Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Hongxiang Lin"'
Publikováno v:
Chemosensors, Vol 12, Iss 11, p 242 (2024)
A theoretical approach is presented to significantly enhance the spectral sensitivity of prism-based SPR sensors. The spectral sensitivity of prism-based SPR sensors is derived based on the coupling conditions of SPR and might exceed 105 nm/RIU for a
Externí odkaz:
https://doaj.org/article/6d4a11a2262643178e78962ffb1cdddc
Autor:
Francesco Grussu, Stefano B. Blumberg, Marco Battiston, Lebina S. Kakkar, Hongxiang Lin, Andrada Ianuş, Torben Schneider, Saurabh Singh, Roger Bourne, Shonit Punwani, David Atkinson, Claudia A. M. Gandini Wheeler-Kingshott, Eleftheria Panagiotaki, Thomy Mertzanidou, Daniel C. Alexander
Publikováno v:
Frontiers in Physics, Vol 9 (2021)
Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI).Methods: We select subsets of measurements within lengthy pilot scans, without
Externí odkaz:
https://doaj.org/article/dde3530e76cb45218a05c5fe9fae8da8
Autor:
Syed Furqan Qadri, Hongxiang Lin, Linlin Shen, Mubashir Ahmad, Salman Qadri, Salabat Khan, Maqbool Khan, Syeda Shamaila Zareen, Muhammad Azeem Akbar, Md Belal Bin Heyat, Saqib Qamar
Publikováno v:
International Journal of Intelligent Systems. 2023:1-14
CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is chal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf7f43af355b5cbde19af834fe8aeb11
https://doi.org/10.1155/2023/2345835
https://doi.org/10.1155/2023/2345835
Autor:
Xiaolei Qu, Chujian Ren, Guo Yan, Dezhi Zheng, Wenzhong Tang, Shuai Wang, Hongxiang Lin, Jingya Zhang, Jue Jiang
Publikováno v:
Ultrasound in Medicine & Biology. 48:2079-2094
Ultrasound sound-speed tomography (USST) is a promising technology for breast imaging and breast cancer detection. Its reconstruction is a complex non-linear mapping from the projection data to the sound-speed image (SSI). The traditional reconstruct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24984a79e11539bfde0e59d77af319cc
https://doi.org/10.1016/j.ultrasmedbio.2022.05.033
https://doi.org/10.1016/j.ultrasmedbio.2022.05.033
Autor:
Hongxiang Lin, Matteo Figini, Felice D’Arco, Godwin Ogbole, Ryutaro Tanno, Stefano B. Blumberg, Lisa Ronan, Biobele J. Brown, David W. Carmichael, Ikeoluwa Lagunju, Judith Helen Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander
Publikováno v:
Medical Image Analysis. 87:102807
Low-field (
Accepted in Medical Image Analysis
Accepted in Medical Image Analysis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7605b8e85bd0f941b456fc1526fff922
https://doi.org/10.1016/j.media.2023.102807
https://doi.org/10.1016/j.media.2023.102807
Autor:
Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu, Yukun Zhou, Matteo Figini, Daniel C. Alexander
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164453
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::528c05b63217aaf2fb1d3a9731fed269
https://doi.org/10.1007/978-3-031-16446-0_40
https://doi.org/10.1007/978-3-031-16446-0_40
Recently, self-supervised representation learning gives further development in multimedia technology. Most existing self-supervised learning methods are applicable to packaged data. However, when it comes to streamed data, they are suffering from a c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca21af55061f3273287122d1caf1a8aa
http://arxiv.org/abs/2107.01776
http://arxiv.org/abs/2107.01776
Publikováno v:
Journal of Healthcare Engineering, Vol 2021 (2021)
Journal of Healthcare Engineering
Journal of Healthcare Engineering
Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generaliza
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f085cacebe5024c0946964ad225484b
https://doaj.org/article/d63d11360d57437e92d37993a5824fbc
https://doaj.org/article/d63d11360d57437e92d37993a5824fbc
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872304
MICCAI (6)
MICCAI (6)
Multi-modal and multi-contrast imaging datasets have diverse voxel-wise intensities. For example, quantitative MRI acquisition protocols are designed specifically to yield multiple images with widely-varying contrast that inform models relating MR si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a9d822a793f29ed3daa2a0ddf31bda6
https://doi.org/10.1007/978-3-030-87231-1_5
https://doi.org/10.1007/978-3-030-87231-1_5
Autor:
Francesco Grussu, Andrada Ianus, Torben Schneider, Stefano B. Blumberg, Claudia A. M. Wheeler-Kingshott, Marco Battiston, Roger Bourne, David Atkinson, Daniel C. Alexander, Lebina S. Kakkar, Shonit Punwani, Thomy Mertzanidou, Hongxiang Lin, Saurabh Singh, Eleftheria Panagiotaki
PurposeWe introduce “Select and retrieve via direct upsampling” network (SARDU-Net), a data-driven framework for model-free quantitative MRI (qMRI) protocol design, and demonstrate it on in vivo brain and prostate diffusion-relaxation imaging (DR
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f338b954d173b6817cd30516255a682
https://doi.org/10.1101/2020.05.26.116491
https://doi.org/10.1101/2020.05.26.116491