Dr. Summer E. Hanson Profile

Dr. Summer E. Hanson

at Univ of Texas M D Anderson Cancer Ctr

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Proceedings Article | 16 March 2020 Paper

Spherical harmonics for modeling shape transformations of breasts following breast surgery

U. Sampathkumar, Z. Nowroozilarki, G. Reece, S. Hanson, F. Merchant

Proceedings Volume 11315, 113152H (2020) https://doi.org/10.1117/12.2549776

KEYWORDS: Breast, Surgery, 3D modeling, 3D image processing, Cancer, Computer simulations

Read Abstract +

Breast shape aesthetic is most desired outcome of cosmetic and reconstructive breast surgery. Post-operative (post-op) patient satisfaction largely depends on patient expectations, hence appropriately communicating information about surgical options to patients and moderating patient expectations is critical in surgical planning. Breast modeling and computational simulation can help mitigate this challenge and provide an effective tool for presenting potential surgical outcomes and eliciting patient preferences. Most available computational models lack the ability to provide realistic estimation of breast shape changes. We have previously developed a Fourier spherical harmonics (SPHARM) based computational approach to model breast shape¹. SPHARM modeling results in 1320 coefficients that are effective descriptors of the 3D breast shape. In this study, we develop a framework to transform the SPHARM coefficients of the pre-operative (pre-op) breast to generate an estimation of the post-op breast shape for cosmetic (e.g. implant-based augmentation) and reconstructive (e.g. implant, autologous tissue, etc.) surgery procedures. Least squares optimization was used to realize the transformation between the pre- and post-op SPHARM coefficients. We demonstrate the feasibility of our approach using data from patients who have undergone bilateral implant-based breast reconstruction as part of cancer treatment. We trained a random forest regression² function using SPHARM coefficients and their corresponding shape transformation vectors for 21 preop breasts and validated the regressor using a test dataset of 41 breasts. Our preliminary results demonstrate feasibility of the proposed data-driven approach to model transformation of the pre-op breast to its post-op form for a given surgical procedure.

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