2026-09-22T10:13:26Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/113842 2023-07-11 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

Sreenivas, Ramavarapu S Masud, Arif Sutton, Brad Kesavadas, Thenkurussi Kesavadas, Thenkurussi 2022-04-29T21:34:26Z 2021-12 Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms The student, Kuocheng Wang, accepted the attached license on 2021-11-17 at 09:42. The student, Kuocheng Wang, submitted this Dissertation for approval on 2021-11-22 at 10:37. This Dissertation was approved for publication on 2021-11-24 at 12:40. DSpace SAF Submission Ingestion Package generated from Vireo submission #17217 on 2022-04-06 at 17:09:47 Made available in DSpace on 2022-04-29T21:34:26Z (GMT). No. of bitstreams: 3 WANG-DISSERTATION-2021.pdf: 5478905 bytes, checksum: 10a5f74d9baffd90e64e4ee995f49de3 (MD5) LICENSE.txt: 4210 bytes, checksum: d5b31b553beae289dd03a76ef9037ea5 (MD5) PROQUEST_LICENSE.txt: 4556 bytes, checksum: 72cba56ded65f65078fde6376cacc4e2 (MD5) Previous issue date: 2021-11-24 application/pdf en text FEA-based simulation of breast deformation in real-time using artificial neural network Wang, Kuocheng 2021-11-24 Engineering 2022-04-29T21:34:26Z 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. Thesis eng http://hdl.handle.net/2142/113842 Copyright 2021 Kuocheng Wang Industrial&Enterprise Sys Eng Systems & Entrepreneurial Engr University of Illinois at Urbana-Champaign Dissertation Ph.D.