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James S Duncan

Publications and source records attributed to James S Duncan.

16 recordsLinked to original sources
PubMed2006

Towards pointwise motion tracking in echocardiographic image sequences--comparing the reliability of different features for speckle tracking.

In this paper, we studied the problem of feature-based motion tracking in echocardiographic image sequences. We described the relation between possible feature variations and different kinds of tissue motion using a linear convolution model. We also showed that motion-feature decorrelation (which means that the motion parameters estimated using feature tracking fail to represent the underlying tissue motion) compensation is an ill-posed inverse problem. Instead of finding a method that may provide better compensation results than previous approaches, we used an quantitative measure to compare the reliability of tracking features. Experiment results showed that the use of the reliability measure improved the robustness of displacement estimation. With the help of the reliability measure, we compared the performance of different features using simulations and phantom examples. While we noticed that the radio frequency (RF) signal outperforms the B-mode (BM) signal in the analysis of small deformation (e.g., less than 0.1% compression), we also found out that the BM signal works better than the RF signal in the analysis of large deformation (e.g., larger than 2% compression). The use of a band-passed filtered feature does not result in significant improvement in tracking.

Algorithms↗
PubMed2006

Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement.

The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with "sticks," short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.

Algorithms↗
PubMed2005

Tetrabromobenzotriazole (TBBt) and tetrabromobenzimidazole (TBBz) as selective inhibitors of protein kinase CK2: evaluation of their effects on cells and different molecular forms of human CK2.

The development of selective cell-permeable inhibitors of protein kinase CK2 has represented an important advance in the field. However, it is important to not overlook the existence of discrete molecular forms of CK2 that arise from the presence of distinct isozymic forms, and the existence of the catalytic CK2 subunits as free subunits and in complexes with the regulatory CK2beta subunits and, possibly, other proteins. This review examines two recently developed, and presently widely applied, CK2 inhibitors, 4,5,6,7-tetrabromobenzotriazole (TBBt) and the related 4,5,6,7-tetrabromobenzimidazole (TBBz), the latter of which was previously shown to discriminate between different molecular forms of CK2 in yeast. We have shown, by spectrophotometric titration, that TBBt, with a pK(a) approximately 5, exists in solution at physiological pH almost exclusively (>99%) as the monoanion; whereas TBBz, with a pKa approximately 9, is predominantly (>95%) in the neutral form, both of obvious relevance to their modes of binding. In vitro, TBBt inhibits different forms of CK2 with Ki values ranging from 80 to 210 nM. TBBz better discriminates between CK2 forms, with Ki values ranging from 70 to 510 nM. Despite their general similar in vitro activities, TBBz is more effective than TBBt in inducing apoptosis and, to a lesser degree, necrosis, in transformed human cell lines. Finally, development of shRNA strategies for the selective knockdown of the CK2alpha and CK2alpha' isoforms reinforces the foregoing results, indicating that inhibition of CK2 leads to attenuation of proliferation.

Apoptosis↗
PubMed2005

Segmentation and 3D reconstruction of microtubules in total internal reflection fluorescence microscopy (TIRFM).

The interaction of the microtubules with the cell cortex plays numerous critical roles in a cell. For instance, it directs vesicle delivery, and modulates membrane adhesions pivotal for cell movement as well as mitosis. Abnormal function of the microtubules is involved in cancer. An effective method to observe microtubule function adjacent to the cortex is TIRFM. To date most analysis of TIRFM images has been done by visual inspection and manual tracing. In this work we have developed a method to automatically process TIRFM images of microtubules so as to enable high throughput quantitative studies. The microtubules are extracted in terms of consecutive segments. The segments are described via Hamilton-Jacobi equations. Subsequently, the algorithm performs a limited reconstruction of the microtubules in 3D. Last, we evaluate our method with phantom as well as real TIRFM images of living cells.

Algorithms↗
PubMed2005

A boundary element-based approach to analysis of LV deformation.

Quantification of left ventricular (LV) deformation from 3D image sequences (4D data) is important for the assessment of myocardial viability, which can have important clinical implications. To date, feature information from either Magnetic Resonance, computed tomographic or echocardiographic image data has been assembled with the help of different interpolative models to estimate LV deformation. These models typically are designed to be computationally efficient (e.g. regularizing strategies using B-splines) or more physically realistic (e.g. finite element approximations to biomechanical models), but rarely incorporate both notions. In this paper, we combine an approach to the extraction and matching of image-derived point features based on local shape properties with a boundary element model. This overall scheme is intended to be both computationally efficient and physically realistic. In order to illustrate this, we compute strains using our method on canine 4D MR image sequences and compare the results to those found from a B-spline-based method (termed extended free-form deformation (EFFD)) and a method based on finite elements (FEM). All results are compared to displacements found using implanted markers, taken to be agold standard.

Algorithms↗
PubMed2004

3D image segmentation of deformable objects with joint shape-intensity prior models using level sets.

We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the object shape and the image gray levels, along with information derived from the input image, is employed. Our method is motivated by the observation that the shape of an object and the gray level variation in an image have consistent relations that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation model using the joint prior information of the object shape and the image gray levels to realize image segmentation. We introduce a representation for the joint density function of the object and the image gray level values, and define a joint probability distribution over the variations of the object shape and the gray levels contained in a set of training images. By estimating the MAP shape of the object, we formulate the shape-intensity model in terms of level set functions as opposed to landmark points of the object shape. In addition, we evaluate the performance of the level set representation of the object shape by comparing it with the point distribution model (PDM). We found the algorithm to be robust to noise and able to handle multidimensional data, while able to avoid the need for explicit point correspondences during the training phase. Results and validation from various experiments on 2D and 3D medical images are shown.

Algorithms↗
PubMed2004

Geometric strategies for neuroanatomic analysis from MRI.

In this paper, we describe ongoing work in the Image Processing and Analysis Group (IPAG) at Yale University specifically aimed at the analysis of structural information as represented within magnetic resonance images (MRI) of the human brain. Specifically, we will describe our applied mathematical approaches to the segmentation of cortical and subcortical structure, the analysis of white matter fiber tracks using diffusion tensor imaging (DTI), and the intersubject registration of neuroanatomical (aMRI) data sets. Many of our methods rally around the use of geometric constraints, statistical (MAP) estimation, and the use of level set evolution strategies. The analysis of gray matter structure and connecting white matter paths combined with the ability to bring all information into a common space via intersubject registration should provide us with a rich set of data to investigate structure and variation in the human brain in neuropsychiatric disorders, as well as provide a basis for current work in the development of integrated brain function-structure analysis.

Algorithms↗
PubMed2004

Neighbor-constrained segmentation with level set based 3-D deformable models.

A novel method for the segmentation of multiple objects from three-dimensional (3-D) medical images using interobject constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring shape and position relationships of a set of training images. In order to estimate the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. This method is useful in situations where there is limited interobject information as opposed to robust global atlases. In addition, we compare our level set representation of the object shape to the point distribution model. Results and validation from experiments on synthetic data and medical imagery in two-dimensional and 3-D are demonstrated.

Algorithms↗
PubMed2003

Neighbor-constrained segmentation with 3D deformable models.

A novel method for the segmentation of multiple objects from 3D medical images using inter-object constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a Maximum A Posteriori(MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring positions and shapes of a set of training images. By estimating the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. We feel that this method is useful in situations where there is limited inter-object information as opposed to robust global atlases. Results and validation from various experiments on synthetic data and medical imagery in 2D and 3D are demonstrated.

Algorithms↗
PubMed2003

A unified non-rigid feature registration method for brain mapping.

This paper describes the design, implementation and results of a unified non-rigid feature registration method for the purposes of anatomical MRI brain registration. An important characteristic of the method is its ability to fuse different types of anatomical features into a single point-set representation. We demonstrate the application of the method using two different types of features: the outer cortical surface and major sulcal ribbons. Non-rigid registration of the combined feature point-sets is then performed using a new robust non-rigid point matching algorithm. The point matching algorithm implements an iterative joint clustering and matching (JCM) strategy which effectively reduces the computational complexity without sacrificing accuracy. We have conducted carefully designed synthetic experiments to gauge the effect of using different types of features either separately or together. A validation study examining the accuracy of non-rigid alignment of many brain structures is also presented. Finally, we present anecdotal results on the alignment of two subject MRI brain data.

Algorithms↗
PubMed2003

Combinative multi-scale level set framework for echocardiographic image segmentation.

In the automatic segmentation of echocardiographic images, a priori shape knowledge has been used to compensate for poor features in ultrasound images. This shape knowledge is often learned via an off-line training process, which requires tedious human effort and is highly expertise-dependent. More importantly, a learned shape template can only be used to segment a specific class of images with similar boundary shape. In this paper, we present a multi-scale level set framework for segmentation of endocardial boundaries at each frame in a multiframe echocardiographic image sequence. We point out that the intensity distribution of an ultrasound image at a very coarse scale can be approximately modeled by Gaussian. Then we combine region homogeneity and edge features in a level set approach to extract boundaries automatically at this coarse scale. At finer scale levels, these coarse boundaries are used to both initialize boundary detection and serve as an external constraint to guide contour evolution. This constraint functions similar to a traditional shape prior. Experimental results validate this combinative framework.

Algorithms↗
PubMed2003

Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation.

For patient setup verification in external beam radiotherapy (EBRT) of prostate cancer, we developed an information theoretic registration framework, called the minimax entropy registration framework, to simultaneously and iteratively segment portal images and register them to three-dimensional (3-D) computed tomography (CT) image data. The registration framework has two steps, the max step and the min step, and evaluates appropriate entropies to estimate segmentations of the portal images and to find the transformation parameters. In the initial version of the algorithm (Bansal et al. 1999), we assumed image pixels to be independently distributed, an assumption not true in general. Thus, to better segment the portal images and to improve the accuracy of the estimated registration parameters, in this initial formulation of the problem, the correlation among pixel intensities is modeled using a one-dimensional Markov random process. Line processes are incorporated into the model to improve the estimation of segmentation of the portal images. In the max step, the principle of maximum entropy is invoked to estimate the probability distribution on the segmentations. The estimated distribution is then incorporated into the min step to estimate the registration parameters. Performance of the proposed framework is evaluated and compared to that of a mutual information-based registration algorithm using both simulated and real patient data. In the proposed registration framework, registration of the 3-D CT image and the portal images is guided by an estimated segmentation of the pelvic bone. However, as the prostate can move with respect to the pelvic structure, further localization of the prostate using ultrasound image data is required, an issue to be further explored in future.

Algorithms↗
PubMed2003

MRI and PET study of deficits in hippocampal structure and function in women with childhood sexual abuse and posttraumatic stress disorder.

OBJECTIVE: Animal studies have suggested that early stress is associated with alterations in the hippocampus, a brain area that plays a critical role in learning and memory. The purpose of this study was to measure both hippocampal structure and function in women with and without early childhood sexual abuse and the diagnosis of posttraumatic stress disorder (PTSD). METHOD: Thirty-three women participated in this study, including women with early childhood sexual abuse and PTSD (N=10), women with abuse without PTSD (N=12), and women without abuse or PTSD (N=11). Hippocampal volume was measured with magnetic resonance imaging in all subjects, and hippocampal function during the performance of hippocampal-based verbal declarative memory tasks was measured by using positron emission tomography in abused women with and without PTSD. RESULTS: A failure of hippocampal activation and 16% smaller volume of the hippocampus were seen in women with abuse and PTSD compared to women with abuse without PTSD. Women with abuse and PTSD had a 19% smaller hippocampal volume relative to women without abuse or PTSD. CONCLUSIONS: These results are consistent with deficits in hippocampal function and structure in abuse-related PTSD.

Adult↗
PubMed2002

Estimation of 3-D left ventricular deformation from medical images using biomechanical models.

The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using a transversely isotropic, linear-elastic model, which accounts for the muscle fiber directions in the left ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion, exhibit a high correlation with strains produced in the same animals using implanted markers. Further, they show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3-D estimates of heart deformation.

Algorithms↗