Zobrazeno 1 - 10
of 930
pro vyhledávání: '"Carass, A"'
Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Despite the significant promise shown by recent deep learning (DL)-based m
Externí odkaz:
http://arxiv.org/abs/2409.13863
Autor:
Hays, Savannah P., Remedios, Samuel W., Zuo, Lianrui, Mowry, Ellen M., Newsome, Scott D., Calabresi, Peter A., Carass, Aaron, Dewey, Blake E., Prince, Jerry L.
Magnetic resonance (MR) imaging is commonly used in the clinical setting to non-invasively monitor the body. There exists a large variability in MR imaging due to differences in scanner hardware, software, and protocol design. Ideally, a processing a
Externí odkaz:
http://arxiv.org/abs/2408.16562
Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning-based deformable registration methods have been widely studied in recent years due to their speed advantage over traditional a
Externí odkaz:
http://arxiv.org/abs/2407.10209
Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both transformation and appearance uncertainties related to the registratio
Externí odkaz:
http://arxiv.org/abs/2403.05111
Autor:
Hays, Savannah P., Zuo, Lianrui, Liu, Yihao, Feng, Anqi, Zhuo, Jiachen, Prince, Jerry L., Carass, Aaron
Deep learning (DL) has led to significant improvements in medical image synthesis, enabling advanced image-to-image translation to generate synthetic images. However, DL methods face challenges such as domain shift and high demands for training data,
Externí odkaz:
http://arxiv.org/abs/2402.12288
Autor:
Bian, Zhangxing, Alshareef, Ahmed, Wei, Shuwen, Chen, Junyu, Wang, Yuli, Woo, Jonghye, Pham, Dzung L., Zhuo, Jiachen, Carass, Aaron, Prince, Jerry L.
Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-proce
Externí odkaz:
http://arxiv.org/abs/2401.17571
Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs) to super-resolve these 2D-acquired LR MR slices. This paper introduc
Externí odkaz:
http://arxiv.org/abs/2312.04385
Autor:
Zhang, Jinwei, Zuo, Lianrui, Dewey, Blake E., Remedios, Samuel W., Pham, Dzung L., Carass, Aaron, Prince, Jerry L.
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation. Current state-of-the-art automatic MS lesion segmentation metho
Externí odkaz:
http://arxiv.org/abs/2312.01460
Autor:
Zhang, Jinwei, Zuo, Lianrui, Dewey, Blake E., Remedios, Samuel W., Hays, Savannah P., Pham, Dzung L., Prince, Jerry L., Carass, Aaron
Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their perform
Externí odkaz:
http://arxiv.org/abs/2310.20586
Autor:
Bian, Zhangxing, Wei, Shuwen, Liu, Yihao, Chen, Junyu, Zhuo, Jiachen, Xing, Fangxu, Woo, Jonghye, Carass, Aaron, Prince, Jerry L.
Tagged magnetic resonance imaging (tMRI) has been employed for decades to measure the motion of tissue undergoing deformation. However, registration-based motion estimation from tMRI is difficult due to the periodic patterns in these images, particul
Externí odkaz:
http://arxiv.org/abs/2308.02949