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.

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.

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.

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.

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.

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.

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.