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

Robust direct vision-based pose tracking using normalized mutual information

This paper presents a novel visual tracking approach that combines the NMI metric and the traditional SSD metric within a gradient-based optimization frame, which can be used for direct visual odometry and SLAM. We firstly derivate the closed form expression for first- and second-order analytical NMI derivatives under the assumption of rigid-body transformations, which then can be used by subsequent Newton-like optimization methods. Then we develop a robust tracking scheme that utilizes the robustness of NMI metric while keeping the optimization characteristics of SSD-based Lucas-Kanade (LK) tracking methods. To validate the robustness and accuracy of the proposed approach, several experiments are performed on synthetic datasets as well as real image datasets. The experimental results demonstrate that our approach can provide fast, accurate pose estimation and obtain better tracking performance over standard SSD-based methods in most cases. © 2018 SPIE

Kohdennusohjelman optimointi pään magneetti- ja tietokonetomografiakuville

In this thesis work, the aim was to find a robust, optimal rigid registration process to accurately and automatically align computed tomography (CT) and magnetic resonance (MR) images of the brain. For patients undergoing, for example, stereoelectroencephalography (epilepsy patients) or implantation of stimulating electrodes in the brain (Parkinson’s patients), it is crucial to be able to combine information from low-dose CT and MR with great precision. Registration was performed with SimpleITK interface to the image registration framework of the United States National Library of Medicine Insight Segmentation and Registration Toolkit (ITK). In the optimization process an existing SimpleITK example was used as a basis for the registration algorithm, which was then optimized one block at a time beginning with the initial alignment. Registration accuracy was determined by comparing the automatic transform of our registration algorithm to the transform of a semiautomatic registration performed with a semiautomatic ITK based software, ipcWorkstation, which is used and developed in HUS Medical Imaging Center. As a result, a robust rigid registration algorithm was developed. The maximum registration errors with the final algorithm were less than 2 mm for 7 out of 15 and less than 4 mm for 12 out of 15 patients. The algorithm performs registration up to initial rotations of 45 degrees. The fast development of automated registration algorithm presented in this thesis appears promising to be used for other applications as well. This kind of block-wise optimization pattern could be used to optimize the registration either for images of other parts of the body or for other imaging modalities such as positron emission tomography (PET) and MR.Tämän diplomityön tarkoituksena oli löytää optimaalinen ja automaattinen tietokonetomografia- ja magneettikuvien kohdennusmenetelmä. Kohdennus suoritettiin käyttäen hyväksi SimpleITK-ohjelmakirjastoa, joka perustuu ITK kuvakohdennus ohjelmakirjastoon (engl. the United States National Library of Medicine Insight Segmentation and Registration Toolkit). Optimointi aloitettiin SimpleITKesimerkin pohjalta, jonka parametreja optimoitiin osa kerrallaan lähtien liikkeelle kohdennuksen alustuksesta. Kohdennustarkkuus määritettiin vertaamalla optimoidulla kohdennusohjelmalla saatua automaattista muunnosmatriisia puoliautomaattisella menetelmällä saatuun muunnosmatriisiin. Puoliautomaattinen muunnos tehtiin HUS-Kuvantamisessa kehitetyllä ipcWorkstation-ohjelmalla, joka myös perustuu ITK-ohjelmakirjastoon. Työn tuloksena saatiin luotettavasti toimiva jäykän kuvakohdennuksen suorittava algoritmi, joka pohjautuu SimpleITK:n Python-kirjastoon. Seitsemällä 15 potilaasta suurin kohdennusvirhe oli alle 2 mm ja 12:lla 15 potilaasta alle 4 mm. Kohdennus onnistuu jopa 45 asteen lähtökohtaisilla kulmaeroilla. Työssä käytettyä nopeaa algoritmikehitystekniikkaa voitaisiin käyttää optimointiin muillekin sovelluksille. Tulevaisuudessa algoritmioptimointia osa kerrallaan voisi hyödyntää kohdennusparametrien optimointiin jonkin muun vartalon alueen rakenteellisten kuvien kohdennukseen tai eri kuvamodaliteettien kohdennukseen kuten positroniemissiotomografia- ja magneettikuvien kohdennukseen

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

Elastic Registration of Biological Images Using Vector-Spline Regularization

We present an elastic registration algorithm for the alignment of biological images. Our method combines and extends some of the best techniques available in the context of medical imaging. We express the deformation field as a B-spline model, which allows us to deal with a rich variety of deformations. We solve the registration problem by minimizing a pixelwise mean-square distance measure between the target image and the warped source. The problem is further constrained by way of a vector-spline regularization which provides some control over two independent quantities that are intrinsic to the deformation: its divergence, and its curl. Our algorithm is also able to handle soft landmark constraints, which is particularly useful when parts of the images contain very little information or when its repartition is uneven. We provide an optimal analytical solution in the case when only landmarks and smoothness considerations are taken into account. We have applied our approach to perform the elastic registration of images such as electrophoretic gels and fly embryos. The validation of the results by experts has been favorable in all cases