Dr. Mohamed A. Naser Profile

Dr. Mohamed A. Naser

Research Engineer at The Univ of Texas MD Anderson Cancer Ctr

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Publications (4)

SPIE Journal Paper | 6 November 2023 Open Access

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform for head and neck cancer

Joint Head and Neck Radiotherapy-MRI Development Cooperative, Travis Salzillo, M. Alex Dresner, Ashley Way, Kareem Wahid, Brigid McDonald, Sam Mulder, Mohamed Naser, Renjie He, Yao Ding, Alison Yoder, Sara Ahmed, Kelsey Corrigan, Gohar Manzar, Lauren Andring, Chelsea Pinnix, R. Jason Stafford, Abdallah S. Mohamed, John Christodouleas, Jihong Wang, Clifton David Fuller

JMI, Vol. 10, Issue 06, 065501, (November 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.065501

KEYWORDS: Image segmentation, Image quality, Signal to noise ratio, Radiotherapy, Muscles, Lymph nodes, Neck, Magnetic resonance imaging, Head, Image analysis

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PurposeTo improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T hybrid magnetic resonance imaging/linear accelerator (MR-Linac), three-dimensional (3D), T2-weighted, fat-suppressed magnetic resonance imaging sequences were developed and optimized.ApproachAfter initial testing, spectral attenuated inversion recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a nonsuppressed, T2-weighted sequence were acquired for five HNC patients using a 1.5T MR-Linac. MR physicists identified persistent artifacts in two of the SPAIR sequences, so the remaining three SPAIR sequences were further analyzed. The gross primary tumor volume, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated using five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences on the basis of qualitative and quantitative metrics.ResultsSequences were analyzed for the signal-to-noise ratio and the contrast-to-noise ratio and compared with fat and muscle, conspicuity, pairwise distance metrics, and segmentor assessments. In this analysis, the nonsuppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but it was superior for the pterygoid muscles. The SPAIR sequence that received the highest combined score among the analysis categories was recommended to Unity MR-Linac users for HNC radiotherapy treatment planning.ConclusionsOur study led to two developments: an optimized, 3D, T2-weighted, fat-suppressed sequence that can be disseminated to Unity MR-Linac users and a robust image quality analysis pathway that can be used to objectively score SPAIR sequences and can be customized and generalized to any image quality optimization protocol. Improved segmentation accuracy with the proposed SPAIR sequence will potentially lead to improved treatment outcomes and reduced toxicity for patients by maximizing the target coverage and minimizing the radiation exposure of organs at risk.

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SPIE Journal Paper | 8 February 2023 Open Access

E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation

Diana Lin, Kareem Wahid, Benjamin Nelms, Renjie He, Mohamed Naser, Simon Duke, Michael Sherer, John Christodouleas, Abdallah S. R. Mohamed, Michael Cislo, James Murphy, Clifton Fuller, Erin Gillespie

JMI, Vol. 10, Issue S1, S11903, (February 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.S1.S11903

KEYWORDS: Image segmentation, Breast, Radiation oncology, Diseases and disorders, Radiotherapy, Tumors, Neck, Lymph nodes, Head, Cancer

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Proceedings Article | 27 March 2018 Paper

Imaging biomarker development based on microbubble perfusion and oxygen saturation in a rat model of liver cancer

Mohamed Naser, Nina Munoz, Diego R. Sampaio, Houra Taghavi, Trevor Mitcham, Kiersten Maldonado, Charles Kingsley, Rony Avritscher, Richard Bouchard

Proceedings Volume 10580, 1058007 (2018) https://doi.org/10.1117/12.2293597

KEYWORDS: Tumors, Oxygen, Signal to noise ratio, Blood, Data modeling, In vivo imaging, Tissues, Tissue optics, Arteries, Image segmentation

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Proceedings Article | 17 February 2011 Paper

Reconstruction algorithm for diffuse optical tomography using x-ray CT anatomical information and application to bioluminescence tomography

Mohamed Naser, Julius Pekar, Michael Patterson

Proceedings Volume 7896, 78961L (2011) https://doi.org/10.1117/12.873189

KEYWORDS: Reconstruction algorithms, Optical properties, Absorption, Scattering, Bioluminescence, Light scattering, Diffuse optical tomography, Sensors, Tissue optics, Signal attenuation

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