Zobrazeno 1 - 10 of 48 pro vyhledávání: '"Daniil I, Nikitichev"'
1
Akademický článek
From medical imaging data to 3D printed anatomical models.
Autor: Thore M Bücking, Emma R Hill, James L Robertson, Efthymios Maneas, Andrew A Plumb, Daniil I Nikitichev
Publikováno v: PLoS ONE, Vol 12, Iss 5, p e0178540 (2017)
Anatomical models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D) printers have made it
Externí odkaz: https://doaj.org/article/37028554fcf84311833f962fb29ab9f3
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2
Multispectral tissue mapping: developing a concept for the optical evaluation of liver disease
Autor: Crispin Schneider, Kurinchi Selvan Gurusamy, Daniil I. Nikitichev, Brian R. Davidson, Wenfeng Xia, Adrien E. Desjardins
Publikováno v: King's College London
Journal of Medical Imaging
Purpose: Alterations in the optical absorption behavior of liver tissue secondary to pathological processes can be evaluated by multispectral analysis, which is increasingly being explored as an imaging adjunct for use in liver surgery. Current metho
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11cee2b3cf505f6a25a0f12a688d2783
https://doi.org/10.1117/1.jmi.7.6.066001
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4
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
Autor: Stefano B. Blumberg, M Tariq, Maria Robu, Mikael Brudfors, Hongxiang Lin, Anita Rau, Maria Del Mar Estarellas Garcia, Cemre Zor, Baris Kanber, Fabio Ferreira, Tong Wu, Janaina Mourao-Miranda, Daniil I. Nikitichev, Agoston Mihalik, Neil P. Oxtoby
Publikováno v: Adolescent brain cognitive development neurocognitive prediction
Lecture Notes in Computer Science
Adolescent Brain Cognitive Development Neurocognitive Prediction ISBN: 9783030319007
ABCD-NP@MICCAI
We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tiss
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d22a0ed5b00658bf36190b854332da0
http://arxiv.org/abs/1905.10831
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5
3D printed kidney phantoms for an LED-based photoacoustic and ultrasound imaging system
Autor: Daniil I. Nikitichev, Efthimios Maneas, Vimoshan Arumuham, Simon Choong, Jeremy C. Hebden, Mithun Kuniyil Ajith Singh, Sandy Mosse, Georgia Harris, Karol Murawski, Wenfeng Xia, Guy Perkins, Adrien E. Desjardins
Publikováno v: Photons Plus Ultrasound: Imaging and Sensing 2019.
Photoacoustic imaging is a powerful and increasingly popular technique for tissue diagnostics. Suitable tissue- equivalent phantoms are in high demand for validating photoacoustic imaging methods and for clinical training. In this work, we describe a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5664d19257ad0b8668e5093aa15fcae8
https://doi.org/10.1117/12.2509831
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6
Wall-less vascular poly(vinyl) alcohol gel ultrasound imaging phantoms using 3D printed vessels
Autor: Elizabeth Carr, Wenfeng Xia, Eleanor C. Mackle, Adrien E. Desjardins, Daniil I. Nikitichev, Callum Little, Malcolm Finlay, Efthymios Maneas, Roby Rakhit
Publikováno v: Design and Quality for Biomedical Technologies XII.
Vascular phantoms are crucial tools for clinical training and for calibration and validation of medical imaging systems. With current methods, it can be challenging to replicate anatomically-realistic vasculature. Here, we present a novel method that
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3f002529445712c09d666737d127074e
https://doi.org/10.1117/12.2510033
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7
ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology
Autor: Stefano B. Blumberg, M Tariq, Mikael Brudfors, Anita Rau, Agoston Mihalik, Hongxiang Lin, Neil P. Oxtoby, Janaina Mourao-Miranda, Tong Wu, Maria Robu, Maria Del Mar Estarellas Garcia, Cemre Zor, Baris Kanber, Fabio Ferreira, Daniil I. Nikitichev
Publikováno v: Adolescent Brain Cognitive Development Neurocognitive Prediction ISBN: 9783030319007
ABCD-NP@MICCAI
Adolescent brain cognitive development neurocognitive prediction
Lecture Notes in Computer Science
We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f09a1e6d1e45ad57df25315a92e188b
https://doi.org/10.1007/978-3-030-31901-4_14
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9
Coded excitation ultrasonic needle tracking: An in vivo study
Autor: Yuval Ginsberg, Daniil I. Nikitichev, Anna L. David, Adrien E. Desjardins, Wenfeng Xia, Simeon J. West, Sebastien Ourselin
Publikováno v: Medical Physics. 43:4065-4073
Purpose: Accurate and efficient guidance of medical devices to procedural targets lies at the heart of interventional procedures. Ultrasound imaging is commonly used for device guidance, but determining the location of the device tip can be challengi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::31aff6434560980205f2eff2788a22e1
https://doi.org/10.1118/1.4953205
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10
Novel 3D printing technology for direct fabrication of tissue-mimicking phantoms (Conference Presentation)
Autor: Daniil I. Nikitichev, Sebastien Ourselin, Tom Vercauteren, Sandy Mosse
Publikováno v: Photons Plus Ultrasound: Imaging and Sensing 2018.
Medical phantoms with accurate tissue-mimicking properties and anatomical structures are vital for evaluation of imaging system performance, calibration of medical devices, and training medical staff in techniques such as ultrasonography. Tissue-mimi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::eebd91132daff744be9de0331f53d14a
https://doi.org/10.1117/12.2287439
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