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Personalized learning-based segmentation of thoracic aorta and main branches for diagnosis and treatment planning

Personalized learning-based segmentation of thoracic aorta a...

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IEEE International Symposium on Biomedical Imaging

作者： Dime Vitanovski Kristof Ralovich Razvan Ionasec Yefeng Zheng Michael Suehling Waldemar Krawtschuk Joachim Hornegger Dorin Comaniciu Image Analytics and Medical Informatics Siemens AG Corporate Research and Development Princeton USA Pattern Recongnition Laboratory University of Erlangen-Nuremberg Germany

Coarctation of the aorta (CoA), is an obstruction of the aortic arch present in 5-8% of congenital heart diseases. For children older than a year, CoA is increasingly treated by aortic stenting instead of surgical repair. In pediatric cardiology, CMR is accepted as the standard non-invasive imaging modality to assess the aortic arch in it's entire spatial context [1]. Interpreting such 3D datasets are required to assess the underlying anatomy during both diagnosis and therapy planning phases. However this process is time consuming and varies with operator skills. Within this study we propose - for the first time in our knowledge - a method of automatic segmentation of the lumen of thoracic aorta and main branches. The personalized model of the aorta and the supra-aortic arteries, automatically estimated from 3D CMR data, will provide better understanding of the complexity of pathology and assist the cardiologist to choose the best treatment and timing of repair. A hierarchical framework based on robust machine-learning algorithms is proposed to estimate the personalized model parameters. Experiments throughout 212 3D CMR volumes demonstrate model estimation error of 3.24 mm and average computation time of 8 sec. combined with clinical evaluation on 32 patients.

关键词： Biomedical imaging Arteries Detectors Computational modeling image segmentation Heart Valves

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Enhancement of organ of interest via background subtraction in cone beam rotational angiocardiogram

Enhancement of organ of interest via background subtraction ...

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IEEE International Symposium on Biomedical Imaging

作者： Mingqing Chen Yefeng Zheng Kerstin Mueller Christopher Rohkohl Guenter Lauritsch Jan Boese Dorin Comaniciu Image Analytics and Informatics Siemens AG Corporate Research and Development USA Healthcare Sector Siemens AG Forchheim Germany Pattern Recognition Laboratory University of Erlangen-Nuremberg Germany

The real time X-ray angiography based on C-arm cone beam system is the workhorse imaging modality for interventional cardiac procedures. These images are two-dimensional (2D) projections of three dimensional (3D) objects along the X-ray direction. The organ of interest (OOI), such as left ventricle (LV) endocardium, in the projection image is superimposed with other anatomical structures and often has low contrast. In this study, a novel approach is proposed to isolate the OOI in projection images by subtracting with a background image, which is generated by numerical projection of 3D tomographic image with OOI masked out. Study based on one patient and one pig image is taken. About two to three-fold increase in the contrast-to-noise ratio (CNR) is achieved for LV endocardium, compared to an unprocessed image.

关键词： X-ray imaging image reconstruction Computed tomography Attenuation Angiography

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Data-driven computational models of heart anatomy, mechanics and hemodynamics: An integrated framework

Data-driven computational models of heart anatomy, mechanics...

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IEEE International Symposium on Biomedical Imaging

作者： T. Mansi V. Mihalef P. Sharma B. Georgescu X. Zheng S. Rapaka A. Kamen D. Mereles H. Steen B. Meder H. Katus D. Comaniciu Image Analytics and Informatics Corporate Research and Technology Siemens Corporation Princeton NJ USA Department of Internal Medicine III Cardiology Angiology and Pneumology University Hospital of Heidelberg Heidelberg Germany

ISBN: (纸本)9781457718571

Cardiac therapies aim to correct pathological blood flow. Patient-specific therapy planning is challenging due to the large variability in disease cause, location and severity. A predictive framework is therefore needed to assess the optimal treatment for a patient in terms of maximizing effectiveness (blood flow velocity, vorticity, cardiac output, etc.) and minimizing the risk of complications. Multi-physics fluid structure interaction models have been proposed to investigate the function of the bi-ventricular myocardium. Yet, no or only partial patient-specific data have been used due to the lack of efficient methods for accurate modeling of patient's cardiac anatomy from images. Current imaging technologies enabling assessment of cardiac anatomy, dynamics and tissue structure, we propose an integrated framework for multi-physics heart modeling based on imaging data. Our approach relies on efficient machine learning methods to estimate an accurate and comprehensive model of patient's anatomy from MRI. A computational model of cardiac electrophysiology and biomechanics is then solved on patient's anatomy.

关键词： Computational modeling Biological system modeling Blood Heart Data models Mathematical model Analytical models

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Discrete optimization of the multiphase piecewise constant Mumford-Shah functional

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Lecture Notes in Computer Science

作者： El-Zehiry, Noha Grady, Leo Siemens Corporate Research Image Analytics and Informatics Princeton NJ United States

ISBN: (纸本)9783642230936

The Mumford-Shah model has been one of the most powerful models in image segmentation and denoising. The optimization of the multiphase Mumford-Shah energy functional has been performed using level sets methods that optimize the Mumford-Shah energy by evolving the level sets via the gradient descent. These methods are very slow and prone to getting stuck in local optima due to the use of the gradient descent. After the reformulation of the bimodal Mumford-Shah functional on a graph, several groups investigated the hierarchical extension of the graph representation to multi class. These approaches, though more effective than level sets, provide approximate solutions and can diverge away from the optimal solution. In this paper, we present a discrete optimization for the multiphase Mumford Shah functional that directly minimizes the multiphase functional without recursive bisection on the labels. Our approach handles the nonsubmodularity of the multiphase energy function and provide a global optimum if prior information is provided. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： image segmentation

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Robust discriminative wire structure modeling with application to stent enhancement in fluoroscopy

Robust discriminative wire structure modeling with applicati...

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作者： Lu, Xiaoguang Chen, Terrence Comaniciu, Dorin Image Analytics and Informatics Siemens Corporate Research Princeton NJ 08540 United States

ISBN: (纸本)9781457703942

Learning-based methods have been widely used in detecting landmarks or anatomical structures in various medical imaging applications. The performance of discriminative learning techniques has been demonstrated superior to traditional low-level filtering in robustness and scalability. Nevertheless, some structures and patterns are more difficult to be defined by such methods and complicated and ad-hoc methods still need to be used, e.g. a non-rigid and highly deformable wire structure. In this paper, we propose a novel scheme to train classifiers to detect the markers and guide wire segment anchored by markers. The classifier utilizes the markers as the end point and parameterizes the wire in-between them. The probabilities of the markers and the wire are integrated in a Bayesian framework. As a result, both the marker and the wire detection are improved by such a unified approach. Promising results are demonstrated by quantitative evaluation on 263 fluoroscopic sequences with 12495 frames. Our training scheme can further be generalized to localize longer guidewire with higher degrees of parameterization. © 2011 IEEE.

关键词： Wire

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Model-based fusion of CT and non-contrasted 3D C-arm CT: Application to transcatheter valve therapies

Model-based fusion of CT and non-contrasted 3D C-arm CT: App...

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IEEE International Symposium on Biomedical Imaging

作者： Sasa Grbic Christian Gesell Razvan Lonasec Matthias John Jan Boese Joachim Hornegger Nassir Navab Dorin Cotnaniciu Image Analytics and Medical Informatics Siemens AG Corporate Research and Development Princeton USA Computer Aided Medical Procedures Technical University Munich Germany Pattern Recongnition Laboratory University of Erlangen-Nuremberg Germany Siemens Corp Corporate Research Princeton NJ US Healthcare Sector Siemens AG Forchheim Germany

ISBN: (纸本)9781457718571

In recent years transcatheter valve therapies are beginning to replace invasive surgical procedures. As there is no direct view and access to the affected anatomy advanced imaging techniques such as 3D rotational angiography (C-arm CT) and real-time fluoroscopy are used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel method to align pre-operative and intra-operative data by using surrogate anatomical structures which are visible in both modalities without adding contrast agent. The trachea bifurcation model is used as a surrogate structure and the model parameters are estimated using robust machine learning algorithms. High quality patient specific models can be extracted from the pre-operative CT and mapped to the intra-operative 3D C-arm CT for guidance. In addition we learn a weighted mapping function for the trachea bifurcation model extracted from the pre-operative and intra-operative images which minimizes the mapping error in respect to the anatomy of interest. Experiments performed on 28 patient pairs of CT and contrasted 3D C-arm CT data sets assure an accuracy of the mapped aortic valve model of 9.08 ± 7.31 deg and 7.57 ± 3.22mm.

关键词： Computed tomography Valves Solid modeling Bifurcation Atmospheric modeling Estimation Computational modeling

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Accelerated parallel magnetic resonance imaging reconstruction using joint estimation with a sparse signal model

Accelerated parallel magnetic resonance imaging reconstructi...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Daniel S. Weller Jonathan R. Polimeni Leo Grady Lawrence L. Wald Elfar Adalsteinsson Vivek K Goyal Department of EECS Massachusetts Institute of Technology Cambridge MA USA Department of Radiology Harvard Medical School Boston MA USA A. A. Martinos Center Department of Radiology Massachusetts General Hospital Charlestown MA USA Department of Image Analytics and Informatics Siemens AG Corporate Research and Development Princeton NJ USA

Accelerating magnetic resonance imaging (MRI) by reducing the number of acquired k-space scan lines benefits conventional MRI significantly by decreasing the time subjects remain in the magnet. In this paper, we formulate a novel method for Joint estimation from Undersampled LinEs in Parallel MRI (JULEP) that simultaneously calibrates the GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) reconstruction kernel and reconstructs the full multi-channel k-space. We employ a joint sparsity signal model for the channel images in conjunction with observation models for both the acquired data and GRAPPA reconstructed k-space. We demonstrate using real MRI data that JULEP outperforms conventional GRAPPA reconstruction at high levels of undersampling, increasing the peak-signal-to-noise ratio by up to 10 dB.

关键词： Magnetic resonance imaging image reconstruction Joints Acceleration Kernel PSNR

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Patient-specific finite-element simulation of respiratory mechanics for radiotherapy guidance, a first evaluation study

Patient-specific finite-element simulation of respiratory me...

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IEEE International Symposium on Biomedical Imaging

作者： B. Fuerst T. Mansi P. Khurd J. Zhang J. Declerck T. Boettger N. Navab J. Bayouth A. Kamen Corporate Research and Technology Image Analytics and Informatics Siemens Corporation Research Princeton NJ USA Technische Universität München Munich Germany Siemens Molecular Imaging Oxford UK Siemens Radiation Oncology Heidelberg Germany Computer Aided Medical Procedures Technische Universität München Munich Germany Radiation Oncology University of Iowa USA

ISBN: (纸本)9781457718571

During radiotherapy of lung tumors, the respiratory motion must be tracked to reduce radiation of healthy tissue. This is usually done by using a respiratory surrogate, but with limited accuracy. We investigate how patient-specific finite element models (FEM) of respiratory mechanics can predict the motion of the lungs. First, the anatomical models of the lungs and thorax are extracted from CT images automatically. Then, a biomechanical model is used to simulate the respiratory motion based on a novel thorax/lung interaction force that simulates the pleural cavity. Our model is not driven by image forces but by thoracic pressures personalized using a multivariate optimizer. The proposed model is validated on three DIR-Lab datasets, yielding a promising internal landmark error of 3.33 ± 0.60 mm. Our model may represent a tool for lung deformation prediction and therapy guidance.

关键词： Lungs Thorax Load modeling Biological system modeling Force Cavity resonators Computational modeling

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Robust Discriminative Wire Structure Modeling with Application to Stent Enhancement in Fluoroscopy

Robust Discriminative Wire Structure Modeling with Applicati...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Xiaoguang Lu Terrence Chen Dorin Comaniciu Image Analytics and Informatics Siemens Corporate Research

ISBN: (纸本)9781457703942

关键词： ELECTRIC WIRE Fluoroscopy Learning-based method Classifiers End Point Structural Models Guidewire Anatomical structure Quantitative Evaluation

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Learning-based hypothesis fusion for robust catheter tracking in 2D X-ray fluoroscopy

Learning-based hypothesis fusion for robust catheter trackin...

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作者： Wu, Wen Chen, Terrence Barbu, Adrian Wang, Peng Strobel, Norbert Zhou, Shaohua Kevin Comaniciu, Dorin Image Analytics and Informatics Siemens Corporate Research Princeton NJ 08540 United States Department of Statistics Florida State University Tallahassee FL 32306 United States Siemens AG Forchheim Germany

ISBN: (纸本)9781457703942

Catheter tracking has become more and more important in recent interventional applications. It provides real time navigation for the physicians and can be used to control a motion compensated fluoro overlay reference image for other means of guidance, e.g. involving a 3D anatomical model. Tracking the coronary sinus (CS) catheter is effective to compensate respiratory and cardiac motion for 3D overlay navigation to assist positioning the ablation catheter in Atrial Fibrillation (Afib) treatments. During interventions, the CS catheter performs rapid motion and non-rigid deformation due to the beating heart and respiration. In this paper, we model the CS catheter as a set of electrodes. Novelly designed hypotheses generated by a number of learning-based detectors are fused. Robust hypothesis matching through a Bayesian framework is then used to select the best hypothesis for each frame. As a result, our tracking method achieves very high robustness against challenging scenarios such as low SNR, occlusion, foreshortening, non-rigid deformation, as well as the catheter moving in and out of ROI. Quantitative evaluation has been conducted on a database of 13221 frames from 1073 sequences. Our approach obtains 0.50mm median error and 0.76mm mean error. 97.8% of evaluated data have errors less than 2.00mm. The speed of our tracking algorithm reaches 5 frames-per-second on most data sets. Our approach is not limited to the catheters inside the CS but can be extended to track other types of catheters, such as ablation catheters or circumferential mapping catheters. © 2011 IEEE.

关键词： Catheters

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