Magnetic Resonance Imaging Datasets Research Articles

Constantly optimized mean teacher for semi-supervised 3D MRI image segmentation.

The mean teacher model and its variants, as important methods in semi-supervised learning, have demonstrated promising performance in magnetic resonance imaging (MRI) data segmentation. However, the superior performance of teacher model through exponential moving average (EMA) is limited by the unreliability of unlabeled image, resulting in potentially unreliable predictions. In this paper, we propose a framework to optimized the teacher model with reliable expert-annotated data while preserving the advantages of EMA. To avoid the tight coupling that results from EMA, we leverage data augmentations to provide two distinct perspectives for the teacher and student models. The teacher model adopts weak data augmentation to provide supervision for the student model and optimizes itself with real annotations, while the student uses strong data augmentation to avoid overfitting on noise information. In addition, double softmax helps the model resist noise and continue learning meaningful information from the images, which is a key component in the proposed model. Extensive experiments show that the proposed method exhibits competitive performance on the Left Atrium segmentation MRI dataset (LA) and the Brain Tumor Segmentation MRI dataset (BraTS2019). For the LA dataset, we achieved a dice of 91.02% using only 20% labeled data, which is close to the dice of 91.14% obtained by the supervised approach using 100% labeled data. For the BraTs2019 dataset, the proposed method achieved 1.02% and 1.92% improvement on 5% and 10% labeled data, respectively, compared to the best baseline method on this dataset. This study demonstrates that the proposed model can be a potential candidate for medical image segmentation in semi-supervised learning scenario.

A novel center-based deep contrastive metric learning method for the detection of polymicrogyria in pediatric brain MRI

Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) containing both PMG and control cases from the Children’s Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG and control MRIs are subtle and the true distribution of the features of the disease is unknown. This makes automatic detection of potential PMG cases in MRI difficult. To enable the automatic detection of potential PMG cases, we propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM). Despite working with a small and imbalanced dataset our method achieves 88.07% recall at 71.86% precision. This will facilitate a computer-aided tool for radiologists to select potential PMG MRIs. To the best of our knowledge, our research is the first to apply machine learning techniques to identify PMG solely from MRI.Our code is available at: https://github.com/RichardChangCA/Deep-Contrastive-Metric-Learning-Method-to-Detect-Polymicrogyria-in-Pediatric-Brain-MRI.Our pediatric MRI dataset is available at: https://www.kaggle.com/datasets/lingfengzhang/pediatric-polymicrogyria-mri-dataset.

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.

Brain tumors recognition based on deep learning

Brain tumors are fatal diseases that require proper treatment, making accurate and timely diagnosis critical for successful treatment. Deep learning (DL) has emerged as a powerful tool for improving the accuracy of brain tumor recognition and underscores the importance of optimizing training parameters and dataset size. These findings demonstrate the feasibility of using DL for accurate and efficient brain tumor recognition, which has significant implications for improving patient outcomes. Accurate and timely diagnosis can greatly improve treatment outcomes and potentially save lives. This paper investigated the impact of DL on brain tumor recognition by utilizing a convolution neural network (CNN) algorithm and Magnetic Resonance Imaging (MRI) dataset of 4000 samples, each with a size of (224×224). The results show that increasing the dataset size led to better performance, with increasing accuracy and generalization of the model. Furthermore, increasing the number of epochs during training improves the accuracy; with 60 epochs as our choice for the DL model, we achieved 97.28℅ accuracy.

Amygdala functional connectivity in borderline personality disorder

Borderline personality disorder (BPD) is characterised by structural and functional brain alterations. Yet, there is little data on functional connectivity (FC) across different levels of brain networks and parameters. In this study, we applied a multi-level approach to analyse abnormal functional connectivity. We analysed resting-state functional magnetic resonance imaging (fMRI) data sets of 69 subjects: 17 female BPD patients and 51 age-matched psychiatrically healthy female controls. fMRI was analysed using CONN toolbox including: a) seed-based FC analysis of amygdala connectivity, b) independent component analysis (ICA) based network analysis of intra- and inter-network FC of selected resting-state networks (DMN, SN, FPN), as well as c) graph-theory based measures of network-level characteristics. We show group-level seed FC differences with higher amygdala to contralateral (superior) occipital cortex connectivity in BPD, which correlated with schema-therapy derived measures of symptoms/traits across the entire cohort. While there was no significant group effect on DMN, SN, or FPN intra-network or inter-network FC, we show a significant group difference for local efficiency and cluster coefficient for a DMN-linked cerebellum cluster. Our findings demonstrate BPD-linked changes in FC across multiple levels of observation, which supports a multi-level analysis for future studies to consider different aspects of functional connectome alterations.

SOmicsFusion: Multimodal coregistration and fusion between spatial metabolomics and biomedical imaging

We present SOmicsFusion, a software toolbox for ’fusing’ spatial omics with classical biomedical imaging modalities, capitalizing on their inherent correspondences and complementarity when characterizing the same subject. By augmenting radiological and histological images with spatially resolved molecular profiling, this fusion offers a panoramic characterization of the biochemical perturbations underlying pathological conditions, thereby advancing our understanding of diseases like brain disorders and cancers. The cornerstone of SOmicsFusion is a coregistration tool that leverages an innovative two-stage machine learning pipeline to tackle the longstanding challenge of spatially aligning data from fundamentally different modalities, priming them for subsequent fusion analysis that often requires precise pixel-wise correspondence between the datasets. Specifically, the pipeline utilizes an original dimension reduction algorithm for representational domain alignment, followed by a Deep Learning-based method for spatial domain alignment. SOmicsFusion is demonstrated using mass spectrometry imaging (MSI)-mediated spatial metabolomics and four other modalities: magnetic resonance imaging (MRI), microscopy, brain atlas, and spatial transcriptomics. By reducing coregistration errors by 38–69% compared to existing pipelines, SOmicsFusion enhances the precision of associating molecule distribution with anatomy and pathology features, ultimately leading to more statistically robust findings. Furthermore, SOmicsFusion incorporates various downstream analysis tools, including overlay visualization, spatial correlation/co-expression analysis, pansharpening, and automated anatomy annotation. These tools facilitate the extraction of biological insights that would be unattainable through individual modalities alone. For instance, the coregistration and correlation between MSI and in vivo MRI datasets unveil that the spatial heterogeneity in metabolites stems from the temporal heterogeneity in the development of cerebral ischemia-reperfusion injury.

Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach

Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality.

Lumbar spine segmentation in MR images: a dataset and a public benchmark

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI.

Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection

The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.

Brain MR Image Classification Using Superpixel-Based Deep Transfer Learning.

Nowadays, brain MR (Magnetic Resonance) images are widely used by clinicians to examine the brain's anatomy to look into various pathological conditions like cerebrovascular incidents and neuro-degenerative diseases. Generally, these diseases can be identified with the MR images as "normal" and "abnormal" brains in a two-class classification problem or as disease-specific classes in a multi-class problem. This article presents an ensemble transfer learning-inspired deep architecture that uses the simple linear iterative clustering (SLIC)-based superpixel algorithm along with convolutional neural network (CNN) to classify the MR images as normal or abnormal. Superpixel algorithm segments the input MR images into clusters of regions defined by similarity measures using perceptual feature space. These superpixel images are beneficial as they can provide a compact and meaningful role in computationally demanding applications. The superpixel images are then fed to the deep convolutional neural network (CNN) to classify the images. Three brain MR image datasets, NITR-DHH, DS-75, and DS-160, are used to conduct the experimentation. Through the use of deep transfer learning, the model achieves performance accuracy of 88.15% (NITR-DHH), 98.15% (DS-160), and 98.33% (DS-75) even with the small-scale medical image dataset. The experimentally obtained results demonstrate that the proposed method is promising and efficient for clinical applications for diagnosing different brain diseases via MR images.

Hierarchical functional differences between gyri and sulci at different scales.

Gyri and sulci are 2 fundamental cortical folding patterns of the human brain. Recent studies have suggested that gyri and sulci may play different functional roles given their structural and functional heterogeneity. However, our understanding of the functional differences between gyri and sulci remains limited due to several factors. Firstly, previous studies have typically focused on either the spatial or temporal domain, neglecting the inherently spatiotemporal nature of brain functions. Secondly, analyses have often been restricted to either local or global scales, leaving the question of hierarchical functional differences unresolved. Lastly, there has been a lack of appropriate analytical tools for interpreting the hierarchical spatiotemporal features that could provide insights into these differences. To overcome these limitations, in this paper, we proposed a novel hierarchical interpretable autoencoder (HIAE) to explore the hierarchical functional difference between gyri and sulci. Central to our approach is its capability to extract hierarchical features via a deep convolutional autoencoder and then to map these features into an embedding vector using a carefully designed feature interpreter. This process transforms the features into interpretable spatiotemporal patterns, which are pivotal in investigating the functional disparities between gyri and sulci. We evaluate the proposed framework on Human Connectome Project task functional magnetic resonance imaging dataset. The experiments demonstrate that the HIAE model can effectively extract and interpret hierarchical spatiotemporal features that are neuroscientifically meaningful. The analyses based on the interpreted features suggest that gyri are more globally activated, whereas sulci are more locally activated, demonstrating a distinct transition in activation patterns as the scale shifts from local to global. Overall, our study provides novel insights into the brain's anatomy-function relationship.

Sliding transformer with uncertainty estimation for vestibular schwannoma automatic segmentation.

Objective.Automated segmentation of vestibular schwannoma (VS) using magnetic resonance imaging (MRI) can enhance clinical efficiency. Though many advanced methods exist for automated VS segmentation, the accuracy is hindered by ambivalent tumor borders and cystic regions in some patients. In addition, these methods provide results that do not indicate segmentation uncertainty, making their translation into clinical workflows difficult due to potential errors. Providing a definitive segmentation result along with segmentation uncertainty or self-confidence is crucial for the conversion of automated segmentation programs to clinical aid diagnostic tools.Approach.To address these issues, we propose a U-shaped cascade transformer structure with a sliding window that utilizes multiple sliding samples, a segmentation head, and an uncertainty head to obtain both the segmentation mask and uncertainty map. We collected multimodal MRI data from 60 clinical patients with VS from Xuanwu Hospital. Each patient case includes T1-weighted images, contrast-enhanced T1-weighted images, T2-weighted images, and a tumor mask. The images exhibit an in-plane resolution ranging from 0.70 × 0.70 to 0.76 × 0.76 mm, an in-plane matrix spanning from 216 × 256 to 284 × 256, a slice thickness varying between 0.50 and 0.80 mm, and a range of slice numbers from 72 to 120.Main results.Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods. On our collected multimodal MRI dataset of clinical VS, our method achieved the dice similarity coefficient (DSC) of 96.08% ± 1.30. On a publicly available VS dataset, our method achieved the mean DSC of 94.23% ± 2.53.Significance.The method efficiently solves the VS segmentation task while providing an uncertainty map of the segmentation results, which helps clinical experts review the segmentation results more efficiently and helps to transform the automated segmentation program into a clinical aid diagnostic tool.

Exploiting macro- and micro-structural brain changes for improved Parkinson’s disease classification from MRI data

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model’s decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Network Localization of State and Trait of Auditory Verbal Hallucinations in Schizophrenia.

Neuroimaging studies investigating the neural substrates of auditory verbal hallucinations (AVH) in schizophrenia have yielded mixed results, which may be reconciled by network localization. We sought to examine whether AVH-state and AVH-trait brain alterations in schizophrenia localize to common or distinct networks. We initially identified AVH-state and AVH-trait brain alterations in schizophrenia reported in 48 previous studies. By integrating these affected brain locations with large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we then leveraged novel functional connectivity network mapping to construct AVH-state and AVH-trait dysfunctional networks. The neuroanatomically heterogeneous AVH-state and AVH-trait brain alterations in schizophrenia localized to distinct and specific networks. The AVH-state dysfunctional network comprised a broadly distributed set of brain regions mainly involving the auditory, salience, basal ganglia, language, and sensorimotor networks. Contrastingly, the AVH-trait dysfunctional network manifested as a pattern of circumscribed brain regions principally implicating the caudate and inferior frontal gyrus. Additionally, the AVH-state dysfunctional network aligned with the neuromodulation targets for effective treatment of AVH, indicating possible clinical relevance. Apart from unifying the seemingly irreproducible neuroimaging results across prior AVH studies, our findings suggest different neural mechanisms underlying AVH state and trait in schizophrenia from a network perspective and more broadly may inform future neuromodulation treatment for AVH.

Cochlear measurement in computed tomography and magnetic resonance imaging data sets by the Otoplan measurement tool: a retrospective comparative study.

Using Otoplan software, it is possible to measure the cochlea before cochlear implant surgery. Until now, computed tomography (CT) of the cochlea has been necessary for this purpose. The aim of this study was to find out whether measuring the cochlea with magnetic resonance imaging (MRI) using Otoplan is possible with the same accuracy. The cochlea of 44 patients of the local cochlear implant centre was measured by Otoplan using high-resolution CT-bone and MRI images, and the determined lengths were compared. No significant difference was found between the cochlear lengths measured, regardless of whether the length measurement was based on a CT or an MRI data set. For the determination of cochlear length prior to cochlear implant surgery, MRI images are just as suitable as CT images, therefore CT is not mandatory for length measurement by Otoplan, which could reduce the patient's radiation exposure.

MRI Plain Scan: A Tool in the Management of Cervical Cancer during Pregnancy.

The purpose of this study was to assess the diagnostic value of magnetic resonance imaging (MRI) in staging and treatment of cervical cancer in pregnancy, and to evaluate the benefit of apparent diffusion coefficient (ADC) during neoadjuvant chemotherapy management. This was a retrospective cohort study. Patients were divided into two groups according to the stage of cervical cancer. The mean term of pregnancy at the time of the diagnosis was the early second trimester (range 10-27 weeks) and the median age was 33 years (range 26-40 years). The abdominal and pelvic MRI images and clinical data of these patients were reviewed. Tumor size, local tumor spread, and nodal involvement were evaluated using an MRI dataset. The treatment and follow-up imaging were analyzed as well, and the ADC was measured before and after the chemotherapy. 16 patients with histopathologically confirmed cervical cancer during pregnancy were retrospectively enrolled. 7 patients were diagnosed with local cervical cancer (FIGO stage IAI) and designated as early stage group, as the lesion was invisible on MRI. In this group, pregnancies were allowed to continue until cesarean delivery (CD) at 38-41 weeks. The other 9 patients presenting with local or extensive cervical cancer (FIGO stage IB2-IIA2) were designated as the advanced-stage group. The lesion could be measured and analyzed on MRI. They were treated with neoadjuvant chemotherapy in pregnancy. Among them, 6 patients underwent TP regimen (paclitaxel 135~175 mg/m2 plus cisplatin 70~75 mg/m2), while 3 patients received TC regimen (paclitaxel 135~175 mg/m2 plus carboplatin AUC=5). NACT was performed for 1 to 2 courses before surgery. ADC demonstrated significant differences before and after chemotherapy administered during pregnancy (1.06 ± 0.12 sec/mm2 vs. 1.34 ± 0.21 sec/mm2). MRI has been found to be helpful in staging cervical cancer in pregnancy. Patients with stage IA confirmed by MRI can choose conservative treatment and continue the pregnancy until term birth. MRI can dynamically monitor the efficacy of chemotherapy for patients with stage IB and above during pregnancy. ADC value can have a potential role in the evaluation of chemotherapy efficacy.

Differentiating the Presence of Brain Stroke Types in MR Images using CNN Architecture.

Stroke is reportedly the biggest cause of death and disability in the world, according to the World Health Organisation (WHO). The severity of a stroke can be lowered by recognising the many stroke warning indicators early on. Using CNN, the primary goal of this study is to predict the likelihood that a brain stroke would develop at an early stage. The novelty of the proposed work is to acquire models that can accurately differentiate between stroke and no-stroke (normal) cases using MR Imaging sequences like DWI, SWI, GRE and T2 FLAIR aiding in timely diagnosis and treatment decisions. A dataset comprising real time MRI scans of patients with stroke and no-stroke conditions was collected and preprocessed for model training. The preprocessing involves standardizing the resolution of the images, normalizing pixel values, and augmenting the dataset to enhance model generalization. The ResNet, DenseNet, EfficientNet, and VGG16 architectures were implemented and trained on the preprocessed dataset. The training process involved optimizing model parameters using stochastic gradient descent and minimizing the loss function. The results demonstrate promising performance across all models by obtaining an accuracy of 98% for ResNet, DenseNet and EfficientNet, while 97% for VGG16 in differentiating the stroke using real time MRI data. The proposed work explored the effectiveness of CNN models, including ResNet, DenseNet, EfficientNet, and VGG16, for the differentiation of stroke and no-stroke cases. The models were trained and evaluated using a real-time dataset of brain MR Images. The obtained accuracies highlight the potential of CNN models in accurately differentiating between stroke and non-stroke cases.

Frequency-dependent alterations of global signal topography in patients with major depressive disorder.

Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear. We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed. The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group. The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.

Quantitative analysis of molecular transport in the extracellular space using physics-informed neural network

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Péclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS.

MRI-based prostate cancer classification using 3D efficient capsule network.

Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi-quantitative interpretation criteria, leading to inter-reader variability. Computer-aided diagnosis employing quantitative MRI analysis has yielded promising results in non-invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS-derived PCa risk using T2-weighted (T2W) MRI images. In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully-connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. Two groups of experiments were performed: (1) we first identified high-risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one-versus-one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five-fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1-score for low versus high grade, 0.79 AUC and 0.75 F1-score for low + medium versus high grade, 0.75 AUC and 0.69 F1-score for medium versus high grade and 0.59 AUC and 0.57 F1-score for low versus medium grade. Our method outperformed state-of-the-art radiomics-based classification and deep learning methods with the highest metrics for each experiment. Our divide-and-conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non-invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies.