Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
40000210
To address the issue of a large number of network parameters and substantial floating-point operations in deep learning networks applied to image segmentation for cardiac magnetic resonance imaging (MRI), this paper proposes a lightweight dilated par...
Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in early diagnosis and patient care. The research paper explores the application of hybrid deep learning me...
Computer methods and programs in biomedicine
39893807
BACKGROUND AND OBJECTIVE: Cardiovascular disease is a leading cause of mortality worldwide. Automated analysis of heart structures in MRI is crucial for effective diagnostics. While supervised learning has advanced the field of medical image segmenta...
The Journal of neuroscience : the official journal of the Society for Neuroscience
39971581
The structure and function of the brain and cardiovascular system change over the lifespan. In this study, we aim to establish the extent to which age-related changes in these two vital organs are linked. Utilizing normative models and data from the ...
BACKGROUND: Computed tomography attenuation correction (CTAC) is commonly used in cardiac SPECT imaging to reduce soft-tissue attenuation artifacts. However, CTAC is prone to inaccuracies due to CT artifacts and SPECT-CT mismatch, along with addition...
IEEE transactions on bio-medical engineering
40030245
This study introduces an innovative approach combining deep-learning techniques with classical physics-based electrocardiographic imaging (ECGI) methods. Our objective is to enhance the accuracy and robustness of ECGI reconstructions. We reshape the ...
IEEE journal of biomedical and health informatics
40030822
Automated medical image segmentation plays a crucial role in assisting doctors in diagnosing diseases. Feature decoding is a critical yet challenging issue for medical image segmentation. To address this issue, this work proposes a novel feature deco...
Cardiac motion estimation is important for assessing the contractile health of the heart, and performing this in 3D can provide advantages due to the complex 3D geometry and motions of the heart. Deep learning image registration (DLIR) is a robust wa...
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hi...
Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). Materials...