Zobrazeno 1 - 10
of 507
pro vyhledávání: '"Chen, ZhaoLin"'
Deep learning-based techniques have potential to optimize scan and post-processing times required for MRI-based fat quantification, but they are constrained by the lack of large training datasets. Generative models are a promising tool to perform dat
Externí odkaz:
http://arxiv.org/abs/2412.08741
Autor:
Peiris, Himashi, Chen, Zhaolin
Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders. While high-field MRIs provide detailed imaging, low-field MRIs are more accessible and cost-effective,
Externí odkaz:
http://arxiv.org/abs/2410.17502
Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising means of incorporating prior information into the image reconstruction pro
Externí odkaz:
http://arxiv.org/abs/2409.06198
Implicit Neural Representations (INRs) have recently advanced the field of deep learning due to their ability to learn continuous representations of signals without the need for large training datasets. Although INR methods have been studied for medi
Externí odkaz:
http://arxiv.org/abs/2409.01013
Autor:
Dayarathna, Sanuwani, Islam, Kh Tohidul, Zhuang, Bohan, Yang, Guang, Cai, Jianfei, Law, Meng, Chen, Zhaolin
Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis, offering diverse contrasts that provide comprehensive diagnostic information. However, acquiring multiple MRI contrasts is often constrained by high costs, long scanning duration
Externí odkaz:
http://arxiv.org/abs/2409.00585
Autor:
Wang, Guoan, Ye, Jin, Cheng, Junlong, Li, Tianbin, Chen, Zhaolin, Cai, Jianfei, He, Junjun, Zhuang, Bohan
Publikováno v:
MICCAI 2024
Volumetric medical image segmentation is pivotal in enhancing disease diagnosis, treatment planning, and advancing medical research. While existing volumetric foundation models for medical image segmentation, such as SAM-Med3D and SegVol, have shown
Externí odkaz:
http://arxiv.org/abs/2407.04938
Motion artifacts in Magnetic Resonance Imaging (MRI) are one of the frequently occurring artifacts due to patient movements during scanning. Motion is estimated to be present in approximately 30% of clinical MRI scans; however, motion has not been ex
Externí odkaz:
http://arxiv.org/abs/2405.17756
Autor:
Sinclair, Benjamin, Vivash, Lucy, Moses, Jasmine, Lynch, Miranda, Pham, William, Dorfman, Karina, Marotta, Cassandra, Koh, Shaun, Bunyamin, Jacob, Rowsthorn, Ella, Jarema, Alex, Peiris, Himashi, Chen, Zhaolin, Shultz, Sandy R, Wright, David K, Kong, Dexiao, Naismith, Sharon L., OBrien, Terence J., Law, Meng
Perivascular spaces(PVSs) form a central component of the brain\'s waste clearance system, the glymphatic system. These structures are visible on MRI images, and their morphology is associated with aging and neurological disease. Manual quantificatio
Externí odkaz:
http://arxiv.org/abs/2405.08337
Autor:
Huang, Jiahao, Wu, Yinzhe, Wang, Fanwen, Fang, Yingying, Nan, Yang, Alkan, Cagan, Abraham, Daniel, Liao, Congyu, Xu, Lei, Gao, Zhifan, Wu, Weiwen, Zhu, Lei, Chen, Zhaolin, Lally, Peter, Bangerter, Neal, Setsompop, Kawin, Guo, Yike, Rueckert, Daniel, Wang, Ge, Yang, Guang
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advan
Externí odkaz:
http://arxiv.org/abs/2401.16564
Multimodal large language models (MLLMs) represent an evolutionary expansion in the capabilities of traditional large language models, enabling them to tackle challenges that surpass the scope of purely text-based applications. It leverages the knowl
Externí odkaz:
http://arxiv.org/abs/2401.02797