Magnetic Resonance Imaging Modalities Research Articles

SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma

Background and objectiveMalignant primary brain tumors cause the greatest number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating: The median survival without any treatment is less than six months and with standard-of-care treatment is only 14.6 months. Accurate identification of the overall survival time of patients with brain tumors is of profound importance in many clinical applications. Automated image analytics with magnetic resonance imaging (MRI) can provide insights into the prognosis of patients with brain tumors. MethodsIn this paper, We propose SurvNet, a low-complexity deep learning architecture based on the convolutional neural network to classify the overall survival time of patients with brain tumors into long-time and short-time survival cohorts. Through the incorporation of diverse MRI modalities as inputs, we facilitate deep feature extraction at various anatomical sites, thereby augmenting the precision of predictive modeling. We compare SurvNet with the Inception V3, VGG 16 and ensemble CNN models on pre-operative magnetic resonance image datasets. We also analyzed the effect of segmented brain tumors and training data on the system performance. ResultsSeveral measures, such as accuracy, precision, and recall, are calculated to examine the perfor-mance of SurvNet on three-fold cross-validation. SurvNet with T1 MRI modality achieved a 62.7 % accuracy, compared with 52.9 % accuracy of the Inception V3 model, 58.5 % accuracy of the VGG 16 model, and 54.9 % of the ensemble CNN model. By increasing the MRI input modalities, SurvNet becomes more accurate and achieves 76.5 % accuracy with four MRI modalities. Combining the segmented data, SurvNet achieved the highest accuracy of 82.4 %. ConclusionsThe research results show that SurvNet achieves higher metrics such as accuracy and f1-score than the comparisons. Our research also proves that by using multiparametric MRI modalities, SurvNet is able to learn more image features and performs a better classification accuracy. We can conclude that SurvNet with the complete scenario, i.e., segmented data and four MRI modalities, achieved the best accuracy, showing the validity of segmentation information during the survival time prediction process.

HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge

Background and purposeTo promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge. Materials and methodsThe challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test. ResultsWhile 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD95 of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities. ConclusionThis challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general.

A comprehensive review on role of magnetic resonance imaging in the precise measurement of corpus callosum

Magnetic Resonance Imaging (MRI) has emerged as a powerful and non-invasive imaging technique, playing a pivotal role in neuroimaging research and clinical diagnosis. This comprehensive review explores the significance of MRI in the precise measurement of the corpus callosum, a key structure connecting the two hemispheres of the brain. The corpus callosum plays a crucial role in inter hemispheric communication and is implicated in various neurological and psychiatric disorders. The review begins by elucidating the anatomy and functions of the corpus callosum, emphasizing its importance in facilitating seamless communication between the cerebral hemispheres. Subsequently, the paper delves into the historical evolution of MRI techniques and how advancements have enhanced the visualization and measurement capabilities of the corpus callosum. A detailed examination of various MRI modalities, including structural MRI, diffusion tensor imaging (DTI), and functional MRI, is presented in the context of corpus callosum analysis. Structural MRI provides high-resolution images for accurate morphological assessments, while DTI offers insights into white matter microstructure and fiber tractography, allowing for a more comprehensive understanding of the corpus callosum's integrity. The review also highlights the role of MRI in investigating alterations in the corpus callosum associated with neurological disorders such as multiple sclerosis, epilepsy, and neurodevelopmental disorders. Additionally, the paper explores how functional MRI contributes to understanding the functional connectivity and inter hemispheric interactions mediated by the corpus callosum. Challenges and limitations in the measurement of the corpus callosum using MRI are discussed, including methodological considerations, image artifacts, and potential sources of variability. The review concludes by outlining future directions in corpus callosum imaging research, including the integration of advanced imaging techniques, artificial intelligence, and large-scale multi-center studies to enhance the precision and reliability of measurements.In summary, this comprehensive review underscores the pivotal role of MRI in advancing our understanding of the corpus callosum, providing valuable insights into both normal brain function and pathological conditions. The continual evolution of MRI techniques holds promise for further refining the assessment of the corpus callosum, ultimately contributing to improved diagnostic accuracy and therapeutic interventions in neurological and psychiatric disorders.

Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices. To address these challenges, we propose a Self-supervised Anatomical Continuity Enhancement (SACE) network to synthesize 7T SWI from 3T SWI using plentiful 3T SWI data and limited 3T–7T paired data. The SACE employs two specifically designed pretext tasks to utilize low-level representations from abundant 3T SWI data for assisting 7T SWI synthesis in a downstream task with limited paired data. One pretext task emphasizes input-specific morphology by balancing the elimination of redundant patterns with the preservation of essential morphology, preventing the blurring of synthetic 7T SWI images. The other task improves the synthesis of tissues that are invisible in a single 3T SWI slice by aligning adjacent slices with the current slice and predicting their difference fields. The downstream task innovatively combines clinical knowledge with brain substructure diagrams to selectively enhance clinically relevant features. When evaluated on a dataset comprising 97 cases (5495 slices), the proposed method achieved a Peak Signal-to-Noise Ratio (PSNR) of 23.05 dB and a Structural Similarity Index (SSIM) of 0.688. Due to the absence of specific methods for 7T SWI, our method was compared with existing enhancement techniques for general 7T MRI synthesis, outperforming these techniques in the context of 7T SWI synthesis. Clinical evaluations have shown that our synthetic 7T SWI is clinically effective, demonstrating its potential as a clinical tool.

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

Modern Magnetic Resonance Imaging Modalities to Advance Neuroimaging in Astronauts.

INTRODUCTION: The rapid development of the space industry requires a deeper understanding of spaceflight's impact on the brain. MRI research reports brain volume changes following spaceflight in astronauts, potentially affecting cognition. Recently, we have demonstrated that this evidence of volumetric changes, as measured by typical T1-weighted sequences (e.g., magnetization-prepared rapid gradient echo sequence; MPRAGE), is error-prone due to the microgravity-related redistribution of cerebrospinal fluid in the brain. More modern neuroimaging methods, particularly dual-echo MPRAGE (DEMPRAGE) and magnetization-prepared rapid gradient echo sequence utilizing two inversion pulses (MP2RAGE), have been suggested to be resilient to this error. Here, we tested if these imaging modalities offered consistent segmentation performance improvements in some commonly employed neuroimaging software packages.METHODS: We conducted manual gray matter tissue segmentation in traditional T1w MRI images to utilize for comparison. Automated tissue segmentation was performed for traditional T1w imaging, as well as on DEMPRAGE and MP2RAGE images from the same subjects. Statistical analysis involved a comparison of total gray matter volumes for each modality, and the extent of tissue segmentation agreement was assessed using a test of similarity (Dice coefficient).RESULTS: Neither DEMPRAGE nor MP2RAGE exhibited consistent segmentation performance across all toolboxes tested.DISCUSSION: This research indicates that customized data collection and processing methods are necessary for reliable and valid structural MRI segmentation in astronauts, as current methods provide erroneous classification and hence inaccurate claims of neuroplastic brain changes in the astronaut population.Berger L, Burles F, Jaswal T, Williams R, Iaria G. Modern magnetic resonance imaging modalities to advance neuroimaging in astronauts. Aerosp Med Hum Perform. 2024; 95(5):245-253.

Combined use of radiomics and artificial neural networks for the three‐dimensional automatic segmentation of glioblastoma multiforme

AbstractGlioblastoma multiforme (GBM) is the most prevalent and aggressive primary brain tumour that has the worst prognosis in adults. Currently, the automatic segmentation of this kind of tumour is being intensively studied. Here, the automatic three‐dimensional segmentation of the GBM is achieved with its related subzones (active tumour, inner necrosis, and peripheral oedema). Preliminary segmentations were first defined based on the four basic magnetic resonance imaging modalities and classic image processing methods (multithreshold Otsu, Chan–Vese active contours, and morphological erosion). After an automatic gap‐filling post processing step, these preliminary segmentations were combined and corrected by a supervised artificial neural network of multilayer perceptron type with a hidden layer of 80 neurons, fed by 30 selected radiomic features of gray intensity and texture. Network classification has an overall accuracy of 83.9%, while the complete combined algorithm achieves average Dice similarity coefficients of 89.3%, 80.7%, 79.7%, and 66.4% for the entire region of interest, active tumour, oedema, and necrosis segmentations, respectively. These values are in the range of the best reported in the present bibliography, but even with better Hausdorff distances and lower computational costs. Results presented here evidence that it is possible to achieve the automatic segmentation of this kind of tumour by traditional radiomics. This has relevant clinical potential at the time of diagnosis, precision radiotherapy planning, or post‐treatment response evaluation.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents

US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms. To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group. This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit. AHIs. Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling. Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs. In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.

Early diagnosis of Charcot neuro-osteoarthropathy using MRI and its effect on patient outcomes: A seven-year retrospective audit

BackgroundThere remains a paucity of research comparing the diagnostic and therapeutic outcomes between Magnetic Resonance Imaging (MRI) and x-ray modalities for Charcot neuro-osteoarthropathy CNO. This retrospective study investigates the use of offloading devices, duration of offloading and final footwear outcomes dependent on imaging at diagnosis. MethodsMedical records from a secondary hospital high-risk foot clinic in Perth, Western Australia, were systematically reviewed. Data collected included baseline medical history, location of CNO, Eichenholtz stage or Chantelau and Grutznel grade at diagnosis, type and duration of offloading, and final footwear outcomes. ResultsTwenty-eight patients met the inclusion criteria. All had diabetes and peripheral neuropathy. All patients received either an MRI (43%) or x-ray (57%) to confirm the diagnosis of active CNO. Five (17.9%) patients who were diagnosed on MRI had grade 0 CNO whilst 23 (82.1%) patients who were diagnosed on x-ray had stage 1 CNO. No statistical significance was found between the type and duration of offloading, resolution of CNO, footwear and transtibial amputation (TTA) outcomes across those diagnosed with MRI or x-ray. ConclusionNo statistical significance in patient outcomes was found between those diagnosed with grade 0 on MRI and those diagnosed with stage 1 on x-ray.

Sex differences in the pleiotropy of hearing difficulty with imaging-derived phenotypes: a brain-wide investigation.

Hearing difficulty (HD) is one of the major health burdens in older adults. While aging-related changes in the peripheral auditory system play an important role, genetic variation associated with brain structure and function could also be involved in HD predisposition. We analyzed a large-scale HD genome-wide association study (GWAS; Ntotal = 501,825, 56% females) and GWAS data related to 3,935 brain imaging-derived phenotypes (IDPs) assessed in up to 33,224 individuals (52% females) using multiple magnetic resonance imaging modalities. To investigate HD pleiotropy with brain structure and function, we conducted genetic correlation, latent causal variable, Mendelian randomization, and multivariable generalized linear regression analyses. Additionally, we performed local genetic correlation and multi-trait colocalization analyses to identify genomic regions and loci implicated in the pleiotropic mechanisms shared between HD and brain IDPs. We observed a widespread genetic correlation of HD with 120 IDPs in females, 89 IDPs in males, and 171 IDPs in the sex-combined analysis. The latent causal variable analysis showed that some of these genetic correlations could be due to cause-effect relationships. For seven correlations, the causal effects were also confirmed by the Mendelian randomization approach: vessel volume→HD in the sex-combined analysis; hippocampus volume→HD, cerebellum grey matter volume→HD, primary visual cortex volume→HD, and HD→fluctuation amplitudes of node 46 in resting-state functional MRI dimensionality 100 in females; global mean thickness→HD and HD→mean orientation dispersion index in superior corona radiata in males. The local genetic correlation analysis identified 13 pleiotropic regions between HD and these seven IDPs. We also observed a colocalization signal for the rs13026575 variant between HD, primary visual cortex volume, and SPTBN1 transcriptomic regulation in females. Brain structure and function may have a role in the sex differences in HD predisposition via possible cause-effect relationships and shared regulatory mechanisms.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data

BackgroundGliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorizing gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. MethodsThis study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the …… Hospital. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. ResultsIn total, 613 patients were enrolled in the study, and 24,455 radiomic features were extracted from each patient’s MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. ConclusionsThis study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Post-COVID-19 conditions: a systematic review on advanced magnetic resonance neuroimaging findings.

Post-COVID conditions (PCCs) cover a wide spectrum of lingering symptoms experienced by survivors of coronavirus disease 2019 (COVID-19). Neurological and neuropsychiatric sequelae are common in PCCs. Advanced magnetic resonance imaging (MRI) techniques can reveal subtle alterations in brain structure, function, and perfusion that underlie these sequelae. This systematic review aimed to synthesize findings from studies that used advanced MRI to characterize brain changes in individuals with PCCs. A detailed literature search was conducted in the PubMed and Scopus databases to identify relevant studies that used advanced MRI modalities, such as structural MRI (sMRI), diffusion tensor imaging (DTI), functional MRI (fMRI), and perfusion-weighted imaging (PWI), to evaluate brain changes in PCCs. Twenty-five studies met the inclusion criteria, comprising 1219 participants with PCCs. The most consistent findings from sMRI were reduced gray matter volume (GMV) and cortical thickness (CTh) in cortical and subcortical regions. DTI frequently reveals increased mean diffusivity (MD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in white matter tracts (WMTs) such as the corpus callosum, corona radiata, and superior longitudinal fasciculus. fMRI demonstrated altered functional connectivity (FC) within the default mode, salience, frontoparietal, somatomotor, subcortical, and cerebellar networks. PWI showed decreased cerebral blood flow (CBF) in the frontotemporal area, thalamus, and basal ganglia. Advanced MRI shows changes in the brain networks and regions of the PCCs, which may cause neurological and neuropsychiatric problems. Multimodal neuroimaging may help understand brain-behavior relationships. Longitudinal studies are necessary to better understand the progression of these brain anomalies.

ReeGAN: MRI image edge-preserving synthesis based on GANs trained with misaligned data.

As a crucial medical examination technique, different modalities of magnetic resonance imaging (MRI) complement each other, offering multi-angle and multi-dimensional insights into the body's internal information. Therefore, research on MRI cross-modality conversion is of great significance, and many innovative techniques have been explored. However, most methods are trained on well-aligned data, and the impact of misaligned data has not received sufficient attention. Additionally, many methods focus on transforming the entire image and ignore crucial edge information. To address these challenges, we propose a generative adversarial network based on multi-feature fusion, which effectively preserves edge information while training on noisy data. Notably, we consider images with limited range random transformations as noisy labels and use an additional small auxiliary registration network to help the generator adapt to the noise distribution. Moreover, we inject auxiliary edge information to improve the quality of synthesized target modality images. Our goal is to find the best solution for cross-modality conversion. Comprehensive experiments and ablation studies demonstrate the effectiveness of the proposed method.

Open-source versatile 3D-print animal conditioning platform design for in vivo preclinical brain imaging in awake mice and anesthetized mice and rats.

Proper animal conditioning is a key factor in the quality and success of preclinical neuroimaging applications. Here, we introduce an open-source easy-to-modify multimodal 3D printable design for rodent conditioning for magnetic resonance imaging (MRI) or other imaging modalities. Our design can be used for brain imaging in anesthetized or awake mice, and in anesthetized rats. We show ease of use and reproducibility of subject conditioning with anatomical T2-weighted imaging for both mice and rats. We also demonstrate the application of our design for awake functional MRI in mice using both visual evoked potential and olfactory stimulation paradigms. In addition, using a combined MRI, positron emission tomography and X-ray computed tomography experiment, we demonstrate that our proposed cradle design can be utilized for multiple imaging modalities.

Neighbouring-slice Guided Multi-View Framework for brain image segmentation

Automated segmentation of brain images is a critical task in neuroscience for brain registration, atlas generation, etc. Deep learning techniques have been widely investigated for segmenting brain images, where most existing methods only consider each brain slice independently in a single view, limiting their ability to explore the correlation among adjacent slices and spatial brain structures. This paper proposes a Neighbouring-slice Guided Multi-view Framework for automated segmentation of brain images. To fully utilize the information between neighbouring slices, we design a dual-decoder network to segment targets (e.g., regions/tumors) in brain images and the edge of each brain targets simultaneously, by calculating the difference between adjacent slices. Considering the fact that some brain images cannot be fully recognized in a single view, we integrate the neighbouring-slice strategy in a multi-view segmentation framework to fully explore spatial structures in 3D brains. The proposed framework is validated on the automated segmentation of diverse brain tumors and brain regions including CTX (cerebellar cortex), CP (caudoputamen), HPF (hippocampal formation), BS (brain stem), CB (cerebellum), and CBX (cerebellar cortex), in LSFM (Light-sheet Fluorescence Microscopy) and MRI (Magnetic Resonance Imaging) modalities, demonstrating superior performance in comparison with state-of-the-arts. The codes are released at: https://github.com/NeuronXJTU/NGMV.

A Convolutional Block Base Architecture for Multiclass Brain Tumor Detection Using Magnetic Resonance Imaging

In the domain of radiological diagnostics, accurately detecting and classifying brain tumors from magnetic resonance imaging (MRI) scans presents significant challenges, primarily due to the complex and diverse manifestations of tumors in these scans. In this paper, a convolutional-block-based architecture has been proposed for the detection of multiclass brain tumors using MRI scans. Leveraging the strengths of CNNs, our proposed framework demonstrates robustness and efficiency in distinguishing between different tumor types. Extensive evaluations on three diverse datasets underscore the model’s exceptional diagnostic accuracy, with an average accuracy rate of 97.52%, precision of 97.63%, recall of 97.18%, specificity of 98.32%, and F1-score of 97.36%. These results outperform contemporary methods, including state-of-the-art (SOTA) models such as VGG16, VGG19, MobileNet, EfficientNet, ResNet50, Xception, and DenseNet121. Furthermore, its adaptability across different MRI modalities underlines its potential for broad clinical application, offering a significant advancement in the field of radiological diagnostics and brain tumor detection.

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

Applications of AI in multi-modal imaging for cardiovascular disease.

Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis. Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05). Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.

Image Diagnosis of Trigeminal Neuralgia

Classic trigeminal neuralgia is mainly caused by arterial compression; most cases involve the superior cerebellar artery, followed by the anterior cerebellar, basilar, and vertebral arteries. The detection of neurovascular conflicts in trigeminal neuralgia requires special magnetic resonance imaging(MRI)modalities, including high-resolution three-dimensional(3D)-T2 sequence, 3D-time of flight angiography, 3D-T1 sequencing with gadolinium injection, and merged images of these sequences. The conflicting sites are not necessarily restricted to the root entry zone of the trigeminal nerve root and can be located more distally, proximal to the Meckel's cavum. Arterial compression and its severity, including displacement, angulation, distortion, and atrophy of the trigeminal root, are good predictors of the long-term efficacy of decompression surgery. Veins, primarily the transverse pontine vein, comprise 10%-20% of all causative vessels in trigeminal neuralgia. Gadolinium-enhanced 3D-T1 MRI and high-resolution 3D-T2 MRI merged with computed tomographic angiography are useful for detecting venous compression.