For applications of real-time MRI, it is important but difficult to efficiently prescribe the next scan plane and obtain visual feedback on the prescription. This work addresses these issues with a 6-degree-of-freedom...
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ISBN:
(纸本)3540229779
For applications of real-time MRI, it is important but difficult to efficiently prescribe the next scan plane and obtain visual feedback on the prescription. This work addresses these issues with a 6-degree-of-freedom Plane Navigator. The Plane Navigator is a mechanical arm with integrated input and output functionality while being statically balanced. In the input mode, by holding and moving the surface normal of the physical representation of the scan plane, the operator can intuitively place the scan plane at a position with any orientation within a few milliseconds. In the output mode, the Plane Navigator automatically places the physical representation of the scan plane to reflect its position and orientation (pose)[PR1] relative to a patient domain with a maximum delay of half a second. Application examples in MRI cardiac imaging are also described.
作者：
Shen, DGUniv Penn
Sch Biomed Image Anal Dept Radiol Philadelphia PA 19104 USA
We previously presented a HAMMER image registration algorithm that demonstrated high accuracy in superposition of images from different individual brains. However, the HAMMER registration algorithm requires presegment...
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ISBN:
(纸本)3540229760
We previously presented a HAMMER image registration algorithm that demonstrated high accuracy in superposition of images from different individual brains. However, the HAMMER registration algorithm requires presegmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented images. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we use local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and more importantly it captures spatial information by integrating a number of local histograms that are calculated from multi-resolution images. The new attribute vectors are able to determine corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed registration method performs as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more general applications in registering images of other organs. Experimental results show good performance of the proposed method in registering MR brain images and CT pelvis images.
Spatial normalization is a key process in cross-sectional studies of brain structure and function using MRI, fMR1, PET and other imaging techniques. A wide range of 2D surface and 3D image deformation algorithms have ...
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Spatial normalization is a key process in cross-sectional studies of brain structure and function using MRI, fMR1, PET and other imaging techniques. A wide range of 2D surface and 3D image deformation algorithms have been developed, all of which involve design choices that are subject to debate. Moreover, most have numerical parameters whose value must be specified by the user. This paper proposes a principled method for evaluating design choices and choosing parameter values. This method can also be used to compare competing spatial normalization algorithms. We demonstrate the method through a performance analysis of a nonaffine registration algorithm for 3D images and a registration algorithm for 2D cortical surfaces. (C) 2004 Elsevier B.V. All rights reserved.
Voxel-based nonrigid image registration can be formulated as an optimisation problem whose goal is to minimise a cost function, consisting of a first term that characterises the similarity between both images and a se...
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ISBN:
(纸本)3540229760
Voxel-based nonrigid image registration can be formulated as an optimisation problem whose goal is to minimise a cost function, consisting of a first term that characterises the similarity between both images and a second term that regularises the transformation and/or penalties improbable or impossible deformations. Within this paper, we extend previous works on nonrigid image registration by the introduction of a new penalty term that expresses the local rigidity of the deformation. A necessary and sufficient condition for the transformation to be locally rigid at a particular location is that its Jacobian matrix J(T) at this location is orthogonal, satisfying the orthogonality condition J(T)J(T)(T) = 1. So we define the penalty term as the weighted integral of the Frobenius norm of J(T)J(T)(T)-1 integrated over the overlap of the images to be registered. We fit the implementation of the penalty term in a multidimensional, continuous and differentiable B-spline deformation framework and analytically determine the derivative of the similarity criterion and the penalty term with respect to the deformation parameters. We show results of the impact of the proposed rigidity constraint on artificial and clinical images demonstrating local shape preservation with the proposed constraint.
In this paper we present a new algorithm for 3D medicalimage segmentation. The algorithm is versatile, fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from ...
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In this paper we present a new algorithm for 3D medicalimage segmentation. The algorithm is versatile, fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise. (C) 2004 Elsevier B.V. All rights reserved.
Currently, minimally invasive cardiac surgery (MICS) faces several limitations, including inadequate training methods using non-realistic models, insufficient surgery planning using 2D images, and the lack of global, ...
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ISBN:
(纸本)3540229760
Currently, minimally invasive cardiac surgery (MICS) faces several limitations, including inadequate training methods using non-realistic models, insufficient surgery planning using 2D images, and the lack of global, 3D guidance during the procedure. To address these issues we are developing the Virtual Cardiac Surgery Platform (VCSP) - a virtual reality model of the patient specific thorax, derived from pre-procedural images. Here we present an image registration-based method for customizing a geometrical template model of the heart to any given patient, and validate it using manual segmentation as the gold standard. On average, the process is accurate to within 3.3 +/- 0.3 mm in MR images, and 2.4 +/- 0.3 min in CT images. These results include inaccuracies in the gold standard, which are on average 1.6 +/- 0.2 and 0.9 +/- 0.2 mm for MR and CT images respectively. We believe this method adequately prepares templates for use within VCSP, prior to and during MICS.
While level sets have demonstrated a great potential for 3D medicalimage segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation...
While level sets have demonstrated a great potential for 3D medicalimage segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. The second problem is compounded by the first. This paper describes a new tool for 3D segmentation that addresses these problems by computing level-set surface models at interactive rates. This tool employs two important, novel technologies. First is the mapping of a 3D level-set solver onto a commodity graphics card (GPU). This mapping relies on a novel mechanism for GPU memory management. The interactive rates level-set PDE solver give the user immediate feedback on the parameter settings, and thus users can tune free parameters and control the shape of the model in real time. The second technology is the use of intensity-based speed functions, which allow a user to quickly and intuitively specify the behavior of the deformable model. We have found that the combination of these interactive tools enables users to produce good, reliable segmentations. To support this observation, this paper presents qualitative results from several different datasets as well as a quantitative evaluation from a study of brain tumor segmentations. (C) 2004 Elsevier B.V. All rights reserved.
We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make t...
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We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation. (C) 2004 Elsevier B.V. All rights reserved.
Otologic surgery is undertaken to treat ailments of the ear including persistent infections, hearing loss, vertigo, and cancer. Typically performed on healthy patients in outpatient facilities, the application of imag...
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Otologic surgery is undertaken to treat ailments of the ear including persistent infections, hearing loss, vertigo, and cancer. Typically performed on healthy patients in outpatient facilities, the application of image-guided surgery has been limited because accurate (
We describe a new 3-D statistical shape model of the heart consisting of atria, ventricles and epicardium. The model was constructed by combining information on standard short- and long-axis cardiac MR images. In the ...
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We describe a new 3-D statistical shape model of the heart consisting of atria, ventricles and epicardium. The model was constructed by combining information on standard short- and long-axis cardiac MR images. In the model, the variability of the shape was modeled with PCA- and ICA-based shape models as well as with non-parametric landmark probability distributions and a probabilistic atlas. The statistical atlas was built from 25 healthy subjects. The shape model was evaluated by applying it to image segmentation. The probabilistic atlas was found to be superior to the other shape models (p < 0.001) in this study. (C) 2004 Elsevier B.V. All rights reserved.
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