Solid-State NMR 13C sensitivity at high magnetic field.

Sensitivity is the foundation of every NMR experiment, and the signal-to-noise ratio (SNR) should increase with static (B0) magnetic field, by a proportionality that primarily depends on the design of the NMR probe and receiver. In the low B0 field limit, where the coil geometry is much smaller than the wavelength of the NMR frequency, SNR can increase in proportion to B0 to the power 7/4. For modern magic-angle spinning (MAS) probes, this approximation holds for rotor sizes up to 3.2 mm at 14.1 Tesla (T), corresponding to 600 MHz 1H and 151 MHz 13C Larmor frequencies. To obtain the anticipated benefit of larger coils and/or higher B0 fields requires a quantitative understanding of the contributions to SNR, utilizing standard samples and protocols that reproduce SNR measurements with high accuracy and precision. Here, we present such a systematic and comprehensive study of 13C SNR under MAS over the range of 14.1 to 21.1 T. We evaluate a range of probe designs utilizing 1.6, 2.5 and 3.2 mm rotors, including 24 different sets of measurements on 17 probe configurations using five spectrometers. We utilize N-acetyl valine as the primary standard and compare and contrast with other commonly used standard samples (adamantane, glycine, hexamethylbenzene, and 3-methylglutaric acid). These robust approaches and standard operating procedures provide an improved understanding of the contributions from probe efficiency, receiver noise figure, and B0 dependence in a range of custom-designed and commercially available probes. We find that the optimal raw SNR is obtained with balanced 3.2 mm design at 17.6 T, that the best mass-limited SNR is achieved with a balanced 1.6 mm design at 21.1 T, and that the raw SNR at 21.1 T reaches diminishing returns with rotors larger than 2.5 mm.

Neural correlates of impulsivity in amphetamine use disorder.

Impulsivity is a trait associated with several psychiatric conditions, not least addictive disorders. While the neural mechanisms behind certain aspects of impulsivity have been studied extensively, there are few imaging studies examining this neurocircuitry in populations with substance use disorders. Therefore, we aimed to examine the functional connectivity of relevant neural networks, and their possible association with trait impulsivity, in a sample with severe amphetamine use disorder and a control group of healthy subjects. We used data collected in a randomized clinical trial studying the acute effects of oral naltrexone in amphetamine use disorder. Our final sample included 32 amphetamine users and 27 healthy controls. Trait impulsivity was rated with the Barratt Impulsiveness Scale-11, and functional connectivity was measured during resting-state fMRI, looking specifically at networks involving prefrontal regions previously implicated in studies of impulsivity. Amphetamine users had higher subjective ratings of impulsivity as compared to healthy controls, and these scores correlated positively with a wide-spread prefrontal hyperconnectivity that was found among the amphetamine users. These findings highlight the importance of aberrant prefrontal function in severe addiction.

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors.

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized assessments is currently discouraged due to a series of technical and biological issues. In this work, we present a deep learning pipeline for segmentation-based brain atrophy quantification that improves upon the automated labels of the reference method from which it learns. This goal is achieved through tissue similarity regularization that exploits the a priori knowledge that scans from the same subject made within a short interval must have similar tissue volumes. To train the presented pipeline, we use unlabeled pairs of T1-weighted MRI scans having a tissue similarity prior, and generate the target brain tissue segmentations in a fully automated manner using the fsl_anat pipeline implemented in the FMRIB Software Library (FSL). Tissue similarity regularization is enforced during training through a weighted loss term that penalizes tissue volume differences between short-interval scan pairs from the same subject. In inference, the pipeline performs end-to-end skull stripping and brain tissue segmentation from a single T1-weighted MRI scan in its native space, i.e., without performing image interpolation. For longitudinal evaluation, each image is independently segmented first, and then measures of change are computed. We evaluate the presented pipeline in two different MRI datasets, MIRIAD and ADNI1, which have longitudinal and short-interval imaging from healthy controls (HC) and Alzheimer's disease (AD) subjects. In short-interval scan pairs, tissue similarity regularization reduces the quantification error and improves the consistency of measured tissue volumes. In the longitudinal case, the proposed pipeline shows reduced variability of atrophy measures and higher effect sizes of differences in annualized rates between HC and AD subjects. Our pipeline obtains a Cohen's d effect size of d=2.07 on the MIRIAD dataset, an increase from the reference pipeline used to train it (d=1.01), and higher than that of SIENA (d=1.73), a well-known state-of-the-art approach. In the ADNI1 dataset, the proposed pipeline improves its effect size (d=1.37) with respect to the reference pipeline (d=0.80) and surpasses SIENA (d=1.33). The proposed data-driven deep learning regularization reduces the biases and systematic errors learned from the reference segmentation method, which is used to generate the training targets. Improving the accuracy and reliability of atrophy quantification methods is essential to unlock brain atrophy as a diagnostic and prognostic marker in neurodegenerative pathologies.

Patient-centric comparative analysis of experiences in open upright and conventional closed MRI scanners.

INTRODUCTION: MRI often induces anxiety, leading to incomplete scans and claustrophobia-related distress. Open MRI systems aim to enhance patient comfort. This study examines how prior MRI experiences impact subsequent encounters in an open upright MRI scanner. METHODS: In this cross-sectional study, 118 adult patients completed a self-administered questionnaire from August 2022 to October 2023. It covered previous MRI experiences, including questions about claustrophobia, premature scan terminations, sedative medication usage, general MRI experiences, and interactions with radiology technologists. RESULTS: Patients in open upright MRI reported less claustrophobia compared to closed MRI systems (18.4% vs. 58.3%), fewer premature scan terminations (5.3% vs. 31.0%), and less sedative use (5.3% vs. 46.9%). Moderate positive correlations were found between past and current claustrophobic events and premature scan terminations. Effective communication with radiology technologists was essential for patient comfort and reduced claustrophobia. Scan duration and noise triggered discomfort in 26.1% and 21.6% of study participants respectively. Persons without prior MRI experience were more satisfied with the examination and expressed no clear preference for future MRI settings, contrasting those with previous exposure favoring the open MRI setup. CONCLUSION: The study emphasizes the benefits of open upright MRI for high-risk claustrophobic patients. It identifies the lasting impact of negative MRI experience on future examinations and highlights the crucial role of radiology technologists. IMPLICATIONS FOR PRACTICE: Integrating open MRI scanners in medical facilities and prioritizing effective communication with radiology technologists enhances patient comfort. Positive experiences with open MRI may improve patient compliance and offer greater flexibility for future examinations.

Ectopic fat in muscle and poor glycemic control are negatively associated with trabecular bone score in type 2 diabetes.

INTRODUCTION: Type 2 Diabetes (T2D) is associated with fractures, despite preserved Bone Mineral Density (BMD). This study aimed to evaluate the relationship between BMD and trabecular bone score (TBS) with the reallocation of fat within muscle in individuals with eutrophy, obesity, and T2D. METHODS: The subjects were divided into three groups: eutrophic controls paired by age and sex with the T2D group (n = 23), controls diagnosed with obesity paired by age, sex, and body mass index with the T2D group (n = 27), and the T2D group (n = 29). BMD and body fat percentage were determined using dual-energy X-Ray absorptiometry. TBS was determined using TBS iNsight software. Intra and extramyocellular lipids in the soleus were measured using proton magnetic resonance spectroscopy. RESULTS: TBS was lower in the T2D group than in the other two groups. Glycated hemoglobin (A1c) was negatively associated with TBS. Body fat percentage was negatively associated with TBS and Total Hip (TH) BMD. TH BMD was positively associated with intramuscular lipids. A trend of negative association was observed between intramuscular lipids and TBS. CONCLUSION: This study showed for the first time that the reallocation of lipids within muscle has a negative association with TBS. Moreover, these results are consistent with previous studies showing a negative association between a parameter related to insulin resistance (intramuscular lipids) and TBS.

Covalent organic framework-functionalized magnetic MXene nanocomposite for efficient pre-concentration and detection of organophosphorus and organochlorine pesticides in tea samples before gas chromatography-triple quadrupole mass spectrometry analysis.

In this study, a hydrophobic covalent organic framework-functionalized magnetic composite (CoFe2O4@Ti3C2@TAPB-TFTA) with a high specific area with 1,3,5-tris(4-aminophenyl)benzene (TAPB) and 2,3,5,6-tetrafluoroterephthalaldehyde (TFTA) was designed and synthesized through Schiff base reaction. An efficient magnetic solid-phase extraction method was established and combined with gas chromatography-triple quadrupole mass spectrometry to sensitively determine 10 organochlorine and organophosphorus pesticides in tea samples. The established method exhibited good linearity in the range of 0.05-120 µg/L and had low limits of detection (0.013-0.018 µg/L). The method was evaluated with tea samples, and the spiked recoveries of pesticides in different tea samples reached satisfactory values of 85.7-96.8%. Moreover, the adsorption of pesticides was spontaneous and followed Redlich-Peterson isotherm and pseudo-second-order kinetic models. These results demonstrate the sensitivity, effectiveness, and reliability of the proposed method for monitoring organochlorine and organophosphorus pesticides in tea samples, providing a preliminary basis for researchers to reasonably design adsorbents for the efficient extraction of pesticides.

Dual-template magnetic molecularly imprinted polymers for selective extraction and sensitive detection of aflatoxin B1 and benzo(α)pyrene in environmental water and edible oil.

The coexistence of multiple contaminates in the environment and food is of growing concern due to their extremely hazard as a well-known class I carcinogen, like aflatoxin B1 (AFB1) and benzo(α)pyrene (BaP). AFB1 and BaP are susceptible to coexistence in environmental water and edible oil, posing a significant potential risk to environmental monitoring and food safety. The remaining challenges in detecting multiple contaminates include unsatisfied sensitivity, insufficient targets selectivity, and interferences in complex matrices. Here, we developed dual-template magnetic molecularly imprinted polymers (DMMIPs) for selective extraction of dual targets in complex matrices from the environment and food. The DMMIPs were fabricated by surface imprinting with vinyl-functionalized Fe3O4 as carrier, 5,7-dimethoxycoumarin and pyrene as dummy templates, and methacrylamide as functional monomer. The DMMIPs showed excellent adsorption ability (12.73-15.80 mg/g), imprinting factors (2.01-2.58), and reusability of three adsorption-desorption cycles for AFB1 and BaP. The adsorption mechanism including hydrogen bond, electrostatic interaction and van der Waals force was confirmed by physical characterization and DFT calculation. Applying DMMIPs in magnetic solid phase extraction (MSPE) followed by high-performance liquid chromatography (HPLC) analysis enabled detection limits of 0.134 µg/L for AFB1 and 0.107 µg/L for BaP. Recovery rates for water and edible oil samples were recorded as 86.2%-110.3% with RSDs of 4.1%-11.9%. This approach demonstrates potential for simultaneous identification and extraction of multiple contaminants in environmental and food.

Muscle mitochondrial function is impaired in adults with type 1 diabetes.

AIMS: Type 1 diabetes has been associated with mitochondrial dysfunction. However, the mechanism of this dysfunction in adults remains unclear. METHODS: A secondary analysis was conducted using data from several clinical trials measuring in-vivo and ex-vivo mitochondrial function in adults with type 1 diabetes (n = 34, age 38.8 ± 14.6 years) and similarly aged controls (n = 59, age 44.6 ± 13.9 years). In-vivo mitochondrial function was assessed before, during, and after isometric exercise with 31phosphorous magnetic resonance spectroscopy. High resolution respirometry of vastus lateralis muscle tissue was used to assess ex-vivo measures. RESULTS: In-vivo data showed higher rates of anaerobic glycolysis (p = 0.013), and a lower maximal mitochondrial oxidative capacity (p = 0.012) and mitochondrial efficiency (p = 0.024) in adults with type 1 diabetes. After adjustment for age and percent body fat maximal mitochondrial capacity (p = 0.014) continued to be lower and anaerobic glycolysis higher (p = 0.040) in adults with type 1 diabetes. Ex-vivo data did not demonstrate significant differences between the two groups. CONCLUSIONS: The in-vivo analysis demonstrates that adults with type 1 diabetes have mitochondrial dysfunction. This builds on previous research showing in-vivo mitochondrial dysfunction in youths with type 1 diabetes and suggests that defects in substrate or oxygen delivery may play a role in in-vivo dysfunction.

Magnetic resonance thermometry for hyperthermia in the oropharynx region.

Magnetic resonance thermometry (MRT) can measure in-vivo 3D-temperature changes in real-time and noninvasively. However, for the oropharynx region and the entire head and neck, motion potentially introduces large artifacts. Considering long treatment times of 60-90 min, this study aims to evaluate whether MRT around the oropharynx is clinically feasible for hyperthermia treatments and quantify the effects of breathing and swallowing on MRT performance. A 3D-ME-FGRE sequence was used in a phantom cooling down and around the oropharynx of five volunteers over ∼75 min. The imaging protocol consisted of imaging with acceleration (ARC = 2), number of image averages (NEX = 1,2 and 3). For volunteers, the acquisitions included a breath-hold scan and scans with deliberate swallowing. MRT performance was quantified in neck muscle, spinal cord and masseter muscle, using mean average error (MAE), mean error (ME) and spatial standard deviation (SD). In phantom, an increase in NEX leads to a significant decrease in SD, but MAE and ME were unchanged. No significant difference was found in volunteers between the different scans. There was a significant difference between the regions evaluated: neck muscle had the best MAE (=1.96 °C) and SD (=0.82 °C), followed by spinal cord (MAE = 3.17 °C, SD = 0.92 °C) and masseter muscle (MAE = 4.53 °C, SD = 1.16 °C). Concerning the ME, spinal cord did best, then neck muscle and masseter muscle, with values of -0.64 °C, 1.15 °C and -3.05 °C respectively. Breathing, swallowing, and different ways of imaging (acceleration and NEX) do not significantly influence the MRT performance in the oropharynx region. The ROI selected however, leads to significant differences.

Intraindividual comparison of CT and MRI for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

OBJECTIVES: To establish and validate scoring models for predicting vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) using computed tomography (CT) and magnetic resonance imaging (MRI), and to intra-individually compare the predictive performance between the two modalities. METHODS: We retrospectively included 324 patients with surgically confirmed HCC who underwent preoperative dynamic CT and MRI with extracellular contrast agent between June 2019 and August 2020. These patients were then divided into a discovery cohort (n = 227) and a validation cohort (n = 97). Imaging features and Liver Imaging Reporting and Data System (LI-RADS) categories of VETC-positive HCCs were evaluated. Logistic regression analyses were conducted on the discovery cohort to identify clinical and imaging predictors associated with VETC-positive cases. Subsequently, separate CT-based and MRI-based scoring models were developed, and their diagnostic performance was compared using generalized estimating equations. RESULTS: On both CT and MRI, VETC-positive HCCs exhibited a higher frequency of size > 5.0 cm, necrosis or severe ischemia, non-smooth tumor margin, targetoid appearance, intratumor artery, and heterogeneous enhancement with septations or irregular ring-like structure compared to VETC-negative HCCs (all p < 0.05). Regarding LI-RADS categories, VETC-positive HCCs were more frequently categorized as LR-M than VETC-negative cases (all p < 0.05). In the validation cohort, the CT-based model showed similar sensitivity (76.7% vs. 86.7%, p = 0.375), specificity (83.6% vs. 74.6%, p = 0.180), and area under the curve value (0.80 vs. 0.81, p = 0.910) to the MRI-based model in predicting VETC-positive HCCs. CONCLUSION: Preoperative CT and MRI demonstrated comparable performance in the identification of VETC-positive HCCs, thus displaying promising predictive capabilities. CLINICAL RELEVANCE STATEMENT: Both computed tomography and magnetic resonance imaging demonstrated promise in preoperatively identifying the vessel-encapsulating tumor cluster pattern in hepatocellular carcinoma, with no statistically significant difference between the two modalities, potentially adding additional prognostic value. KEY POINTS: Computed tomography (CT) and magnetic resonance imaging (MRI) show promise in the preoperative identification of vessels encapsulating tumor clusters-positive hepatocellular carcinoma (HCC). HCC with vessels encapsulating tumor cluster patterns were more frequently LR-M compared to those without. These CT and MRI models showed comparable ability in identifying vessels encapsulating tumor clusters-positive HCC.

A visual marker for early atrophy of the supraspinatus muscle on conventional MRI: introduction of the blackbird sign.

OBJECTIVES: The aim of this study was to introduce the blackbird sign as a fast, qualitative measure of early supraspinatus (SSP) muscle atrophy and to correlate the sign with quantitatively assessed muscle volume and intramuscular fat fraction (FF) in patients with full-thickness SSP tears. MATERIALS AND METHODS: The blackbird sign describes the asymmetric pattern of early SSP atrophy: on sagittal MR images, the supero-posterior contour of the muscle becomes concave, resembling the shape of a blackbird. MRIs of patients with full-thickness SSP tears were retrospectively reviewed for the presence of the blackbird and tangent signs. Patients were then divided into group 1: negative tangent sign and negative blackbird sign (n = 67), group 2: negative tangent sign and positive blackbird sign (n = 31), and group 3: positive tangent sign (n = 32). A 2-point Dixon sequence was acquired in all patients from which quantitative FF and muscle volumes were calculated. RESULTS: In total 130 patients (mean age 67 ± 11 years) were included. Mean SSP volume was significantly smaller in group 3 (15.8 ± 8.1 cm3) compared to group 2 (23.9 ± 7.0 cm3, p = 0.01) and group 1 (29.7 ± 9.1 cm3, p < 0.01). Significantly lower muscle volumes were also found in group 2 compared to group 1 (p = 0.02), confirming that the blackbird sign is able to identify early SSP atrophy. Mean FF in the SSP was significantly higher in group 3 (18.5 ± 4.4%) compared to group 2 (10.9 ± 4.7%, p < 0.01) and group 1 (6.1 ± 2.6%, p < 0.01). CONCLUSION: Visual assessment of early muscle atrophy of the SSP is feasible and reproducible using the blackbird sign, allowing the diagnosis of early SSP atrophy. CLINICAL RELEVANCE STATEMENT: In routine clinical practice, the blackbird sign may be a useful tool for assessing early muscle degeneration before the risk of postoperative rotator cuff re-tears increases with progressive muscle atrophy and fatty infiltration. KEY POINTS: Quantitative measurements of rotator cuff injuries require time, limiting clinical practicality. The proposed blackbird sign is able to identify early SSP atrophy. Reader agreement for the blackbird sign was substantial, demonstrating reproducibility and ease of implementation in the clinical routine.

Ultra-high b-value DWI in rectal cancer: image quality assessment and regional lymph node prediction based on radiomics.

OBJECTIVES: This study aims to evaluate image quality and regional lymph node metastasis (LNM) in patients with rectal cancer (RC) on multi-b-value diffusion-weighted imaging (DWI). METHODS: This retrospective study included 199 patients with RC who had undergone multi-b-value DWI. Subjective (five-point Likert scale) and objective assessments of quality images were performed on DWIb1000, DWIb2000, and DWIb3000. Patients were randomly divided into a training (n = 140) or validation cohort (n = 59). Radiomics features were extracted within the whole volume tumor on ADC maps (b = 0, 1000 s/mm2), DWIb1000, DWIb2000, and DWIb3000, respectively. Five prediction models based on selected features were developed using logistic regression analysis. The performance of radiomics models was evaluated with a receiver operating characteristic curve, calibration, and decision curve analysis (DCA). RESULTS: The mean signal intensity of the tumor (SItumor), signal-to-noise ratio (SNR), and artifact and anatomic differentiability score gradually were decreased as the b-value increased. However, the contrast-to-noise (CNR) on DWIb2000 was superior to those of DWIb1000 and DWIb3000 (4.58 ± 0.86, 3.82 ± 0.77, 4.18 ± 0.84, p < 0.001, respectively). The overall image quality score of DWIb2000 was higher than that of DWIb3000 (p < 0.001) and showed no significant difference between DWIb1000 and DWIb2000 (p = 0.059). The area under curve (AUC) value of the radiomics model based on DWIb2000 (0.728) was higher than conventional ADC maps (0.690), DWIb1000 (0.699), and DWIb3000 (0.707), but inferior to multi-b-value DWI (0.739) in predicting LNM. CONCLUSION: DWIb2000 provides better lesion conspicuity and LNM prediction than DWIb1000 and DWIb3000 in RC. CLINICAL RELEVANCE STATEMENT: DWIb2000 offers satisfactory visualization of lesions. Radiomics features based on DWIb2000 can be applied for preoperatively predicting regional lymph node metastasis in rectal cancer, thereby benefiting the stratified treatment strategy. KEY POINTS: Lymph node staging is required to determine the best treatment plan for rectal cancer. DWIb2000 provides superior contrast-to-noise ratio and lesion conspicuity and its derived radiomics best predict lymph node metastasis. DWIb2000 may be recommended as the optimal b-value in rectal MRI protocol.

Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance.

BACKGROUND: Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine. METHODS: A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects. RESULTS: The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91-0.98 and 0.89-0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99-1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001). CONCLUSION: Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment. TRIALS REGISTRATION: ClinicalTrials.gov: NCT05114785. RELEVANCE STATEMENT: Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes. KEY POINTS: • Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.

In vivo Hyperpolarized Metabolic Imaging to Monitor the Progression of Hepatitis B Virus (HBV)-Related Hepatitis to Liver Fibrosis.

PURPOSE: This study aimed to assess metabolic changes to monitor the progression from normal liver to hepatitis B virus (HBV)-related hepatitis and liver fibrosis using hyperpolarized 13C magnetic resonance imaging (MRI). PROCEDURES: Hepatitis was induced in mice (n = 16) via hydrodynamic injection of HBV 1.2 plasmid (25 µg). Among them, liver fibrosis was induced in the mice (n = 8) through weight-adapted administration of thioacetamide with ethanol. Normal control mice (n = 8) were injected with a phosphate buffer solution. Subsequently, a hyperpolarized 13C MRI was performed on the mouse liver in vivo. The level of hepatitis B surface antigen (HBsAg) in blood serum was measured. Statistical analysis involved comparing the differential metabolite ratios, blood biochemistry values, and body weight among the three groups using the Kruskal-Wallis one-way analysis of variance. RESULTS: HBsAg was absent in the normal and fibrosis groups, while it was detected in the hepatitis group. The ratios of [1-13C] lactate/pyruvate, [1-13C] alanine/pyruvate, [1-13C] lactate/total carbon, and [1-13C] alanine/total carbon were significantly lower in the normal control group than in the hepatitis and fibrosis groups (p < 0.05). Moreover, these ratios were significantly higher in the fibrosis group than in the hepatitis group (p < 0.05). However, no significant differences were observed in either [1-13C] pyruvate-hydrate/pyruvate or [1-13C] pyruvate-hydrate/total carbon among the three groups. CONCLUSIONS: The levels of [1-13C] lactate and [1-13C] alanine in vivo may serve as valuable indicators for differentiating between HBV-related hepatitis, liver fibrosis, and normal liver.

Effect of spin-lock frequency on quantitative myocardial T1ρ mapping.

OBJECTIVES: To use T1ρ mapping to assess myocardial fibrosis and to provide a reference for future clinical application, it is necessary to understand the factors influencing T1ρ values. This study explored the influence of different spin-locking frequencies on T1ρ values under a 3.0-T MR system. METHODS: Fifty-seven healthy subjects were prospectively and consecutively included in this study, and T1ρ mapping was performed on them in 3 short-axis slices with three spin-lock frequencies at the amplitude of 300 Hz, 400 Hz, and 500 Hz, then nine T1ρ images were acquired per subject. Four T1ρ-weighted images were acquired using a spin-lock preparation pulse with varying durations (0 msec, 13.3 msec, 26.6 msec, 40 msec). T1ρ relaxation times were quantified for each slice and each myocardial segment. The results were analyzed using Student's t-test and one-way analysis of variance (ANOVA) methods. RESULTS: Mean T1ρ relaxation times were 43.5 ± 2.8 msec at 300 Hz, 44.9 ± 3.6 msec at 400 Hz, and 46.2 ± 3.1 msec at 500 Hz, showing a significant progressive increase from low to high spin-lock frequency (300 Hz vs. 400 Hz, p = 0.046; 300 Hz vs. 500 Hz, p < 0.001; 400 Hz vs. 500 Hz, p = 0.043). In addition, The T1ρ values of females were significantly higher than those of males (300 Hz, p = 0.049; 400 Hz, p = 0.01; 500 Hz, p = 0.002). CONCLUSION: In this prospective study, myocardial T1ρ values for the specific CMR setting are provided, and we found that gender and spin-lock frequency can affect the T1ρ values. CRITICAL RELEVANCE STATEMENT: T1ρ mapping could supersede late gadolinium enhancement for detection of myocardial fibrosis. Establishing reference mean values that take key technical elements into account will facilitate interpretation of data in disease states. KEY POINTS: This study established myocardial T1ρ reference values for different spin-lock frequencies. T1ρ values increased with spin-lock frequency, but numerical differences were minimal. Females had higher T1ρ values than males at all frequencies.

Altered activation of the dorsal anterior cingulate cortex during oddball performance in individuals at risk for Alzheimer's disease.

INTRODUCTION: The neural mechanisms underlying neurodegenerative disorders in the elderly remain elusive, despite extensive neuroimaging research in recent decades. Amnestic type mild cognitive impairment (aMCI) and late-life major depressive disorder (MDD) are two such conditions characterized by intersecting cognitive and affective symptomatology, and they are at a higher risk for Alzheimer's disease. MATERIALS AND METHODS: This study analyzed the neural underpinnings of cognitive and depressive symptoms in a cohort comprising 12 aMCI subjects, 24 late-life MDD patients, and 26 healthy controls (HCs). Participants underwent a detailed neuropsychological assessment and completed a visual attentional oddball task during functional magnetic resonance imaging (fMRI), with evaluations at baseline and at 2-year follow-up. RESULTS: Initial findings showed that aMCI subjects had reduced dACC activation during oddball (target) stimulus detection, a pattern that persisted in longitudinal analyses and correlated with cognitive functioning measures. For HCs, subsequent dACC activation was linked to depression scores. Furthermore, in the affective-cognitive altered groups, later dACC activation correlated with oddball and memory performance. CONCLUSIONS: These findings enhance our comprehension of the neurobiological basis of cognitive and depressive disturbances in aging, indicating that dACC activation in response to a visual attentional oddball task could serve as a neural marker for assessing cognitive impairment and depression in conditions predisposing to Alzheimer's disease.

DCE-MRI to distinguish all monoclonal plasma cell disease stages and correlation with diffusion-weighted MRI/PET-based biomarkers in a hybrid simultaneous whole body-2-[18F]FDG-PET/MRI imaging approach.

BACKGROUND: Dynamic contrast-enhanced-MRI (DCE-MRI) is able to study bone marrow angiogenesis in patients with multiple myeloma (MM) and asymptomatic precursor diseases but its role in the management of MM has not yet been established. The aims of this prospective study was to compare DCE-MRI-based parameters between all monoclonal plasma cell disease stages in order to find out discriminatory parameters and to seek correlations with other diffusion-weighted MRI and positron emission tomography (PET)-based biomarkers in a hybrid simultaneous whole-body-2-[18F]fluorodeoxyglucose (FDG)-PET/MRI (WB-2-[18F]FDG-PET/MRI) imaging approach. METHODS: Patients with newly diagnosed Monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM) or symptomatic MM according to international myeloma working group and underwent WB-2-[18F]FDG-PET/MRI imaging including bone marrow DCE sequences at the Nantes University Hospital were prospectively enrolled in this study before receiving treatment. RESULTS: One hundred and sixty-seven patients (N = 167, mean age: 64 years ± 11 [Standard deviation], 66 males) were considered for the analysis. DCE-MRI-based Peak Enhancement Intensity (PEI), Time to PEI (TPEI) and their maximum intensity time ratio (MITR: PEI/TPEI) values were significantly different between the different monoclonal plasma cell disease stages, PEI values increasing and TPEI values decreasing progressively along the spectrum of plasma cell disorders, from MGUS stage to symptomatic multiple myeloma. PEI values were significantly higher in patients with diffuse bone marrow involvement (either in PET or in MRI images) than in those without diffuse bone marrow involvement, unlike TPEI values. PEI and TPEI values were not significantly different between patients with or without focal bone lesions. CONCLUSION: Different DCE-MRI-based parameters (PEI, TPEI, MITR) could significantly differentiate all monoclonal plasma cell disease stages and complemented conventional MRI and PET-based biomarkers.

How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T.

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.

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OBJECTIVES: Peritoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients. METHODS: All enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group. RESULTS: There was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively. CONCLUSIONS: GRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.

Joint Brain Tumor Segmentation from Multi-magnetic Resonance Sequences through a Deep Convolutional Neural Network.

Background: Brain tumor segmentation is highly contributive in diagnosing and treatment planning. Manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologist's skill. Automated brain tumor segmentation is of high importance and does not depend on either inter- or intra-observation. The objective of this study is to automate the delineation of brain tumors from the Fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1W), T2-weighted (T2W), and T1W contrast-enhanced (T1ce) magnetic resonance (MR) sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein. Methods: The BraTS-2020 challenge dataset, containing 370 subjects with four MR sequences and manually delineated tumor masks, is applied to train a residual neural network. This network is trained and assessed separately for each one of the MR sequences (single-channel input) and any combination thereof (dual- or multi-channel input). Results: The quantitative assessment of the single-channel models reveals that the FLAIR sequence would yield higher segmentation accuracy compared to its counterparts with a 0.77 ± 0.10 Dice index. As to considering the dual-channel models, the model with FLAIR and T2W inputs yields a 0.80 ± 0.10 Dice index, exhibiting higher performance. The joint tumor segmentation on the entire four MR sequences yields the highest overall segmentation accuracy with a 0.82 ± 0.09 Dice index. Conclusion: The FLAIR MR sequence is considered the best choice for tumor segmentation on a single MR sequence, while the joint segmentation on the entire four MR sequences would yield higher tumor delineation accuracy.