Iterative Principal Axes Registration method for analysis of MR-PET brain images

Multitemporal Volume Registration for the Analysis of Rheumatoid Arthritis Evolution in the Wrist

This paper describes a method based on an automatic segmentation process to coregister carpal bones of the same patient imaged at different time points. A rigid registration was chosen to avoid artificial bone deformations and to allow finding eventual differences in the bone shape due to erosion, disease regression, or other eventual pathological signs. The actual registration step is performed on the basis of principal inertial axes of each carpal bone volume, as estimated from the inertia matrix. In contrast to already published approaches, the proposed method suggests splitting the 3D rotation into successive rotations about one axis at a time (the so-called basic or elemental rotations). In such a way, singularity and ambiguity drawbacks affecting other classical methods, for instance, the Euler angles method, are addressed. The proposed method was quantitatively evaluated using a set of real magnetic resonance imaging (MRI) sequences acquired at two different times from healthy wrists and by choosing a direct volumetric comparison as a cost function. Both the segmentation and registration steps are not based on a priori models, and they are therefore able to obtain good results even in pathological cases, as proven by the visual evaluation of actual pathological cases.

Online spatial normalization for real-time FMRI

Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short time period. However, spatial normalization may be critical for accurate functional localization in a stereotactic space and is an essential procedure for some emerging applications of rtfMRI. In this study, we introduced an online spatial normalization method that adopts a novel affine registration (AFR) procedure based on principal axes registration (PA) and Gauss-Newton optimization (GN) using the self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration (NLR) based on discrete cosine transform (DCT). In AFR, PA provides an appropriate initial estimate of GN to induce the rapid convergence of GN. In addition, the β parameter, which relies on the change rate of cost function, is employed to self-adaptively adjust the iteration step of GN. The accuracy and performance of PA-GN(β) AFR were confirmed using both simulation and real data and compared with the traditional AFR. The appropriate cutoff frequency of the DCT basis function in NLR was determined to balance the accuracy and calculation load of the online spatial normalization. Finally, the validity of the online spatial normalization method was further demonstrated by brain activation in the rtfMRI data.

Segmentation of the striatum from MR brain images to calculate the 99mTc-TRODAT-1 binding ratio in SPECT images

Quantification of regional ^99mTc-TRODAT-1 binding ratio in the striatum regions in SPECT images is essential for differential diagnosis between Alzheimer's and Parkinson's diseases. Defining the region of the striatum in the SPECT image is the first step toward success in the quantification of the TRODAT-1 binding ratio. However, because SPECT images reveal insufficient information regarding the anatomical structure of the brain, correct delineation of the striatum directly from the SPECT image is almost impossible. We present a method integrating the active contour model and the hybrid registration technique to extract regions from MR T1-weighted images and map them into the corresponding SPECT images. Results from three normal subjects suggest that the segmentation accuracy using the proposed method was compatible with the expert decision but has a higher efficiency and reproducibility than manual delineation. The binding ratio derived by this method correlated well (R² = 0.76) with those values calculated by commercial software, suggesting the feasibility of the proposed method.

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.

RIGID MEDICAL IMAGE REGISTRATION AND ITS ASSOCIATION WITH MUTUAL INFORMATION

Image registration plays a crucial role in the computer vision and medical imaging field where it is used to develop a spatial mapping between different sets of data. These transformations can range from simple rigid registrations to complex nonrigid deformations. Mutual information (MI) is a popular entropy-based similarity measure which has recently experienced a prolific expansion in a number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it only assumes a statistical dependence between images. This paper provides a thorough introduction to the MI measure and its use in rigid medical image registration. A look at the extensions proposed to the original measure will also be provided. These were developed to improve the robustness of the measure and to avoid certain cases when maximizing MI does not lead to the correct spatial alignment.

High-Speed Registration of Three- and Four-dimensional Medical Images by Using Voxel Similarity

A generalized, accurate, automatic, retrospective method of image registration for three-dimensional images has been developed. The method is based on mutual information, a specific measure of voxel similarity, and is applicable to a wide range of imaging modalities and organs, rigid or deformable. A drawback of mutual information-based image registration is long execution times. To overcome the speed problem, low-cost, customized hardware to accelerate this computationally intensive task was developed. Individual hardware accelerator units (each, in principle, 25-fold faster than a comparable software implementation) can be concatenated to perform image registration at any user-desired speed. A first-generation prototype board with two processing units provided a 12- to 16-fold increase in speed. Enhancements for increasing the speed further are being developed. These advances have enabled many nontraditional applications of image registration and have made the traditional applications more efficient. Clinical applications include fusion of computed tomographic (CT), magnetic resonance, and positron emission tomographic (PET) images of the brain; fusion of whole-body CT and PET images; fusion of four-dimensional spatiotemporal ultrasonographic (US) and single photon emission CT images of the heart; and correction of misalignment between pre- and poststress four-dimensional US images.

Automatic landmark extraction from image data using modified growing neural gas network

A new method for automatic landmark extraction from MR brain images is presented. In this method, landmark extraction is accomplished by modifying growing neural gas (GNG), which is a neural-network-based cluster-seeking algorithm. Using modified GNG (MGNG) corresponding dominant points of contours extracted from two corresponding images are found. These contours are borders of segmented anatomical regions from brain images. The presented method is compared to: 1) the node splitting-merging Kohonen model and 2) the Teh-Chin algorithm (a well-known approach for dominant points extraction of ordered curves). It is shown that the proposed algorithm has lower distortion error, ability of extracting landmarks from two corresponding curves simultaneously, and also generates the best match according to five medical experts.

Iterative normalization of cDNA microarray data

This paper describes a new approach to normalizing microarray expression data. The novel feature is to unify the tasks of estimating normalization coefficients and identifying control gene set. Unification is realized by constructing a window function over the scatter plot defining the subset of constantly expressed genes and by affecting optimization using an iterative procedure. The structure of window function gates contributions to the control gene set used to estimate normalization coefficients. This window measures the consistency of the matched neighborhoods in the scatter plot and provides a means of rejecting control gene outliers. The recovery of normalizational regression and control gene selection are interleaved and are realized by applying coupled operations to the mean square error function. In this way, the two processes bootstrap one another. We evaluate the technique on real microarray data from breast cancer cell lines and complement the experiment with a data cluster visualization study.

Image fusion in open-architecture PACS-environment

Multimodal digital imaging is common in many fields of diagnosis and therapy planning - there is great interest in matching globally, fusing or registering data from the same part of the body. In practice, there are still difficulties in customizing image fusion in hospitals. Efficient routine use of image fusion requires, among others, an image management infrastructure - a picture archiving and communication system (PACS) - to provide storage of image data in a standard digital format, intelligent image management and fault-tolerant high-speed image networking. In order to customize image fusion, advances in both fusion software and hardware are also needed. The algorithms should be automatic, fast and accurate enough. Registration of multimodal data also creates a need for different display techniques and user-friendly interfaces. Image fusion has been impractical and too tedious to be performed in routine work, but in the future, fused images will be used in clinical practice - even in teleradiological consultation.

Efficient nonlinear registration of 3D images using high order co-ordinate transfer functions

There is an increasing interest in image registration for a variety of medical imaging applications. Image registration is achieved through the use of a co-ordinate transfer function (CTF) which maps voxels in one image to voxels in the other image, including in the general case changes in mapped voxel intensity. If images of the same subject are to be registered the co-ordinate transfer function needs to implement a spatial transformation consisting of a displacement and a rigid rotation. In order to achieve registration a common approach is to choose a suitable quality-of-registration measure and devise a method for the efficient generation of the parameters of the CTF which minimize this measure. For registration of images from different subjects more complex transforms are required. In general function minimization is too slow to allow the use of CTFs with more than a small number of parameters. However, provided the images are from the same modality and the CTF can be expanded in terms of an appropriate set of basis functions this paper will show how relatively complex CTFs can be used for registration. The use of increasingly complex CTFs to minimize the within group standard deviation of a set of normal single photon emission tomography brain images is used to demonstrate the improved registration of images from different subjects using CTFs of increasing complexity.

Registration of MR/MR and MR/SPECT brain images by fast stochastic optimization of robust voxel similarity measures

This paper describes a robust, fully automated algorithm to register intrasubject 3D single and multimodal images of the human brain. The proposed technique accounts for the major limitations of the existing voxel similarity-based methods: sensitivity of the registration to local minima of the similarity function and inability to cope with gross dissimilarities in the two images to be registered. Local minima are avoided by the implementation of a stochastic iterative optimization technique (fast simulated annealing). In addition, robust estimation is applied to reject outliers in case the images show significant differences (due to lesion evolution, incomplete acquisition, non-Gaussian noise, etc.). In order to evaluate the performance of this technique, 2D and 3D MR and SPECT human brain images were artificially rotated, translated, and corrupted by noise. A test object was acquired under different angles and positions for evaluating the accuracy of the registration. The approach has also been validated on real multiple sclerosis MR images of the same patient taken at different times. Furthermore, robust MR/SPECT image registration has permitted the representation of functional features for patients with partially complex seizures. The fast simulated annealing algorithm combined with robust estimation yields registration errors that are less than 1 degree in rotation and less than 1 voxel in translation (image dimensions of 128(3)). It compares favorably with other standard voxel similarity-based approaches.

Three- to five-dimensional biomedical multisensor imaging for the assessment of neurological (dys) function

This report describes techniques and protocols implemented at the Geneva Canton University Hospitals (HUG) for the combination of various biomedical imaging modalities and sensors including electromagnetic tomography, to study, assess, and localize neurological (dys) function. The interest for this combination stems from the broad variety of information brought out by (functional) magnetic resonance imaging, magnetic resonance spectroscopy, computed tomography, single-photon emission tomography, positron emission tomography, and electromagnetic tomography. Combining these data allows morphology, metabolism, and function to be studied simultaneously, the complementary nature of the information from these modalities becoming evident when studying pathologies reflected by metabolic or electrophysiologic dysfunctions. Compared with other current multimodality approaches, the one at the HUG is totally compatible with both clinical and research protocols, and efficiently addresses the multidimensional registration and visualization issues. It also smoothly integrates electrophysiology and related data as fully featured modalities.

Volumetric measurement of multifocal brain lesions. Implications for treatment trials of vascular dementia and multiple sclerosis

This pilot study examined the reproducibility of serial magnetic resonance (MR) measurements of brain, ventricular, sulcal, and lesion volumes in patients with ischemic brain disease using an image analysis protocol designed at the University of Cincinnati. Five patients with a clinical history of brain ischemia had two separate MR brain imaging studies using the standard clinical MR imaging protocol at the University of Cincinnati Medical Center. The MR images on both film and tape were digitized and then analyzed according to the standardized image analysis protocol. Based on tape data, variability in volume measurements between the two MR studies, as measured by the coefficient of variation, ranged from 1% for intracranial volume to 8% for ventricular volume. Variability based on film data was slightly greater, ranging from 2% for intracranial volume to 12% for lesion volume. As part of a multicenter treatment trial of vascular dementia, this method was then used to analyze MR films in 13 patients with vascular dementia who all had an MR study at baseline and at 1 year. The mean annual change in lesion volume was 4 +/- 5 cm3 (a 24% increase from the baseline lesion volume); in ventricular volume, 7 +/- 8 cm3 (a 10% increase from baseline); and in sulcal volume, 13 +/- 25 cm3 (a 5% increase from baseline). This method of image analysis, using MR film or tape-generated data, can provide reproducible serial measurements of brain, ventricular, sulcal, and ischemic lesion volumes. This method, if applied in randomized treatment trials of vascular dementia or multiple sclerosis, can be used to monitor disease progression and to evaluate the effectiveness of a given therapy.