FEA-based simulation of breast deformation in real-time using artificial neural network

Wang, Kuocheng

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https://hdl.handle.net/2142/113842 Copy

Description

Title

FEA-based simulation of breast deformation in real-time using artificial neural network

Author(s)

Wang, Kuocheng

Issue Date

2021-11-24

Director of Research (if dissertation) or Advisor (if thesis)

Kesavadas, Thenkurussi

Doctoral Committee Chair(s)

Kesavadas, Thenkurussi

Committee Member(s)

• Sreenivas, Ramavarapu S
• Masud, Arif
• Sutton, Brad

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Systems & Entrepreneurial Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Breast deformation simulation
• Artificial neural network
• Finite element analysis
• Augment reality

Abstract

Treatment of breast cancer involves two stages: diagnosis and treatment. It is difficult to correlate the imaging results at the two stages because as the patient’s posture changes during treatment, the images captured during diagnosis do not represent the tumor location during the treatment. In the absence of real-time imaging during treatment, the visualization of tumor location is challenging for surgeons. There are many challenges for breast deformation simulation. For example, material properties are very important to simulate the deformation accurately. The simulation speed will decide whether the technology is applicable for clinical use. But because of the limit of hardware, achieving real time simulation is difficult. This thesis focuses on investigating visualization of breast deformation for different patient’s positions. We utilized magnetic resonance imaging (MRI) of a patient collected during diagnosis for this study. This data was preprocessed to form a 3D reconstructed model that was used to run a finite element analysis (FEA) simulation. FEA simulates the deformation of breast tissues for different constraints, such as glandular ratio and gravity angle. However, FEA simulation of such deformation can take a few minutes to as much as 40 minutes to complete using a 8 cores computer. To obtain real-time visualization, we constructed a neural network (NN) model that takes breast gravity angle and glandular / fat ratio (breast material) as input to estimate breast deformation for different patient’s positions offline. This NN is used to predict the deformation of the breast and provide visualization in real-time (5 ms prediction time). To further validate our result, we carried out MRI of a breast phantom in several angles (to mimic various patient postures). We also implemented an iterative technique to estimate material properties. This data was used to simulate breast deformations at different posture angles. A similar approach was implemented to build an NN model. Our results show that NN has the ability to map the gravity direction to the breast shape and tumor location accurately, while, keeping run time to a minimum.

Graduation Semester

2021-12

Type of Resource

Thesis

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http://hdl.handle.net/2142/113842

Copyright and License Information

Copyright 2021 Kuocheng Wang

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