IMAGE-BASED RESPIRATORY MOTION EXTRACTION AND RESPIRATION-CORRELATED CONE BEAM CT (4D-CBCT) RECONSTRUCTION

Accounting for respiration motion during imaging helps improve targeting precision in radiation therapy. Respiratory motion can be a major source of error in determining the position of thoracic and upper abdominal tumor targets during radiotherapy. Thus, extracting respiratory motion is a key task in radiation therapy planning. Respiration-correlated or four-dimensional CT (4DCT) imaging techniques have been recently integrated into imaging systems for verifying tumor position during treatment and managing respiration-induced tissue motion. The quality of the 4D reconstructed volumes is highly affected by the respiratory signal extracted and the phase sorting method used. This thesis is divided into two parts. In the first part, two image-based respiratory signal extraction methods are proposed and evaluated. Those methods are able to extract the respiratory signals from CBCT images without using external sources, implanted markers or even dependence on any structure in the images such as the diaphragm. The first method, called Local Intensity Feature Tracking (LIFT), extracts the respiratory signal depending on feature points extracted and tracked through the sequence of projections. The second method, called Intensity Flow Dimensionality Reduction (IFDR), detects the respiration signal by computing the optical flow motion of every pixel in each pair of adjacent projections. Then, the motion variance in the optical flow dataset is extracted using linear and non-linear dimensionality reduction techniques to represent a respiratory signal. Experiments conducted on clinical datasets showed that the respiratory signal was successfully extracted using both proposed methods and it correlates well with standard respiratory signals such as diaphragm position and the internal markers’ signal. In the second part of this thesis, 4D-CBCT reconstruction based on different phase sorting techniques is studied. The quality of the 4D reconstructed images is evaluated and compared for different phase sorting methods such as internal markers, external markers and image-based methods (LIFT and IFDR). Also, a method for generating additional projections to be used in 4D-CBCT reconstruction is proposed to reduce the artifacts that result when reconstructing from an insufficient number of projections. Experimental results showed that the feasibility of the proposed method in recovering the edges and reducing the streak artifacts

Plausibility of Image Reconstruction Using a Proposed Flexible and Portable CT Scanner

The very hot and power-hungry x-ray filaments in today's computed tomography (CT) scanners constrain their design to be big and stationary. What if we built a CT scanner that could be deployed at the scene of a car accident to acquire tomographic images before moving the victim? Recent developments in nanotechnology have shown that carbon nanotubes can produce x-rays at room temperature, and with relatively low power needs. We propose a design for a portable and flexible CT scanner made up of an addressable array of tiny x-ray emitters and detectors. In this paper, we outline a basic design, propose a strategy for reconstruction, and demonstrate the feasibility of reconstruction using experiments on a software simulation of the flexible scanner. These simulations show that reconstruction quality is stable over a wide range of scanner geometries, while progressively larger errors in the scanner geometry induce progressively larger errors. We also raise a number of issues that still need to be overcome to build such a scanner.This work was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation, and the Ontario Innovation Trust

Developments in PET-MRI for Radiotherapy Planning Applications

The hybridization of magnetic resonance imaging (MRI) and positron emission tomography (PET) provides the benefit of soft-tissue contrast and specific molecular information in a simultaneous acquisition. The applications of PET-MRI in radiotherapy are only starting to be realised. However, quantitative accuracy of PET relies on accurate attenuation correction (AC) of, not only the patient anatomy but also MRI hardware and current methods, which are prone to artefacts caused by dense materials. Quantitative accuracy of PET also relies on full characterization of patient motion during the scan. The simultaneity of PET-MRI makes it especially suited for motion correction. However, quality assurance (QA) procedures for such corrections are lacking. Therefore, a dynamic phantom that is PET and MR compatible is required. Additionally, respiratory motion characterization is needed for conformal radiotherapy of lung. 4D-CT can provide 3D motion characterization but suffers from poor soft-tissue contrast. In this thesis, I examine these problems, and present solutions in the form of improved MR-hardware AC techniques, a PET/MRI/CT-compatible tumour respiratory motion phantom for QA measurements, and a retrospective 4D-PET-MRI technique to characterise respiratory motion. Chapter 2 presents two techniques to improve upon current AC methods that use a standard helical CT scan for MRI hardware in PET-MRI. One technique uses a dual-energy computed tomography (DECT) scan to construct virtual monoenergetic image volumes and the other uses a tomotherapy linear accelerator to create CT images at megavoltage energies (1.0 MV) of the RF coil. The DECT-based technique reduced artefacts in the images translating to improved μ-maps. The MVCT-based technique provided further improvements in artefact reduction, resulting in artefact free μ-maps. This led to more AC of the breast coil. In chapter 3, I present a PET-MR-CT motion phantom for QA of motion-correction protocols. This phantom is used to evaluate a clinically available real-time dynamic MR images and a respiratory-triggered PET-MRI protocol. The results show the protocol to perform well under motion conditions. Additionally, the phantom provided a good model for performing QA of respiratory-triggered PET-MRI. Chapter 4 presents a 4D-PET/MRI technique, using MR sequences and PET acquisition methods currently available on hybrid PET/MRI systems. This technique is validated using the motion phantom presented in chapter 3 with three motion profiles. I conclude that our 4D-PET-MRI technique provides information to characterise tumour respiratory motion while using a clinically available pulse sequence and PET acquisition method

Doctor of Philosophy

dissertationThe statistical study of anatomy is one of the primary focuses of medical image analysis. It is well-established that the appropriate mathematical settings for such analyses are Riemannian manifolds and Lie group actions. Statistically defined atlases, in which a mean anatomical image is computed from a collection of static three-dimensional (3D) scans, have become commonplace. Within the past few decades, these efforts, which constitute the field of computational anatomy, have seen great success in enabling quantitative analysis. However, most of the analysis within computational anatomy has focused on collections of static images in population studies. The recent emergence of large-scale longitudinal imaging studies and four-dimensional (4D) imaging technology presents new opportunities for studying dynamic anatomical processes such as motion, growth, and degeneration. In order to make use of this new data, it is imperative that computational anatomy be extended with methods for the statistical analysis of longitudinal and dynamic medical imaging. In this dissertation, the deformable template framework is used for the development of 4D statistical shape analysis, with applications in motion analysis for individualized medicine and the study of growth and disease progression. A new method for estimating organ motion directly from raw imaging data is introduced and tested extensively. Polynomial regression, the staple of curve regression in Euclidean spaces, is extended to the setting of Riemannian manifolds. This polynomial regression framework enables rigorous statistical analysis of longitudinal imaging data. Finally, a new diffeomorphic model of irrotational shape change is presented. This new model presents striking practical advantages over standard diffeomorphic methods, while the study of this new space promises to illuminate aspects of the structure of the diffeomorphism group

Improving Statistical Image Reconstruction for Cardiac X-ray Computed Tomography.

Technological advances in CT imaging pose new challenges such as increased X-ray radiation dose and complexity of image reconstruction. Statistical image reconstruction methods use realistic models that incorporate the physics of the measurements and the statistical properties of the measurement noise, and they have potential to provide better image quality and dose reduction compared to the conventional filtered back-projection (FBP) method. However, statistical methods face several challenges that should be addressed before they can replace the FBP method universally. In this thesis, we develop various methods to overcome these challenges of statistical image reconstruction methods. Rigorous regularization design methods in Fourier domain were proposed to achieve more isotropic and uniform spatial resolution or noise properties. The design framework is general so that users can control the spatial resolution and the noise characteristics of the estimator. In addition, a regularization design method based on the hypothetical geometry concept was introduced to improve resolution or noise uniformity. Proposed designs using the new concept effectively improved the spatial resolution or noise uniformity in the reconstructed image. The hypothetical geometry idea is general enough to be applied to other scan geometries. Statistical weighting modification, based on how much each detector element affects insufficiently sampled region, was proposed to reduce the artifacts without degrading the temporal resolution within the region-of-interest (ROI). Another approach using an additional regularization term, that exploits information from the prior image, was investigated. Both methods effectively removed short-scan artifacts in the reconstructed image. We accelerated the family of ordered-subsets algorithms by introducing a double surrogate so that faster convergence speed can be achieved. Furthermore, we present a variable splitting based algorithm for motion-compensated image reconstruction (MCIR) problem that provides faster convergence compared to the conjugate gradient (CG) method. A sinogram-based motion estimation method that does not require any additional measurements other than the short-scan amount of data was introduced to provide decent initial estimates for the joint estimation. Proposed methods were evaluated using simulation and real patient data, and showed promising results for solving each challenge. Some of these methods can be combined to generate more complete solutions for CT imaging.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110319/1/janghcho_1.pd

Time dependent cone-beam CT reconstruction via a motion model optimized with forward iterative projection matching

The purpose of this work is to present the development and validation of a novel method for reconstructing time-dependent, or 4D, cone-beam CT (4DCBCT) images. 4DCBCT can have a variety of applications in the radiotherapy of moving targets, such as lung tumors, including treatment planning, dose verification, and real time treatment adaptation. However, in its current incarnation it suffers from poor reconstruction quality and limited temporal resolution that may restrict its efficacy. Our algorithm remedies these issues by deforming a previously acquired high quality reference fan-beam CT (FBCT) to match the projection data in the 4DCBCT data-set, essentially creating a 3D animation of the moving patient anatomy. This approach combines the high image quality of the FBCT with the fine temporal resolution of the raw 4DCBCT projection data-set. Deformation of the reference CT is accomplished via a patient specific motion model. The motion model is constrained spatially using eigenvectors generated by a principal component analysis (PCA) of patient motion data, and is regularized in time using parametric functions of a patient breathing surrogate recorded simultaneously with 4DCBCT acquisition. The parametric motion model is constrained using forward iterative projection matching (FIPM), a scheme which iteratively alters model parameters until digitally reconstructed radiographs (DRRs) cast through the deforming CT optimally match the projections in the raw 4DCBCT data-set. We term our method FIPM-PCA 4DCBCT. In developing our algorithm we proceed through three stages of development. In the first, we establish the mathematical groundwork for the algorithm and perform proof of concept testing on simulated data. In the second, we tune the algorithm for real world use; specifically we improve our DRR algorithm to achieve maximal realism by incorporating physical principles of image formation combined with empirical measurements of system properties. In the third stage we test our algorithm on actual patient data and evaluate its performance against gold standard and ground truth data-sets. In this phase we use our method to track the motion of an implanted fiducial marker and observe agreement with our gold standard data that is typically within a millimeter