Dr. Joseph A. Paydarfar Profile

Dr. Joseph A. Paydarfar

Associate Professor of Otolaryngology at Dartmouth-Hitchcock Medical Ctr

SPIE Involvement:

Author

Publications (13)

Proceedings Article | 29 March 2024 Poster + Paper

Initial implementation of robot-integrated specimen imaging in transoral robotic surgery

Michael Kokko, Andrew Lee, Joseph Paydarfar, Ryan Halter

Proceedings Volume 12928, 129282J (2024) https://doi.org/10.1117/12.3008832

KEYWORDS: 3D modeling, Resection, Pathology, Robotic surgery, Surgery, Anatomy, Tumors, Point clouds, Visualization

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Proceedings Article | 29 March 2024 Presentation + Paper

A surgical navigation framework for image-guided transoral robotic surgery

Yuan Shi, Michael Kokko, Joseph Paydarfar, Ryan Halter

Proceedings Volume 12928, 1292804 (2024) https://doi.org/10.1117/12.3008805

KEYWORDS: Optical tracking, Heads up displays, Computed tomography, Visualization

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Proceedings Article | 3 April 2023 Poster + Paper

Initial image-plane assessment of kinematic-based stereo overlay in trans-oral robotic surgery

Michael Kokko, Yuan Shi, Joseph Paydarfar, Ryan Halter

Proceedings Volume 12466, 124662I (2023) https://doi.org/10.1117/12.2655867

KEYWORDS: Kinematics, Endoscopes, Image segmentation, Optical tracking, Robotic surgery, Image registration, Video, Imaging systems, Calibration

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Proceedings Article | 15 March 2023 Presentation + Paper

First demonstration of a novel nerve-targeting fluorophore in a cohort of ex vivo human tissues

Kendra Hebert, Logan Bateman, Dylan Parker, Jenna Nunziata, Joseph Paydarfar, Jennifer Hong, Darcy Kerr, George Zanazzi, Connor Barth, Lei Wang, Summer Gibbs, Eric Henderson

Proceedings Volume 12361, 123610D (2023) https://doi.org/10.1117/12.2650748

KEYWORDS: Fluorophores, Nervous system, Nerve, Adipose tissue, Lead, Biological samples, Surgery, Injuries, Muscles, Visualization

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Proceedings Article | 15 March 2023 Presentation + Paper

Correlation of fluorescence optomics method classification performance to varying expression level of epidermal growth factor receptor

Yao Chen, Samuel Streeter, Brady Hunt, Hira Sardar, Jason Gunn, Laura Tafe, Joseph Paydarfar, Keith Paulsen, Brian Pogue, Kimberley Samkoe

Proceedings Volume 12361, 123610G (2023) https://doi.org/10.1117/12.2649399

KEYWORDS: Tumors, Fluorescence, Surgery, Fluorescence imaging, Cancer detection, Radiomics, Image classification, Correlation coefficients, Machine learning

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Fluorescence molecular imaging using ABY-029, an epidermal growth factor receptor (EGFR)-targeted synthetic Affibody peptide labeled with a near-infrared fluorophore, is under investigation for surgical guidance during head and neck squamous cell carcinoma (HNSCC) resection. However, tumor-to-normal tissue contrast is confounded by intrinsic physiological limitations of heterogeneous EGFR expression. In this study, a machine learning-based optomics analysis, which interprets the textural pattern differences in EGFR expression conveyed by fluorescence, was applied to optical ABY-029 fluorescence image data of HNSCC surgical specimens. The study objective was to determine the correlations between optomics method classification performance and tissue inherent EGFR expression level. Fluorescence image data were collected through a Phase 0 clinical trial of ABY-029, which involved a total of 20,073 sub-image patches (size of 1.8×1.8 mm2) extracted from 24 bread-loafed slices of HNSCC surgical resections from 12 patients who were stratified into three dose groups (30, 90, and 171 nanomoles). The optomics approach utilized a supervised machine learning pipeline. Each dose group was randomly partitioned on the specimen-level 75%/25% into training/testing sets, then all training and testing sets were aggregated. A total of 1,472 standardized optomic features were extracted from each patch and evaluated by minimum redundancy maximum relevance feature selection, and 25 top-ranked features were used to train a support vector machine classifier. A conceptual framework of correlation analysis to evaluate the relationship between optomics tumor classification performance and underlying EGFR expression level was provided, but the present results are underpowered. Some generalized conclusions about the ABY-029 fluorescence optomics method correlating to varied levels of EGFR expression were summarized, suggesting that optomics method using fluorescence molecular imaging data offers a potentially stable image analysis technique for cancer detection for fluorescence-guided surgery applications; however, further study with additional samples is needed to validate this conclusion.

Proceedings Article | 4 April 2022 Presentation + Paper

Surgical navigation system for image-guided transoral robotic surgery: a proof of concept

Yuan Shi, Yajie Lou, Iroha Shirai, Xiaotian Wu, Joseph Paydarfar, Ryan Halter

Proceedings Volume 12034, 1203409 (2022) https://doi.org/10.1117/12.2613486

KEYWORDS: Head, Surgery, 3D modeling, Navigation systems, Computed tomography, Sensors, Robotic surgery, Cancer, Image-guided intervention

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Proceedings Article | 7 March 2022 Presentation

Towards clinical translation of paired-agent imaging for FGS

Kimberley Samkoe, Cheng Wang, Hira Sardar, Jason Gunn, Sassan Hodge, Laura Tafe, Konstantinos Linos, Joseph Paydarfar, Eric Henderson, Brian Pogue, Eunice Chen, Jonathan Elliott, Kenneth Tichauer, Keith Paulsen

Proceedings Volume PC11943, PC119430D (2022) https://doi.org/10.1117/12.2617796

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Proceedings Article | 15 February 2021 Presentation + Paper

Imaging-compatible oral retractor system for use in image-guided transoral robotic surgery

Yuan Shi, Xiaotian Wu, Joseph Paydarfar, Ryan Halter

Proceedings Volume 11598, 1159807 (2021) https://doi.org/10.1117/12.2582233

KEYWORDS: Robotic surgery, Polymers, Magnetic resonance imaging, Imaging systems, Computed tomography, Tumors, Robotics, Navigation systems, Metals, Haptic technology

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Proceedings Article | 16 March 2020 Paper

Development of a novel tumor phantom model for head and neck squamous cell carcinoma and its applications

Michael Sramek, Yuan Shi, Erick Quintanilla, Xiaotian Wu, Aravind Ponukumati, David Pastel, Ryan Halter, Joseph Paydarfar

Proceedings Volume 11315, 113152X (2020) https://doi.org/10.1117/12.2550597

KEYWORDS: Tumors, Tongue, Head, Neck, Surgery, 3D modeling, Visualization

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Proceedings Article | 16 March 2020 Presentation + Paper

Estimating tongue deformation during laryngoscopy using hybrid FEM-multibody model and intraoperative tracking: a cadaver pilot study

Xiaotian Wu, C. Antonio Sánchez, John Lloyd, Heather Borgard, Sidney Fels, Joseph Paydarfar, Ryan Halter

Proceedings Volume 11315, 113151E (2020) https://doi.org/10.1117/12.2550471

KEYWORDS: Modeling and simulation, Tongue, Skull, Tumors, Surgery, Head, Data modeling, Image segmentation, Computed tomography, Tissues, Solid modeling

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Proceedings Article | 24 February 2020 Presentation + Paper

Considerations for NIR-I and short-wave infrared (SWIR) fluorescence imaging within a clinical operating room

B. Byrd, J. Paydarfar, D. Wirth, L. Tafe, K. Samkoe, K. Paulsen, S. Davis

Proceedings Volume 11222, 112220T (2020) https://doi.org/10.1117/12.2543869

KEYWORDS: Short wave infrared radiation, LCDs, Luminescence, Infrared imaging, Imaging systems, Infrared radiation, Reflectivity, Infrared search and track, Navigation systems, Light

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Proceedings Article | 19 February 2020 Presentation + Paper

Perspectives on the phase 0 clinical trial of microdose administration of ABY-029 for fluorescence guided surgery: stability testing

Kimberley Samkoe, Sally Hull, Jonathan Elliott, Hira Shahzad Sardar, Jason Gunn, Konstantinos Linos, Laura Tafe, Brent Harris, Joachim Feldwisch, Brian Pogue, David Roberts, Eric Henderson, Joseph Paydarfar, Keith Paulsen

Proceedings Volume 11222, 112220X (2020) https://doi.org/10.1117/12.2547110

KEYWORDS: Molecular imaging, Clinical trials, Luminescence, Surgery, Proteins, Manufacturing, Receptors, Absorbance

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