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

Gradient Based Image Registration Using Importance Sampling

Analytical gradient based non-rigid image registration methods, using intensity based similarity measures (e.g. mutual information), have proven to be capable of accurately handling many types of deformations. While their versatility is largely in part to their high degrees of freedom, the computation of the gradient of the similarity measure with respect to the many warp parameters becomes very time consuming. Recently, a simple stochastic approximation method using a small random subset of image pixels to approximate this gradient has been shown to be effective. We propose to use importance sampling to improve the accuracy and reduce the variance of this approximation by preferentially selecting pixels near image edges. Initial empirical results show that a combination of stochastic approximation methods and importance sampling greatly improves the rate of convergence of the registration process while preserving accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86019/1/Fessler217.pd

Accelerated Nonrigid Intensity-Based Image Registration Using Importance Sampling

Nonrigid image registration methods using intensity-based similarity metrics are becoming increasingly common tools to estimate many types of deformations. Nonrigid warps can be very flexible with a large number of parameters and gradient optimization schemes are widely used to estimate them. However, for large datasets, the computation of the gradient of the similarity metric with respect to these many parameters becomes very time consuming. Using a small random subset of image voxels to approximate the gradient can reduce computation time. This work focuses on the use of importance sampling to reduce the variance of this gradient approximation. The proposed importance sampling framework is based on an edge-dependent adaptive sampling distribution designed for use with intensity-based registration algorithms. We compare the performance of registration based on stochastic approximations with and without importance sampling to that using deterministic gradient descent. Empirical results, on simulated magnetic resonance brain data and real computed tomography inhale-exhale lung data from eight subjects, show that a combination of stochastic approximation methods and importance sampling accelerates the registration process while preserving accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85955/1/Fessler13.pd

Improved fMRI Time-Series Registration Using Joint Probability Density Priors

Functional MRI (fMRI) time-series studies are plagued by varying degrees of subject head motion. Faithful head motion correction is essential to accurately detect brain activation using statistical analyses of these time-series. Mutual information (MI) based slice-to-volume (SV) registration is used for motion estimation when the rate of change of head position is large. SV registration accounts for head motion between slice acquisitions by estimating an independent rigid transformation for each slice in the time-series. Consequently each MI optimization uses intensity counts from a single time-series slice, making the algorithm susceptible to noise for low complexity endslices (i.e., slices near the top of the head scans). This work focuses on improving the accuracy of MI-based SV registration of end-slices by using joint probability density priors derived from registered high complexity centerslices (i.e., slices near the middle of the head scans). Results show that the use of such priors can significantly improve SV registration accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85928/1/Fessler236.pd