Multimodal Magnetic Resonance Research Articles

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

BackgroundNeuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response.MethodsSixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline.ResultsWe found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54).ConclusionOur findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response.Relevance statementDynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease.Key points• rTMS at the left angular gyrus could induce cognitive improvement.• rTMS could suppress primary network connections to compensate for higher-order networks.• Dynamic reorganization of brain networks could predict individual treatment response to rTMS.Graphical

Assessment of white matter hyperintensity severity using multimodal magnetic resonance imaging.

White matter hyperintensities are radiological abnormalities reflecting cerebrovascular dysfunction detectable using MRI. White matter hyperintensities are often present in individuals at the later stages of the lifespan and in prodromal stages in the Alzheimer's disease spectrum. Tissue alterations underlying white matter hyperintensities may include demyelination, inflammation and oedema, but these are highly variable by neuroanatomical location and between individuals. There is a crucial need to characterize these white matter hyperintensity tissue alterations in vivo to improve prognosis and, potentially, treatment outcomes. How different MRI measure(s) of tissue microstructure capture clinically-relevant white matter hyperintensity tissue damage is currently unknown. Here, we compared six MRI signal measures sampled within white matter hyperintensities and their associations with multiple clinically-relevant outcomes, consisting of global and cortical brain morphometry, cognitive function, diagnostic and demographic differences and cardiovascular risk factors. We used cross-sectional data from 118 participants: healthy controls (n = 30), individuals at high risk for Alzheimer's disease due to familial history (n = 47), mild cognitive impairment (n = 32) and clinical Alzheimer's disease dementia (n = 9). We sampled the median signal within white matter hyperintensities on weighted MRI images [T1-weighted (T1w), T2-weighted (T2w), T1w/T2w ratio, fluid-attenuated inversion recovery (FLAIR)] as well as the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. qT2* and fluid-attenuated inversion recovery signals within white matter hyperintensities displayed different age- and disease-related trends compared to normal-appearing white matter signals, suggesting sensitivity to white matter hyperintensity-specific tissue deterioration. Further, white matter hyperintensity qT2*, particularly in periventricular and occipital white matter regions, was consistently associated with all types of clinically-relevant outcomes in both univariate and multivariate analyses and across two parcellation schemes. qT1 and fluid-attenuated inversion recovery measures showed consistent clinical relationships in multivariate but not univariate analyses, while T1w, T2w and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to white matter hyperintensities. Our results suggest that combining volumetric and signal measures of white matter hyperintensity should be considered to fully characterize the severity of white matter hyperintensities in vivo. These findings may have implications in determining the reversibility of white matter hyperintensities and the potential efficacy of cardio- and cerebrovascular treatments.

The outstanding diagnostic value of DKI in multimodal magnetic resonance imaging for benign and malignant breast tumors: A diagnostic accuracy study.

To explore the value of applying different magnetic resonance imaging MRI sequences in the differential diagnosis of benign and malignant breast tumors. Routine breast magnetic resonance scans (T1-weighted image, T1WI; T2-weighted image, T2WI), dynamically enhanced scans, diffusion-weighted Imaging, and diffusion kurtosis imaging (DKI) scans were performed on 63 female patients with breast-occupying lesions. The benign and malignant lesions were confirmed by biopsy, excision-histopathology reports. There are 70 lesions, of which 46 are benign and 24 are malignant. Analyze the primary conditions, such as the shape, size, and boundary of the lesion, and determine the apparent diffusion coefficient (ADC), mean kurtosis (MK), and mean diffusion (MD) values. The receiver operating characteristic curve was used to evaluate the value and difference in differentiating benign and malignant lesions. In this study, the results of the 2 testers both showed that the MK of malignant lesions was significantly higher than that of benign lesions (P < .001), and the MD of benign lesions was higher than that of malignant lesions (P < .05). The ADC of benign lesions was higher than that of malignant lesions (P < .05). For MK, the area under the curve of the 2 testers was 0.855/0.869, respectively. When the best cutoff value of MK for tester 1 was 0.515, the sensitivity and specificity of MK for diagnosing malignant tumors were 83.3%/87.0%, respectively. For the 2 testers MD, and ADC, the area under the curve was < 0.5, and the diagnostic value was low. The MK value obtained by DKI has a specific value in the differential diagnosis of benign and malignant breast lesions. DKI is helpful in the identification of benign and malignant breast tumors. The diagnostic value is outstanding, and its importance to the changes in the microstructure of the organization needs to be further explored.

An integrated neuroimaging-omics approach for the gut-brain communication pathways in Alzheimer's disease.

A key role of the gut microbiota in the pathogenesis of neurodegenerative diseases, such as Alzheimer's disease (AD), has been identified over the past decades. Increasing clinical and preclinical evidence implicates that there is bidirectional communication between the gut microbiota and the central nervous system (CNS), which is also known as the microbiota-gut-brain axis. Nevertheless, current knowledge on the interplay between gut microbiota and the brain remains largely unclear. One of the primary mediating factors by which the gut microbiota interacts with the host is peripheral metabolites, including blood or gut-derived metabolites. However, mechanistic knowledge about the effect of the microbiome and metabolome signaling on the brain is limited. Neuroimaging techniques, such as multi-modal magnetic resonance imaging (MRI), and fluorodeoxyglucose-positron emission tomography (FDG-PET), have the potential to directly elucidate brain structural and functional changes corresponding with alterations of the gut microbiota and peripheral metabolites in vivo. Employing a combination of gut microbiota, metabolome, and advanced neuroimaging techniques provides a future perspective in illustrating the microbiota-gut-brain pathway and further unveiling potential therapeutic targets for AD treatments.

Multimodal Transformer for Accelerated MR Imaging.

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets.

Multimodal magnetic resonance imaging in the diagnosis of cervical cancer and its correlation with the differentiation process of cervical cancer

PurposeThis study seeks to evaluate the value of MRI (Magnetic resonance imaging) diffusion weighted images (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in the diagnosis of cervical carcinoma.MethodsSeventy-nine cases of cervical cancer (CC group) (39 cases of squamous carcinoma (SCC group) and 40 cases of adenocarcinoma (ACC group)) and 30 cases of healthy controls (HC group) were included in this study. All the subjects were informed of the purpose of this study. The study was approved by the Ethics Committee of Beihua University Hospital, Jinlin, China. In this study, images were acquired based on a 3T MR scanner (Ingenia; Philips, Best, the Netherlands) and measured the imaging parameters by DWI, IVIM and DKI techniques. The parameters were obtained by Philips post-processing workstation, DKE and IVIM. These ROIs (region of interest) were manually drawn on each parameter mapping image by MRI physicians. Finally, SPSS 23.0 statistical software was used for data analysis.ResultsThe ADC (apparent diffusion coefficient) value of M group was lower than that of N group, and the difference was statistically significant (P < 0.05). The D (true diffusion coefficient) value, D*(pseudo diffusion coefficient) value, f (perfusion fraction) value, MD (mean diffusivity) value, and ADC value in the SCC group were lower than those in the ACC group with statistically significant differences (P < 0.05). The MK (mean kurtosis) value was higher than that of the ACC group, and the difference was statistically significant (P < 0.05). Compared with the HC group, the ADC values, D values, MD values of group CC group were lower, and the D* values, f values, MK values were higher; all the parameters were statistically significant (P < 0.05). The higher the differentiation degree of cervical cancer, the higher ADC values, D values, MD values, and the smaller D* values, f values, MK values. The difference of ADC values, D values and MK values was statistically significant (P < 0.05). MK value had the best diagnostic efficiency in the differential diagnosis of cervical cancer with low and medium differentiation, high and low differentiation (P < 0.05). There was no significant difference in the f value between high and low differentiation cervical cancer (P > 0.05). There was no significant difference in the MD value between low and high differentiation cervical cancer (P > 0.05). The strongest correlation between MK values (r = 0.796) and the degree of pathological differentiation of cervical cancer is positively correlated. The D values, MD values, and ADC values are negatively correlated with the degree of pathological differentiation of cervical cancer.ConclusionThe ADC value of DWI parameters has important diagnostic value for different menstrual states of cervical cancer. The parameter values of DWI, IVIM, and DKI can be used to differentiate cervical cancer from normal cervical tissue, and thus have important diagnostic value for differentiating pathological types of cervical cancer. This means that these parameter values may have great significance in the differential diagnosis of cervical cancer with different degrees of pathological differentiation. The pathological differentiation degree of cervical cancer is significantly positively correlated with the MK value in the parameter values of DWI, IVIM, and DKI, while negatively correlated with the D value, MD value, and ADC value.

Neurovascular coupling dysfunction in high myopia patients: Evidence from a multi-modal magnetic resonance imaging analysis

Background and purposeTo investigate neurovascular coupling dysfunction in high myopia (HM) patients. Materials and methodsA total of 37 HM patients and 36 healthy controls were included in this study. Degree centrality (DC), regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) maps were employed to represent neuronal activity. Cerebral blood perfusion was characterized by cerebral blood flow (CBF). The correlation coefficient was calculated to reflect the relationship between neuronal activity and cerebral blood perfusion. Pearson partial correlation analysis was utilized to evaluate the association between HM dysfunction and clinical indicators. ResultsHM patients exhibited significant alterations in neurovascular coupling across 37 brain regions compared to healthy controls. The brain regions with marked changes varied among the four neurovascular coupling patterns, including the middle frontal gyrus, superior occipital gyrus, middle occipital gyrus, and fusiform gyrus. Additionally, the superior frontal gyrus orbital part, medial superior frontal gyrus, inferior occipital gyrus, and dorsolateral superior frontal gyrus displayed significant changes in three coupling patterns. In HM patients, the ReHo-CBF changes in the inferior frontal gyrus orbital part were positively correlated with best-corrected visual acuity (BCVA) and refractive diopter changes. Similarly, the ALFF-CBF changes in the inferior frontal gyrus orbital part showed a positive correlation with refractive diopter changes. ReHo-CBF and ALFF-CBF alterations in the paracentral lobule were positively correlated with BCVA and refractive diopter changes. ConclusionOur findings underscore the abnormal alterations in neurovascular coupling across multiple brain regions in HM patients. These results suggest that neurovascular dysfunction in HM patients may be associated with an aberrant visual regulation mechanism.

Predicting acute ischemic stroke using the revised Framingham stroke risk profile and multimodal magnetic resonance imaging.

Patients with transient ischemic attacks (TIA) have a significant risk of developing acute ischemic strokes (AIS), emphasizing the critical need for hierarchical management. This study aims to develop a clinical-imaging model utilizing multimodal magnetic resonance imaging (mMRI) and the revised Framingham Stroke Risk Profile (FSRP) to predict AIS and achieve early secondary prevention. mMRI scans were conducted on patients with symptomatic intracranial atherosclerotic disease (ICAD) to assess vascular wall features and cerebral perfusion parameters. Based on diffusion-weighted imaging (DWI), patients were divided into two groups: TIA and AIS. Clinical data were evaluated to calculate the FSRP score. Differences in clinical and imaging characteristics between the groups were analyzed, and a predictive model for AIS probability in patients with ICAD was established. A total of 112 TIA and AIS patients were included in the study. The results showed that the AIS group had higher proportions of FSRP-high risk, hyperhomocysteinemia, and higher value of low-density lipoprotein (LDL), standardized plaque index (SQI), and enhancement rate (ER) compared to the TIA group (p < 0.05). Mean transit time (MTT) and time to peak (TTP) in the lesion area were significantly longer in the AIS group (p < 0.05). Multivariate analysis identified FSRP-high risk (p = 0.027) and high ER (p = 0.046) as independent risk factors for AIS. The combined clinical and mMRI model produced an area under the curve (AUC) of 0.791 in receiver operating characteristic (ROC) analysis. The constructed nomogram model combining clinical and mMRI features demonstrated favorable clinical net benefits. FSRP-high risk and high ER were confirmed as independent risk factors for AIS. The combined prediction model utilizing clinical and imaging markers effectively predicts stroke risk in symptomatic ICAD patients.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Preoperative serum cortisone levels are associated with cognition in preschool-aged children with tetralogy of Fallot after corrective surgery: new evidence from human populations and mice

BackgroundTetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease. Children with TOF would be confronted with neurological impairment across their lifetime. Our study aimed to identify the risk factors for cerebral morphology changes and cognition in postoperative preschool-aged children with TOF.MethodsWe used mass spectrometry (MS) technology to assess the levels of serum metabolites, Wechsler preschool and primary scale of intelligence-Fourth edition (WPPSI-IV) index scores to evaluate neurodevelopmental levels and multimodal magnetic resonance imaging (MRI) to detect cortical morphological changes.ResultsMultiple linear regression showed that preoperative levels of serum cortisone were positively correlated with the gyrification index of the left inferior parietal gyrus in children with TOF and negatively related to their lower visual spaces index and nonverbal index. Meanwhile, preoperative SpO2 was negatively correlated with levels of serum cortisone after adjusting for all covariates. Furthermore, after intervening levels of cortisone in chronic hypoxic model mice, total brain volumes were reduced at both postnatal (P) 11.5 and P30 days.ConclusionsOur results suggest that preoperative serum cortisone levels could be used as a biomarker of neurodevelopmental impairment in children with TOF. Our study findings emphasized that preoperative levels of cortisone could influence cerebral development and cognition abilities in children with TOF.

Vascular Disease Is Associated With Differences in Brain Structure and Lower Cognitive Functioning in Inflammatory Bowel Disease: A Cross-Sectional Study.

Vascular disease and cognitive impairment have been increasingly documented in inflammatory bowel disease (IBD), and both have been individually correlated with changes in brain structure. This study aimed to determine if both macro- and microstructural brain changes are prevalent in IBD and whether alterations in brain structure mediate the relationship between vascular disease and cognitive functioning. Eighty-four IBD participants underwent multimodal magnetic resonance imaging. Volumetric and mean diffusivity measures of the thalamus, hippocampus, normal-appearing white matter, and white matter lesions were converted to age- and sex-adjusted z scores. Vascular comorbidity was assessed using a modified Framingham Risk Score and cognition was assessed using a battery of neuropsychological tests. Test scores were standardized using local regression-based norms. We generated summary statistics for the magnetic resonance imaging metrics and cognitive tests, and these were examined using canonical correlation analysis and linear regression modeling. Greater vascular comorbidity was negatively correlated with thalamic, normal-appearing white matter, and white matter lesion volumes. Higher Framingham Risk Score were also correlated with lower processing speed, learning and memory, and verbal fluency. Increased vascular comorbidity was predictive of poorer cognitive functioning, and this effect was almost entirely mediated (94.76%) by differences in brain structure. Vascular comorbidity is associated with deleterious effects on brain structure and lower cognitive functioning in IBD. These findings suggest that proper identification and treatment of vascular disease is essential to the overall management of IBD, and that certain brain areas may serve as critical targets for predicting the response to therapeutic interventions.

Deep wavelet scattering orthogonal fusion network for glioma IDH mutation status prediction

Accurately predicting the isocitrate dehydrogenase (IDH) mutation status of gliomas is greatly significant for formulating appropriate treatment plans and evaluating the prognoses of gliomas. Although existing studies can accurately predict the IDH mutation status of gliomas based on multimodal magnetic resonance (MR) images and machine learning methods, most of these methods cannot fully explore multimodal information and effectively predict IDH status for datasets acquired from multiple centers. To address this issue, a novel wavelet scattering (WS)-based orthogonal fusion network (WSOFNet) was proposed in this work to predict the IDH mutation status of gliomas from multiple centers. First, transformation-invariant features were extracted from multimodal MR images with a WS network, and then the multimodal WS features were used instead of the original images as the inputs of WSOFNet and were fully fused through an adaptive multimodal feature fusion module (AMF2M) and an orthogonal projection module (OPM). Finally, the fused features were input into a fully connected classifier to predict IDH mutation status. In addition, to achieve improved prediction accuracy, four auxiliary losses were also used in the feature extraction modules. The comparison results showed that the prediction area under the curve (AUC) of WSOFNet on a single-center dataset was 0.9966 and that on a multicenter dataset was approximately 0.9655, which was at least 3.9% higher than that of state-of-the-art methods. Moreover, the ablation experimental results also proved that the adaptive multimodal feature fusion strategy based on orthogonal projection could effectively improve the prediction performance of the model, especially for an external validation dataset.

Multimodal magnetic resonance imaging on brain structure and function changes in subjective cognitive decline: a mini-review.

Subjective cognitive decline (SCD) is the initial stage of Alzheimer's disease (AD). Early identification of SCD and its risk factors is of great importance for targeted interventions and for delaying the onset of AD. We reviewed the relevant literature on structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and other techniques regarding SCD research in recent years. This study applied sMRI and fMRI techniques to explore abnormal brain structures and functions, which may help provide a basis for SCD diagnosis.

MSA-YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images

Background and objectiveAcute ischemic stroke (AIS) is a common neurological disorder characterized by the sudden onset of cerebral ischemia, leading to functional impairments. Swift and precise detection of AIS lesions is crucial for stroke diagnosis and treatment but poses a significant challenge. This study aims to leverage multimodal fusion technology to combine complementary information from various modalities, thereby enhancing the detection performance of AIS target detection models. MethodsIn this retrospective study of AIS, we collected data from 316 AIS patients and created a multi-modality magnetic resonance imaging (MRI) dataset. We propose a Multi-Scale Attention-based YOLOv5 (MSA-YOLOv5), targeting challenges such as small lesion size and blurred borders at low resolutions. Specifically, we augment YOLOv5 with a prediction head to detect objects at various scales. Next, we replace the original prediction head with a Multi-Scale Swin Transformer Prediction Head (MS-STPH), which reduces computational complexity to linear levels and enhances the ability to detect small lesions. We incorporate a Second-Order channel attention (SOCA) module to adaptively rescale channel features by employing second-order feature statistics for more discriminative representations. Finally, we further validate the effectiveness of our method using the ISLES 2022 dataset. ResultsOn our in-house AIS dataset, MSA-YOLOv5 achieves a 79.0% mAP0.5, substantially surpassing other single-stage models. Compared to two-stage models, it maintains a comparable performance level while significantly reducing the number of parameters and resolution. On the ISLES 2022 dataset, MSA-YOLOv5 attains an 80.0% mAP0.5, outperforming other network models by a considerable margin. MS-STPH and SOCA modules can significantly increase mAP0.5 by 2.7% and 1.9%, respectively. Visualization interpretability results show that the proposed MSA-YOLOv5 restricts high attention in the small regions of AIS lesions. ConclusionsThe proposed MSA-YOLOv5 is capable of automatically and effectively detecting acute ischemic stroke lesions in multimodal images, particularly for small lesions and artifacts. Our enhanced model reduces the number of parameters while improving detection accuracy. This model can potentially assist radiologists in providing more accurate diagnosis, and enable clinicians to develop better treatment plans.

The value of multimodal functional magnetic resonance imaging in differentiating p53abn from p53wt endometrial carcinoma.

Endometrial carcinoma (EC) is the sixth most common cancer in women. P53 gene expression in patients with endometrial cancer can predict the efficacy and prognosis of patients with neoadjuvant therapy. To explore the value of multimodal magnetic resonance imaging (MRI) in differentiating p53 abnormal (p53abn) from p53 wild-type (p53wt) EC. Data from 47 EC patients, including 14 p53abn cases and 33 p53wt cases, were retrospectively analyzed. The preoperative MRI sequences included amide proton transfer weighted (APTw) imaging, T2 mapping, mDIXON-Quant imaging and diffusion-weighted imaging (DWI). After post-processing, APT, T2, transverse relaxation rate (R2*), fat fraction (FF) and apparent diffusion coefficient (ADC) maps were obtained. The APT, T2, R2*, FF and ADC values for lesions of the two groups of cases were measured by two observers who were blind to the pathological data. The APT value and R2* value in the p53abn group were higher than those in the p53wt group, while the ADC value was lower (all P < 0.05). There was no statistically significant difference in T2 value and FF value between the two groups (all P > 0.05). The area under curve of APT, R2*, ADC and combined APT + R2*+ADC values for identification of p53abn and p53wt EC were 0.739, 0.689, 0.718 and 0.820, respectively (all P > 0.05). APTw, mDIXON-Quant and DWI techniques can be usedfor quantitative identification of p53abn and p53wt EC. The multimodal MRI provides a new way for preoperative quantitative evaluation of EC molecular typing, which has certain clinical application value.

The value of multimodal magnetic resonance imaging in breast cancer and its correlation with pathological features and prognosis.

This study aimed to explore the diagnostic value of multimodal magnetic resonance imaging (MRI) in breast cancer (BC) and its correlation with pathological features of the disease. Clinical data of 85 BC patients (malignant group) and 85 patients with benign breast diseases (benign group), admitted to 3201 Hospital of Xi'an Jiaotong University Health Science Center from May 2020 to May 2022, were retrospectively collected. Both groups underwent multimodal MRI examinations. We compared the differences in multimodal MRI examination parameters between the groups, as well as between patients with different pathological characteristics and prognoses in the malignant group. The correlation between multimodal MRI examination parameters and pathological features of BC was analyzed. The apparent diffusion coefficient (ADC) of the malignant group was lower than that of the benign group, while the extravascular extracellular volume fraction (Ve), reaction rate constant (Kep), and volume transfer constant (Ktrans) of the malignant group were higher compared to the benign group (p<0.05). In the malignant group, patients with stage III+IV disease, lymph node metastases, and low differentiation had lower ADC values, and higher Ve, Ktrans, and Kep compared to patients with stage I+II disease, no lymph node metastasis, and medium to high differentiation (p<0.05). ADC value negatively correlated with the stage of the disease, lymph node metastases but positively correlated with the degree of differentiation (p<0.05). Ve, Ktrans, and Kep positively correlated with the stage of the disease and lymph node metastasis, and negatively correlated with the degree of differentiation (p<0.05). ADC value of patients with poor prognosis was lower, while Ve, Ktrans, and Kep were higher compared to patients with good prognosis (p<0.05). The Receiver operating characteristic (ROC) analysis showed that ADC, Ve, Ktrans, and Kep have certain predictive values for the poor prognosis of BC patients (p<0.05). Multimodal MRI examination detected obvious differences in the examination parameters of BC patients, and the increase or decrease in these parameters is closely correlated with the pathological characteristics and prognosis of the disease. Multimodal MRI examination can be used for pathological evaluation and prognosis prediction in BC patients.

Structural and spontaneous functional brain changes in visual and oculomotor areas identified by functional localization task in intermittent exotropia children

Intermittent exotropia (IXT) is characterizedby an intermittent outward deviation of the eyes. Yet, the neural substrates associated with IXT are not fully understood. This study investigated brain structure and spontaneous functional activity changes in children with IXT. All participants underwent detailed ophthalmological examinations and multimodal magnetic resonance imaging (MRI) scanning. During functional scanning, binocular visual stimuli were presented to subjects to determine brain areas involved in visual and oculomotor processing. Regions of interest(ROI) were subsequently selected based on functional activation to investigate brain structural and spontaneous functional differences between IXT children and healthy controls (HCs) using small volume correction (SVC). Reduced gray matter density (GMD) was found in the right frontal eye field (FEF) and bilateral inferior parietal lobe (IPL) in IXT children compared with HCs. Besides, reduced fractional amplitude of low-frequency fluctuations (fALFF) values were observed in the left lingual gyrus, right inferior occipital gyrus (IOG), bilateral IPL, and bilateral cerebellum in the IXT children compared to the HCs. IXT children with worse eye position control ability exhibited lower GMD and fALFF values in these areas. Finally, resting state functional connectivity (RSFC) was reduced in frontoparietal oculomotor processing areas in IXT children compared to HCs. In addition, increased cortical thickness was found in the right visual areas and bilateral IPL. These results showed that IXT-related structural and functional brain abnormalities occurred in childhood and may be related to underlying neuropathological mechanisms.

A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities

To propose a Dual-Aware deep learning framework for genotyping of isocitrate dehydrogenase (IDH) in gliomas based on magnetic resonance amide proton transfer (APT) modality data as a means to assist non-invasive diagnosis of gliomas. We collected multimodal magnetic resonance imaging (MRI) imaging data of the brain from 118 cases of gliomas, including 68 wild-type and 50 mutant type cases. The delineation of the ROI of brain glioma was completed in all the cases. APT modality imaging does not require contrast agents, and its signal intensity on tumors is positively correlated with tumor malignancy, and the signal intensity on wild-type IDH is higher than that on mutant IDH. For APT modalities, tumor imaging and derived areas are morphologically variable and lack prominent edge contour characteristics compared with other modalities. Based on these characteristics, we propose the Dual-Aware framework, which introduces the Multi-Aware framework to mine multi-scale features, and the Edge Aware module mines the edge features for automatic genotype identification. The introduction of two types of Aware mechanisms effectively improved the identification rate of the model for glioma IDH genotyping. The accuracy and AUC for each modality data were enhanced, and the best performance was achieved on the APT modality with a prediction accuracy of 83.1% and an AUC of 0.822, suggesting its advantages and effectiveness for identifying glioma IDH genotypes. The proposed deep learning algorithm model constructed based on the image characteristics of the APT modality is effective for glioma IDH genotyping and identification task and may potentially replace the commonly used T1CE modality to avoid contrast agent injection and achieve non- invasive IDH genotyping.

MLP-Res-Unet：MLPs and residual blocks-based U-shaped network intervertebral disc segmentation of multi-modal MR spine images.

Intervertebral disc degeneration (IVD) is now the most prevalent disease in the world; thus, precise intervertebral disc segmentation is essential for the assessment and diagnosis of spinal diseases. Multi-modal magnetic resonance (MR) imaging is more multi-dimensional and thorough than unimodal imaging. However, manual segmentation of multi-modal MRI not only imposes a huge burden on physicians but also has a high error rate. In this study, we propose a new method that can efficiently and accurately segment intervertebral discs from multi-modal MR spine images, providing a reproducible usage scheme for the diagnosis of spinal disorders. We suggest a network structure called MLP-Res-Unet that reduces the amount of computational load and the number of parameters while maintaining performance. Our contribution is two-fold. First, a medical image segmentation network that fuses residual blocks and a multilayer perceptron (MLP) is proposed. Secondly, we design a new deep supervised method and pass the features extracted from the encoder to the decoder through the residual path to achieve a new full-scale residual connection. We evaluate the network on the MICCAI-2018 IVD dataset and obtain Dice similarity coefficient equal to 94.77 (%) and Jaccard coefficient equal to 84.74 (%), while we reduce the amount of parameters by a factor of 3.9 and computation by a factor of 2.4 compared to the IVD-Net. Experiments show that MLP-Res-Unet improves segmentation performance and creates a simpler model structure while reducing the number of parameters and computation.

MRAβ: A multimodal MRI-derived amyloid-β biomarker for Alzheimer's disease.

Florbetapir 18 F (AV45), a highly sensitive and specific positron emission tomographic (PET) molecular biomarker binding to the amyloid-β of Alzheimer's disease (AD), is constrained by radiation and cost. We sought to combat it by combining multimodal magnetic resonance imaging (MRI) images and a collaborative generative adversarial networks model (CollaGAN) to develop a multimodal MRI-derived Amyloid-β (MRAβ) biomarker. We collected multimodal MRI and PET AV45 data of 380 qualified participants from the ADNI dataset and 64 subjects from OASIS3 dataset. A five-fold cross-validation CollaGAN were applied to generate MRAβ. In the ADNI dataset, we found MRAβ could characterize the subject-level AV45 spatial variations in both AD and mild cognitive impairment (MCI). Voxel-wise two-sample t-tests demonstrated amyloid-β depositions identified by MRAβ in AD and MCI were significantly higher than healthy controls (HCs) in widespread cortices (p < .05, corrected) and were much similar to those by AV45 (r > .92, p < .001). Moreover, a 3D ResNet classifier demonstrated that MRAβ was comparable to AV45 in discriminating AD from HC in both the ADNI and OASIS3 datasets, and in discriminate MCI from HC in ADNI. Finally, we found MRAβ could mimic cortical hyper-AV45 in HCs who later converted to MCI (r = .79, p < .001) and was comparable to AV45 in discriminating them from stable HC (p > .05). In summary, our work illustrates that MRAβ synthesized by multimodal MRI could mimic the cerebral amyloid-β depositions like AV45 and lends credence to the feasibility of advancing MRI toward molecular-explainable biomarkers.