Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Matthew, Tivnan"'
Publikováno v:
IEEE transactions on medical imaging. 41(9)
Spectral CT has shown promise for high-sensitivity quantitative imaging and material decomposition. This work presents a new device called a spatial-spectral filter (SSF) which consists of a tiled array of filter materials positioned near the x-ray s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e52fd4b6e121bca69cf2e4160b24dd0
https://pubmed.ncbi.nlm.nih.gov/35377842
https://pubmed.ncbi.nlm.nih.gov/35377842
Publikováno v:
Journal of Medical Imaging. 10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e04c3f1127bf58764cdb4d16fadd7e4c
https://doi.org/10.1117/1.jmi.10.3.033501
https://doi.org/10.1117/1.jmi.10.3.033501
Publikováno v:
Medical Imaging 2023: Physics of Medical Imaging.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29525468f48add81108dc90bdad29bfd
https://doi.org/10.1117/12.2655806
https://doi.org/10.1117/12.2655806
Autor:
Matthew Tivnan, Tzu-Cheng Lee, Ruoqiao Zhang, Kirsten Boedeker, Liang Cai, Jeremias Sulam, J. Webster Stayman
Publikováno v:
Medical Imaging 2023: Physics of Medical Imaging.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54d467151ea3e4dbe461bad742f05a2f
https://doi.org/10.1117/12.2653936
https://doi.org/10.1117/12.2653936
Publikováno v:
Sensors, Vol 15, Iss 8, Pp 19709-19722 (2015)
Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autoco
Externí odkaz:
https://doaj.org/article/bb6427744da540eb8029930240866762
Autor:
Matthew, Tivnan, Grace, Gang, Ruoqiao, Zhang, Peter, Noël, Jeremias, Sulam, J Webster, Stayman
Publikováno v:
Proc SPIE Int Soc Opt Eng
Quantitative estimation of multi-material density images is an important goal for Spectral CT imaging. However, material decomposition is a poorly-conditioned nonlinear inverse problem. Maximum-likelihood model-based material decomposition results in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::800ec7563894505d09380880b1a01a61
https://pubmed.ncbi.nlm.nih.gov/36329993
https://pubmed.ncbi.nlm.nih.gov/36329993
Publikováno v:
Medical Physics. 48:6401-6411
Purpose Spectral CT has great potential for a variety of clinical applications due to improved tissue and material discrimination over conventional single-energy CT. Many clinical and preclinical spectral CT systems have two spectral channels enablin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a1f305ba65a77c72a8dcd1afbdefbb9
https://doi.org/10.1002/mp.14930
https://doi.org/10.1002/mp.14930
Autor:
Junyuan Li, Wenying Wang, Matthew Tivnan, Jeremias Sulam, Jerry L. Prince, Michael McNitt-Gray, Joseph W. Stayman, Grace J. Gang
Publikováno v:
Proc SPIE Int Soc Opt Eng
The rapid development of deep-learning methods in medical imaging has called for an analysis method suitable for non-linear and data-dependent algorithms. In this work, we investigate a local linearity analysis where a complex neural network can be r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19d8d18c9a17a2ae8553b1915ab8c3c1
https://europepmc.org/articles/PMC9621688/
https://europepmc.org/articles/PMC9621688/
Publikováno v:
Proc SPIE Int Soc Opt Eng
Recent years have seen the increasing application of deep learning methods in medical imaging formation, processing, and analysis. These methods take advantage of the flexibility of nonlinear neural network models to encode information and features i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eab004c5309fa5f536f949fdb565edd0
https://pubmed.ncbi.nlm.nih.gov/35601024
https://pubmed.ncbi.nlm.nih.gov/35601024
Publikováno v:
Proc SPIE Int Soc Opt Eng
The proliferation of deep learning image processing calls for a quantitative image quality assessment framework that is suitable for nonlinear, data-dependent algorithms. In this work, we propose a method to systematically evaluate the system and noi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0370c65063b907cb094ec34af9492a68
https://pubmed.ncbi.nlm.nih.gov/35585939
https://pubmed.ncbi.nlm.nih.gov/35585939