Fourier registration of three-dimensional brain MR images: exploiting the axis of rotation

Medical image registration: a review

This paper presents a review of automated image registration methodologies that have been used in the medical field. The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application. The registration methodologies under review are classified into intensity or feature based. The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described.

Robust initialization of 2D-3D image registration using the projection-slice theorem and phase correlation

PURPOSE

The image registration literature comprises many methods for 2D-3D registration for which accuracy has been established in a variety of applications. However, clinical application is limited by a small capture range. Initial offsets outside the capture range of a registration method will not converge to a successful registration. Previously reported capture ranges, defined as the 95% success range, are in the order of 4-11 mm mean target registration error. In this article, a relatively computationally inexpensive and robust estimation method is proposed with the objective to enlarge the capture range.

METHODS

The method uses the projection-slice theorem in combination with phase correlation in order to estimate the transform parameters, which provides an initialization of the subsequent registration procedure.

RESULTS

The feasibility of the method was evaluated by experiments using digitally reconstructed radiographs generated from in vivo 3D-RX data. With these experiments it was shown that the projection-slice theorem provides successful estimates of the rotational transform parameters for perspective projections and in case of translational offsets. The method was further tested on ex vivo ovine x-ray data. In 95% of the cases, the method yielded successful estimates for initial mean target registration errors up to 19.5 mm. Finally, the method was evaluated as an initialization method for an intensity-based 2D-3D registration method. The uninitialized and initialized registration experiments had success rates of 28.8% and 68.6%, respectively.

CONCLUSIONS

The authors have shown that the initialization method based on the projection-slice theorem and phase correlation yields adequate initializations for existing registration methods, thereby substantially enlarging the capture range of these methods.

A projection-based image registration algorithm and its application

Proposed is a projection-based image registration technique where, by rearranging the projections of characteristic images, the image registration is implemented with two independent steps - rotation and translation, to perform the two-dimensional or three-dimensional rigid-body image registration addressing the head motion problem in functional magnetic resonance imaging (fMRI). For a 2D problem, the approach is based on a one-dimensional projection of a segmented two-dimensional characteristic image, in which the translation and rotation parameters are obtained with a one-dimensional cross-correlation-based estimator. This is then used to compute the cross-correlation between the projection of an image and a registration table that is created by rearranged projections of a selected two-dimensional image with various rotation angles. In this approach, the translation registration table may be created by rearranged projections of sub-voxel level two-dimensional images with various sub-voxel level parameters, and so it may be applied into a sub-voxel registration. Such an approach replaced the general multi-dimensional optimization procedure with a linear projection calculation and a finite cross-correlation with a registration table, thus the amount of computation is considerably reduced. The performance of this method was confirmed by simulation study different SNRs and applications to 2D and 3D actual functional MRI images.

Using the axis of rotation of polar navigator echoes to rapidly measure 3D rigid-body motion

An improved technique to prospectively correct three-dimensional rigid-body motion using polar spherical navigator (pNAV) echoes is presented. The technique is based on acquiring pNAVs of an object in a baseline and rotated position and determining the axis of rotation (AOR) between data sets, thereby reducing 3D rotations to a 2D, planar rotation. Finding the AOR is simplified by prerotating the baseline trajectory, which forces the axis to lie within a specific polar region of a spherical shell in k-space. Orbital navigator echoes are interpolated from the pNAV data in planes orthogonal to the AOR and cross-correlated to determine the 2D rotation. The rotation about the AOR is used in conjunction with its orientation to calculate the overall 3D rotation. The pNAV-AOR technique was tested for its precision, accuracy, and processing speed in detecting compound rotations and translations of varying magnitude. In comparison to the spherical navigator echo technique, the pNAV-AOR technique is noniterative, fast, and independent of rotation magnitude and direction. At low SNR, the technique can detect compound rotations to 0.5 degrees accuracy in an estimated 100 msec, indicating that prospective 3D rigid-body motion correction may be feasible with this technique.

The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization

The purpose of this work is to detect and assess the significance of subtle signal changes in mixed-signal lesions based on serial MRI scan matching. Pairs of serially acquired T1-weighted volume MR images from 20 normal controls and seven patients with epilepsy were matched and difference images obtained. The precision and consistency of the registration were evaluated. The Gaussian noise level in the difference images was determined automatically. A structured difference filter was then used to segment structured (changed) voxels from the Gaussian noise. In the controls, the structured difference images were normalized into Talairach space, resulting in a structured noise map. The significance of changes in patients was assessed by spatial normalization and comparison with the structured noise map. The precision and consistency of the co-registration were < or = 0.06 mm with a registration success rate of 100%. The Gaussian noise level in the difference images was in the range 3.0-6.9. In the controls, an average of 1.6% of the brain voxels were classified as structured. Sine-based registration resulted in a reduction of < 1% in the amount of structure compared to linear interpolation. The structured noise map in controls showed high noise density in areas affected by image artefacts. We show examples of significant changes found in lesions which had been reported as unchanged on visual inspection. A novel quantitative approach has been presented for the detection and quantification of subtle signal changes in lesions. This method is of potential clinical value in the non-invasive characterization of signal change and biological behaviour of neoplastic lesions.