OBJECTIVES: To construct a bone tumor classification model based on feature decoupling and fusion for processing modality loss and fusing multimodal information to improve classification accuracy. METHODS: A decoupling completion module was designed to extract local and global bone tumor image features from available modalities. These features were then decomposed into shared and modality-specific features, which were used to complete the missing modality features, thereby reducing completion bias caused by modality differences. To address the challenge of modality differences that hinder multimodal information fusion, a cross-attention-based fusion module was introduced to enhance the model's ability to learn cross-modal information and fully integrate specific features, thereby improving the accuracy of bone tumor classification. RESULTS: The experiment was conducted using a bone tumor dataset collected from the Third Affiliated Hospital of Southern Medical University for training and testing. Among the 7 available modality combinations, the proposed method achieved an average AUC, accuracy, and specificity of 0.766, 0.621, and 0.793, respectively, which represent improvements of 2.6%, 3.5%, and 1.7% over existing methods for handling missing modalities. The best performance was observed when all the modalities were available, resulting in an AUC of 0.837, which still reached 0.826 even with MRI alone. CONCLUSIONS The proposed method can effectively handle missing modalities and successfully integrate multimodal information, and show robust performance in bone tumor classification under various complex missing modality scenarios.
Humans ; Bone Neoplasms/diagnosis* ; Multimodal Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Image Processing, Computer-Assisted/methods* ; Algorithms

OBJECTIVES: To propose a single repetition time (TR) quantitative magnetic susceptibility imaging method for the lumbar spine using bipolar readout gradient, and compare the quantitative magnetic susceptibility measurement using single TR and dual TR methods for the lumbar spine with different bone densities. METHODS: A translation correction method was proposed to correct spatial misalignment along the frequency encoding direction between positive and negative gradient readout images, and the phase difference between the images was eliminated using a phase correction method. The data of lumbar vertebrae L1-L5 were collected using single TR and dual TR methods from 6 normal individuals, 2 patients with osteopenia, and 2 patients with osteoporosis. The magnetic susceptibility map was reconstructed, the quantitative results of single TR before and after correction were compared with those of the dual TR method. RESULTS: The linear regression result of the lumbar spine magnetic susceptibility values obtained by the single TR method before calibration and the dual TR method is Y=0.64*X-11.61. The linear regression result of the lumbar spine magnetic susceptibility values corrected by the single TR method and the dual TR method is Y=1.03*X+0.25. The results of the corrected single TR method were highly consistent with those of the dual TR method, and the calibrated single TR method could effectively distinguish osteopenia and osteoporosis patients from normal individuals. CONCLUSIONS The calibrated single TR bipolar readout gradient method can generate artifact-free lumbar spine quantitative magnetic susceptibility distribution maps and reduce data acquisition time by 50%.
Humans ; Lumbar Vertebrae/pathology* ; Magnetic Resonance Imaging/methods* ; Female ; Middle Aged ; Male ; Osteoporosis/diagnosis* ; Adult ; Bone Density ; Aged ; Bone Diseases, Metabolic/diagnosis*

OBJECTIVES: To qualitatively assess the diagnostic performance of dynamic contrast enhancement (DCE), diffusion-weighted imaging (DWI), and T2-weighted imaging (T2WI), alone or in combination, in the evaluation of breast cancer. METHODS: We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging (MRI). The morphological characteristics of breast lesions were evaluated using DCE, DWI, and T2WI based on BI-RADS lexicon descriptors by trained radiologists. Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions, and the differences between benign and malignant lesions in each group were compared. Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis. Diagnostic efficacies were compared using the area under the receiver operating characteristic curve (AUC) and DeLong test. RESULTS: For mass-like lesions, all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE, DWI, and T2WI (P<0.05). The combined method (DCE+DWI+T2WI) had a higher AUC (0.865) than any of the individual modality (DCE: 0.786; DWI: 0.793; T2WI: 0.809) (P<0.05). For non-mass-like lesions, DWI signal intensity was a significant predictor of malignancy (P=0.036), but the model using DWI alone had a low AUC (0.669). CONCLUSIONS Morphological assessment using the combination of DCE, DWI, and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.
Humans ; Female ; Breast Neoplasms/pathology* ; Retrospective Studies ; Middle Aged ; Adult ; Diffusion Magnetic Resonance Imaging/methods* ; Aged ; Magnetic Resonance Imaging/methods* ; Young Adult ; Aged, 80 and over

OBJECTIVES: To evaluate the effect of artificial intelligence-assisted compressed sensing (ACS) acceleration on MRI radiomic feature extraction and performance of diagnostic staging models for nasopharyngeal carcinoma (NPC) in comparison with conventional parallel imaging (PI). METHODS: A total of 64 patients with newly diagnosed NPC underwent 3.0T MRI using axial T1-weighted (T1W), T2-weighted (T2W), and contrast-enhanced T1-weighted (CE-T1W) sequences. Both PI and ACS protocols were performed using identical imaging parameters. The total scan time for the 3 sequences in ACS group was 227 s, representing a 30% reduction from 312 s in the PI group. Eighteen first-order and 75 texture features were extracted using Pyradiomics. Intraclass correlation coefficients (ICCs) were calculated to assess the agreement between the two acceleration methods. After feature selection using the least absolute shrinkage and selection operator (LASSO), random forest regression models were constructed to distinguish early-stage (T1 and T2) from advanced-stage (T3 and T4) NPC. The diagnostic performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. RESULTS: ACS-accelerated images demonstrated good radiomic reproducibility, with 86.0% (240/279) of features showing good agreement (ICC>0.75), with mean ICCs for T1W, T2W and CE-T1W sequences of 0.91±0.09, 0.89±0.13 and 0.88±0.11, respectively. The staging prediction models achieved similar AUCs for ACS and PI (0.89 vs 0.90, P=0.991). CONCLUSIONS The MRI radiomic features extracted using ACS and PI techniques are highly consistent, and the ACS-based model shows comparable diagnostic performance to the PI-based model, but ACS significantly reduces the scan time and provides an efficient and reliable acceleration strategy for radiomics in NPC.
Humans ; Nasopharyngeal Neoplasms/diagnosis* ; Magnetic Resonance Imaging/methods* ; Nasopharyngeal Carcinoma ; Neoplasm Staging ; Artificial Intelligence ; Carcinoma ; Female ; Male ; Middle Aged ; Adult ; Radiomics

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.
Humans ; Cerebral Cortex/anatomy & histology* ; Male ; Female ; Magnetic Resonance Imaging ; Adult ; Connectome/methods* ; Young Adult ; Nerve Net/anatomy & histology* ; Neural Pathways ; Depressive Disorder, Major/diagnostic imaging*

Mild repetitive head injury is a serious health problem with long-term negative consequences. Changes in brain neurobiology were assessed with MRI in a model of head injury designed to reflect the human experience. Rats were maintained on a reverse light-dark cycle and head impacted daily at 24 h intervals over three days while fully awake under red light illumination. There was no neuroradiological evidence of brain damage. Rats were imaged for changes in blood brain barrier permeability, edema and gray matter microarchitecture, and resting state functional connectivity. Data were registered to a 3D MRI rat atlas with 173 segmented brain areas providing site-specific information on each imaging modality. Changes in BBB permeability were minimal and localized to the hippocampus and cerebellum. There was evidence of cytotoxic edema in the basal ganglia, thalamus, and cerebellum. There was a global decrease in connectivity and an increase in gliosis in the thalamus, cerebellum, and hippocampus. This study shows a sequelae of neuropathology caused by mild repetitive head injury that is commonly observed in clinical practice using MRI in patients. As such, it may serve as a model for testing the efficacy of new therapeutics using any or all of the measures as biomarkers to assess drug efficacy.
Animals ; Magnetic Resonance Imaging/methods* ; Disease Models, Animal ; Brain/physiopathology* ; Male ; Rats ; Rats, Sprague-Dawley ; Blood-Brain Barrier/diagnostic imaging* ; Multimodal Imaging ; Wakefulness/physiology* ; Craniocerebral Trauma/physiopathology*

Image-guided thermal ablation (IGTA) has been widely used in the treatment of liver tumors. MRI-guided thermal ablation of liver tumors offers several advantages, including the absence of ionizing radiation; excellent soft-tissue contrast; multi-parametric and multiplanar imaging; non-invasive, real-time temperature monitoring of the thermal field during the procedure; and accurate post-procedural assessment of therapeutic efficacy. To standardize and promote MRI-guided thermal ablation for liver tumors in China, advance the technological development of IGTA for tumor treatment, and enhance the efficacy of "precision ablation" for hepatic tumors, this expert consensus (2025 edition) was collaboratively developed by national experts from multiple societies and committees through multidisciplinary discussions. The contributing groups included the Society of Tumor Ablation Therapy of the Chinese Anti-Cancer Association, the Ablation Expert Committee of the Chinese Society of Clinical Oncology (CSCO), the Tumor Ablation Subgroup of the National Health Commission Comprehensive Interventional Therapy Quality Control Center, the Expert Group on Tumor Ablation Therapy of the Chinese Medical Doctors Association, and the Tumor Ablation Committee of the Chinese College of Interventionalists. The main contents of the consensus include: (1) indications, contraindications, and characteristics of MRI-guided thermal ablation for liver tumors; (2) MRI magnets, guidance sequences, and MRI-compatible thermal ablation equipment and instruments; and (3) protocols for MRI-guided thermal ablation of liver tumors, efficacy assessment, and the prevention and management of complications.
Humans ; Liver Neoplasms/surgery* ; Magnetic Resonance Imaging ; Consensus ; China ; Catheter Ablation/methods* ; Ablation Techniques/methods* ; Surgery, Computer-Assisted

Objective To compare the diagnostic value of transabdominal intestinal ultrasound (IUS) and magnetic resonance enterography (MRE) for intestinal stenosis in inflammatory bowel disease (IBD). Methods A retrospective analysis was conducted on the imaging features of 51 IBD patients who underwent both IUS and MRE at Beijing Friendship Hospital,Capital Medical University,between January 2021 and February 2025.With endoscopy as the gold standard,the diagnostic performance of the two methods was compared. Results The sensitivity (84.2% vs. 52.6%,P=0.008) and accuracy (66.7% vs. 45.1%,P=0.035) of IUS for stenosis were higher than those of MRE.In the localization of stenosis,IUS demonstrated higher sensitivity than MRE for detecting stenosis in the terminal ileum (78.6% vs. 35.7%,P=0.070) and colorectum (86.7% vs. 53.3%,P=0.060).Furthermore,IUS showed higher diagnostic accuracy than MRE for terminal ileum stenosis (70.6% vs. 29.4%,P=0.039).The intestinal wall thickness[(8.2±2.7) mm vs. (10.3±3.8) mm;t=3.20,P=0.002)] and stenosis inner diameter[(3.0±1.6) mm vs. (4.3±1.8) mm;t=2.15,P=0.035] measured by IUS were lower than those measured by MRE,with a moderate level of consistency (ICC:0.19-0.53).In addition,IUS demonstrated a higher detection rate for mesenteric fat hypertrophy (70.6% vs. 27.5%,Kappa=0.27,P=0.005),whereas MRE was more sensitive in detecting lymphadenopathy (90.2% vs. 56.9%,Kappa=0.16,P=0.080). Conclusions IUS is superior to MRE in the diagnosis and localization sensitivity for intestinal stenosis in IBD.However,the two methods showcase poor consistency in detecting and quantitating some inflammatory signs.IUS can be used as a first-line screening method for diagnosing intestinal stenosis in IBD patients,while its clinical application should be combined with specific needs to optimize diagnosis.
Humans ; Retrospective Studies ; Constriction, Pathologic/diagnostic imaging* ; Ultrasonography/methods* ; Magnetic Resonance Imaging/methods* ; Inflammatory Bowel Diseases/diagnostic imaging* ; Male ; Female ; Adult ; Middle Aged ; Intestines/diagnostic imaging* ; Sensitivity and Specificity

INTRODUCTION: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc., Seoul, Republic of Korea) segmentation software in a Southeast Asian community-based cohort with normal cognition, mild cognitive impairment (MCI) and dementia. METHOD: Study participants belonged to the community-based Biomarker and Cognition Study in Singapore. Participants aged between 30 and 95 years, having cognitive concerns, with no diagnosis of major psychiatric, neurological or systemic disorders who were recruited consecutively between April 2022 and July 2023 were included. Participants underwent neuropsychological assessments and structural MRI, and were classified as cognitively normal, with MCI or with dementia. MRI pre-processing using automated pipelines, along with human-based visual ratings, were compared against AI-based automated AQUA output. Default mode network grey matter (GM) volumes were compared between cognitively normal, MCI and dementia groups. RESULTS: A total of 90 participants (mean age at visit was 63.32±10.96 years) were included in the study (30 cognitively normal, 40 MCI and 20 dementia). Non-parametric Spearman correlation analysis indicated that AQUA-based and human-based visual ratings were correlated with total (ρ=0.66; P<0.0001), periventricular (ρ=0.50; P<0.0001) and deep (ρ=0.57; P<0.0001) white matter hyperintensities (WMH). Additionally, volumetric WMH obtained from AQUA and automated pipelines was also strongly correlated (ρ=0.84; P<0.0001) and these correlations remained after controlling for age at visit, sex and diagnosis. Linear regression analyses illustrated significantly different AQUA-derived default mode network GM volumes between cognitively normal, MCI and dementia groups. Dementia participants had significant atrophy in the posterior cingulate cortex compared to cognitively normal participants (P=0.021; 95% confidence interval [CI] -1.25 to -0.08) and in the hippocampus compared to cognitively normal (P=0.0049; 95% CI -1.05 to -0.16) and MCI participants (P=0.0036; 95% CI -1.02 to -0.17). CONCLUSION Our findings demonstrate high concordance between human-based visual ratings and AQUA-based ratings of WMH. Additionally, the AQUA GM segmentation pipeline showed good differentiation in key regions between cognitively normal, MCI and dementia participants. Based on these findings, the automated AQUA software could aid clinicians in examining MRI scans of patients with cognitive impairment.
Humans ; Cognitive Dysfunction/pathology* ; Magnetic Resonance Imaging/methods* ; Male ; Middle Aged ; Female ; Aged ; Artificial Intelligence ; Software ; Dementia/diagnostic imaging* ; Aged, 80 and over ; Adult ; Singapore ; Neuropsychological Tests ; Brain/pathology* ; Cohort Studies ; Gray Matter/pathology* ; Southeast Asian People

BACKGROUND: Iron deposition plays a crucial role in the pathophysiology of Parkinson's disease (PD), yet the distribution pattern of iron deposition in the subcortical nuclei has been inconsistent across previous studies. We aimed to assess the difference patterns of iron deposition detected by quantitative iron-sensitive magnetic resonance imaging (MRI) between patients with PD and patients with atypical parkinsonian syndromes (APSs), and between patients with PD and healthy controls (HCs). METHODS: A systematic literature search was conducted on PubMed, Embase, and Web of Science databases to identify studies investigating the iron content in PD patients using the iron-sensitive MRI techniques (R2 * and quantitative susceptibility mapping [QSM]), up until May 1, 2023. The quality assessment of case-control and cohort studies was performed using the Newcastle-Ottawa Scale, whereas diagnostic studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Standardized mean differences and summary estimates of sensitivity, specificity, and area under the curve (AUC) were calculated for iron content, using a random effects model. We also conducted the subgroup-analysis based on the MRI sequence and meta-regression. RESULTS: Seventy-seven studies with 3192 PD, 209 multiple system atrophy (MSA), 174 progressive supranuclear palsy (PSP), and 2447 HCs were included. Elevated iron content in substantia nigra (SN) pars reticulata ( P <0.001) and compacta ( P <0.001), SN ( P <0.001), red nucleus (RN, P <0.001), globus pallidus ( P <0.001), putamen (PUT, P = 0.021), and thalamus ( P = 0.029) were found in PD patients compared with HCs. PD patients showed lower iron content in PUT ( P <0.001), RN ( P = 0.003), SN ( P = 0.017), and caudate nucleus ( P = 0.017) than MSA patients, and lower iron content in RN ( P = 0.001), PUT ( P <0.001), globus pallidus ( P = 0.004), SN ( P = 0.015), and caudate nucleus ( P = 0.001) than PSP patients. The highest diagnostic accuracy distinguishing PD from HCs was observed in SN (AUC: 0.85), and that distinguishing PD from MSA was found in PUT (AUC: 0.90). In addition, the best diagnostic performance was achieved in the RN for distinguishing PD from PSP (AUC: 0.86). CONCLUSIONS: Quantitative iron-sensitive MRI could quantitatively detect the iron content of subcortical nuclei in PD and APSs, while it may be insufficient to accurately diagnose PD. Future studies are needed to explore the role of multimodal MRI in the diagnosis of PD. REGISTRISION PROSPERO (CRD42022344413).
Humans ; Parkinson Disease/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Iron/metabolism*