Pg Batchelor

Medical image registration

Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies. Current registration algorithms can, in many cases, automatically register images that are related by a rigid body transformation (i.e. where tissue deformation can be ignored). There has also been substantial progress in non-rigid registration algorithms that can compensate for tissue deformation, or align images from different subjects. Nevertheless many registration problems remain unsolved, and this is likely to continue to be an active field of research in the future.

Validation of a two- to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images.

We present a validation of an intensity based two- to three-dimensional image registration algorithm. The algorithm can register a CT volume to a single-plane fluoroscopy image. Four routinely acquired clinical data sets from patients who underwent endovascular treatment for an abdominal aortic aneurysm were used. Each data set was comprised of two intraoperative fluoroscopy images and a preoperative CT image. Regions of interest (ROI) were drawn around each vertebra in the CT and fluoroscopy images. Each CT image ROI was individually registered to the corresponding ROI in the fluoroscopy images. A cross validation approach was used to obtain a measure of registration consistency. Spinal movement between the preoperative and intraoperative scene was accounted for by using two fluoroscopy images. The consistency and robustness of the algorithm when using two similarity measures, pattern intensity and gradient difference, was investigated. Both similarity measures produced similar results. The consistency values were rotational errors below 0.74 degree and in-plane translational errors below 0.90 mm. These errors approximately relate to a two-dimensional projection error of 1.3 mm. The failure rate was less than 8.3% for three of the four data sets. However, for one of the data sets a much larger failure rate (28.5%) occurred.

Symmetric Positive-Definite Matrices: From Geometry to Applications and Visualization

In many engineering applications that use tensor analysis, such as tensor imaging, the underlying tensors have the characteristic of being positive definite. It might therefore be more appropriate to use techniques specially adapted to such tensors. We will describe the geometry and calculus on the Riemannian symmetric space of positive-definite tensors. First, we will explain why the geometry, constructed by Emile Cartan, is a natural geometry on that space. Then, we will use this framework to present formulas for means and interpolations specific to positive-definite tensors.

Study of Connectivity in the Brain Using the Full Diffusion Tensor from MRI

In this paper we propose a novel technique for the analysis of diffusion tensor magnetic resonance images. This method involves solving the full diffusion equation over a finite element mesh derived from the MR data. It calculates connection probabilities between points of interest, which can be compared within or between subjects. Unlike traditional tractography, we use all the data in the diffusion tensor at each voxel which is likely to increase robustness and make intersubject comparisons easier.

Coil-based artifact reduction.

Multiple MRI receiver coils provide extra information and can enable the reconstruction of multiple images using data from different combinations of coils. Comparison of these images shows that artifacts due to motion or flowing blood appear with different intensities due to the differing coil sensitivities. Typically, the artifact appears amplified in regions of low coil sensitivity. An optimization routine was developed to correct for the artifact by comparing reconstructions from various coil combinations and favoring a self‐consistent solution. It is demonstrated that images artifacted by blood flowing in the aorta, or translational motion of the head, can be improved. Magn Reson Med 52:825–830, 2004.

A Stochastic Iterative Closest Point Algorithm (stochastICP)

We present a modification to the iterative closest point algorithm which improves the algorithms robustness and precision. At the start of each iteration, before point correspondence is calculated between the two feature sets, the algorithm randomly perturbs the point positions in one feature set. These perturbations allow the algorithm to move out of some local minima to find a minimum with a lower residual error. The size of this perturbation is reduced during the registration process. The algorithm has been tested using multiple starting positions to register three sets of data: a surface of a femur, a skull surface and a registration to hepatic vessels and a liver surface. Our results show that, if local minima are present, the stochastic ICP algorithm is more robust and is more precise than the standard ICP algorithm.

Robust registration between cardiac MRI images and atlas for segmentation propagation

We propose a new framework to propagate the labels in a heart atlas to the cardiac MRI images for ventricle segmentations based on image registrations. The method employs the anatomical information from the atlas as priors to constrain the initialisation between the atlas and the MRI images using region based registrations. After the initialisation which minimises the possibility of local misalignments, a fluid registration is applied to fine-tune the labelling in the atlas to the detail in the MRI images. The heart shape from the atlas does not have to be representative of that of the segmented MRI images in terms of morphological variations of the heart in this framework. In the experiments, a cadaver heart atlas and a normal heart atlas were used to register to in-vivo data for ventricle segmentation propagations. The results have shown that the segmentations based on the proposed method are visually acceptable, accurate (surface distance against manual segmentations is 1.0 ± 1.0 mm in healthy volunteer data, and 1.6 ± 1.8 mm in patient data), and reproducible (0.7 ± 1.0 mm) for in-vivo cardiac MRI images. The experiments also show that the new initialisation method can correct the local misalignments and help to avoid producing unrealistic deformations in the nonrigid registrations with 21% quantitative improvement of the segmentation accuracy.

Estimating Sparse Deformation Fields Using Multiscale Bayesian Priors and 3-D Ultrasound

This paper presents an extension to the standard Bayesian image analysis paradigm to explicitly incorporate a multiscale approach. This new technique is demonstrated by applying it to the problem of compensating for soft tissue deformation of pre-segmented surfaces for image-guided surgery using 3-D ultrasound. The solution is regularised using knowledge of the mean and Gaussian curvatures of the surface estimate. Results are presented from testing the method on ultrasound data acquired from a volunteers liver. Two structures were segmented from an MR scan of the volunteer: the liver surface and the portal vein. Accurate estimates of the deformed surfaces were successfully computed using the algorithm, based on prior probabilities defined using a minimal amount of human intervention. With a more accurate prior model, this technique has the possibility to completely automate the process of compensating for intraoperative deformation in image-guided surgery.

k-t group sparse: A method for accelerating dynamic MRI: k-t Group Sparse for Dynamic MRI

Compressed sensing (CS) is a data‐reduction technique that has been applied to speed up the acquisition in MRI. However, the use of this technique in dynamic MR applications has been limited in terms of the maximum achievable reduction factor. In general, noise‐like artefacts and bad temporal fidelity are visible in standard CS MRI reconstructions when high reduction factors are used. To increase the maximum achievable reduction factor, additional or prior information can be incorporated in the CS reconstruction. Here, a novel CS reconstruction method is proposed that exploits the structure within the sparse representation of a signal by enforcing the support components to be in the form of groups. These groups act like a constraint in the reconstruction. The information about the support region can be easily obtained from training data in dynamic MRI acquisitions. The proposed approach was tested in two‐dimensional cardiac cine MRI with both downsampled and undersampled data. Results show that higher acceleration factors (up to 9‐fold), with improved spatial and temporal quality, can be obtained with the proposed approach in comparison to the standard CS reconstructions. Magn Reson Med, 2011.

Multiple Coils for Reduction of Flow Artefacts in MR Images

Flowing blood can cause streak or blob artefacts in MR images and these may degrade subsequent image analysis. Multiple MRI receiver coils enable the reconstruction of images using data from different combinations of coils. The artefact intensities differ in these images due to the differing coil sensitivities. The artefact cause is parameterised and an optimisation routine is used to find self-consistent image reconstructions which have reduced artefacts.