BIMODAL DYNAMIC IMAGING SYSTEM FOR TUMOR CHARACTERIZATION USING HYBRID HIERARCHICAL STATISTICAL CONTROL

Abstract

Conventional medical imaging technologies for cancer diagnosis utilize fixed geometric configuration of the source and the detector to image the target. In this dissertation, we hypothesize that dynamic utilization of source and detector geometry will lead to better performance of medical imaging devices. Interrogating a target in a three dimensional space requires cooperation and coordination between the source and detector positions. The goal of this dissertation is to develop a dynamic imaging method, which will improve the tumor characterization performance, and provide a control scheme appropriate for the dynamic interrogation. This dissertation proposes a bimodal dynamic imaging (BDI) method for improving tumor characterization and a hybrid hierarchical statistical control scheme for the autonomous control of the sources and detectors. The tactile imaging sensor has high specificity but low sensitivity in tumor characterization. The spectral sensor has high sensitivity but low specificity. The BDI system integrates the tactile sensing and the spectral sensing modalities with the capability of dynamic positioning of the source and detector to determine the mechanical and spectral properties of a tumor. The tactile sensing can estimate the mechanical properties of the tumor, such as size, depth, and elastic modulus, while the spectral sensing can determine the absorption coefficient of the tumor through diffuse optical imaging. These properties help us characterize the tumor, and differentiate cancerous tissues from healthy tissues. We designed and experimentally evaluated the BDI system for estimating the size, depth, elastic modulus, and absorption coefficient of embedded inclusions. The system performance in characterizing mechanical properties was then compared to that of the tactile imaging sensor. The proposed BDI method was experimentally validated using fabricated bimodal phantom. The experimental results showed that the tactile imaging system (TIS) estimated the tumor phantom size with 7.23% error; BDI measured the size with 0.8% error. The TIS depth estimation error was 41.83%; BDI reduced the depth measurement error to 20.00%. The TIS elastic modulus estimation error was 96.80%; the BDI method showed 74.79% error. Additionally, BDI estimated the absorption coefficient with 14%-25% estimation error. For further improvement the system performance, this bimodal imaging system is implemented on a dual-arm robot, Baxter, where the laser source and the tactile imaging sensors were mounted on the end-effectors. Each arm of Baxter robot has seven Degree-of- Freedom. This provides more flexibility in terms of interrogating the target compared to the fixed geometric configuration. We devised a hybrid statistical controller for maneuvering the source and the detector of the system. In this control architecture, a high-level supervisory controller was used for the functions at a higher level for coordinating two arms. At lower level, a full-state feedback statistical controller was used to facilitate the minimum position variation. A linear model for the dual-arm Baxter robot was derived for testing the proposed architecture. We performed the simulations of hybrid hierarchical statistical controller on the Baxter model for trajectory tracking. The simulation studies demonstrated accurate sequential task execution for the bimodal dynamic imaging system using a hybrid hierarchical statistical control.