AIMC Topic: Brain
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TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 23, 2025
Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration...
Deformable image registration with strategic integration pyramid framework for brain MRI.
Magnetic resonance imaging
Mar 21, 2025
Medical image registration plays a crucial role in medical imaging, with a wide range of clinical applications. In this context, brain MRI registration is commonly used in clinical practice for accurate diagnosis and treatment planning. In recent yea...
Radiomics-based MRI model to predict hypoperfusion in lacunar infarction.
Magnetic resonance imaging
Mar 21, 2025
BACKGROUND: Approximately 20-30 % of patients with acute ischemic stroke due to lacunar infarction experience early neurological deterioration (END) within the first three days after onset, leading to disability or more severe sequelae. Hemodynamic p...
An extragradient and noise-tuning adaptive iterative network for diffusion MRI-based microstructural estimation.
Medical image analysis
Mar 20, 2025
Diffusion MRI (dMRI) is a powerful technique for investigating tissue microstructure properties. However, advanced dMRI models are typically complex and nonlinear, requiring a large number of acquisitions in the q-space. Deep learning techniques, spe...
Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.
Nature communications
Mar 19, 2025
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and v...
Weighted Multi-Modal Contrastive Learning Based Hybrid Network for Alzheimer's Disease Diagnosis.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 19, 2025
Multiple imaging modalities and specific proteins in the cerebrospinal fluid, providing a comprehensive understanding of neurodegenerative disorders, have been widely used for computer-aided diagnosis of Alzheimer's disease (AD). Given the proven eff...
Neuro_DeFused-Net: A novel multi-scale 2DCNN architecture assisted diagnostic model for Parkinson's disease diagnosis using deep feature-level fusion of multi-site multi-modality neuroimaging data.
Computers in biology and medicine
Mar 18, 2025
BACKGROUND: Neurological disorders, particularly Parkinson's Disease (PD), are serious and progressive conditions that significantly impact patients' motor functions and overall quality of life. Accurate and timely diagnosis is still crucial, but it ...
AI-driven framework to map the brain metabolome in three dimensions.
Nature metabolism
Mar 18, 2025
High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currentl...
Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.
Scientific reports
Mar 18, 2025
Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitive Impairment (MCI). However, the progressive nature of AD makes it challenging to identify its causa...
Neural correlates of the uncanny valley effect for robots and hyper-realistic masks.
PloS one
Mar 18, 2025
Viewing artificial objects and images that are designed to appear human can elicit a sense of unease, referred to as the 'uncanny valley' effect. Here we investigate neural correlates of the uncanny valley, using still images of androids (robots desi...
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