Improved efficiency for MRI-SPET registration based on mutual information

Mutual information has been proposed as a criterion for image registration. The criterion is calculated from a two-dimensional grey-scale histogram of the image pair being registered. In this paper we study how sparse sampling can be used to increase speed performance using the registration algorithm of Maes et al. (IEEE Trans Med Imaging 1997; 16: 187–198) with a focus on registration of MRI-SPET brain images. In particular we investigate how sparse sampling and parameters such as the number of bins used for the grey-scale histograms and smoothing of the data prior to registration affect accuracy and robustness of the registration. The method was validated using both simulated and human data. Our results show that sparse sampling introduced local maxima into the mutual information similarity function when the number of bins used for the histograms was large. To speed up registration while retaining robustness, smoothing of the data prior to registration was used and a coarse to fine subsampling protocol, where the number of bins in the histograms were dependent on the subsampling factor, was employed. For the simulated data, the method was able to recover known transformations with an accuracy of about 1 mm. Using the human data, there were no significant differences in the recovered transformation parameters when the suggested subsampling scheme was used compared with when no subsampling was used, but there was a more than tenfold increase in speed. Our results show that, with the appropriate choice of parameters, the method can accurately register MRI-SPET brain images even when very efficient sampling protocols are used. Received 14 December 1999 and in revised form 15 March 2000     

Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation

Mutual information (MI) is a popular similarity measure for performing image registration between different modalities. MI makes a statistical comparison between two images by computing the entropy from the probability distribution of the data. Therefore, to obtain an accurate registration it is important to have an accurate estimation of the true underlying probability distribution. Within the statistics literature, many methods have been proposed for finding the ‘optimal’ probability density, with the aim of improving the estimation by means of optimal histogram bin size selection. This provokes the common question of how many bins should actually be used when constructing a histogram. There is no definitive answer to this. This question itself has received little attention in the MI literature, and yet this issue is critical to the effectiveness of the algorithm. The purpose of this paper is to highlight this fundamental element of the MI algorithm. We present a comprehensive study that introduces methods from statistics literature and incorporates these for image registration. We demonstrate this work for registration of multi-modal retinal images: colour fundus photographs and scanning laser ophthalmoscope images. The registration of these modalities offers significant enhancement to early glaucoma detection, however traditional registration techniques fail to perform sufficiently well. We find that adaptive probability density estimation heavily impacts on registration accuracy and runtime, improving over traditional binning techniques.     

First-pass myocardial perfusion image registration by maximization of normalized mutual information

PURPOSE: To evaluate a left ventricular image registration algorithm for first-pass MR myocardial perfusion. MATERIALS AND METHODS: A normalized mutual information based motion correction algorithm was proposed and tested on 27 adenosine stressed myocardial perfusion cases consisting of pretreatment and posttreatment of 15 patients undergone autologous bone marrow mononuclear cell transplant therapy. An image mask approximately covering the left and right ventricles was manually defined to include a region of interest for registration. A two-dimensional multiresolution registration approach was used to register consecutively acquired multislice images with in-plane translations. The method was validated by manual registration and singular value deconvolution based perfusion analysis. RESULTS: The proposed image registration algorithm was found to be robust in minimizing the in-plane motion of the left ventricle in first-pass myocardial perfusion. The image mask including the left and right ventricle was found to be more robust than including the left ventricle alone. A smooth estimate of normalized mutual information coefficients were achieved for images with large contrast changes. CONCLUSION: The proposed semiautomatic multiresolution registration algorithm was able to register first-pass MR myocardial perfusion images and may be useful in quantitative perfusion analysis.     

Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images

We implemented a 3D co-registration technique based on mutual information (MI) including 2D image matching as a coarse pre-registration. The 2D coarse pre-registration was performed in the transverse, sagittal and coronal planes sequentially, and all six parameters were then optimized as fine registration. Normalized mutual information (NMI) was also examined as another entropy-based measure that was invariant to the overlapped area of two images. In order to compare accuracy and precision of the present method with a conventional two-level multiresolution approach, simulation was performed by 100 trials with the random initial mismatch of +/-10 degrees and +/-17.92 mm (Type-I) and +/-20 degrees and +/-40.32 mm (Type-II). For Type-I, no significant differences were found between registration errors of the multiresolution approach and the present method with the MI criterion. No biases were observed (< or =0.13 degrees and < or =0.57 mm for the multiresolution approach; < or =0.12 degrees and < or =0.57 mm for the present method) and the SDs were very small (< or =0.18 degrees and < or =0.12 mm for the multiresolution approach; < or =0.11 degrees and < or =0.11 mm for the present method). For Type-II, SDs for the multiresolution approach (< or =1.8 degrees and < or =0.88 mm) were markedly larger than those for the present method (< or =0.64 degrees and < or =0.20 mm) with MI. Success rate for the present method was 99.9%, which was higher than 97.6% for the multiresolution approach. Simulation also revealed that MI and NMI performance were almost equivalent. The choice of optimization strategy more affected accuracy and reproducibility than the choice of the registration criterion (MI or NMI) in our simulation condition. The present method is sufficiently accurate and reproducible for MRI-SPECT registration in clinical use.     

Subtraction SPECT for parathyroid scintigraphy based on maximization of mutual information

Our objective was to investigate the feasibility of subtraction for SPECT images of (99m)Tc-MIBI double-phase parathyroid scintigraphy. METHODS: Fourteen patients with hyperparathyroidism were enrolled in the present study. Histopathologically, excised tissue specimens showed hyperplasia in 11 patients and adenoma in 3 patients. Both ultrasonography and (99m)Tc-sestamibi (MIBI) SPECT images were obtained from all patients. As standard lines to ensure that patient positioning remained identical between the different phases, we used the cross-marker produced by a pair of laser pointers, the orbitomeatal line, and the vertical midline through the patient's nose. Data processing was performed with software that enables image registration by maximization of mutual information. The results of subtraction SPECT imaging were compared with those of ultrasonography. RESULTS: The registration of double-phase SPECT images was successful in all patients when the salivary glands were excluded from the image reconstruction region. The overall sensitivities of scintigraphy and ultrasonography were 90.9% (40/44) and 70.5% (31/44), respectively, with respective specificities of 83.3% (10/12) and 75.0% (9/12). Scintigraphy and ultrasonography showed accuracies of 92.8% (52/56) and 71.4% (40/56), respectively. CONCLUSION: The new technique used in the present study allowed the subtraction for SPECT images. The sensitivity of parathyroid lesion detection using this technique was superior to that of ultrasonography.     

The use of mutual information in registration of CT and MRI datasets post permanent implant

PURPOSE: To determine the feasibility of registration of MRI and CT datasets post permanent prostate implant by the use of mutual information. METHODS AND MATERIALS: Five patients who underwent permanent (125)I implant for prostate carcinoma were studied. Two weeks postimplant an axial CT, T2-weighted-axial, sagittal and coronal MRI, and T1-fat-saturation MRI scans were obtained. Registrations of MRI to CT and MRI to MRI datasets were performed by mutual information, an automated process of data registration matching all information in specified dataset regions of interest. Registration quality was evaluated by visual inspection, agreement with seed- to-seed registration, and histogram analysis. RESULTS: Rapid registration (<30 minutes) of CT and MRI datasets can be accomplished through the use of mutual information. All methods of registration evaluation confirmed excellent registration quality. Although D90 and V100 for the prostate were comparable between MRI- and CT-based dosimetry, dose to critical structures/microenvironments (anterior base, posterior base, bladder outlet, lower sphincter, bulbar urethra) defined on MRI varied widely. CONCLUSIONS: Efficient and accurate registration of MRI and CT datasets following prostate implant is possible, and improves the accuracy of postimplant dosimetry by superior definition of the prostate. Definition of critical microenvironments and adjacent structures will improve dose and toxicity correlation and ultimately improve planning strategies.     

Influence of implementation parameters on registration of MR and SPECT brain images by maximization of mutual information.

Mutual-information maximization is one of the most popular algorithms for automatic image registration. However, many implementation issues have not been evaluated in a single, coherent context. METHODS: Twenty-one registrations between MR and SPECT brain images (8 patients) were achieved by mutual-information maximization with different implementation strategies. The results of a popular strategy were chosen as the standard. All other results were compared with the standard, and the statistics of misregistrations were computed. The registration speed, accuracy, precision, and success rate were assessed. RESULTS: Compared with trilinear interpolation, nearest-neighbor interpolation slightly sped the registration process, but with a lower success rate. The number of bins used to estimate the probability density function (pdf) affects the speed and robustness. Using fewer bins yielded a less robust registration. Adaptively changing the number of bins increased the registration speed and robustness. Simplex optimization increased the registration speed considerably, with a slightly degraded success rate. Simplex optimization with adaptive bin strategy improved the success rate and further decreased the registration time. Multiresolution optimization yielded a better success rate, with little effect on the accuracy and precision of registration. An increase in the number of resolution levels increased the success rate. Multisampling optimization also improved the success rate, but the results were less accurate and precise than those obtained with multiresolution optimization, with an increase in the number of levels decreasing the performance. Segmentation affected the registration speed and success rate. Because segmentation is problem specific, the effects were not conclusive. CONCLUSION: Different implementation strategies considerably affect the performance of automatic image registration by mutual-information maximization. On the basis of the experimental findings, we suggest that the best implementation strategy would include trilinear interpolation, adaptive change of the number of bins when estimating pdf, and exploitation of a simplex optimization algorithm with a multiresolution scheme.     

Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images.

A global optimization technique for image registration, based on mutual information, that can be used in conjunction with a multi-resolution paradigm is described. This technique combines genetic algorithm in continuous space, which is a stochastic method and is very efficient in large search space, with dividing rectangle, which is a deterministic method that theoretically guarantees global optimization and is efficient in small search space. Calculations were performed for determining the optimum parameters for implementing this method. This technique was applied to register magnetic resonance images of brain. For comparison, the registration results using AIR, a commonly employed software package, are presented.     

Improved image registration by using fourier interpolation

This paper presents a technique for performing two-dimensional rigid-body image registration for functional magnetic resonance images (fMRI). The method provides accurate motion correction without local distortion. The approach is to perform the translation and rotation in the Fourier domain. For images sampled on a grid, such as in echo-planar imaging (EPI), one potential stumbling block to this approach is the computational burden of reconstruction, since the rotated image will no longer be on the Cartesian grid. A method of approximating rotations via local translations (shearing) is presented, which keeps the data on the Cartesian grid. This can provide quite accurate approximations with only a moderate amount of computation. A mean squared error (MSE) criterion is used for determining the registration parameters. This method is tested on several sets of simulated images and shown to have an accuracy ranging from 0.02 to 0.3 pixels for images with SNRs ranging from 100 to 10, respectively. They techniques have been tested on several sets of images. They are shown to work well on real subjects, for both echo-planar and spiral data acquisition schemes. The techniques are used in an activation study in which the subject moved his head during image collection. After use of this registration technique, the activation is easily detected.     

A contrast correction method for dental images based on histogram registration

Contrast correction is often required in digital subtraction radiography when comparing medical data acquired over different time periods owing to dissimilarities in the acquisition process. This paper focuses on dental radiographs and introduces a novel approach for correcting the contrast in dental image pairs.The proposed method modifies the subject images by applying typical registration techniques on their histograms. The proposed histogram registration method reshapes the histograms of the two subject images in such a way that these images are matched in terms of their contrast deviation. The method was extensively tested over 4 sets of dental images, consisting of 72 registered dental image pairs with unknown contrast differences as well as 20 dental pairs with known contrast differences. The proposed method was directly compared against the well-known histogram-based contrast correction method.The two methods were qualitatively and quantitatively evaluated for all 92 available dental image pairs. The two methods were compared in terms of the contrast root mean square difference between the reference image and the corrected image in each case. The obtained results were also verified statistically using appropriate t-tests in each set.The proposed method exhibited superior performance compared with the well-established method, in terms of the contrast root mean square difference between the reference and the corrected images. After suitable statistical analysis, it was deduced that the performance advantage of the proposed approach was statistically significant.     

Directed graph based image registration

In this paper, a novel image registration method is proposed to achieve accurate registration between images having large shape differences with the help of a set of appropriate intermediate templates. We first demonstrate that directionality is a key factor in both pairwise image registration and groupwise registration, which is defined in this paper to describe the influence of the registration direction and paths on the registration performance. In our solution, the intermediate template selection and intermediate template guided registration are two coherent steps with directionality being considered. To take advantage of the directionality, a directed graph is built based on the asymmetric distance defined on all ordered image pairs in the image population, which is fundamentally different from the undirected graph with symmetric distance metrics in all previous methods, and the shortest distance between template and subject on the directed graph is calculated. The allocated directed path can be thus utilized to better guide the registration by successively registering the subject through the intermediate templates one by one on the path towards the template. The proposed directed graph based solution can also be used in groupwise registration. Specifically, by building a minimum spanning arborescence (MSA) on the directed graph, the population center, i.e., a selected template, as well as the directed registration paths from all the rest of images to the population center, is determined simultaneously. The performance of directed graph based registration algorithm is demonstrated by the spatial normalization on both synthetic dataset and real brain MR images. It is shown that our method can achieve more accurate registration results than both the undirected graph based solution and the direct pairwise registration.     

A digital subtraction radiography scheme based on automatic multiresolution registration

OBJECTIVES: To establish a digital subtraction radiography scheme for aligning clinical in vivo radiographs based on the implementations of an automatic geometric registration method and a contrast correction technique. METHODS: Thirty-five pairs of in vivo dental radiographs from four clinical studies were used in this work. First, each image pair was automatically aligned by applying a multiresolution registration strategy using the affine transformation followed by the implementation of the projective transformation at full resolution. Then, a contrast correction technique was applied in order to produce subtraction radiographs and fused images for further clinical evaluation. The performance of the proposed registration method was assessed against a manual method based on the projective transformation. RESULTS: The qualitative assessment of the experiments based on visual inspection has shown advantageous performance of the proposed automatic registration method against the manual method. Furthermore, the quantitative analysis showed statistical difference in terms of the root mean square (RMS) error estimated over the whole images and specific regions of interest. CONCLUSIONS: The proposed automatic geometric registration method is capable of aligning radiographs acquired with or without rigorous a priori standardization. The methodology is pixel-based and does not require the application of any segmentation process prior to alignment. The employed projective transformation provides a reliable model for registering intraoral radiographs. The implemented contrast correction technique sequentially applied provides subtraction radiographs and fused images for clinical evaluation regarding the evolution of a disease or the response to a therapeutic scheme.     

Improved quantitative dynamic regional oxygen-enhanced pulmonary imaging using image registration.

Oxygen-enhanced MR imaging has been demonstrated in a number of recent studies as a potential method to visualize regional ventilation in the lung. A quantitative pixel-by-pixel analysis is hampered by motion and volume change due to breathing. In this study, image registration via active shape modeling is shown to produce significant improvements in the regional analysis of both static and dynamic oxygen-enhanced pulmonary MRI for five normal volunteers. The method enables the calculation of regional change in relaxation rate between breathing air and 100% oxygen, which is proportional to the change in regional oxygen concentration, and regional oxygen wash-in and wash-out time constants. Registration-corrected mapping of these parameters is likely to provide improved information in the regional assessment of a range of lung diseases.     

Prostate positioning using cone-beam computer tomography based on manual soft-tissue registration

Purpose

To check the interobserver agreement between radiation oncologists and therapists (RTT) using an on- and off-line cone-beam computer tomography (CBCT) protocol for setup verification in the radiotherapy of prostate cancer.

Materials and methods

The CBCT data from six prostate cancer patients treated with hypofractionated intensity-modulated radiotherapy (IMRT) were independently reviewed off-line by four observers (one radiation oncologist, one junior and two senior RTTs) and benchmarked with on-line CBCT positioning performed by a radiation oncologist immediately prior to treatment. CBCT positioning was based on manual soft-tissue registration. Agreement between observers was evaluated using weighted Cohen’s kappa statistics.

Results

In total, 152 CBCT-based prostate positioning procedures were reviewed by each observer. The mean (± standard deviation) of the differences between off- and on-line CBCT–simCT registration translations along the three directions (antero-posterior, latero-lateral and cranio-caudal) and rotation around the antero-posterior axis were ??0.7 (3.6) mm, 1.9 (2.7) mm, 0.9 (3.6) mm and ??1.8 (5.0) degrees, respectively. Satisfactory interobserver agreement was found, being substantial (weighted kappa >?0.6) in 10 of 16 comparisons and moderate (0.41–0.60) in the remaining six comparisons.

Conclusions

CBCT interpretation performed by RTTs is comparable to that of radiation oncologists. Our study might be helpful in the quality assurance of radiotherapy and the optimization of competencies. Further investigation should include larger sample sizes, a greater number of observers and validated methodology in order to assess interobserver variability and its impact on high-precision prostate cancer IGRT. In the future, it should enable the wider implementation of complex and evolving radiotherapy technologies.     

改进式的二维图像配准算法在头颈部肿瘤中的应用

目的 研究摆位误差导致的二维图像投影变化及其对图像配准的影响,提出一种改进的互信息配准算法。方法 借助仿真头部体模,分别模拟旋转误差和平移误差,通过互信息的变化来反映投影形变。以3mm平移误差和3°旋转误差为界,模拟10例较小摆位误差和10例较大摆位误差,拍摄正侧位射野图像,分别使用目前加速器自带的传统互信息配准方法和本研究改进的互信息配准方法获取摆位误差,并与实际摆位误差相比较,以判断本研究提出的改进配准方法的优劣。结果 对于摆位误差较小的实例,加速器自带的传统互信息配准方法的x、y、z轴平均平移误差分别为0.3、0.4和0.3 mm,x、z轴平均旋转误差均为0.4°,平均耗时28.7 s。本研究改进的互信息配准方法的平均误差为0.4、0.3和0.3 mm,x、z轴平均旋转误差分别为0.5°和0.4°,平均耗时31.1 s。对于摆位误差较大的实例,加速器自带的传统互信息配准方法的平均误差分别为0.9、0.7和0.8 mm,x、z轴平均旋转误差为0.9°和0.8°,平均耗时29.9 s,本研究的改进互信息方法平均误差分别为0.5、0.4和0.5 mm,x、z轴平均旋转误差分别为0.6°和0.5°,平均耗时33.2 s。结论 对于较小的摆位误差,两种方法都具有较高的配准精度,但对于较大的摆位误差,本研究改进互信息配准方法较加速器自带的互信息配准方法具有显着的精度优势,并且配准耗时也在临床可以接受的范围内。     

Improved efficiency in double-inversion fast spin-echo imaging.

Double-inversion fast spin-echo (FSE) pulse sequences can be designed to provide excellent suppression of blood signal in black-blood MRI. However, because a nonselective inversion is used, these sequences typically have been highly inefficient. In this work it is demonstrated that the efficiency of double-inversion sequences can be greatly improved by a form of interleaving in which all of the slices to be imaged in a single pass are reinverted each time a signal is obtained from any single slice. To date, several studies have demonstrated a high level of blood suppression with these more efficient techniques.     

Retrospective distortion correction for 3D MR diffusion tensor microscopy using mutual information and Fourier deformations.

Magnetic resonance diffusion tensor imaging (DTI) can be complicated by distortions that contribute to errors in tissue characterization and loss of fine structures. This work presents a correction scheme based on retrospective registration via mutual information (MI), using Fourier transform (FT)-based deformations to enhance the reliability of the entropy-based image registration. The registration methodology is applied to correct distortions in 3D high-resolution DTI datasets, incorporating a complete set of affine deformations. The results demonstrate that the proposed methodology can consistently and significantly reduce the number of misregistered pixels, leading to marked improvement in the visualization of internal brain white matter (WM) structure via DTI. Post-registration analysis revealed that eddy-current effects cannot fully account for the observed image distortions. Combined, these findings support the non-model-based, postprocessing approach for correcting distortions, and demonstrate the advantages of combining FT-based deformations and MI registration to enhance the practical utility of DTI.     

Improved quality and information in thyroid scintigraphy

We compared rectilinear scanning, analogous gamma-camera imaging and imaging using a gamma camera interfaced to a computer in order to determine which of these techniques is superior for thyroid scintigraphy. Forty patients with and without goitre and irregular activity distribution were imaged using each of the three techniques. The sets of three scintigrams per patient were rated by seven physicians. These ratings were statistically analysed using a new distribution-free test. Thyroid scintigraphy using a gamma camera interfaced to a computer turned out to be the best method, especially for detecting circumscribed areas of increased uptake. We conclude that this was due to a combination of the following factors: a computer matrix of 128 X 128 pixels in combination with an acquisition zoom of 1.5, the high count density of the scintigrams, computer-controlled documentation after data acquisition and the use of a continuous grey scale for documentation. The improved quality and information content of the scintigrams obtained using a gamma-camera/computer system could improve the diagnostic value of thyroid scintigraphy, especially when the scintigrams are also evaluated quantitatively.     

Improved quality and information in thyroid scintigraphy

We compared rectilinear scanning, analogous gamma-camera imaging and imaging using a gamma camera interfaced to a computer in order to determine which of these techniques is superior for thyroid scintigraphy. Forty patients with and without goitre and irregular activity distribution were imaged using each of the three techniques. The sets of three scintigrams per patient were rated by seven physicians. These ratings were statistically analysed using a new distribution-free test. Thyroid scintigraphy using a gamma camera interfaced to a computer turned out to be the best method, especially for detecting circumscribed areas of increased uptake. We conclude that this was due to a combination of the following factors: a computer matrix of 128x128 pixels in combination with an acquisition zoom of 1.5, the high count density of the scintigrams, computer-controlled documentation after data acquisition and the use of a continuous grey scale for documentation. The improved quality and information content of the scintigrams obtained using a gamma-camera/computer system could improve the diagnostic value of thyroid scintigraphy, especially when the scintigrams are also evaluated quantitatively.     

Automatic registration of brain magnetic resonance images based on Talairach reference system

PURPOSE: To demonstrate a robust registration method of brain magnetic resonance (MR) images based on the Talairach reference system with automatic determinations of the fiducial points. MATERIALS AND METHODS: Eight specified landmark points of the Talairach reference system are determined after successfully extracting the midsagittal plane from three-dimensional MR imaging (MRI) data. Projection information of the image intensity is used to determine the midline of the cerebrum in axial and coronal view images, which is a necessary step for extraction of the midsagittal plane. To find the landmarks of anterior commissure (AC) and posterior commissure (PC) in the midsagittal plane, we adopt two-step shape matching that properly finds locations of the corpus callosum (CC), and then AC and PC, respectively. The shape matching is performed on the edge-enhanced midsagittal plane image to minimize dependency on image intensity variation. Other landmark points of the Talairach reference system are determined by fitting the intensity curve of the cutview with the Gaussian model. RESULTS: The proposed method automatically determines seven landmark points, except the inferior point (IP), and the brain MR images can be successfully registered with the Talairach reference system. CONCLUSION: The suggested registration method can be applied to any MR images for functional studies. It can also be applied to patients unless their brains are highly deformed or have a highly deformed CC.