Medical Image Analysis RSS feed: Current Issue. Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. A bi-monthly journal, it publishes the highest quality, original papers that contribute to the basic science of processing, analysing and utilizing medical and biological images for these purposes. The journal is interested in approaches that utilize biomedical image datasets at all spatial scales, ranging from molecular / cellular imaging to tissue / organ imaging. While not limited to these alone, the typical biomedical image datasets of interest include those acquired from: Magnetic resonance Ultrasound Computed tomography Nuclear medicine X-ray Optical and Confocal Microscopy Video and range data images The types of papers accepted include those that cover the development and implementation of algorithms and strategies based on the use of various models (geometrical, statistical, physical, functional, etc.) to solve the following types of problems, using biomedical image datasets: representation of pictorial data, visualization, feature extraction, segmentation, inter-study and inter-subject registration, longitudinal / temporal studies, image-guided surgery and intervention, texture, shape and motion measurements, spectral analysis, digital anatomical atlases, statistical shape analysis, computational anatomy (modelling normal anatomy and its variations), computational physiology (modelling organs and living systems for image analysis, simulation and training), virtual and augmented reality for therapy planning and guidance, telemedicine with medical images, telepresence in medicine, telesurgery and image-guided medical robots, etc. http://www.medicalimageanalysisjournal.com/?rss=yesElsevier Inc.en © 2011 Published by Elsevier Inc. All rights reserved. Medical Image Analysis1361-8415162February 2012 © 2011 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.medicalimageanalysisjournal.com/article/PIIS1361841512000060/abstract?rss=yesEditorial board10.1016/S1361-8415(12)00006-0Medical Image Analysis 16, 2 (2012)2012-02-01Medical Image Analysis2012-02-01162S1361-8415(12)X0002-1IFCIFChttp://www.medicalimageanalysisjournal.com/article/PIIS1361841511001307/abstract?rss=yesGraphical abstract: Highlights: ► We segment left ventricular endocardial boundaries from RF ultrasound. ► Our M.A.P. segmentation uses a joint spatial model and a multiframe conditional. ► The conditional model relates neighboring frames using a linear predictor. ► The linear predictor exploits spatio-temporal coherence in the data. ► We overcome problems due to image inhomogeneities amplified in B-mode segmentation.Abstract: This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies.Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictorP.C. Pearlman, H.D. Tagare, B.A. Lin, A.J. Sinusas, J.S. Duncan10.1016/j.media.2011.09.002Medical Image Analysis 16, 2 (2012)2011-10-17Medical Image Analysis2011-10-17162S1361-8415(12)X0002-1351360http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001319/abstract?rss=yesGraphical abstract: Highlights: ► Glioma cells infiltrate for several cm beyond the margin visible in MRI. ► Doctors treat the brain volume that extends 2cm out from the visible margin. ► Tumour cells preferentially move in the direction of brain fibers. ► Use a geodesic distance on DTI to define a better anisotropic radiation margin.Abstract: Glioma is one of the most challenging types of brain tumors to treat or control locally. One of the main problems is to determine which areas of the apparently normal brain contain glioma cells, as gliomas are known to infiltrate several centimeters beyond the clinically apparent lesion that is visualized on standard Computed Tomography scans (CT) or Magnetic Resonance Images (MRIs). To ensure that radiation treatment encompasses the whole tumor, including the cancerous cells not revealed by MRI, doctors treat the volume of brain that extends 2cm out from the margin of the visible tumor. This approach does not consider varying tumor-growth dynamics in different brain tissues, thus it may result in killing some healthy cells while leaving cancerous cells alive in the other areas. These cells may cause recurrence of the tumor later in time, which limits the effectiveness of the therapy.Knowing that glioma cells preferentially spread along nerve fibers, we propose the use of a geodesic distance on the Riemannian manifold of brain diffusion tensors to replace the Euclidean distance used in the clinical practice and to correctly identify the tumor invasion margin. This mathematical model results in a first-order Partial Differential Equation (PDE) that can be numerically solved in a stable and consistent way. To compute the geodesic distance, we use actual Diffusion Weighted Imaging (DWI) data from 11 patients with glioma and compare our predicted infiltration distance map with actual grwoth in follow-up MRI scans. Results show improvement in predicting the invasion margin when using the geodesic distance as opposed to the 2cm conventional Euclidean distance.Tumor invasion margin on the Riemannian space of brain fibersParisa Mosayebi, Dana Cobzas, Albert Murtha, Martin Jagersand10.1016/j.media.2011.10.001Medical Image Analysis 16, 2 (2012)2011-11-16Medical Image Analysis2011-11-16162S1361-8415(12)X0002-1361373http://www.medicalimageanalysisjournal.com/article/PIIS136184151100140X/abstract?rss=yesGraphical abstract: Highlights: ► A multiscale shape representation scheme is proposed. ► A fully automatic multiscale shape-based segmentation framework is proposed. ► The multiscale shape representation can be used with other shape analysis and shape-based techniques.Abstract: Extracting structure of interest from medical images is an important yet tedious work. Due to the image quality, the shape knowledge is widely used for assisting and constraining the segmentation process. In many previous works, shape knowledge was incorporated by first constructing a shape space from training cases, and then constraining the segmentation process to be within the learned shape space. However, such an approach has certain limitations due to the number of variations, eigen-shapemodes, that can be captured in the learned shape space. Moreover, small scale shape variances are usually overwhelmed by those in the large scale, and therefore the local shape information is lost. In this work, we present a multiscale representation for shapes with arbitrary topology, and a fully automatic method to segment the target organ/tissue from medical images using such multiscale shape information and local image features. First, we handle the problem of lacking eigen-shapemodes by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances existing in the training shapes captured by the statistical learning step are also represented at various scales. Note that by doing so, one can greatly enrich the eigen-shapemodes as well as capture small scale shape changes. Furthermore, in order to make full use of the training information, not only the shape but also the grayscale training images are utilized in a multi-atlas initialization procedure. By combining such initialization with the multiscale shape knowledge, we perform segmentation tests for challenging medical data sets where the target objects have low contrast and sharp corner structures, and demonstrate the statistically significant improvement obtained by employing such multiscale representation, in representing shapes as well as the overall shape based segmentation tasks.Multiscale 3D shape representation and segmentation with applications to hippocampal/caudate extraction from brain MRIYi Gao, Benjamin Corn, Dan Schifter, Allen Tannenbaum10.1016/j.media.2011.10.002Medical Image Analysis 16, 2 (2012)2011-11-03Medical Image Analysis2011-11-03162S1361-8415(12)X0002-1374385http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001411/abstract?rss=yesGraphical abstract: Highlights: ► We present a method to segment the whole myocardium in 2D-echocardiography. ► The algorithm works for the four main views used in clinical routine. ► The heart is approximated by a combination of hyperquadrics used as a shape prior. ► Comparison is made with experts references on images with clinical interest.Abstract: The segmentation of the myocardium in echocardiographic images is an important task for the diagnosis of heart disease. This task is difficult due to the inherent problems of echographic images (i.e. low contrast, speckle noise, signal dropout, presence of shadows). In this article, we propose a method to segment the whole myocardium (endocardial and epicardial contours) in 2D echographic images. This is achieved using a level-set model constrained by a new shape formulation that allows to model both contours. The novelty of this work also lays in the fact that our framework allows to segment the whole myocardium for the four main views used in clinical routine. The method is validated on a dataset of clinical images and compared with expert segmentation.Detection of the whole myocardium in 2D-echocardiography for multiple orientations using a geometrically constrained level-setT. Dietenbeck, M. Alessandrini, D. Barbosa, J. D’hooge, D. Friboulet, O. Bernard10.1016/j.media.2011.10.003Medical Image Analysis 16, 2 (2012)2011-11-02Medical Image Analysis2011-11-02162S1361-8415(12)X0002-1386401http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001423/abstract?rss=yesGraphical abstract: Liver mosaic (right) made from 60+ 3D ultrasound volumes (left). Image-based rigid registration of volumes took an average of 24.4ms per volume.Highlights: ► Presented is a fast image-based rigid registration method for 3D ultrasound. ► Registrations are computed in real-time (i.e. as fast as volumes are acquired). ► Feature detection and descriptor formation account for 3D ultrasound characteristics. ► Efficient use of a global feature set limits the accumulation of registration error. ► Accuracy of the method is comparable to existing rigid registration methods.Abstract: Registration of three-dimensional ultrasound (3DUS) volumes is necessary in several applications, such as when stitching volumes to expand the field of view or when stabilizing a temporal sequence of volumes to cancel out motion of the probe or anatomy. Current systems that register 3DUS volumes either use external tracking systems (electromagnetic or optical), which add expense and impose limitations on acquisitions, or are image-based methods that operate offline and are incapable of providing immediate feedback to clinicians. This paper presents a real-time image-based algorithm for rigid registration of 3DUS volumes designed for acquisitions in which small probe displacements occur between frames. Described is a method for feature detection and descriptor formation that takes into account the characteristics of 3DUS imaging. Volumes are registered by determining a correspondence between these features. A global set of features is maintained and integrated into the registration, which limits the accumulation of registration error. The system operates in real-time (i.e. volumes are registered as fast or faster than they are acquired) by using an accelerated framework on a graphics processing unit. The algorithm’s parameter selection and performance is analyzed and validated in studies which use both water tank and clinical images. The resulting registration accuracy is comparable to similar feature-based registration methods, but in contrast to these methods, can register 3DUS volumes in real-time.Real-time image-based rigid registration of three-dimensional ultrasoundRobert J. Schneider, Douglas P. Perrin, Nikolay V. Vasilyev, Gerald R. Marx, Pedro J. del Nido, Robert D. Howe10.1016/j.media.2011.10.004Medical Image Analysis 16, 2 (2012)2011-11-16Medical Image Analysis2011-11-16162S1361-8415(12)X0002-1402414http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001435/abstract?rss=yesGraphical abstract: Highlights: ► We formulate a general framework of atlas-based implant surgical planning on 3D data. ► Automated planning of the femoral stem in total hip arthroplasty (THA) is addressed. ► Two types of statistical atlas are developed for modeling the surgeon’s expertise. ► Planning datasets prepared for actual computer-guided THA were used for evaluation. ► The proposed methods will be potentially applied to various implants.Abstract: Atlas-based methods for automated preoperative planning of the femoral stem implant in total hip arthroplasty are described. Statistical atlases are constructed from a number of past preoperative plans prepared by experienced surgeons in order to represent the surgeon’s expertise of the planning. Two types of atlases are considered. One is a statistical distance map atlas, which represents surgeon’s preference of the contact pattern between the femoral canal (host bone) and stem (implant) surfaces. The other is an optimal reference plan, which is selected as the best representative plan expected to minimize the deviation from the surgeon’s preferred contact pattern. These atlases are fitted to the patient data to automatically generate the preoperative plan of the femoral stem. In this paper, we formulate a general framework of atlas-based implant planning, and then describe the methods for construction and utilization of the two proposed atlases. In the experiments, we used 40 cases to evaluate the proposed methods and compare them with previous methods by defining the errors as differences between automated and surgeon’s plans. By using the proposed methods, the positional and orientation errors were significantly reduced compared with the previous methods and the size error was superior to inter-surgeon variability in size selection using 2D templates on an X-ray image reported in previous work.Automated preoperative planning of femoral stem in total hip arthroplasty from 3D CT data: Atlas-based approach and comparative studyItaru Otomaru, Masahiko Nakamoto, Yoshiyuki Kagiyama, Masaki Takao, Nobuhiko Sugano, Noriyuki Tomiyama, Yukio Tada, Yoshinobu Sato10.1016/j.media.2011.10.005Medical Image Analysis 16, 2 (2012)2011-11-07Medical Image Analysis2011-11-07162S1361-8415(12)X0002-1415426http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001605/abstract?rss=yesGraphical abstract: Quantification of 3D Myocardial strain in one patient undergoing CRT before therapy and at follow-up.Highlights: ► We propose a new diffeomorphic temporal registration algorithm. ► It recovers strain and motion from an input 3D ultrasound image sequence. ► Longitudinal strain was quantified on 9 healthy volunteers and 13 CRT patients. ► On volunteers, results are in agreement with clinical literature. ► On patients, results match CRT outcome as quantified by reverse remodeling.Abstract: This paper presents a new registration algorithm, called Temporal Diffeomorphic Free Form Deformation (TDFFD), and its application to motion and strain quantification from a sequence of 3D ultrasound (US) images. The originality of our approach resides in enforcing time consistency by representing the 4D velocity field as the sum of continuous spatiotemporal B-Spline kernels. The spatiotemporal displacement field is then recovered through forward Eulerian integration of the non-stationary velocity field. The strain tensor is computed locally using the spatial derivatives of the reconstructed displacement field. The energy functional considered in this paper weighs two terms: the image similarity and a regularization term. The image similarity metric is the sum of squared differences between the intensities of each frame and a reference one. Any frame in the sequence can be chosen as reference. The regularization term is based on the incompressibility of myocardial tissue. TDFFD was compared to pairwise 3D FFD and 3D+t FFD, both on displacement and velocity fields, on a set of synthetic 3D US images with different noise levels. TDFFD showed increased robustness to noise compared to these two state-of-the-art algorithms. TDFFD also proved to be more resistant to a reduced temporal resolution when decimating this synthetic sequence. Finally, this synthetic dataset was used to determine optimal settings of the TDFFD algorithm. Subsequently, TDFFD was applied to a database of cardiac 3D US images of the left ventricle acquired from 9 healthy volunteers and 13 patients treated by Cardiac Resynchronization Therapy (CRT). On healthy cases, uniform strain patterns were observed over all myocardial segments, as physiologically expected. On all CRT patients, the improvement in synchrony of regional longitudinal strain correlated with CRT clinical outcome as quantified by the reduction of end-systolic left ventricular volume at follow-up (6 and 12months), showing the potential of the proposed algorithm for the assessment of CRT.Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiographyMathieu De Craene, Gemma Piella, Oscar Camara, Nicolas Duchateau, Etelvino Silva, Adelina Doltra, Jan D’hooge, Josep Brugada, Marta Sitges, Alejandro F. Frangi10.1016/j.media.2011.10.006Medical Image Analysis 16, 2 (2012)2011-11-16Medical Image Analysis2011-11-16162S1361-8415(12)X0002-1427450http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001629/abstract?rss=yesGraphical abstract: Multisubject brain response estimated using the three methods under study, KNN maps are shown in serial and parallel versions. Raw maps (left panel) and their responses after thresholding them (at a 95% level) are displayed. Talairach coordinates of axial slices are 66mm, 55mm, −20mm and −23mm.Highlights: ► A mutual information method is used to identify specific effects produced by a task. ► Two MI estimators are proposed for fMRI brain mapping: Parzen windows and KNN. ► A statistical measure has been introduced to automatically threshold the MI maps. ► MI estimators outperform SPM in single subject studies. ► KNN MI shows improved performance in multisubject studies.Abstract: Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). Here, a mutual information (MI) criterion is used to identify regionally specific effects produced by a task. In particular, two MI estimators are presented for its use in fMRI data. The first one uses a Parzen probability density estimation, and the second one is based on a K Nearest Neighbours (KNN) estimation. Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.Analysis of fMRI time series with mutual informationVanessa Gómez-Verdejo, Manel Martínez-Ramón, José Florensa-Vila, Antonio Oliviero10.1016/j.media.2011.11.002Medical Image Analysis 16, 2 (2012)2011-11-28Medical Image Analysis2011-11-28162S1361-8415(12)X0002-1451458http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001630/abstract?rss=yesGraphical abstract: Red circled tensors are original tensors before interpolation, the color of a tensor represent its principal eigenvector orientation.Highlights: ► A novel method for diffusion tensor interpolation. ► A diffusion tensor is represented by tensor eigenvalues and tensor orientation. ► Eliminate swelling effect, preserve simultaneously the monotonicity of FA and MD. ► No artificial crossing fibers introduced.Abstract: Diffusion tensor interpolation is an important issue in the application of diffusion tensor magnetic resonance imaging (DT-MRI) to the human heart, all the more as the points representing the myocardium of the heart are often sparse. We propose a feature-based interpolation framework for the tensor fields from cardiac DT-MRI, by taking into account inherent relationships between tensor components. In this framework, the interpolation consists in representing a diffusion tensor in terms of two tensor features, eigenvalues and orientation, interpolating the Euler angles or the quaternion relative to tensor orientation and the logarithmically transformed eigenvalues, and reconstructing the tensor to be interpolated from the interpolated eigenvalues and tensor orientations. The results obtained with the aid of both synthetic and real cardiac DT-MRI data demonstrate that the feature-based schemes based on Euler angles or quaternions not only maintain the advantages of Log-Euclidean and Riemannian interpolation as for preserving the tensor’s symmetric positive-definiteness and the monotonic determinant variation, but also preserve, at the same time, the monotonicity of fractional anisotropy (FA) and mean diffusivity (MD) values, which is not the case with Euclidean, Cholesky and Log-Euclidean methods. As a result, both interpolation schemes remove the phenomenon of FA collapse, and consequently avoid introducing artificial fiber crossing, with the difference that the quaternion is independent of coordinate system while Euler angles have the property of being more suitable for sophisticated interpolations.Feature-based interpolation of diffusion tensor fields and application to human cardiac DT-MRIFeng Yang, Yue-Min Zhu, Isabelle E. Magnin, Jian-Hua Luo, Pierre Croisille, Peter B. Kingsley10.1016/j.media.2011.11.003Medical Image Analysis 16, 2 (2012)2011-11-18Medical Image Analysis2011-11-18162S1361-8415(12)X0002-1459481http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001678/abstract?rss=yesGraphical abstract: Highlights: ► A biopsy site is re-localised in endoscopic images using epipolar geometry. ► The uncertainty of the re-localised biopsy site is computed analytically. ► Biopsy sites were re-localised with accuracies greater than 1mm in patient data. ► The analytical uncertainty approximates accurately the experimental uncertainty.Abstract: Endoscopy guided probe-based optical biopsy is a new method for detecting sites for tissue biopsy and treatment. After detection, it can be useful to provide a visual aid in the endoscopic images to the endoscopist for example for guidance of forceps to the biopsy sites detected optically. A new method for re-localisation of these sites during the endoscopic examination is presented in this paper. It makes use of a sequence of endoscopic images, where the biopsy site location is known, in order to derive the same number of epipolar lines as images in the sequence projected onto a subsequent target image where the re-localised biopsy site needs to be computed. The location of the re-localised biopsy site is found by minimisation of the sum of squared distances to the epipolar lines. The method also determines analytically the uncertainty of the re-localised biopsy site. This provides the endoscopist with a confidence region around the re-localised biopsy site and a measure of the re-localisation precision. Simulations confirmed that the analytical uncertainty has the potential to be a good estimation of the experimental uncertainty. The method was tested on a physical phantom and on real data from four patients with eight sequences of images acquired during gastroscopy. The re-localisation precision and accuracy were estimated at 1 millimetre or better, which is sufficient for re-localisation of optical biopsy sites.Re-localisation of a biopsy site in endoscopic images and characterisation of its uncertaintyBaptiste Allain, Mingxing Hu, Laurence B. Lovat, Richard J. Cook, Tom Vercauteren, Sebastien Ourselin, David J. Hawkes10.1016/j.media.2011.11.005Medical Image Analysis 16, 2 (2012)2011-12-02Medical Image Analysis2011-12-02162S1361-8415(12)X0002-1482496http://www.medicalimageanalysisjournal.com/article/PIIS136184151100168X/abstract?rss=yesGraphical abstract: 3DMAS Method (3D Mitral Annulus Segmentation Method): Algorithm to segment the mitral valve annulus in a 3D ultrasound frame showing a closed mitral valve.CLKOF Method (Constrained Lucas and Kanade Optical Flow Method): Geometrically constrained optical flow method designed to robustly track the mitral valve annulus between noisy ultrasound volumes.Highlights: ► 4D mitral annulus segmentation algorithm changes methods based on the valve state. ► Valve state is automatically determined from the 3D ultrasound images. ► Closed valve annuli are directly segmented, whereas open valve annuli are tracked. ► Tracking is done using a geometrically constrained optical flow algorithm. ► Annulus delineations are user-independent given reasonable user inputs.Abstract: Measurement of the shape and motion of the mitral valve annulus has proven useful in a number of applications, including pathology diagnosis and mitral valve modeling. Current methods to delineate the annulus from four-dimensional (4D) ultrasound, however, either require extensive overhead or user-interaction, become inaccurate as they accumulate tracking error, or they do not account for annular shape or motion. This paper presents a new 4D annulus segmentation method to account for these deficiencies. The method builds on a previously published three-dimensional (3D) annulus segmentation algorithm that accurately and robustly segments the mitral annulus in a frame with a closed valve. In the 4D method, a valve state predictor determines when the valve is closed. Subsequently, the 3D annulus segmentation algorithm finds the annulus in those frames. For frames with an open valve, a constrained optical flow algorithm is used to the track the annulus. The only inputs to the algorithm are the selection of one frame with a closed valve and one user-specified point near the valve, neither of which needs to be precise. The accuracy of the tracking method is shown by comparing the tracking results to manual segmentations made by a group of experts, where an average RMS difference of 1.67±0.63mm was found across 30 tracked frames.Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flowRobert J. Schneider, Douglas P. Perrin, Nikolay V. Vasilyev, Gerald R. Marx, Pedro J. del Nido, Robert D. Howe10.1016/j.media.2011.11.006Medical Image Analysis 16, 2 (2012)2011-12-06Medical Image Analysis2011-12-06162S1361-8415(12)X0002-1497504http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001691/abstract?rss=yesGraphical abstract: Highlights: ► Successful construction of 3D MRI-based models of pathologic pig hearts. ► 3D model accurately depicts anatomy, scar heterogeneity and fiber directions. ► Categorization of heterogeneous zones was validated using histology. ► Model parameterization used action potential waves from optical imaging.Abstract: Cardiac computer models can help us understand and predict the propagation of excitation waves (i.e., action potential, AP) in healthy and pathologic hearts. Our broad aim is to develop accurate 3D MR image-based computer models of electrophysiology in large hearts (translatable to clinical applications) and to validate them experimentally. The specific goals of this paper were to match models with maps of the propagation of optical AP on the epicardial surface using large porcine hearts with scars, estimating several parameters relevant to macroscopic reaction–diffusion electrophysiological models. We used voltage-sensitive dyes to image AP in large porcine hearts with scars (three specimens had chronic myocardial infarct, and three had radiofrequency RF acute scars). We first analyzed the main AP waves’ characteristics: duration (APD) and propagation under controlled pacing locations and frequencies as recorded from 2D optical images. We further built 3D MR image-based computer models that have information derived from the optical measures, as well as morphologic MRI data (i.e., myocardial anatomy, fiber directions and scar definition). The scar morphology from MR images was validated against corresponding whole-mount histology. We also compared the measured 3D isochronal maps of depolarization to simulated isochrones (the latter replicating precisely the experimental conditions), performing model customization and 3D volumetric adjustments of the local conductivity. Our results demonstrated that mean APD in the border zone (BZ) of the infarct scars was reduced by ∼13% (compared to ∼318ms measured in normal zone, NZ), but APD did not change significantly in the thin BZ of the ablation scars. A generic value for velocity ratio (1:2.7) in healthy myocardial tissue was derived from measured values of transverse and longitudinal conduction velocities relative to fibers direction (22cm/s and 60cm/s, respectively). The model customization and 3D volumetric adjustment reduced the differences between measurements and simulations; for example, from one pacing location, the adjustment reduced the absolute error in local depolarization times by a factor of 5 (i.e., from 58ms to 11ms) in the infarcted heart, and by a factor of 6 (i.e., from 60ms to 9ms) in the heart with the RF scar. Moreover, the sensitivity of adjusted conductivity maps to different pacing locations was tested, and the errors in activation times were found to be of approximately 10–12ms independent of pacing location used to adjust model parameters, suggesting that any location can be used for model predictions.Construction of 3D MR image-based computer models of pathologic hearts, augmented with histology and optical fluorescence imaging to characterize action potential propagationMihaela Pop, Maxime Sermesant, Garry Liu, Jatin Relan, Tommaso Mansi, Alan Soong, Jean-Marc Peyrat, Michael V. Truong, Paul Fefer, Elliot R. McVeigh, Herve Delingette, Alexander J. Dick, Nicholas Ayache, Graham A. Wright10.1016/j.media.2011.11.007Medical Image Analysis 16, 2 (2012)2011-12-07Medical Image Analysis2011-12-07162S1361-8415(12)X0002-1505523http://www.medicalimageanalysisjournal.com/article/PIIS136184151100171X/abstract?rss=yesGraphical abstract: Highlights: ► Noise in magnetic resonance images should be modeled as Rician distribution. ► Rician distribution fits the MR image histogram better than a Gaussian one. ► Cortical surfaces from the brain MR images can be better delineated using Rician models in a segmentation algorithm compared to a Gaussian one. ► Segmentations between same brain MR images acquired under different pulse sequences are more consistent using Rician modeling.Abstract: Several popular classification algorithms used to segment magnetic resonance brain images assume that the image intensities, or log-transformed intensities, satisfy a finite Gaussian mixture model. In these methods, the parameters of the mixture model are estimated and the posterior probabilities for each tissue class are used directly as soft segmentations or combined to form a hard segmentation. It is suggested and shown in this paper that a Rician mixture model fits the observed data better than a Gaussian model. Accordingly, a Rician mixture model is formulated and used within an expectation maximization (EM) framework to yield a new tissue classification algorithm called Rician Classifier using EM (RiCE). It is shown using both simulated and real data that RiCE yields comparable or better performance to that of algorithms based on the finite Gaussian mixture model. As well, we show that RiCE yields more consistent segmentation results when used on images of the same individual acquired with different T1-weighted pulse sequences. Therefore, RiCE has the potential to stabilize segmentation results in brain studies involving heterogeneous acquisition sources as is typically found in both multi-center and longitudinal studies.Consistent segmentation using a Rician classifierSnehashis Roy, Aaron Carass, Pierre-Louis Bazin, Susan Resnick, Jerry L. Prince10.1016/j.media.2011.12.001Medical Image Analysis 16, 2 (2012)2011-12-14Medical Image Analysis2011-12-14162S1361-8415(12)X0002-1524535http://www.medicalimageanalysisjournal.com/article/PIIS1361841511001721/abstract?rss=yesGraphical abstract: Highlights: ► Spatially variable noise correction algorithm is applied with the Rician correction. ► Automatic detection of a regions with the Gaussian or Rician noise distributions. ► Improved noise correction scheme for the diffusion-weighted imaging.Abstract: Magnetic resonance images tend to be influenced by various random factors usually referred to as “noise”. The principal sources of noise and related artefacts can be divided into two types: arising from hardware (acquisition coil arrays, gradient coils, field inhomogeneity); and arising from the subject (physiological noise including body motion, cardiac pulsation or respiratory motion). These factors negatively affect the resolution and reproducibility of the images. Therefore, a proper noise treatment is important for improving the performance of clinical and research investigations. Noise reduction becomes especially critical for the images with a low signal-to-noise ratio, such as those typically acquired in diffusion tensor imaging at high diffusion weightings. The standard methods of signal correction usually assume a uniform distribution of the standard deviation of the noise across the image and evaluate a single correction parameter for the whole image. We pursue a more advanced approach based on the assumption of an inhomogeneous distribution of noise in space and evaluate correction factors for each voxel individually. The Rician nature of the underlying noise is considered for low and high signal-to-noise ratios. The approach developed here has been examined using numerical simulations and in vivo brain diffusion tensor imaging experiments. The efficacy and usefulness of this approach is demonstrated here and the resultant effective tool is described.Spatially variable Rician noise in magnetic resonance imagingIvan I. Maximov, Ezequiel Farrher, Farida Grinberg, N. Jon Shah10.1016/j.media.2011.12.002Medical Image Analysis 16, 2 (2012)2011-12-12Medical Image Analysis2011-12-12162S1361-8415(12)X0002-1536548