Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological...
Journal of neuroengineering and rehabilitation
Mar 7, 2025
BACKGROUND: This prospective cohort study was designed to investigate and compare the effectiveness of rehabilitation training robots versus conventional rehabilitation training on stroke survivors by monitoring alterations in brain network of stroke...
Breast cancer (BC) remains a predominant and deadly cancer in women worldwide. By 2040, projections indicate that more than 3 million new cases of breast cancer will emerge annually, culminating in more than 1 million deaths worldwide. Early detectio...
IEEE journal of biomedical and health informatics
Mar 6, 2025
Recent advancements in the classification of Alzheimer's disease have leveraged the automatic feature generation capability of convolutional neural networks (CNNs) using neuroimaging biomarkers. However, most of the existing CNN-based methods often d...
IEEE journal of biomedical and health informatics
Mar 6, 2025
The performance of modern U-shaped neural networks for medical image segmentation has been significantly enhanced by incorporating Transformer layers. Although Transformer architectures are powerful at extracting global information, its ability to ca...
AJNR. American journal of neuroradiology
Mar 4, 2025
BACKGROUND AND PURPOSE: Lumbar spine MRIs can be time consuming, stressful for patients, and costly to acquire. In this work, we train and evaluate open-source generative adversarial network (GAN) to create synthetic lumbar spine MRI STIR volumes fro...
Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly resul...
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...
BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.