Advancing Hyperspectral and Multispectral Image Fusion: An Information-Aware Transformer-Based Unfolding Network

Advancing Hyperspectral and Multispectral Image Fusion: An Information-Aware Transformer-Based Unfolding Network

Information-aware Transformer Unfolding Network for Hyperspectral and Multispectral Image Fusion Background Introduction Hyperspectral images (HSIs) play a crucial role in remote sensing applications such as material identification, image classification, target detection, and environmental monitoring, due to their spectral information across multip...

Imaging Bioluminescence by Detecting Localized Haemodynamic Contrast from Photosensitized Vasculature

Imaging Bioluminescence by Detecting Localized Haemodynamic Contrast from Photosensitized Vasculature

Academic News Report: New MRI Technology Achieves Biological Fluorescence Imaging by Detecting Local Hemodynamics of Photosensitive Blood Vessels Academic Background Introduction Bioluminescent probes are widely used for monitoring biomedical processes and cellular targets in living animals. However, the absorption and scattering of visible light b...

Staged Bilateral MRI-Guided Focused Ultrasound Subthalamotomy for Parkinson Disease

MRI-Guided Staged Bilateral Focused Ultrasound Subthalamotomy for Parkinson’s Disease Background Parkinson’s Disease (PD) is a common neurodegenerative disorder, characterized mainly by motor symptoms such as tremor, rigidity, and bradykinesia. Traditionally, treatments for PD include medication and surgical interventions such as Deep Brain Stimula...

Cortical Networks Relating to Arousal Are Differentially Coupled to Neural Activity and Hemodynamics

Differences in Coupling Between Cortical Networks Related to Arousal in Neural Activity and Hemodynamics Academic Background In the absence of specific sensory inputs or behavioral tasks, the brain generates structured activity patterns. This organized activity is modulated by the state of arousal. The relationship between arousal and cortical acti...

Deep Geometric Learning with Monotonicity Constraints for Alzheimer’s Disease Progression

Using Monotonicity-Constrained Deep Geometric Learning to Predict Alzheimer’s Disease Progression Background Introduction Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder that gradually leads to irreversible cognitive decline, eventually resulting in dementia. Early identification and progression prediction of this disease are c...