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This work describes a protocol for creating a population of pediatric, patient specific, computational dosimetry phantoms. Pediatric CT data are mapped to a detailed adult template using multi channel (MC) - large deformation diffeomorphic metric mapping (LDDMM) applied to whole body images. Challenges are studied and overcome using 2D LDDMM applied to simulated phantoms. The protocol involves automatic...
Incorporating biomedical information into nonrigid image registration is an important approach to improve the registration quality and provide realistic results. However, previous tissue-dependent deformation field filtering incur a relatively high computation cost in order to obtain results of improved quality. In this paper, we propose a collapsed-cone based adaptive filtering method to reduce the...
In prostate cancer radiotherapy, accurate segmentation of prostate and organs at risk in planning CT and follow-up CBCT images is an essential part of the therapy planning and optimization. Automatic segmentation is challenging because of the poor constrast in soft tissues. Although atlas-based approaches may provide a priori structural information by propagating manual expert delineations to a new...
To diagnose the osteoporosis accurately, the bone mineral density (BMD) measurements of the vertebral bodies (VBs) are required. In this paper, we propose a new segmentation and registration method in order to assist the BMD measurements and fracture analysis (FA) accurately. In this experiment, image appearance and shape information of VBs are used. Our shape model is required to be registered to...
Plaque composition analysis is a critical tool in identifying vulnerable atherosclerotic plaques. Intravascular ultrasound with spectral analysis of the backscattered radio frequency (RF) signals (IVUS-VH) is currently considered as the gold standard for the evaluation of coronary plaque composition, while CT coronary angiography (CTA) has been proposed as a potential non-invasive counterpart. In...
This paper builds on the tradition of developing computationally efficient nonrigid registration techniques that are easy to implement. All of the standard approaches for variational registration must either implicitly or explicitly assume boundary conditions for the deformation field being sought. Unfortunately, the most commonly used boundary conditions (homogeneous Dirichlet, homogeneous Neumann,...
Lung function depends on mechanical lung expansion and contraction during the respiratory cycle. Recently developed dynamic 4D CT imaging and 3D image registration can be used to analyze regional lung function, which are significant for lung disease diagnosis, treatment and lung ventilation change during radiation therapy. 4D CT images of the lung can be reconstructed at any respiratory phase point...
We present a local affine-based adaptive regularization approach as an alternative to the homogeneous regularization used in Thirion's demons non-rigid registration algorithm, which does not preserve the deformation field discontinuities related to the independent motion of different organs, as typically occurs during CT and MR imaging of the abdomen and pelvis. By contrast, our method fits local...
We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration...
This work presents an integrated framework for elastic image registration with log-unbiased deformations and a spatially variable constraint to reduce image folding and preserve the rigidity of bony structures. The framework has been applied to the data provided by the workshop on Evaluation of Methods for Pulmonary Image Registration 2010 (EMPIRE10). We have compared our new method to the classic...
We present a method to detect damage in time-lapsed, micro-CT images of progressively loaded bone. The method we have developed splits the image into regions and performs registration on each region individually. The procedure is repeated with progressively smaller regions until either a minimum size or a maximum number of levels is reached. The regions are then classified as damaged or undamaged...
The accuracy of optical flow estimation algorithms has been improving steadily by refining the objective function which should be optimized. A novel energy function for computing 2-D optical flow from X-ray CT images is presented. One advantage of the optical flow framework is the possibility to enforce physical constraints on the numerical solutions. The physical constraints which have been included...
Current generation minimally invasive surgeries present many visualization challenges, including two-dimensional representation of three-dimensional anatomy and a lack of visualization of deeply recessed structures. Coupled with the loss of tactile feedback which places greater emphasis on available visual cues, improved surgical visualization remains a long-standing need. Our response to address...
This study proposed an off-line method to quantitively analyze the dosimetric impact of daily setup variations on Intensity-modulated radiotherapy (IMRT) when these variations are not corrected due to the lack of image guidance. Series of daily Cone-beam computed tomography (CBCT) images and treatment plan data of a patient with esophageal cancer previously treated using Image-guided radiotherapy...
Graphs are useful representations of the liver vasculature. They support tree matching algorithms in landmark-based registration algorithms, they are useful to separate connected vessels from two different vessel systems and are the basis of vessel annotation tools. In this paper, we propose a hierarchical decomposition of vessel skeletons into sub-branches. This simplifies the process of creating...
Maximization of a voxel based similarity metric like mutual information is the state of the art for the multimodal rigid registration of the head. To achieve satisfactory results the transform needs to be initialized properly and the region of interest (ROI) containing only rigid structures has to be defined in both images. In this paper we present and comprehensively evaluate an automated initialization...
A methodology is presented for registering three-dimensional preoperative and postoperative CT scan volumes and for evaluating their differences in selected areas of interest. The proposed method was applied to CT cone beam data from ten patients in order to assess the volume of augmented bone in the alveolar region. A 3D affine model was used to align the preoperative and postoperative data. The...
PET and CT image registration is an important tool of clinical diagnosis of diseases. For PET and CT images, a preprocessing algorithm of medical image registration is proposed in this paper. The algorithm process includes image normalization, CT image adaptive threshold adjustment and automatic extraction of tissues based on morphology, edge detection and statistical analysis theory, and improved...
In medical image processing, automatic parameter regulation reduces labor costs of the visually checking that the organ contours of CT and MRA image are overlapped well or not. The present paper compares three similarity measures of the mutual information, the gradient difference, and the gradient image mutual information for automatic parameter regulation. The parameter regulation chooses one of...
Image registration seeks to compare and combine images acquired from multiple modalities, at different time or at different viewpoints by feature based approach or optimizing the similarity measure of two image sets. In the landmark based registration, the transformation function is required to spatially match the features. Image guidance systems designed for neurosurgery, hip surgery, and spine surgery...
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