Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional <i>t</i>-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Mingwu Jin

Rajesh Nandy

Tim Curran

Dietmar Cordes

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging problem due to the difference of
  information contained in each image modality. To overcome this difficulty, we
  introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that the proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use.

Pierrick Coup&#233;

Pierre Hellier

Xavier Morandi

Christian Barillot

3D Rigid Registration of Intraoperative Ultrasound and Preoperative MR Brain Images Based on Hyperechogenic Structures

Texture and feature extraction is an important research area with a wide range of applications in science and technology. Selective extraction of entangled textures is a challenging task due to spatial entanglement, orientation mixing, and high-frequency overlapping. The partial differential equation (PDE) transform is an efficient method for functional mode decomposition. The present work introduces adaptive PDE transform algorithm to appropriately threshold the statistical variance of the local variation of functional modes. The proposed adaptive PDE transform is applied to the selective extraction of entangled textures. Successful separations of human face, clothes, background, natural landscape, text, forest, camouflaged sniper and neuron skeletons have validated the proposed method.

Yang Wang

Guo-Wei Wei

Siyang Yang

Selective Extraction of Entangled Textures via Adaptive PDE Transform

Current neuronavigation systems cannot adapt to changing intraoperative conditions over time. To overcome this limitation, we present an experimental end-to-end system capable of updating 3D preoperative images in the presence of brain shift and successive resections. The heart of our system is a nonrigid registration technique using a biomechanical model, driven by the deformations of key surfaces tracked in successive intraoperative images. The biomechanical model is deformed using FEM or XFEM, depending on the type of deformation under consideration, namely, brain shift or resection. We describe the operation of our system on two patient cases, each comprising five intraoperative MR images, and we demonstrate that our approach significantly improves the alignment of nonrigidly registered images.

Lara M. Vigneron

Ludovic Noels

Simon K. Warfield

Jacques G. Verly

Pierre A. Robe

Serial FEM/XFEM-Based Update of Preoperative Brain Images Using Intraoperative MRI

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed  on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately.

Jie Wu

Pavani Davuluri

Kevin R. Ward

Charles Cockrell

Rosalyn Hobson

Kayvan Najarian

Fracture Detection in Traumatic Pelvic CT Images

Attention is crucial for encoding information into memory, and current dual-process models seek to explain the roles of attention in both recollection memory and incidental-perceptual memory processes. The present study combined an incidental memory paradigm with event-related functional MRI to examine the effect of attention at encoding on the subsequent neural activation associated with unintended perceptual memory for spoken words. At encoding, we systematically varied attention levels as listeners heard a list of single English nouns. We then presented these words again in the context of a recognition task and assessed the effect of modulating attention at encoding on the BOLD responses to words that were either attended strongly, weakly, or not heard previously. MRI revealed activity in right-lateralized inferior parietal and prefrontal regions, and positive BOLD signals varied with the relative level of attention present at encoding. Temporal analysis of hemodynamic responses further showed that the time course of BOLD activity was modulated differentially by unintentionally encoded words compared to novel items. Our findings largely support current models of memory consolidation and retrieval, but they also provide fresh evidence for hemispheric differences and functional subdivisions in right frontoparietal attention networks that help shape auditory episodic recall.

Thomas A. Christensen

Kyle R. Almryde

Lesley J. Fidler

Julie L. Lockwood

Sharon M. Antonucci

Elena Plante

Modulating the Focus of Attention for Spoken Words at Encoding Affects Frontoparietal Activation for Incidental Verbal Memory

Multiple sclerosis (MS) is a complicated disease characterized by heterogeneous pathology that varies across individuals. Accurate identification and quantification of pathological changes may facilitate a better understanding of disease pathogenesis and progression and help identify novel therapies for MS patients. Texture analysis evaluates interpixel relationships that generate characteristic organizational patterns in an image, many of which are beyond the ability of visual perception. Given its promise detecting subtle structural alterations texture analysis may be an attractive means to evaluate disease activity and evolution. It may also become a new tool to assess therapeutic efficacy if technique issues are resolved and pathological correlates are further confirmed. This paper describes the concept, strategies, and considerations of MRI texture analysis; summarizes applications of texture analysis in MS as a measure of tissue integrity and its clinical relevance; then discusses potentially future directions of texture analysis in MS.

Yunyan Zhang

MRI Texture Analysis in Multiple Sclerosis

<i  >Purpose</i>. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. <i  >Materials and Methods</i>. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (<svg style="vertical-align:-0.17555pt;width:35.099998px;" id="M1" height="11.1" version="1.1" viewBox="0 0 35.099998 11.1" width="35.099998"  xmlns="http://www.w3.org/2000/svg">
	<g transform="matrix(1.25,0,0,-1.25,0,11.1)">
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</svg> each). In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. <i  >Results</i>. Statistical analyses demonstrated significant improvement (<svg style="vertical-align:-0.30096pt;width:56.150002px;" id="M2" height="11.25" version="1.1" viewBox="0 0 56.150002 11.25" width="56.150002"  xmlns="http://www.w3.org/2000/svg">
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				<tspan style="font-size: 12.50px; " x="0" y="0">𝑃</tspan>
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</svg>) in registration accuracy by landmark optimization in most data sets and trends towards improvement (<svg style="vertical-align:-0.30096pt;width:48.337502px;" id="M3" height="11.0625" version="1.1" viewBox="0 0 48.337502 11.0625" width="48.337502"  xmlns="http://www.w3.org/2000/svg">
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</svg>) in others as compared to manual landmark selection.

Yutong Liu

Balasrinivasa R. Sajja

Mariano G. Uberti

Howard E. Gendelman

Tammy Kielian

Michael D. Boska

Landmark Optimization Using Local Curvature for Point-Based Nonlinear Rodent Brain Image Registration

About this journal

<p><em>International Journal of Biomedical Imaging</em> aims to promote research and development of biomedical imaging by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.</p>


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<p><em>International Journal of Biomedical Imaging</em> maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.</p>


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International Journal of Biomedical Imaging - Table of contents 2012

International Journal of Biomedical Imaging | Hindawi

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International Journal of Biomedical Imaging aims to promote research and development of biomedical imaging by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

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International Journal of Biomedical Imaging

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International Journal of Biomedical Imaging