Image Registration for Quantitative Parametric Response Mapping of Cancer Treatment Response

AbstractImaging biomarkers capable of early quantification of tumor response to therapy would provide an opportunity to individualize patient care. Image registration of longitudinal scans provides a method of detecting treatment-associated changes within heterogeneous tumors by monitoring alterations in the quantitative value of individual voxels over time, which is unattainable by traditional volumetric-based histogram methods. The concepts involved in the use of image registration for tracking and quantifying breast cancer treatment response using parametric response mapping (PRM), a voxel-based analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) scans, are presented. Application of PRM to breast tumor response detection is described, wherein robust registration solutions for tracking small changes in water diffusivity in breast tumors during therapy are required. Methodologies that employ simulations are presented for measuring expected statistical accuracy of PRM for response assessment. Test-retest clinical scans are used to yield estimates of system noise to indicate significant changes in voxel-based changes in water diffusivity. Overall, registration-based PRM image analysis provides significant opportunities for voxel-based image analysis to provide the required accuracy for early assessment of response to treatment in breast cancer patients receiving neoadjuvant chemotherapy

Non-Rigid Registration via Global to Local Transformation

Non-rigid point set and image registration are key problems in plenty of computer vision and pattern recognition tasks. Typically, the non-rigid registration can be formulated as an optimization problem. However, registration accuracy is limited by local optimum. To solve this problem, we propose a method with global to local transformation for non-rigid point sets registration and it also can be used to infrared (IR) and visible (VIS) image registration. Firstly, an objective function based on Gaussian fields is designed to make a problem of non-rigid registration transform into an optimization problem. A global transformation model, which can describe the regular pattern of non-linear deformation between point sets, is then proposed to achieve coarse registration in global scale. Finally, with the results of coarse registration as initial value, a local transformation model is employed to implement fine registration by using local feature. Meanwhile, the optimal global and local transformation models estimated from edge points of IR and VIS image pairs are used to achieve non-rigid image registration. The qualitative and quantitative comparisons demonstrate that the proposed method has good performance under various types of distortions. Moreover, our method can also produce accurate results of IR and VIS image registration

Registration of prostate surfaces for image-guided robotic surgery via the da Vinci System

Organ-confined prostate cancer represents a commonly diagnosed cancer among men rendering an early diagnosis and screening a necessity. The prostate laparoscopic surgery using the da Vinci system is a minimally invasive, computer assisted and image-guided surgery application that provides surgeons with (i) navigational assistance by displaying targeting lesions of the intraoperative prostate anatomy onto aligned preoperative high-field magnetic resonance imaging (MRI) scans of the pelvis; and (ii) an effective clinical management of intra-abdominal cancers in real time. Such an image guidance system can improve both functional and oncological outcomes as well as augment the learning curve of the process increasing simultaneously the eligibility of patients for surgical resection. By segmenting MRI scans into 3D models of intraprostatic anatomy preoperatively, and overlaying them onto 3D stereoendoscopic images acquired intraoperatively using the da Vinci surgical system, a graphical representation of intraoperative anatomy can be provided for surgical navigation. The preoperative MRI surfaces are full 3D models and the stereoendoscopic images represent partial 3D views of the prostate due to occlusion. Hence achieving an accurate non-rigid image registration of full prostate surfaces onto occluded ones in real time becomes of critical importance, especially for use intraoperatively with the stereoendoscopic and MRI imaging modalities. This work exploits the registration accuracy that can be achieved from the application of selected state-of-the-art non-rigid registration algorithms and in doing so identifies the most accurate technique(s) for registration of full prostate surfaces onto occluded ones; a series of rigorous computational registration experiments is performed on synthetic target prostate data, which are aligned manually onto the MRI prostate models before registration is initiated. This effort extends to using real target prostate data leading to visually acceptable non-rigid registration results. A great deal of emphasis is placed on examining the capacity of the selected non-rigid algorithms to recover the deformation of the intraoperative prostate surfaces; the deformation of prostate can become pronounced during the surgical intervention due to surgical-induced anatomical deformities and pathological or other factors. The warping accuracy of the non-rigid registration algorithms is measured within the space of common overlap (established between the full MRI model and the target scene) and beyond. From the results of the registrations to occluded and deformed prostate surfaces (in the space beyond common overlap) it is concluded that the modified versions of the Kernel Correlation/Thin-plane Spline (KC/TPS) and Gaussian Mixture Model/Thin-plane Spline (GMM/TPS) methodologies can provide the clinical accuracy required for image-guided prostate surgery procedures (performed by the da Vinci system) as long as the size of the target scene is greater than ca. 30% of the full MRI surface. For the modified KC/TPS and GMM/TPS non-rigid registration techniques to be clinically acceptable when the measurement noise is also included in the simulations: (i) the size of the target model must be greater than ca. 38% of the full MRI surface; (ii) the standard deviation σ of the contributing Gaussian noise must be less than 0.345 for μ=0; and (iii) the observed deformation must not be characterized by excessively increased complexity. Otherwise the contribution of Gaussian noise must be explicitly parameterized in the objective cost functions of these non-rigid algorithms. The Expectation Maximization/Thin-plane Spline (EM/TPS) non-rigid registration algorithm cannot recover the prostate surface deformation accurately in full-model-to-occluded-model registrations due to the way that the correspondences are estimated. However, EM/TPS is more accurate than KC+TPS and GMM+TPS in recovering the deformation of the prostate surface in full-model-to-full-model registrations

Analysis and Strategies to Enhance Intensity-Base Image Registration.

The availability of numerous complementary imaging modalities allows us to obtain a detailed picture of the body and its functioning. To aid diagnostics and surgical planning, all available information can be presented by visually aligning images from different modalities using image registration. This dissertation investigates strategies to improve the performance of image registration algorithms that use intensity-based similarity metrics. Nonrigid warp estimation using intensity-based registration can be very time consuming. We develop a novel framework based on importance sampling and stochastic approximation techniques to accelerate nonrigid registration methods while preserving their accuracy. Registration results for simulated brain MRI data and human lung CT data demonstrate the efficacy of the proposed framework. Functional MRI (fMRI) is used to non-invasively detect brain-activation by acquiring a series of brain images, called a time-series, while the subject performs tasks designed to stimulate parts of the brain. Consequently, these studies are plagued by subject head motion. Mutual information (MI) based slice-to-volume (SV) registration algorithms used to estimate time-series motion are less accurate for end-slices (i.e., slices near the top of the head scans), where a loss in image complexity yields noisy MI estimates. We present a strategy, dubbed SV-JP, to improve SV registration accuracy for time-series end-slices by using joint pdf priors derived from successfully registered high complexity slices near the middle of the head scans to bolster noisy MI estimates. Although fMRI time-series registration can estimate head motion, this motion also spawns extraneous intensity fluctuations called spin saturation artifacts. These artifacts hamper brain-activation detection. We describe spin saturation using mathematical expressions and develop a weighted-average spin saturation (WASS) correction scheme. An algorithm to identify time-series voxels affected by spin saturation and to implement WASS correction is outlined. The performance of registration methods is dependant on the tuning parameters used to implement their similarity metrics. To facilitate finding optimal tuning parameters, we develop a computationally efficient linear approximation of the (co)variance of MI-based registration estimates. However, empirically, our approximation was satisfactory only for a simple mono-modality registration example and broke down for realistic multi-modality registration where the MI metric becomes strongly nonlinear.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61552/1/rbhagali_1.pd