Prediction of MRI R 2 * $$ {\mathrm{R}}_2

To develop Monte Carlo simulations to predict the relationship of R 2 * $$ {\mathrm{R}}_2^{\ast } $$ with liver fat content at 1.5 T and 3.0 T. For various fat fractions (FFs) from 1% to 25%, four types of virtual liver models were developed by incorporating the size and spatial distribution of fat droplets. Magnetic fields were then generated under different fat susceptibilities at 1.5 T and 3.0 T, and proton movement was simulated for phase accrual and MRI signal synthesis. The synthesized signal was fit to single-peak and multi-peak fat signal models for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and proton density fat fraction (PDFF) predictions. In addition, the relationships between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and PDFF predictions were compared with in vivo calibrations and Bland-Altman analysis was performed to quantitatively evaluate the effects of these components (type of virtual liver model, fat susceptibility, and fat signal model) on R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions. A virtual liver model with realistic morphology of fat droplets was demonstrated, and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and PDFF values were predicted by Monte Carlo simulations at 1.5 T and 3.0 T. R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions were linearly correlated with PDFF, while the slope was unaffected by the type of virtual liver model and increased as fat susceptibility increased. Compared with in vivo calibrations, the multi-peak fat signal model showed superior performance to the single-peak fat signal model, which yielded an underestimation of liver fat. The R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF relationships by simulations with fat susceptibility of 0.6 ppm and the multi-peak fat signal model were R 2 * = 0.490 × PDFF + 28.0 $$ {\mathrm{R}}_2^{\ast }=0.490\times \mathrm{PDFF}+28.0 $$ ( R 2 = 0.967 $$ {R}^2=0.967 $$ , p < 0.01 $$ p<0.01 $$ ) at 1.5 T and R 2 * = 0.928 × PDFF + 39.4 $$ {\mathrm{R}}_2^{\ast }=0.928\times \mathrm{PDFF}+39.4 $$ ( R 2 = 0.972 $$ {R}^2=0.972 $$ , p < 0.01 $$ p<0.01 $$ ) at 3.0 T. Monte Carlo simulations provide a new means for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF prediction, which is primarily determined by fat susceptibility, fat signal model, and magnetic field strength. Accurate R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF calibration has the potential to correct the effect of fat on R 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification, and may be helpful for accurate R 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements in liver iron overload. In this study, a Monte Carlo simulation of hepatic steatosis was developed to predict the relationship between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and PDFF. Furthermore, the effects of fat droplet morphology, fat susceptibility, fat signal model, and magnetic field strength were evaluated for the R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF calibration. Our results suggest that Monte Carlo simulations provide a new means for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF prediction and this means can be easily generated for various regimes, such as simulations with higher fields and different echo times, as well as correction of magnetic susceptibility measurements for liver iron quantification.

Resolving heterogeneity in obsessive-compulsive disorder through individualized differential structural covariance network analysis.

BACKGROUND: The high heterogeneity of obsessive-compulsive disorder (OCD) denies attempts of traditional case-control studies to derive neuroimaging biomarkers indicative of precision diagnosis and treatment. METHODS: To handle the heterogeneity, we uncovered subject-level altered structural covariance by adopting individualized differential structural covariance network (IDSCN) analysis. The IDSCN measures how structural covariance edges in a patient deviated from those in matched healthy controls (HCs) yielding subject-level differential edges. One hundred patients with OCD and 106 HCs were recruited and whose T1-weighted anatomical images were acquired. We obtained individualized differential edges and then clustered patients into subtypes based on these edges. RESULTS: Patients presented tremendously low overlapped altered edges while frequently shared altered edges within subcortical-cerebellum network. Two robust neuroanatomical subtypes were identified. Subtype 1 presented distributed altered edges while subtype 2 presented decreased edges between default mode network and motor network compared with HCs. Altered edges in subtype 1 predicted the total Yale-Brown Obsessive Compulsive Scale score while that in subtype 2 could not. CONCLUSIONS: We depict individualized structural covariance aberrance and identify that altered connections within subcortical-cerebellum network are shared by most patients with OCD. These 2 subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of OCD.

Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information.

Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group-level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety-nine first-episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel-based morphometric and amplitude of low-frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure-function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.

Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis.

Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1-weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale-Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.

Epigallocatechin-3-Gallate Ameliorated Iron Accumulation and Apoptosis and Promoted Neuronal Regeneration and Memory/Cognitive Functions in the Hippocampus Induced by Exposure to a Chronic High-Altitude Hypoxia Environment.

We aimed to explore the protective effects and potential treatment mechanism of Epigallocatechin-3-gallate (EGCG) in an animal model of chronic exposure in a natural high-altitude hypoxia (HAH) environment. Behavioral alterations were assessed with the Morris water maze test. Iron accumulation in the hippocampus was detected by using DAB enhanced Perls' staining, MRI, qPCR and colorimetry, respectively. Oxidative stress (malondialdehyde, MDA), apoptosis (Caspase-3), and neural regeneration (brain-derived neurotrophic factor, BDNF) were detected by using ELISA and western blotting. Neural ultrastructural changes were evaluated by transmission electron microscopy (TEM). The results showed that learning and memory performance of rats decreased when exposure to HAH environment. It was followed by iron accumulation, dysfunctional iron metabolism, reduced BDNF and the upregulation of MDA and Caspase-3. TEM confirmed the ultrastructural changes in neurons and mitochondria. EGCG reduced HAH-induced cognitive impairment, iron deposition, oxidative stress, and apoptosis and promoted neuronal regeneration against chronic HAH-mediated neural injury.

Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient values.

OBJECTIVE: We used radiomics feature-based machine learning classifiers of apparent diffusion coefficient (ADC) maps to differentiate small round cell malignant tumors (SRCMTs) and non-SRCMTs of the nasal and paranasal sinuses. MATERIALS: A total of 267 features were extracted from each region of interest (ROI). Datasets were randomized into two sets, a training set (∼70%) and a test set (∼30%). We performed dimensional reductions using the Pearson correlation coefficient and feature selection analyses (analysis of variance [ANOVA], relief, recursive feature elimination [RFE]) and classifications using 10 machine learning classifiers. Results were evaluated with a leave-one-out cross-validation analysis. RESULTS: We compared the AUC for all the pipelines in the validation dataset using FeAture Explorer (FAE) software. The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUCs with ten features. When the "one-standard error" rule was used, FAE produced a simpler model with eight features, including Perc.01%, Perc.10%, Perc.90%, Perc.99%, S(1,0) SumAverg, S(5,5) AngScMom, S(5,5) Correlat, and WavEnLH_s-2. The AUCs of the training, validation, and test datasets achieved 0.995, 0.902, and 0.710, respectively. For ANOVA, the pipeline with the auto-encoder classifier yielded the highest AUC using only one feature, Perc.10% (training/validation/test datasets: 0.886/0.895/0.809, respectively). For the relief, the AUCs of the training, validation, and test datasets that used the LRLasso classifier using five features (Perc.01%, Perc.10%, S(4,4) Correlat, S(5,0) SumAverg, S(5,0) Contrast) were 0.892, 0.886, and 0.787, respectively. Compared with the RFE and relief, the results of all algorithms of ANOVA feature selection were more stable with the AUC values higher than 0.800. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence with the radiomics from ADC values in the differential diagnosis of SRCMTs and non-SRCMTs and the potential of this non-invasive approach for clinical applications. KEY POINTS: • The parameter with the best diagnostic performance in differentiating SRCMTs from non-SRCMTs was the Perc.10% ADC value. • Results of all the algorithms of ANOVA feature selection were more stable and the AUCs were higher than 0.800, as compared with RFE and relief. • The pipeline using RFE feature selection and Gaussian process classifier yielded the highest AUC.

A novel phase-unwrapping method by using phase-jump detection and local surface fitting: application to Dixon water-fat MRI.

PURPOSE: This study aims to develop an accurate and robust phase-unwrapping method that works effectively under severe noise, rapid-varying phase, and disconnected regions for water-fat Dixon MRI. METHODS: The proposed method first segments the phase map into blocks by automatically detecting phase jumps, and then clusters the pixels near phase jumps into residual pixels. Thereafter, the proposed method sequentially performs intrablock, interblock, and residual-pixel unwrapping using the local surface fitting approach. To address intrablock wraps, the proposed method segments each block into subblocks using the phase partition approach and then performs inter-subblock unwrapping using a block-growing approach. The phase derivative variance is used as the quality criterion to determine the region-growing path of residual pixels. The performance of the proposed method was evaluated on simulation and in vivo Dixon data. RESULTS: The proposed method obtained accurate phase-unwrapping results in the simulation experiment with severe noise, rapid-varying phase, and disconnected regions, and the mean and SD error ratio was 0.26 ± 0.07%. For 505 in vivo knee and ankle images, the total water-fat swap ratio by the proposed method was 1.78%, whereas those by phase region expanding labeler for unwrapping discrete estimates and clustering and local surface fitting were 38.42% and 7.72%, respectively. CONCLUSION: The proposed method achieves accurate and robust performance in phase unwrapping and can benefit phase-related MRI applications such as Dixon water-fat separation.

A novel phase-unwrapping method based on pixel clustering and local surface fitting with application to Dixon water-fat MRI.

PURPOSE: To develop and evaluate a novel 2D phase-unwrapping method that works robustly in the presence of severe noise, rapid phase changes, and disconnected regions. THEORY AND METHODS: The MR phase map usually varies rapidly in regions adjacent to wraps. In contrast, the phasors can vary slowly, especially in regions distant from tissue boundaries. Based on this observation, this paper develops a phase-unwrapping method by using a pixel clustering and local surface fitting (CLOSE) approach to exploit different local variation characteristics between the phase and phasor data. The CLOSE approach classifies pixels into easy-to-unwrap blocks and difficult-to-unwrap residual pixels first, and then sequentially performs intrablock, interblock, and residual-pixel phase unwrapping by a region-growing surface-fitting method. The CLOSE method was evaluated on simulation and in vivo water-fat Dixon data, and was compared with phase region expanding labeler for unwrapping discrete estimates (PRELUDE). RESULTS: In the simulation experiment, the mean error ratio by CLOSE was less than 1.50%, even in areas with signal-to-noise ratio equal to 0.5, phase changes larger than π, and disconnected regions. For 350 in vivo knee and ankle images, the water-fat swap ratio of CLOSE was 4.29%, whereas that of PRELUDE was 25.71%. CONCLUSIONS: The CLOSE approach can correctly unwrap phase with high robustness, and benefit MRI applications that require phase unwrapping. Magn Reson Med 79:515-528, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI.

Objective. The acceleration of magnetic resonance imaging (MRI) acquisition is crucial for both clinical and research applications, particularly in dynamic MRI. Existing compressed sensing (CS) methods, despite being effective for fast imaging, face limitations such as the need for incoherent sampling and residual noise, which restrict their practical use for rapid MRI.Approach. To overcome these challenges, we propose a novel image reconstruction framework that integrates the MRI physical model with a flexible, self-adjusting, decoupling data-driven model. We validated this method through experiments using both simulated andin vivodynamic contrast-enhanced MRI datasets.Main results. The experimental results demonstrate that the proposed framework achieves high spatial and temporal resolution reconstructions. Additionally, when compared to state-of-the-art image reconstruction approaches, our method significantly enhances acceleration capabilities, enabling sparse and rapid imaging with high resolution.Significance. Our proposed framework offers a promising solution for real-time imaging and image-guided radiation therapy applications by providing superior performance in achieving high spatial and temporal resolution reconstructions, thus addressing the limitations of existing CS schemes.

Peak width of skeletonized mean diffusivity as a neuroimaging biomarker in first-episode schizophrenia.

Background and objective: Peak width of skeletonized mean diffusivity (PSMD), a fully automated diffusion tensor imaging (DTI) biomarker of white matter (WM) microstructure damage, has been shown to be associated with cognition in various WM pathologies. However, its application in schizophrenic disease remains unexplored. This study aims to investigate PSMD along with other DTI markers in first-episode schizophrenia patients compared to healthy controls (HCs), and explore the correlations between these metrics and clinical characteristics. Methods: A total of 56 first-episode drug-naive schizophrenia patients and 64 HCs were recruited for this study. Participants underwent structural imaging and DTI, followed by comprehensive clinical assessments, including the Positive and Negative Syndrome Scale (PANSS) for patients and cognitive function tests for all participants. We calculated PSMD, peak width of skeletonized fractional anisotropy (PSFA), axial diffusivity (PSAD), radial diffusivity (PSRD) values, skeletonized average mean diffusivity (MD), average fractional anisotropy (FA), average axial diffusivity (AD), and average radial diffusivity (RD) values as well as structural network global topological parameters, and examined between-group differences in these WM metrics. Furthermore, we investigated associations between abnormal metrics and clinical characteristics. Results: Compared to HCs, patients exhibited significantly increased PSMD values (t = 2.467, p = 0.015), decreased global efficiency (Z = -2.188, p = 0.029), and increased normalized characteristic path length (lambda) (t = 2.270, p = 0.025). No significant differences were observed between the groups in the remaining metrics, including PSFA, PSAD, PSRD, average MD, FA, AD, RD, local efficiency, normalized cluster coefficient, small-worldness, assortativity, modularity, or hierarchy (p > 0.05). After adjusting for relevant variables, both PSMD and lambda values exhibited a significant negative correlation with reasoning and problem-solving scores (PSMD: r = -0.409, p = 0.038; lambda: r = -0.520, p = 0.006). No statistically significant correlations were observed between each PANSS score and the aforementioned metrics in the patient group (p > 0.05). Multivariate linear regression analysis revealed that increased PSMD (ß = -0.426, t = -2.260, p = 0.034) and increased lambda (ß = -0.490, t = -2.994, p = 0.007) were independently associated with decreased reasoning and problem-solving scores respectively ( R a d j 2 = 0.295, F = 2.951, p = 0.029). But these significant correlations did not withstand FDR correction (p_FDR > 0.05). Conclusion: PSMD can be considered as a valuable neuroimaging biomarker that complements conventional diffusion measurements for investigating abnormalities in WM microstructural integrity and cognitive functions in schizophrenia.

Hollow magnetic vortex nanorings loaded with quercetin encapsulated in polydopamine: A high-performance, intelligent nanotheranostic platform for enhanced tumor imaging and dual thermal treatment.

Nanoparticle-mediated thermotherapeutic research strives innovative, multifunctional, efficient, and safe treatments. Our study introduces a novel nanoplatform: the hollow magnetic vortex nanorings within a polydopamine layer (HMVNp), which exhibit dual functionality as magnetic and photothermal agents. Utilizing a "Dual-mode" approach, combining an alternating magnetic field (AMF) with near-infrared (NIR) laser irradiation, HMVNp demonstrated a significant enhancement in heating efficacy (58 ± 8 %, SAR = 1441 vs 1032 W/g) over traditional solid magnetite nanoparticles coated with polydopamine (SMNp). The unique geometry larger surface area to volume ratio facilitates efficient magnetic vortex dynamics and enhanced heat transfer. Addressing the challenge of heat resistant heat shock protein (Hsp) expression, encapsulated quercetin (Q) within HMVNp leverages tumor acidity and dual-mode thermal therapy to enhance release, showing a 28.8 ± 6.81 % increase in Q loading capacity compared to traditional SMNp. Moreover, HMVNp significantly improves contrast for both magnetic resonance imaging (MRI) and photoacoustic imaging (PAI), with an approximately 62 % transverse relaxation (R2 = 81.5 vs 31.6 mM-1s-1 [Fe]). In vivo studies showed that while single treatments slowed tumor growth, dual-mode therapy with quercetin significantly reduced tumors and effectively prevented metastases. Our study highlights the potential of HMVNp/Q as a versatile agent in thermotherapeutic interventions, offering improved diagnostic imaging capabilities.

Functional differentiation of the dorsal striatum: a coordinate-based neuroimaging meta-analysis.

Background: The dorsal striatum, a nucleus in the basal ganglia, plays a key role in the execution of cognitive functions in the human brain. Recent studies have focused on how the dorsal striatum participates in a single cognitive function, whereas the specific roles of the caudate and putamen in performing multiple cognitive functions remain unclear. In this paper we conducted a meta-analysis of the relevant neuroimaging literature to understand the roles of subregions of the dorsal striatum in performing different functions. Methods: PubMed, Web of Science, and BrainMap Functional Database were searched to find original functional magnetic resonance imaging (fMRI) studies conducted on healthy adults under reward, memory, emotion, and decision-making tasks, and relevant screening criteria were formulated. Single task activation, contrast activation, and conjunction activation analyses were performed using the activation likelihood estimation (ALE) method for the coordinate-based meta-analysis to evaluate the differences and linkages. Results: In all, 112 studies were included in this meta-analysis. Analysis revealed that, of the 4 single activation tasks, reward, memory, and emotion tasks all activated the putamen more, whereas decision-making tasks activated the caudate body. Contrast analysis showed that the caudate body played an important role in the 2 cooperative activation tasks, but conjunction activation results found that more peaks appeared in the caudate head. Discussion: Different subregions of the caudate and putamen assume different roles in processing complex cognitive behaviors. Functional division of the dorsal striatum identified specific roles of 15 different subregions, reflecting differences and connections between the different subregions in performing different cognitive behaviors.

New 3D phase-unwrapping method based on voxel clustering and local polynomial modeling: application to quantitative susceptibility mapping.

Background: To develop an accurate and robust 3-dimensional (3D) phase-unwrapping method that works effectively in the presence of severe noise, disconnected regions, rapid phase changes, and open-ended lines for quantitative susceptibility mapping (QSM). Methods: We developed a 3D phase-unwrapping method based on voxel clustering and local polynomial modeling named CLOSE3D, which firstly explores the 26-neighborhood to calculate local variation of the phasor and the phase, and then according to the local variation of the phasor, clusters the phase data into easy-to-unwrap blocks and difficult-to-unwrap residual voxels. Next, CLOSE3D sequentially performs intrablock, interblock, and residual-voxel unwrapping by using the region-growing local polynomial modeling method. CLOSED3D was evaluated in simulation and using in vivo brain QSM data, and was compared with classical region-growing and region-expanding labeling for unwrapping estimates methods. Results: The simulation experiments showed that CLOSE3D achieved accurate phase-unwrapping results with a mean error ratio <0.39%, even in the presence of serious noise, disconnected regions, and rapid phase changes. The error ratios of region-growing (P=0.000 and P=0.000) and region-expanding labeling for unwrapping estimates (P=0.007, P=0.014) methods were both significantly higher than that of CLOSE3D, when the noise level was ≥60%. The results of the in vivo brain QSM experiments showed that CLOSE3D unwrapped the phase data and accurately reconstructed quantitative susceptibility data, even with serious noise, rapid-varying phase, or an open-ended cutline. Conclusions: CLOSE3D achieves phase unwrapping with high accuracy and robustness, which will help phase-related 3D magnetic resonance imaging (MRI) applications such as QSM and susceptibility weighted imaging.

A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping.

Background: Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline. Methods: In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods. Results: Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view. Conclusion: The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications.

Accelerated Brain Aging in Patients With Obsessive-Compulsive Disorder.

Obsessive-compulsive disorder (OCD) may be accompanied by an accelerated structural decline of the brain with age compared to healthy controls (HCs); however, this has yet to be proven. To answer this question, we built a brain age prediction model using mean gray matter volumes of each brain region as features, which were obtained by voxel-based morphometry derived from T1-weighted MRI scans. The prediction model was built using two Chinese Han datasets (dataset 1, N = 106 for HCs and N = 90 for patients with OCD; dataset 2, N = 270 for HCs) to evaluate its performance. Then, a new prediction model was trained using data for HCs in dataset 1 and applied to patients with OCD to investigate the brain aging trajectory. The brain-predicted age difference (brain-PAD) scores, defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with matched HCs in dataset 1. It was demonstrated that the prediction model performs consistently across different datasets. Patients with OCD presented higher brain-PAD scores than matched HCs, suggesting that patients with OCD presented accelerated brain aging. In addition, brain-PAD scores were negatively correlated with the duration of illness, suggesting that brain-PAD scores might capture progressive structural brain changes. These results identified accelerated brain aging in patients with OCD for the first time and deepened our understanding of the pathogenesis of OCD.

Down-regulating circular RNA_0004674 delays the progression of oral squamous cell carcinoma through microRNA-377-3p/THBS1 axis.

OBJECTIVE: Circular RNAs (CircRNAs) are of great significance in oral squamous cell carcinoma (OSCC) cell progression. Insufficiently, the performance of Circ_0004674 has not been specified in the disease, which alighted our desire to unmask its actions in OSCC cell progression with microRNA (miR)-377-3p and thrombospondin-1 (THBS1). METHODS: OSCC expression chip were collected through GEO database and analyzed. The upstream mechanism of THBS1 was predicted through databases. OSCC cancer tissues and normal tissues were resected, in which Circ_0004674, miR-377-3p and THBS1 expression were examined. The relationship of Circ_0004674, miR-377-3p and THBS1 was identified. Circ_0004674- and/or miR-377-3p-related oligonucleotides were transfected into CAL27 cells for detecting cell biological behaviors. Tumors in mice were implanted to monitor the tumor-forming ability of cells. RESULTS: THBS1 showed high expression in the three OSCC chips, and it was enriched in PI3K-AKT signaling pathway. The upstream mechanism of THBS1 predicted that Circ_0004674 regulated THBS1 through miR-377-3p. Circ_0004674 and THBS1 levels were enhanced while miR-377-3p level was reduced in OSCC. Down-regulating Circ_0004674 restricted the growth of CAL27 cells in vivo and in vitro. Restoring miR-377-3p, the target gene of Circ_0004674, destroyed CAL27 cell progression and tumor growth. miR-377-3p suppression rescued the effects of down-regulated Circ_0004674 on OSCC. THBS1 was negatively mediated by miR-377-3p. CONCLUSION: It is clarified that depleting Circ_0004674 mediates miR-377-3p to restrain THBS1, after which OSCC cell progression can be suppressed. It widens the way to control OSCC from a novel perspective.

Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors.

OBJECTIVE: We used texture analysis and machine learning (ML) to classify small round cell malignant tumors (SRCMTs) and Non-SRCMTs of nasal and paranasal sinus on fat-suppressed T2 weighted imaging (Fs-T2WI). MATERIALS: Preoperative MRI scans of 164 patients from 1 January 2018 to 1 January 2021 diagnosed with SRCMTs and Non-SRCMTs were included in this study. A total of 271 features were extracted from each regions of interest. Datasets were randomly divided into two sets, including a training set (∼70%) and a test set (∼30%). The Pearson correlation coefficient (PCC) and principal component analysis (PCA) methods were performed to reduce dimensions, and the Analysis of Variance (ANOVA), Kruskal-Wallis (KW), and Recursive Feature Elimination (RFE) and Relief were performed for feature selections. Classifications were performed using 10 ML classifiers. Results were evaluated using a leave one out cross-validation analysis. RESULTS: We compared the AUC of all pipelines on the validation dataset with FeAture Explorer (FAE) software. The pipeline using a PCC dimension reduction, relief feature selection, and gaussian process (GP) classifier yielded the highest area under the curve (AUC) using 15 features. When the "one-standard error" rule was used, FAE also produced a simpler model with 13 features, including S(5,-5)SumAverg, S(3,0)InvDfMom, Skewness, WavEnHL_s-3, Horzl_GlevNonU, Horzl_RLNonUni, 135dr_GlevNonU, WavEnLL_s-3, Teta4, Teta2, S(5,5)DifVarnc, Perc.01%, and WavEnLH_s-2. The AUCs of the training/validation/test datasets were 1.000/0.965/0.979, and the accuracies, sensitivities, and specificities were 0.890, 0.880, and 0.920, respectively. The best algorithm was GP whose AUCs of the training/validation/test datasets by the two-dimensional reduction methods and four feature selection methods were greater than approximately 0.800. Especially, the AUCs of different datasets were greater than approximately 0.900 using the PCC, RFE/Relief, and GP algorithms. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence and the radiomics from Fs-T2WI to differentially diagnose SRCMTs and Non-SRCMTs. This non-invasive approach could be very promising in clinical oncology.

Brain iron assessment in patients with First-episode schizophrenia using quantitative susceptibility mapping.

PURPOSE: Decreased serum ferritin level was recently found in schizophrenia. Whether the brain iron concentration in schizophrenia exists abnormality is of research significance. Quantitative susceptibility mapping (QSM) was used in this study to assess brain iron changes in the grey matter nuclei of patients with first-episode schizophrenia. METHODS: The local ethics committee approved the study, and all subjects gave written informed consent. Thirty patients with first-episode schizophrenia and 30 age and gender-matched healthy controls were included in this study. QSM and effective transverse relaxation rate (R2*) maps were reconstructed from a three-dimensional multi-echo gradient-echo sequence. The inter-group differences of regional QSM values, R2* values and volumes were calculated in the grey matter nuclei, including bilateral caudate nucleus, putamen, globus pallidus, substantia nigra, red nucleus, and thalamus. The diagnostic performance of QSM and R2* was evaluated using receiver operating characteristic curve. The correlations between regional iron variations and clinical PANSS (Positive and Negative Syndrome Scale) scores were assessed using partial correlation analysis. RESULTS: Compared to healthy controls, patients with first-episode schizophrenia had significantly decreased QSM values (less paramagnetic) in the bilateral substantia nigra, left red nucleus and left thalamus (p < 0.05, FDR correction). QSM proved more sensitive than R2* regarding inter-group differences. The highest diagnostic performance for first-episode schizophrenia was observed in QSM value of the left substantia nigra (area under the curve, AUC = 0.718, p = 0.004). Regional volumes of bilateral putamen and bilateral substantia nigra were increased (p < 0.05, FDR correction) in first-episode schizophrenia. However, both QSM and R2* values did not show significant correlations with PANSS scores (p > 0.05). CONCLUSION: This study reveals decreased iron concentration in grey matter nuclei of patients with first-episode schizophrenia. QSM provides superior sensitivity over R2* in the evaluation of schizophrenia-related brain iron changes. It demonstrated that QSM may be a potential biomarker for further understanding the pathophysiological mechanism of first-episode schizophrenia.

[A two-point Dixon technique for water-fat separation using multiresolution and region-growing algorithm].

OBJECTIVE: An improved water-fat separation method based on region-growing was proposed for use in regions with low signal-noise ratio (SNR). METHODS: Region-growing method was applied to 4 sub-images acquired by a down- sampling operation on the acquired phasor maps. The spatial smoothing constraint was exploited to calculate 4 error phasor maps to construct the final smooth error phasor map, which was used in two-point Dixon technique for water-fat separation. RESULTS: The simulation experiment showed that the proposed method produced smaller errors, and for clinical images of the knees, abdomen and lower limbs, the proposed method achieved accurate water-fat separations. CONCLUSION: The proposed method is more robust and reliable than the original global region-growing algorithm, and serves as a promising water-fat separation method for clinical applications.