Zobrazeno 1 - 10
of 569
pro vyhledávání: '"Dharmakumar, Rohan"'
Autor:
Yalcinkaya, Dilek M., Youssef, Khalid, Heydari, Bobak, Wei, Janet, Merz, Noel Bairey, Judd, Robert, Dharmakumar, Rohan, Simonetti, Orlando P., Weinsaft, Jonathan W., Raman, Subha V., Sharif, Behzad
Background. Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-cente
Externí odkaz:
http://arxiv.org/abs/2408.04805
Despite the high diagnostic accuracy of Magnetic Resonance Imaging (MRI), using MRI as a Point-of-Care (POC) disease identification tool poses significant accessibility challenges due to the use of high magnetic field strength and lengthy acquisition
Externí odkaz:
http://arxiv.org/abs/2406.16754
Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. In neuroimaging, DL methods can reconstruct high-quality images from undersampled data. However, it is essential to con
Externí odkaz:
http://arxiv.org/abs/2309.14392
Autor:
Yalcinkaya, Dilek M., Youssef, Khalid, Heydari, Bobak, Simonetti, Orlando, Dharmakumar, Rohan, Raman, Subha, Sharif, Behzad
Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of my
Externí odkaz:
http://arxiv.org/abs/2308.13488
Autor:
Vora, Keyur P., Kumar, Andreas, Krishnam, Mayil S., Prato, Frank S., Raman, Subha V., Dharmakumar, Rohan
Publikováno v:
In JACC: Cardiovascular Imaging July 2024 17(7):795-810
Autor:
Jiang, Haochuan, Chartsias, Agisilaos, Zhang, Xinheng, Papanastasiou, Giorgos, Semple, Scott, Dweck, Mark, Semple, David, Dharmakumar, Rohan, Tsaftaris, Sotirios A.
Publikováno v:
MICCAI-2020 DART workshop
Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An
Externí odkaz:
http://arxiv.org/abs/2009.02564
Akademický článek
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Autor:
Kumar, Andreas, Connelly, Kim, Vora, Keyur, Bainey, Kevin R., Howarth, Andrew, Leipsic, Jonathon, Betteridge-LeBlanc, Suzanne, Prato, Frank S., Leong-Poi, Howard, Main, Anthony, Atoui, Rony, Saw, Jacqueline, Larose, Eric, Graham, Michelle M., Ruel, Marc, Dharmakumar, Rohan
Publikováno v:
In Canadian Journal of Cardiology January 2024 40(1):1-14
Autor:
Chartsias, Agisilaos, Papanastasiou, Giorgos, Wang, Chengjia, Semple, Scott, Newby, David E., Dharmakumar, Rohan, Tsaftaris, Sotirios A.
Publikováno v:
IEEE Transactions on Medical Imaging (2020)
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common informat
Externí odkaz:
http://arxiv.org/abs/1911.04417
Autor:
Chartsias, Agisilaos, Joyce, Thomas, Papanastasiou, Giorgos, Williams, Michelle, Newby, David, Dharmakumar, Rohan, Tsaftaris, Sotirios A.
Publikováno v:
Medical Image Analysis 58 (2019) 101535
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in
Externí odkaz:
http://arxiv.org/abs/1903.09467