Region-growing segmentation of brain vessels: an atlas-based automatic approach

PURPOSE: To propose an atlas-based method that uses both phase and magnitude images to integrate anatomical information in order to improve the segmentation of blood vessels in cerebral phase-contrast magnetic resonance angiography (PC-MRA). MATERIAL AND METHODS: An atlas of the whole head was developed to store the anatomical information. The atlas divides a magnitude image into several vascular areas, each of which has specific vessel properties. It can be applied to any magnitude image of an entire or nearly entire head by deformable matching, which helps to segment blood vessels from the associated phase image. The segmentation method used afterwards consists of a topology-preserving, region-growing algorithm that uses adaptive threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels that are selected according to their grayscale value and the variation of values in their neighborhood. The topology preservation is guaranteed because only simple points are selected during the growing process. RESULTS: The method was performed on 40 PC-MRA images of the brain. The results were validated using maximum-intensity projection (MIP) and three-dimensional surface rendering visualization, and compared with results obtained with two non-atlas-based methods. CONCLUSION: The results show that the proposed method significantly improves the segmentation of cerebral vascular structures from PC-MRA. These experiments tend to prove that the use of vascular atlases is an effective way to optimize vessel segmentation of cerebral images.     

Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images

RATIONALE AND OBJECTIVES: To establish the range of normal values for quantitative CT-based measures of lung structure and function, the authors developed a method for matching pulmonary structures across individuals and creating a normative human lung atlas. MATERIALS AND METHODS: A computerized human lung atlas was synthesized from computed tomographic (CT) images from six subjects by means of three-dimensional image registration. The authors identified a set of reproducible feature points for each CT image and used these points to establish correspondences across subjects, used a landmark- and intensity-based consistent image registration algorithm to register a template image volume from the population to the rest of the pulmonary CT volumes in the population, averaged these transformations, and constructed an atlas by deforming the template with the average transformation. RESULTS: The effectiveness of the authors' method was evaluated and visualized by means of both gray-level and segmented CT images. The method reduced the average landmark registration error from 10.5 mm to 0.4 mm and the average relative volume overlap error from 0.7 to 0.11 for the six data sets studied. CONCLUSION: The method, and the computerized human lung atlas constructed and visualized by the authors with this method, provides a basis for establishing regional ranges of normative values for structural and functional measures of the human lung.     

Automated analysis of small animal PET studies through deformable registration to an atlas

Purpose

This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.

Methods

A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.

Results

The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6?mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10?% in most of the organs considered.

Conclusion

The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.     

Automatic method to assess local CT-MR imaging registration accuracy on images of the head

BACKGROUND AND PURPOSE: Precise registration of CT and MR images is crucial in many clinical cases for proper diagnosis, decision making or navigation in surgical interventions. Various algorithms can be used to register CT and MR datasets, but prior to clinical use the result must be validated. To evaluate the registration result by visual inspection is tiring and time-consuming. We propose a new automatic registration assessment method, which provides the user a color-coded fused representation of the CT and MR images, and indicates the location and extent of poor registration accuracy. METHODS: The method for local assessment of CT-MR registration is based on segmentation of bone structures in the CT and MR images, followed by a voxel correspondence analysis. The result is represented as a color-coded overlay. The algorithm was tested on simulated and real datasets with different levels of noise and intensity non-uniformity. RESULTS: Based on tests on simulated MR imaging data, it was found that the algorithm was robust for noise levels up to 7% and intensity non-uniformities up to 20% of the full intensity scale. Due to the inability to distinguish clearly between bone and cerebro-spinal fluids in the MR image (T1-weighted), the algorithm was found to be optimistic in the sense that a number of voxels are classified as well-registered although they should not. However, nearly all voxels classified as misregistered are correctly classified. CONCLUSION: The proposed algorithm offers a new way to automatically assess the CT-MR image registration accuracy locally in all the areas of the volume that contain bone and to represent the result with a user-friendly, intuitive color-coded overlay on the fused dataset.     

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

Purpose

The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT generation approach.

Methods

The proposed approach consists of only one online registration between the target and reference images, regardless of the number of atlas images (N), while for the remaining atlas images, the pre-computed transformation matrices to the reference image are used to align them to the target image. The performance characteristics of the proposed method were evaluated and compared with conventional atlas-based attenuation map generation strategies (direct registration of the entire atlas images followed by voxel-wise weighting (VWW) and arithmetic averaging atlas fusion). To this end, four different positron emission tomography (PET) attenuation maps were generated via arithmetic averaging and VWW scheme using both direct registration and ORMA approaches as well as the 3-class attenuation map obtained from the Philips Ingenuity TF PET/MRI scanner commonly used in the clinical setting. The evaluation was performed based on the accuracy of extracted whole-body bones by the different attenuation maps and by quantitative analysis of resulting PET images compared to CT-based attenuation-corrected PET images serving as reference.

Results

The comparison of validation metrics regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82?±?0.04 compared to arithmetic averaging atlas fusion (0.60?±?0.02), which uses conventional direct registration. Application of the ORMA approach modestly compromised the accuracy, yielding a Dice similarity measure of 0.76?±?0.05 for ORMA-VWW and 0.55?±?0.03 for ORMA-averaging. The results of quantitative PET analysis followed the same trend with less significant differences in terms of SUV bias, whereas massive improvements were observed compared to PET images corrected for attenuation using the 3-class attenuation map. The maximum absolute bias achieved by VWW and VWW-ORMA methods was 06.4?±?5.5 in the lung and 07.9?±?4.8 in the bone, respectively.

Conclusions

The proposed algorithm is capable of generating decent attenuation maps. The quantitative analysis revealed a good correlation between PET images corrected for attenuation using the proposed pseudo-CT generation approach and the corresponding CT images. The computational time is reduced by a factor of 1/N at the expense of a modest decrease in quantitative accuracy, thus allowing us to achieve a reasonable compromise between computing time and quantitative performance.

     

Bestrahlungsplanung rückenmarksnaher Tumoren unter Zuhilfenahme der CT-Myelographie

BACKGROUND AND AIM: Artifacts due to metal implants are an important problem in diagnostic radiology and radiotherapy planning in tumors such as chordoma of the spine. A strict differentiation between target and radiosensitive structures e.g. spinal cord is absolutely essential for high-dose radiotherapy. Up to now CT and MRI techniques have provided only limited image quality in such situations. We introduce an approach to facilitate segmentation by using the technique of CT-myelography for radiation treatment. PATIENT AND METHOD: A 48-year-old woman with multiple inoperable relapses of a chordoma in the lumbar spine and extensive metal instrumentation in this area was given to radiotherapy using IMRT-technique (intensity modulated). MRI- and CT-planning images did not allow differentiation between myelon, cauda equina, dural sac and tumor. In this situation we performed a CT-myelography with the patient in treatment position. RESULT: CT-myelographic images enabled precise differentiation between myelon, cauda equina and intraspinal tumor. A substantial improvement of the segmentation of the spinal cord was obtained. There was no compression of the dural sac along the spine. This information provided the basis for a precise radiotherapy planning in IMRT-technique. CONCLUSION: In situations where CT- and MRI-techniques are not able to generate precise images which allow differentiation between tumor, myelon and cauda equina because of metal artifacts, CT-myelography is a promising technique which may help the diagnostic radiologist and radiation oncologist in planning radiotherapy.     

Segmentation of brain MRI in young children

RATIONALE AND OBJECTIVES: This article deals with an automatic tissue segmentation of brain magnetic resonance imaging (MRI) in young children. MATERIALS AND METHODS: We examine the suitability of state-of-the-art methods developed for the adult brain when applied to the segmentation of the brain MRI in young children. We develop a method of creation of a population-specific atlas in young children using a single manual segmentation. The method is based on nonlinear propagation of the segmentation into population and subsequent affine alignment into a reference space and averaging. RESULTS: Using this approach, we significantly improve the performance of the popular expectation-maximization algorithm on brain MRI in young children. The method can be used for building probabilistic atlases with any number of structures. We compare resulting algorithm with nonrigid registration-based label propagation. CONCLUSIONS: Finally, both methods are used to measure the volume of seven brain structures and measure the growth between 1 and 2 years of age.     

Nonrigid registration for subtraction CT angiography applied to the carotids and cranial arteries

RATIONALE AND OBJECTIVES: Computed tomography angiography (CTA) is an established tool for vascular imaging. However, high-intensity nonvascular structures in the contrast image can seriously hamper luminal visualization. This is an issue for three-dimensional visualization, where high-intensity structures might cover the underlying vasculature. But also in two dimensions, calcified plaques adjacent to the contrast-enhanced vessel lumen impede correct determination of the vessel boundary. High-intensity structures can be eliminated using subtraction CTA, where a native image is subtracted from the contrast image. However, patient and organ motion limits the widespread application of this technique. We propose to use nonrigid image registration to solve this problem. MATERIALS AND METHODS: For each patient, a native image and a contrast image are recorded, respectively, before and after contrast administration. The native image is registered to the contrast image using an automatic intensity-based nonrigid three-dimensional registration algorithm. Both images are merged in a fused image, allowing the user to switch between a view of the arteries, the bone or both. The procedure has been applied to 95 patients. RESULTS: In all cases, subtraction CTA using nonrigid registration allows for a significantly better artifacts removal than subtraction CTA without registration. Image quality of all images was judged adequate for clinical use. The average total processing time for each dataset is about 30 minutes. CONCLUSION: Nonrigid registration can allow for a great reduction in subtraction artifacts for subtraction CTA, resulting in a clear view of the vasculature.     

Integration of computer assisted bone age assessment with clinical PACS.

Computer assisted bone age assessment (BAA) integrated with a clinical PACS is described. The image analysis is performed on a DICOM compliant workstation able to accept images from a PACS server or directly from an image modality (digital radiography or film scanner). Images can be processed in two modes. If the image is acquired from a normally developed subject, it can be added to the digital hand atlas. An image may also be subjected only to a diagnostic analysis for the BAA without archiving the features in the database. The image analysis is performed in three steps. A location of six region of interest is followed by their segmentation and feature extraction. The features analysis results in retrieving the closest image match from the standard database. Based on currently analyzed image data in the hand atlas, the standard deviation of the assessment bone age does not exceed 1 yr of age.     

Automatic measurement of the labyrinth using image registration and a deformable inner ear atlas

RATIONALE AND OBJECTIVES: This article presents a new method for measuring the shape of the cochlea, vestibule, semi-circular canals, and internal auditory canal using image registration and a deformable inner ear atlas. MATERIALS AND METHODS: Computed tomography images of the inner ear are analyzed by placing them into a common orientation and then registering a digital atlas of the inner ear to the data set. The atlas is deformed from its original shape to match the shape of the inner ear in the computed tomography data set using inverse consistent elastic image registration. This process produces an individualized inner ear atlas containing subject-specific measurements and segmentations of the inner ear anatomy in the target computed tomography data set. The shape measurements include the volume and length of the cochlea, vestibule, semi-circular canals, and internal auditory canal; and the angles between the semi-circular canals. RESULTS: A simulated population of inner ear shapes were generated based on the shape of a real population of inner ear shapes and were used to characterize the measurement error of this method. The deformable atlas was used to measure the shape of the left and right inner ear of six individuals. CONCLUSION: Measurement error for 15 of the 24 measurements of our simulated population had an average error of less than 1% and only one measurement had an average error greater than 2.54%. The deformable human inner ear atlas shows promise as a new method for automatically measuring the shape of the labyrinth.     

Hybrid segmentation and virtual bronchoscopy based on CT images

RATIONALE AND OBJECTIVES: Introduction of combination of the segmentation tool SegoMeTex and the virtual endoscopy system VIVENDI to perform virtual endoscopic inspections of the human lung. This virtual bronchoscopy system enables visualization of the tracheobronchial tree down to seventh generation. Furthermore, the modified virtual system visualizes hidden structures such as segmented vascular system or tumors. MATERIALS AND METHODS: The segmentation is based on image data acquired by a multislice computed tomography scanner. SegoMeTex is used to segment the tracheobronchial tree by a hybrid system with minimal user action. Similarly, the complementary pulmonary arterial can be segmented, whereas additional structures such as tumors are marked manually. On this dataset, subsequently, data structures of the inner surface for virtual endoscopy are generated. Finally, the dataset can be explored by a virtual bronchoscopy procedure using the VIVENDI system. RESULTS: The segmentation method was successfully tested on 22 patients. The hybrid segmentation system identified bronchi up to the sixth generation with a sensitivity of more than 58%, and a positive predictive value of more than 90%. After the segmentation, the datasets are explored interactively (>30 fps on a standard personal computer platform in real-time rendering) using the virtual endoscopy software. The exploration exposed a high-quality reconstruction, even of small structures throughout the dataset. CONCLUSION: Virtual bronchoscopy in combining with a highly sensitive segmentation is a valuable tool for the localization and measurement of stenosis for resection planning.     

MRI brain image segmentation by multi-resolution edge detection and region selection

Combining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multi-resolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures.     

Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images

Medical image segmentation and anatomical structure labeling according to the types of the tissues are important for accurate diagnosis and therapy. In this paper, we propose a novel approach for multi-region labeling and segmentation, which is based on a topological graph prior and the topological information of an atlas, using a modified multi-level set energy minimization method in brain images. We consider a topological graph prior and atlas information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level in low resolution brain image that would be difficult to segment correctly using standard methods. The topological information of an atlas are transformed to the topological graph of a low resolution (noisy) brain image to obtain region labeling. We explain our algorithm and show the topological graph prior and label transformation techniques to explain how it gives precise multi-region segmentation and labeling. The proposed algorithm is capable of segmenting and labeling different regions in noisy or low resolution MRI brain images of different modalities. We compare our approaches with other state-of-the-art approaches for multi-region labeling and segmentation.     

锥形束CT图像分割方法研究进展

图像引导放射治疗(IGRT)是一种可视化的影像引导放疗技术, 具有提高肿瘤靶区剂量, 降低正常器官受照剂量等诸多优点。锥形束CT(CBCT)是IGRT中最常用的医学图像之一, 对CBCT进行快速、准确的靶区及危及器官的分割对放疗具有重大意义。目前的研究方法主要有基于配准的分割方法和基于深度学习的分割方法。本研究针对CBCT图像分割方法、存在问题及发展方向进行综述。     

A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model

The CT uroscan consists of three to four time-spaced acquisitions of the same patient. After registration of these acquisitions, the data forms a volume in which each voxel contains a vector of elements corresponding to the information of the CT uroscan acquisitions. In this paper we will present a segmentation tool in order to differentiate the anatomical structures within the vectorial volume. Because of the partial volume effect (PVE), soft segmentation is better suited because it allows regions or classes to overlap. Gaussian mixture model is often used in statistical classifier to realize soft segmentation by getting classes probability distributions. But this model relies only on the intensity distributions, which will lead a misclassification on the boundaries and on inhomogeneous regions with noise. In order to solve this problem, a neighborhood weighted Gaussian mixture model is proposed in this paper. Expectation maximization algorithm is used as optimization method. The experiments demonstrate that the proposed method can get a better classification result and is less affected by the noise.     

Automatic registration of MR and SPECT images for treatment planning in prostate cancer

RATIONALE AND OBJECTIVES: To aid in surgical and radiation therapy planning for prostate adenocarcinoma, a general-purpose automatic registration method that is based on mutual information was used to align magnetic resonance (MR) images and single photon emission computed tomographic (SPECT) images of the pelvis and prostate. MATERIALS AND METHODS: The authors assessed the effects of various factors on alignment between pairs of MR and SPECT images, including the use of particular pulse sequences in MR imaging, image voxel intensity scaling, the use of different regions on the MR-SPECT histogram, spatial masking of nonoverlapping visual data between images, and multiresolution optimization. A mutual information algorithm was used as the cost function for automatic registration. Automatic registration was deemed acceptable when it resulted in a transformation with less than 2 voxel units (6 mm) difference in translation and less than 2 degree difference in rotation from that obtained with manual registration performed independently by nuclear medicine radiologists. RESULTS: Paired sets of MR and SPECT image volumes from four of five patients were successfully registered. For successful registration, MR images must be optimal and registration must be performed at full spatial resolution and at the full intensity range. Masking, cropping, and the normalization of mutual information, used to register partially overlapping MR-SPECT volumes, were not successful. Multiresolution optimization had little effect on the accuracy and speed of the registration. CONCLUSION: Automatic registration between MR and SPECT images of the pelvis can be achieved when data acquisition and image processing are performed properly. It should prove useful for prostate cancer diagnosis, staging, and treatment planning.     

Automated image registration of gated cardiac single-photon emission computed tomography and magnetic resonance imaging

PURPOSE: To develop an automatic registration method for electrocardiogram-gated myocardial perfusion single-photon emission computed tomography (SPECT) and cardiac cine-magnetic resonance imaging (MRI). MATERIALS AND METHODS: Paired myocardial perfusion SPECT (MPS) and MRI from 20 patients were considered. MR images were presegmented by heart localization based on detection of cardiac motion and optimal thresholding. A registration algorithm based on mutual information was subsequently applied to all time frames or a selected subset from both modalities. RESULTS: A preprocessing step significantly improved the accuracy of the registration when compared to automatic registration performed without preprocessing. Errors in translation parameters (T(x), T(y), T(z)) averaged (1.0 +/- 1.5, 1.1 +/- 1.3, 0.9 +/- 0.9) pixels with MRI segmentation and (4.6 +/- 3.2, 3.4 +/- 2.6, 3.0 +/- 3.4) pixels without MRI segmentation. Errors in rotation parameters (R(x), R(y), R(z)) averaged (5.4 +/- 2.9, 3.4 +/- 2.7, 4.5 +/- 3.6) degrees with MRI segmentation and (9.3 +/- 6.1, 4.8 +/- 4.3, 14.6 +/- 12.6) degrees without MRI segmentation. Error was calculated as the absolute difference between the expert manual and the automatic registration transformation. CONCLUSION: Automatic registration of gated MPS and cine MRI is possible with the use of a mutual information-based technique when MR images are presegmented. Cardiac motion can be used to isolate the left ventricle (LV) on the MR images automatically, and subsequently the segmented MR images can be coregistered with gated MPS.     

Quantitation of T2 lesion load in patients with multiple sclerosis: a novel semiautomated segmentation technique

RATIONALE AND OBJECTIVES: The authors designed a segmentation technique that requires only minimal operator input at the initial and final supervision stages of segmentation and has computer-driven segmentation as the primary determinant of lesion boundaries. The technique was applied to compute total T2-hyperintense lesion volumes in patients with multiple sclerosis (MS). A semi-automated segmentation technique is presented and shown to have a test-retest reliability of <5%. MATERIALS AND METHODS: The method used a single segmented section with MS lesions. A probabilistic neural net performed segmentation into four tissue classes after supervised training. This reference section was deconstructed into the entire set of possible 4 x 4-pixel subregions, which was used to segment all-brain sections in steps of 4 x 4-pixel, adjacent image blocks. Intra- and interimage variabilities were tested by using 3-mm-thick, T2-weighted, dual-echo, spin-echo MR images from five patients, each of whom was imaged twice on the same day. Five different reference sections and three temporally separated. training sessions involving the same reference section were used to test the segmentation technique. RESULTS: The coefficient of variation ranged from 0.013 to 0.068 (mean +/- standard deviation, 0.037 +/- 0.039) for results from five different reference sections for each brain and from 0.007 to 0.037 (mean, 0.027 +/- 0.021) for brains segmented with the same reference section on three temporally separated occasions. Test-retest (intra-imaging) reliability did not exceed 5% (except for a small lesion load of 1 cm3 in one patient). Interimaging differences were approximately 10%. CONCLUSION: The segmentation technique yielded intra-imaging variabilities (2%-3%, except for very small MS lesion loads) that compare favorably with previously published results. New repositioning techniques that minimize imaging-repeat imaging variability could make this approach attractive for resolving MS lesion detection problems.     

A knowledge-guided active model method of cortical structure segmentation on pediatric MR images

PURPOSE: To develop an automated method for quantification of cortical structures on pediatric MR images. MATERIALS AND METHODS: A knowledge-guided active model (KAM) approach was proposed with a novel object function similar to the Gibbs free energy function. Triangular mesh models were transformed to images of a given subject by maximizing entropy, and then actively slithered to boundaries of structures by minimizing enthalpy. Volumetric results and image similarities of 10 different cortical structures segmented by KAM were compared with those traced manually. Furthermore, the segmentation performances of KAM and SPM2, (statistical parametric mapping, a MATLAB software package) were compared. RESULTS: The averaged volumetric agreements between KAM- and manually-defined structures (both 0.95 for structures in healthy children and children with medulloblastoma) were higher than the volumetric agreement for SPM2 (0.90 and 0.80, respectively). The similarity measurements (kappa) between KAM- and manually-defined structures (0.95 and 0.93, respectively) were higher than those for SPM2 (both 0.86). CONCLUSION: We have developed a novel automatic algorithm, KAM, for segmentation of cortical structures on MR images of pediatric patients. Our preliminary results indicated that when segmenting cortical structures, KAM was in better agreement with manually-delineated structures than SPM2. KAM can potentially be used to segment cortical structures for conformal radiation therapy planning and for quantitative evaluation of changes in disease or abnormality.     

Correction of motion artifacts from cardiac cine magnetic resonance images

RATIONALE AND OBJECTIVES: An image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images. MATERIALS AND METHODS: The location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods. RESULTS: The algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3%. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10(-9)). CONCLUSIONS: The novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.