Dr. Ashkan A. Malayeri Profile

Dr. Ashkan A. Malayeri

at National Institutes of Health Clinical Ctr

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Publications (4)

Proceedings Article | 2 April 2024 Presentation + Paper

Lung lesion segmentation of CT scans after SARS-CoV-2 infection: combining nonhuman primate with human data for interspecies transfer learning

Linh Shinguyen, Winston Chu, Mark Rustad, Ashkan Malayeri, Maryam Homayounieh, Shiva Singh, Claudia Calcagno, Philip Sayre, Ian Crozier, Gabriella Worwa, Jens Kuhn, Bradford Wood, Marcelo Castro, Jeffrey Solomon

Proceedings Volume 12930, 1293010 (2024) https://doi.org/10.1117/12.3006206

KEYWORDS: Computed tomography, Data modeling, Lung, Education and training, Viruses, Pulmonary disorders, Deep learning, Medical imaging, Image segmentation, Image enhancement

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

An experimental evaluation of material separability in photon-counting CT

Jayasai Rajagopal, Faraz Farhadi, Ayele Negussie, Ehsan Abadi, Pooyan Sahbaee, Babak Saboury, Ashkan Malayeri, William Pritchard, Elizabeth Jones, Ehsan Samei

Proceedings Volume 11595, 115954K (2021) https://doi.org/10.1117/12.2581081

KEYWORDS: Scanners, Mahalanobis distance, Iodine, Gadolinium, Receivers, Photon counting, Computed tomography

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Multivariate SNR in spectral computed tomography

Jayasai Rajagopal, Faraz Farhadi, Ayele Negussie, Ehsan Abadi, Pooyan Sahbaee, Babak Saboury, Ashkan Malayeri, William Pritchard, Elizabeth Jones, Ehsan Samei

Proceedings Volume 11595, 115954I (2021) https://doi.org/10.1117/12.2581079

KEYWORDS: Signal to noise ratio, Spectral computed tomography, CT reconstruction, Iodine, Scanners, Photons, Matrices, Computing systems, Computed tomography

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Proceedings Article | 3 March 2017 Paper

Texture analysis of common renal masses in multiple MR sequences for prediction of pathology

Uyen Hoang, Ashkan Malayeri, Nathan Lay, Ronald Summers, Jianhua Yao

Proceedings Volume 10134, 101343J (2017) https://doi.org/10.1117/12.2254717

KEYWORDS: Magnetic resonance imaging, Pathology, Kidney, Image classification, Feature extraction, Tumor growth modeling, Data modeling, Tumors, Performance modeling, Imaging systems, Statistical modeling

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