相关论文: Analysis of BOLD fMRI Signal Preprocessing Pipelin…
Accurate predictive models are crucial for analysing COVID-19 mortality trends. This study evaluates the impact of a custom data preprocessing pipeline on ten machine learning models predicting COVID-19 mortality using data from Our World…
Self-supervised learning has greatly facilitated medical image analysis by suppressing the training data requirement for real-world applications. Current paradigms predominantly rely on self-supervision within uni-modal image data, thereby…
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains. The disparities in anatomical structures and functional…
Functional magnetic resonance imaging (fMRI) technology is popularly used in many fields for studying how the brain reacts to mental stimuli. The identification of optimal fMRI experimental designs is crucial for rendering precise…
Due to the growing number of MRI data, automated quality control (QC) has become essential, especially for larger scale analysis. Several attempts have been made in order to develop reliable and scalable QC pipelines. However, the…
Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in…
Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy, artifacts, or imaging parameters are unusual or protocols…
Reducing the scanning time of very-low field (VLF) magnetic resonance imaging (MRI) scanners, commonly employed for stroke diagnosis, can enhance patient comfort and operational efficiency. The conventional parallel imaging (PI) technique…
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and excessive movement of fetuses has limited clinical application…
Functional neuroimaging can measure the brain?s response to an external stimulus. It is used to perform brain mapping: identifying from these observations the brain regions involved. This problem can be cast into a linear supervised…
Brain-mapping techniques have proven to be vital in understanding the molecular, cellular, and functional mechanisms of the brain. Normal anatomical imaging can provide structural information on certain abnormalities in the brain. However…
Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through blood-oxygen-level-dependent (BOLD) signals, which represent…
Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides…
This study proposes an approach to estimate the functional localization and connectivity from CBF and BOLD signals simultaneously measured by ASL (arterial spin labeling) MRI, especially using exploratory Structural Equation Modeling…
Autism spectrum disorder (ASD) is one of the major developmental disorders affecting children. Recently, it has been hypothesized that ASD is associated with atypical brain connectivities. A substantial body of researches use Pearson's…
Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades, advances in functional Magnetic Resonance Imaging (fMRI) and…
The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conventional statistical techniques. However, even deep learning…
Purpose: To develop a data-efficient strategy for accelerated MRI reconstruction with Diffusion Probabilistic Generative Models (DPMs) that enables faster scan times in clinical stroke MRI when only limited fully-sampled data samples are…
Recently, leveraging big data in deep learning has led to significant performance improvements, as confirmed in applications like mental state decoding using fMRI data. However, fMRI datasets remain relatively small in scale, and the…
The brain perfusion ROI detection being a preliminary step, designed to exclude non-brain tissues from analyzed DSC perfusion MR images. Its accuracy is considered as the key factor for delivering correct results of perfusion data analysis.…