a regulatory relationship between lncRNAs, miRNAs, and mRNAs in antifungal immunity. These findings will serve as a genetic resource for lncRNAs in polymorphic eusocial ants and provide a theoretical basis for exploring the function of lncRNAs from a unique and novel perspective.

Evaluation of brown adipose tissue with intermolecular double-quantum coherence magnetic resonance spectroscopy at 3.0 T.

In the current study, we propose a single-voxel (SV) magnetic resonance spectroscopy (MRS) pulse sequence, based on intermolecular double-quantum coherence (iDQC), for in vivo specific assessment of brown adipose tissue (BAT) at 3 T. The multilocular adipocyte, present in BAT, typically contains a large number of small lipid droplets surrounded by abundant intracellular water, while the monolocular adipocyte, present in white adipose tissue (WAT), accommodates only a single large lipid droplet with much less water content. The SV-iDQC sequence probes the spatial correlation between water and fat spins at a distance of about the size of an adipocyte, thus can be used for assessment of BAT, even when mixed with WAT and/or muscle tissues. This sequence for measurement of water-to-fat (water-fat) iDQC signals was tested on phantoms and mouse BAT and WAT tissues. It was then used to differentiate adipose tissues in the supraclavicular and subcutaneous regions of healthy youth human volunteers (n = 6). Phantom results with water-fat emulsions demonstrated enhanced water-fat iDQC signal with increased voxel size, increased energy level of emulsification, or increased distribution balance of water and fat spins. The animal tissue experiments resulted in obvious water-fat iDQC signal in mouse BAT, while this signal was almost absent in the WAT spectrum. The optimal choice of the dipolar coupling distance for the observation was approximately 100 µm, as tested on both emulsion phantom and animal tissue. The water-fat iDQC signals observed in the supraclavicular adipose tissues were higher than in the subcutaneous adipose tissues in healthy young volunteers (0.43 ± 0.36 vs. 0.10 ± 0.06, p = 0.06). It was concluded that the iDQC-based sequence has potential for assessment of mouse and human BAT at 3 T, which is of interest for clinical research and the diagnosis of obesity and associated diseases.

Timely Diagnosis and Treatment Shortens the Time to Resolution of Coronavirus Disease (COVID-19) Pneumonia and Lowers the Highest and Last CT Scores From Sequential Chest CT.

OBJECTIVE. This study aims to assess correlations of the time from symptom onset to diagnosis and treatment with the time to disease resolution and CT scores as based on findings from sequential chest CT examinations. MATERIALS AND METHODS. Thirty patients with coronavirus disease (COVID-19) confirmed by reverse transcription-polymerase chain reaction analysis underwent chest CT examinations. Five patients who did not have positive CT findings or who had not yet fulfilled criteria for discharge from the hospital were excluded. CT scores were determined according to CT findings and lung involvement. The time from symptom onset to diagnosis and treatment was recorded for each patient, and on the basis of this information, patients with COVID-19 were divided into group 1 (patients for whom this interval was ≤ 3 days) and group 2 (those for whom this interval was > 3 days). The CT scores for each group were fitted using a Lorentzian line-shape curve to show the variation tendency during treatment. The differences in age, sex, and last CT scores determined before discharge between the two groups were analyzed, and correlations of the time from symptom onset to diagnosis and treatment with the time to disease resolution as well as with the highest CT score also underwent statistical analysis. RESULTS. A total of 25 subjects were enrolled in the study. The fitted tendency curves for group 1 and group 2 were significantly different, with peak points showing that the estimated highest CT score was 10 and 16 for each group, respectively, and the time to disease resolution was 6 and 13 days, respectively. The Mann-Whitney test showed that the last CT scores were lower for group 1 than for group 2 (p = 0.025), although the chi-square test found no difference in age and sex between the groups. The time from symptom onset to diagnosis and treatment had a positive correlation with the time to disease resolution (r = 0.93; p = 0.000) as well as with the highest CT score (r = 0.83; p = 0.006). CONCLUSION. Timely diagnosis and treatment are key to providing a better prognosis for patients with COVID-19.

Water removal in MR spectroscopic imaging with L2 regularization.

PURPOSE: An L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T. METHODS: Water signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals. RESULTS: The L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect. CONCLUSIONS: With usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.

Ultrahigh-Resolution NMR Spectroscopy for Rapid Chemical and Biological Applications in Inhomogeneous Magnetic Fields.

NMR spectroscopy is a commonly used analytical technique in practical applications, and its applicability is further promoted by pure chemical shift techniques based on spectral simplification for analyses. Unfortunately, magnetic field inhomogeneity caused by adverse experimental conditions remains an obstacle restricting NMR applications. In this study, we introduce a new NMR method for high-resolution pure shift proton (1H) NMR measurements in inhomogeneous magnetic fields. We demonstrate that the method allows one to perform chemical analyses on complex solutions in deshimmed magnetic fields, to obtain metabolite information on intact biological tissues with intrinsic field inhomogeneities and to achieve in situ electrochemical detection under externally adverse field conditions. This approach is readily implemented on common commercial NMR instruments without field shimming and locking procedures, specialized hardware requirements as well as complicated sample pretreatments. It provides an effective tool for NMR applications to high-resolution chemical and biological measurements under inhomogeneous magnetic field conditions.

In Situ Monitoring Potential-Dependent Electrochemical Process by Liquid NMR Spectroelectrochemical Determination: A Proof-of-Concept Study.

We report the design and the performance of a two-chamber thin-layer electrochemical device for in situ potential-dependent liquid NMR measurement. Liquid NMR spectra, simultaneously recorded with cyclic voltammetry (CV), have been obtained to reveal molecular changes with potentials scanning. As a proof of concept, redox properties of 1,4-benzoquinone based systems have been investigated, and a π dimerization has been identified by combining both in situ and ex situ NMR analyses. This work provides a new approach for spectroelectrochemistry, which will contribute to developing electrochemical NMR (EC-NMR) as an important tool for the analysis of electrochemical process at a molecular level.

Accelerating two-dimensional nuclear magnetic resonance correlation spectroscopy via selective coherence transfer.

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool for both qualitative and quantitative analyses of various systems in chemistry, biology, and medicine. However, applications of one-dimensional 1H NMR are often restrained by the presence of severe overlap among different resonances. The advent of two-dimensional (2D) 1H NMR constitutes a promising alternative by extending the crowded resonances into a plane and thereby alleviating the spectral congestions. However, the enhanced ability in discriminating resonances is achieved at the cost of extended experimental duration due to necessity of various scans with progressive delays to construct the indirect dimension. Therefore, in this study, we propose a selective coherence transfer (SECOT) method to accelerate acquisitions of 2D correlation spectroscopy by converting chemical shifts into spatial positions within the effective sample length and then performing an echo planar spectroscopic imaging module to record the spatial and spectral information, which generates 2D correlation spectrum after 2D Fourier transformation. The feasibility and effectiveness of SECOT have been verified by a set of experiments under both homogeneous and inhomogeneous magnetic fields. Moreover, evaluations of SECOT for quantitative analyses are carried out on samples with a series of different concentrations. Based on these experimental results, the SECOT may open important perspectives for fast, accurate, and stable investigations of various chemical systems both qualitatively and quantitatively.

Localized one-dimensional single voxel magnetic resonance spectroscopy without J coupling modulations.

PURPOSE: To acquire single voxel localized one-dimensional 1 H magnetic resonance spectroscopy (MRS) without J coupling modulations, free from amplitude and phase distortions. METHODS: A pulse sequence, named PRESSIR, is developed for volume localized MRS without J modulations at arbitrary echo time (TE). The J coupling evolution is suppressed by the J-refocused module that uses a 90° pulse at the midpoint of a double spin echo. RESULTS: The localization performance of the PRESSIR sequence was tested with a two-compartment phantom. The proposed sequence shows similar voxel localization accuracy as PRESS. Both PRESSIR and PRESS sequences were performed on MRS brain phantom and pig brain tissue. PRESS spectra suffer from amplitude and phase distortions due to J modulations, especially under moderate and long TEs, while PRESSIR spectra are almost free from distortions. CONCLUSION: The PRESSIR sequence proposed herein enables the acquisition of single voxel in-phase MRS within a single scan. It allows an enhanced signal intensity of J coupling metabolites and reducing undesired broad resonances with short T2s while suppressing J modulations. Moreover, it provides an approach for direct measurement of nonoverlapping J coupling peaks and of transverse relaxation times T2s. Magn Reson Med 76:1661-1667, 2016. © 2015 International Society for Magnetic Resonance in Medicine.

High-resolution localized spatiotemporal encoding correlated spectra under inhomogeneous magnetic fields via asymmetrical gradient encoding/decoding.

Applications of conventional localized nuclear magnetic resonance correlated spectroscopy are restrained by long acquisition times and poor performance under inhomogeneous magnetic fields. Here, a method that combines the spatiotemporal encoding technique with the localization technique and implements the encoding and decoding in unison with suitable asymmetrical gradients is proposed to obtain high-resolution localized correlated spectra under inhomogeneous fields in greatly reduced times. Experiments on phantom solutions prove its insensitivity to linear field inhomogeneities along three orthogonal axes. Moreover, this method is applied to adipose study of marrow tissue with resolution improvements. The proposed method may offer promising perspectives for fast analyses of biological tissues.

Discrete decoding based ultrafast multidimensional nuclear magnetic resonance spectroscopy.

The three-dimensional (3D) nuclear magnetic resonance (NMR) spectroscopy constitutes an important and powerful tool in analyzing chemical and biological systems. However, the abundant 3D information arrives at the expense of long acquisition times lasting hours or even days. Therefore, there has been a continuous interest in developing techniques to accelerate recordings of 3D NMR spectra, among which the ultrafast spatiotemporal encoding technique supplies impressive acquisition speed by compressing a multidimensional spectrum in a single scan. However, it tends to suffer from tradeoffs among spectral widths in different dimensions, which deteriorates in cases of NMR spectroscopy with more dimensions. In this study, the discrete decoding is proposed to liberate the ultrafast technique from tradeoffs among spectral widths in different dimensions by focusing decoding on signal-bearing sites. For verifying its feasibility and effectiveness, we utilized the method to generate two different types of 3D spectra. The proposed method is also applicable to cases with more than three dimensions, which, based on the experimental results, may widen applications of the ultrafast technique.

Reverse detection for spectral width improvements in spatially encoded dimensions of ultrafast two-dimensional NMR spectra.

Recently, the spatially encoded technique has been broadly used in the fast analyses of chemical systems and real-time detections of chemical reactions. In spatially encoded ultrafast 2D spectra, spectral widths and resolution in spatially encoded dimensions are contradictive, leading to the risk of insufficient spectral widths when providing satisfactory resolution values for all resonances. Here, a method named as reverse detection is proposed to improve the spectral width in the spatially encoded dimension. Experimental results show that spectral width improvements are at least twofold with reverse detection solely, and more improvements can be expected along with the gradient-controlled folding method. The proposed method can be applied to almost any spatially encoded scheme with echo planar spectroscopic imaging--like detection module and may promote wide applications of ultrafast 2D spectroscopy techniques in chemical analyses.

Preliminary exploration of amide proton transfer weighted imaging in differentiation between benign and malignant bone tumors.

Purpose: To explore the value of 3D amide proton transfer weighted imaging (APTWI) in the differential diagnosis between benign and malignant bone tumors, and to compare the diagnostic performance of APTWI with traditional diffusion-weighted imaging (DWI). Materials and methods: Patients with bone tumors located in the pelvis or lower limbs confirmed by puncture or surgical pathology were collected from January 2021 to July 2023 in the First Affiliated Hospital of Zhengzhou University. All patients underwent APTWI and DWI examinations. The magnetization transfer ratio with asymmetric analysis at the frequency offset of 3.5 ppm [MTRasym(3.5 ppm)] derived by APTWI and the apparent diffusion coefficient (ADC) derived by DWI for the tumors were measured. The Kolmogorou-Smirnou and Levene normality test was used to confirm the normal distribution of imaging parameters; and the independent sample t test was used to compare the differences in MTRasym(3.5 ppm) and ADC between benign and malignant bone tumors. In addition, the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different imaging parameters in differentiation between benign and malignant bone tumors. P<0.05 means statistically significant. Results: Among 85 bone tumor patients, 33 were benign and 52 were malignant. The MTRasym(3.5 ppm) values of malignant bone tumors were significantly higher than those of benign tumors, while the ADC values were significantly lower in benign tumors. ROC analysis shows that MTRasym(3.5 ppm) and ADC values perform well in the differential diagnosis of benign and malignant bone tumors, with the area under the ROC curve (AUC) of 0.798 and 0.780, respectively. Combination of MTRasym(3.5 ppm) and ADC values can further improve the diagnostic performance with the AUC of 0.849 (sensitivity = 84.9% and specificity = 73.1%). Conclusion: MTRasym(3.5 ppm) of malignant bone tumors was significantly higher than that of benign bone tumors, reflecting the abnormal increase of protein synthesis in malignant tumors. APTWI combined with DWI can achieve a high diagnostic efficacy in differentiation between benign and malignant bone tumors.