Catálogo de publicaciones - libros
Medical Image Computing and Computer-Assisted Intervention: MICCAI 2005: 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I
James S. Duncan ; Guido Gerig (eds.)
En conferencia: 8º International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) . Palm Springs, CA, USA . October 26, 2005 - October 29, 2005
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Image Processing and Computer Vision; Pattern Recognition; Computer Graphics; Artificial Intelligence (incl. Robotics); Imaging / Radiology; Health Informatics
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2005 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-29327-9
ISBN electrónico
978-3-540-32094-4
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2005
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2005
Tabla de contenidos
doi: 10.1007/11566465_11
Elastic Registration of 3D Ultrasound Images
Pezhman Foroughi; Purang Abolmaesumi
3D registration of ultrasound images is an important and fast-growing research area with various medical applications, such as image-guided radiotherapy and surgery. However, this registration process is extremely challenging due to the deformation of soft tissue and the existence of speckles in these images. This paper presents a novel intra-modality elastic registration technique for 3D ultrasound images. It uses the general concept of attribute vectors to find the corresponding voxels in the fixed and moving images. The method does not require any pre-segmentation and does not employ any numerical optimization procedure. Therefore, the computational requirements are very low and it has the potential to be used for real-time applications. The technique is implemented and tested for 3D ultrasound images of liver, captured by a 3D ultrasound transducer. The results show that the method is sufficiently accurate and robust and is not easily trapped with local minima.
- Image Registration I | Pp. 83-90
doi: 10.1007/11566465_12
Tracer Kinetic Model-Driven Registration for Dynamic Contrast Enhanced MRI Time Series
Giovanni A. Buonaccorsi; Caleb Roberts; Sue Cheung; Yvonne Watson; Karen Davies; Alan Jackson; Gordon C. Jayson; Geoff J. M. Parker
Motion during time-series data acquisition causes model-fitting errors in quantitative dynamic contrast-enhanced (DCE) MRI studies. Motion correction techniques using conventional registration cost functions may produce biased results because they were not designed to deal with the time-varying information content due to contrast enhancement. We present a locally-controlled, 3D translational registration process driven by tracer kinetic modeling that successfully registers abdominal DCE-MRI data at high temporal resolution and compare this method to a similar approach based on registration to the time series mean image in data from 8 patients. When the registration is driven by an appropriate model, we find significant improvements in model-fitting. Also, model-driven registration influences parameter estimates and reduces repeat study variability in measurements of blood volume.
- Image Registration I | Pp. 91-98
doi: 10.1007/11566465_13
Generalised Overlap Measures for Assessment of Pairwise and Groupwise Image Registration and Segmentation
William R. Crum; Oscar Camara; Daniel Rueckert; Kanwal K. Bhatia; Mark Jenkinson; Derek L. G. Hill
Effective validation techniques are an essential pre-requisite for segmentation and non-rigid registration techniques to enter clinical use. These algorithms can be evaluated by calculating the overlap of corresponding test and gold-standard regions. Common overlap measures compare pairs of binary labels but it is now common for multiple labels to exist and for fractional (partial volume) labels to be used to describe multiple tissue types contributing to a single voxel. Evaluation studies may involve multiple image pairs. In this paper we use results from fuzzy set theory and fuzzy morphology to extend the definitions of existing overlap measures to accommodate multiple fractional labels. Simple formulas are provided which define single figures of merit to quantify the total overlap for ensembles of pairwise or groupwise label comparisons. A quantitative link between overlap and registration error is established by defining the overlap tolerance. Experiments are performed on publicly available labeled brain data to demonstrate the new measures in a comparison of pairwise and groupwise registration.
- Image Registration I | Pp. 99-106
doi: 10.1007/11566465_14
Uncertainty in White Matter Fiber Tractography
Ola Friman; Carl-Fredrik Westin
In this work we address the uncertainty associated with fiber paths obtained in white matter fiber tractography. This uncertainty, which arises for example from noise and partial volume effects, is quantified using a Bayesian modeling framework. The theory for estimating the probability of a connection between two areas in the brain is presented, and a new model of the local water diffusion profile is introduced. We also provide a theorem that facilitates the estimation of the parameters in this diffusion model, making the presented method simple to implement.
- Diffusion Tensor Image Analysis | Pp. 107-114
doi: 10.1007/11566465_15
Fast and Simple Calculus on Tensors in the Log-Euclidean Framework
Vincent Arsigny; Pierre Fillard; Xavier Pennec; Nicholas Ayache
Computations on tensors have become common with the use of DT-MRI. But the classical Euclidean framework has many defects, and affine-invariant Riemannian metrics have been proposed to correct them. These metrics have excellent theoretical properties but lead to complex and slow algorithms. To remedy this limitation, we propose new metrics called Log-Euclidean. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. Indeed, Log-Euclidean computations are Euclidean computations in the domain of matrix logarithms. Theoretical aspects are presented and experimental results for multilinear interpolation and regularization of tensor fields are shown on synthetic and real DTI data.
- Diffusion Tensor Image Analysis | Pp. 115-122
doi: 10.1007/11566465_16
3D Curve Inference for Diffusion MRI Regularization
Peter Savadjiev; Jennifer S. W. Campbell; G. Bruce Pike; Kaleem Siddiqi
We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibers as 3D space curves and to then extend Parent and Zucker’s 2D curve inference approach [8] by using a notion of to indicate compatibility between fibre orientation estimates at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired from a human brain.
- Diffusion Tensor Image Analysis | Pp. 123-130
doi: 10.1007/11566465_17
Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis
Isabelle Corouge; P. Thomas Fletcher; Sarang Joshi; John H. Gilmore; Guido Gerig
Diffusion tensor imaging (DTI) has become the major modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics based on tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics calculated within cross-sections. Examples from a clinical neuroimaging study of the early developing brain illustrate the potential of this new method to assess white matter fiber maturation and integrity.
- Diffusion Tensor Image Analysis | Pp. 131-139
doi: 10.1007/11566465_18
White Matter Tract Clustering and Correspondence in Populations
Lauren O’Donnell; Carl-Fredrik Westin
We present a novel method for finding white matter fiber correspondences and clusters across a population of brains. Our input is a collection of paths from tractography in every brain. Using spectral methods we embed each path as a vector in a high dimensional space. We create the embedding space so that it is common across all brains, consequently similar paths in all brains will map to points near each other in the space. By performing clustering in this space we are able to find matching fiber tract clusters in all brains. In addition, we automatically obtain correspondence of tractographic paths across brains: by selecting one or several paths of interest in one brain, the most similar paths in all brains are obtained as the nearest points in the high-dimensional space.
- Diffusion Tensor Image Analysis | Pp. 140-147
doi: 10.1007/11566465_19
76-Space Analysis of Grey Matter Diffusivity: Methods and Applications
Tianming Liu; Geoffrey Young; Ling Huang; Nan-Kuei Chen; Stephen TC Wong
Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are widely used in the study and diagnosis of neurological diseases involving the White Matter (WM). However, many neurological and neurodegenerative diseases (e.g., Alzheimer’s disease and Creutzfeldt-Jakob disease) are generally considered to involve the Grey Matter (GM). Investigation of GM diffusivity of normal aging and pathological brains has both scientific significance and clinical applications. Most of previous research reports on quantification of GM diffusivity were based on the manually labeled Region of Interests (ROI) analysis of specific neuroanatomic regions. The well-known drawbacks of ROI analysis include inter-rater variations, irreproducible results, tediousness, and requirement of a priori definition of interested regions. In this paper, we present a new framework of automated 76-space analysis of GM diffusivity using DWI/DTI. The framework will be evaluated using clinical data, and applied for study of normal brain, Creutzfeldt-Jakob disease and Schizophrenia.
- Diffusion Tensor Image Analysis | Pp. 148-155
doi: 10.1007/11566465_20
Fast Orientation Mapping from HARDI
Evren Özarslan; Timothy M. Shepherd; Baba C. Vemuri; Stephen J. Blackband; Thomas H. Mareci
This paper introduces a new, accurate and fast method for fiber orientation mapping using high angular resolution diffusion imaging (HARDI) data. The approach utilizes the Fourier relationship between the water displacement probabilities and diffusion attenuated magnetic resonance (MR) signal expressed in spherical coordinates. The Laplace series coefficients of the water displacement probabilities are evaluated at a fixed distance away from the origin. The computations take under one minute for most three-dimensional datasets. We present orientation maps computed from excised rat optic chiasm, brain and spinal cord images. The developed method will improve the reliability of tractography schemes and make it possible to correctly identify the neural connections between functionally connected regions of the nervous system.
- Diffusion Tensor Image Analysis | Pp. 156-163