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Effective image registration for motion estimation in medical imaging environments

University of British Columbia

Motion estimation is a key enabler for many advanced medical imaging / image analysis applications, and hence is of significant clinical interest. In this thesis, we study image registration for motion estimation in medical imaging environments, and focus on two clinically interesting problems: 1) deformable respiratory motion estimation from dynamic Magnetic Resonance Imagings (MRIs), and 2) rigid-body object motion estimation (e.g., surgical devices, implants) from fluoroscopic images. Respiratory motion is a major complicating factor in many image acquisition applications and image-guided interventions. Existing respiratory motion estimation methods typically rely on motion models learned from retrospective data, and therefore are vulnerable to unseen respiratory motion patterns. To address this limitation, we propose to use dynamic MRI acquisition protocol to monitor respiratory motion, and a scatter to volume registration method that can directly recover the dense motion fields from the dynamic MRI data without explicitly modeling the motion. The proposed method achieves significantly higher motion estimation accuracy than the state-of-the-art methods in addressing varying respiratory motion patterns. Object motion estimation from fluoroscopic images is an enabling technology for advanced image guidance applications for Image-Guided Therapy (IGT). Complex and time-critical clinical procedures typically require the motion estimation to be accurate, robust and real-time, which cannot be achieved by existing methods at the same time. We study 2-D/3-D registration for rigid-body object motion estimation to address the above challenges, and propose two new approaches to significantly improve the robustness and computational efficiency of 2-D/3-D registration. We first propose to use pre-generated canonical form Digitally Reconstructed Radiographs (DRRs) to accelerate the DRR generation during intensity-based 2-D/3-D registration, which boosts the computational efficiency by ten-fold with little degradation in registration accuracy and robustness. We further demonstrate that the widely adopted intensity-based formulation for 2-D/3-D registration is ineffective, and propose a more effective regression-based formulation, solved using Convolutional Neural Network (CNN). The proposed regression-based approach achieves significantly higher robustness, capture range and computational efficiency than state-of-the-art intensity-base approaches.

Text

2016-08-23

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58912

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/