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Evaluation of GMI and PMI diffeomorphic-based demons algorithms for aligning PET and CT Images

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS 2015年第4期16卷 18-30页

作者： Yang, Juan Wang, Hongjun Zhang, You Yin, Yong Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China Duke Univ Med Ctr Med Phys Program Durham NC USA Shandong Canc Hosp & Inst Dept Radiat Oncol Jinan Shandong Peoples R China

Fusion of anatomic information in computed tomography (CT) and functional information in F-18-FDG positron emission tomography (PET) is crucial for accurate differentiation of tumor from benign masses, designing radiotherapy treatment plan and staging of cancer. Although current PET and CT images can be acquired from combined F-18-FDG PET/CT scanner, the two acquisitions are scanned separately and take a long time, which may induce potential positional errors in global and local caused by respiratory motion or organ peristalsis. So registration (alignment) of whole-body PET and CT images is a prerequisite for their meaningful fusion. The purpose of this study was to assess the performance of two multimodal registration algorithms for aligning PET and CT images. The proposed gradient of mutual information (GMI)-based demons algorithm, which incorporated the GMI between two images as an external force to facilitate the alignment, was compared with the point-wise mutual information (PMI) diffeomorphic-based demons algorithm whose external force was modified by replacing the image intensity difference in diffeomorphic demons algorithm with the PMI to make it appropriate for multimodal image registration. Eight patients with esophageal cancer(s) were enrolled in this IRB-approved study. Whole-body PET and CT images were acquired from a combined F-18-FDG PET/CT scanner for each patient. The modified Hausdorff distance (d(MH)) was used to evaluate the registration accuracy of the two algorithms. Of all patients, the mean values and standard deviations (SDs) of d(MH) were 6.65 (+/- 1.90) voxels and 6.01 (+/- 1.90) after the GMI-based demons and the PMI diffeomorphic-based demons registration algorithms respectively. Preliminary results on oncological patients showed that the respiratory motion and organ peristalsis in PET/CT esophageal images could not be neglected, although a combined F-18-FDG PET/CT scanner was used for image acquisition. The PMI diffeomorphic-based demon

关键词： deformable image registration PET/CT mutual information demons algorithm multimodal

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MIND demons for MR-to-CT Deformable Image Registration In Image-Guided Spine Surgery

MIND Demons for MR-to-CT Deformable Image Registration In Im...

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Conference on SPIE Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Reaungamornrat, S. De Silva, T. Uneri, A. Wolinsky, J. -P. Khanna, A. J. Kleinszig, G. Vogt, S. Prince, J. L. Siewerdsen, J. H. Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA Johns Hopkins Univ Hosp Dept Neurosurg Spine Baltimore MD 21287 USA Johns Hopkins Hlth Care & Surg Ctr Dept Orthopaed Surg Bethesda MD USA Siemens Healthcare XP Div Erlangen Germany Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

ISBN: (纸本)9781510600218

Purpose: Intraoperative localization of target and normal anatomy can be achieved through multi-modality deformable image registration to resolve planning data (e.g., tumor boundaries and adjacent vital structures) defined in preoperative MR in up-to-date intraoperative CT. We propose a new symmetric diffeomorphic deformable registration method to align preoperative MR to intraoperative CT using a modality-independent neighborhood descriptor (MIND) for image-guided spine intervention. Method: The method estimates symmetric time-dependent diffeomorphisms with smoothness priors on both the velocity fields and the diffeomorphisms using a demons framework. Analysis of energy formulation (asymmetry vs symmetry), similarity metrics (L-2 vs Huber norms), and optimization techniques (a gradient descent method (GD), a Gauss-Newton method (GN) with an explicit matrix inversion (EMI), and GN with the Sherman-Morrison formula (SMF)) was performed in simulation, and performance was measured in terms of target registration error (TRE), properties of diffeomorphisms, and computational efficiency. Result: MIND demons with the symmetric energy functional and the Huber metric yielded topology-preserving and invertible deformations with nonzero-positive Jacobian determinants, sub-voxel invertibility errors (<0.01 mm), and ability to resolve large deformation with TRE = 1.8 mm compared to 6.2 mm for an asymmetric method with L2, 4.9 mm for the asymmetric method with Huber, and 2.9 mm for the symmetric method with L2. MIND demons using GN+SMF yielded TRE comparable to its variant using GD and GN+EMI but improved convergence and computation time. Conclusions: A promising method for MR-to-CT image registration has been developed, incorporating the symmetric energy functional and the robust Huber metric for a stable and reliable estimate of symmetric diffeomorphisms, and GN with SMF for computational efficiency. The approach yielded viscoelastic deformations with registration accuracy pote

关键词： deformable image registration demons algorithm symmetric diffeomorphism multimodality image registration MIND CT MRI image-guided surgery

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Performance evaluation of MIND demons deformable registration of MR and CT images in spinal interventions

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PHYSICS IN MEDICINE AND BIOLOGY 2016年第23期61卷 8276-8297页

作者： Reaungamornrat, S. De Silva, T. Uneri, A. Goerres, J. Jacobson, M. Ketcha, M. Vogt, S. Kleinszig, G. Khanna, A. J. Wolinsky, J-P Prince, J. L. Siewerdsen, J. H. Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21202 USA Siemens Healthcare XP Div D-91052 Erlangen Germany Johns Hopkins Orthopaed Surgery DC Dept Orthopaed Surg Bethesda MD 20817 USA Johns Hopkins Univ Hosp Dept Neurosurg Baltimore MD 21202 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Accurate intraoperative localization of target anatomy and adjacent nervous and vascular tissue is essential to safe, effective surgery, and multimodality deformable registration can be used to identify such anatomy by fusing preoperative CT or MR images with intraoperative images. A deformable image registration method has been developed to estimate viscoelastic diffeomorphisms between preoperative MR and intraoperative CT using modality-independent neighborhood descriptors ( MIND) and aHuber metric for robust registration. The method, called MIND demons, optimizes a constrained symmetric energy functional incorporating priors on smoothness, geodesics, and invertibility by alternating between Gauss-Newton optimization and Tikhonov regularization in a multiresolution scheme. Registration performance was evaluated for the MIND demons method with a symmetric energy formulation in comparison to an asymmetric form, and sensitivity to anisotropic MR voxel-size was analyzed in phantom experiments emulating image-guided spine-surgery in comparison to a free-form deformation (FFD) method using local mutual information (LMI). Performance was validated in a clinical study involving 15 patients undergoing intervention of the cervical, thoracic, and lumbar spine. The target registration error (TRE) for the symmetric MIND demons formulation (1.3 +/- 0.8 mm (median +/- interquartile)) outperformed the asymmetric form (3.6 +/- 4.4 mm). The method demonstrated fairly minor sensitivity to anisotropic MR voxel size, with median TRE ranging 1.3-2.9 mm for MR slice thickness ranging 0.9-9.9 mm, compared to TRE = 3.2-4.1 mm for LMI FFD over the same range. Evaluation in clinical data demonstrated sub-voxel TRE (< 2 mm) in all fifteen cases with realistic deformations that preserved topology with sub-voxel invertibility (0.001 mm) and positive-determinant spatial Jacobians. The approach therefore appears robust against realistic anisotropic resolution characteristics in MR and yields reg

关键词： deformable image registration demons algorithm symmetric diffeomorphism multimodality image registration MIND CT image-guided surgery

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B-spline registration versus demons algorithm - a quantitative comparison of accuracy and invertibility based on artificially created test cases for the lung.

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11th International Congress of the IUPESM/World Congress on Medical Physics and Biomedical Engineering

作者： Hub, M. Kessler, M. L. Karger, C. P. German Canc Res Ctr Neuenheimer Feld 280 D-69120 Heidelberg Germany German Canc Res Ctr Dept Med Phys Radiat Oncol Heidelberg Germany Univ Michigan Dept Med Phys Radiat Oncolo Ann Arbor MI 48109 USA

ISBN: (纸本)9783642038815

In this study the accuracy as well as the physical fidelity of a b-spline registration algorithm and a demons registration algorithm were quantitatively compared based on artificially created test cases for the lung. The test cases were created based on a deformation model. The Jacobean was regarded as a measure of the physical fidelity.

关键词： Elastic image registration demons algorithm b-spline registration accuracy physical fidelity

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Performance Evaluations of demons and Free Form Deformation algorithms for the Liver Region

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TECHNOLOGY IN CANCER RESEARCH & TREATMENT 2014年第2期13卷 101-108页

作者： Wang, Hui Gong, Guanzhong Wang, Hongjun Li, Dengwang Yin, Yong Lu, Jie Shandong Acad Med Sci Dept Radiat Oncol Shandong Canc Hosp Shandong Prov Key Lab Radiat Oncol Jinan 250117 Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Shandong Peoples R China Shandong Normal Univ Sch Phys & Elect Sci Jinan 250014 Shandong Peoples R China

We investigated the influence of breathing motion on radiation therapy according to four-dimensional computed tomography (4D-CT) technology and indicated the registration of 4D-CT images was significant. The demons algorithm in two interpolation modes was compared to the FFD model algorithm to register the different phase images of 4D-CT in tumor tracking, using iodipin as verification. Linear interpolation was used in both mode 1 and mode 2. Mode 1 set outside pixels to nearest pixel, while mode 2 set outside pixels to zero. We used normalized mutual information (NMI), sum of squared differences, modified Hausdorff-distance, and registration speed to evaluate the performance of each algorithm. The average NMI after demons registration method in mode 1 improved 1.76% and 4.75% when compared to mode 2 and FFD model algorithm, respectively. Further, the modified Hausdorff-distance was no different between demons modes 1 and 2, but mode 1 was 15.2% lower than FFD. Finally, demons algorithm has the absolute advantage in registration speed. The demons algorithm in mode 1 was therefore found to be much more suitable for the registration of 4D-CT images. The subtractions of floating images and reference image before and after registration by demons further verified that influence of breathing motion cannot be ignored and the demons registration method is feasible.

关键词： Four-dimensional computed tomography (4D-CT) demons algorithm FFD model lodipin deposition Dose-volume indices Radiation-induced liver disease (RILD)

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Kernel-based Approximation Method for Medical Imaging Problem

Kernel-based Approximation Method for Medical Imaging Proble...

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The 5th Asia-Pacific International Conference on Computational Methods in Engineering and the 11th Chinese National Conference on Computational Methods in Engineering(ICOME 2015)

作者： KS NG SM WONG WK CHEUNG YC HON School of Science and Technology Open University of Hong Kong Department of Clinical Oncology Pamela Youde Nethersole Eastern Hospital Department of Mathematics City University of Hong Kong

The success of radiotherapy treatment relies in accurately localizing the target region close to tumor as well as avoiding damage to the nearby healthy organ. In order to control the tumor growth and minimize side effects to the patient, it is crucial to make some substantial modifications on the patient's anatomy due to the reduction of the patient's weight;shrinkage of the tumor;and the edema of the tissue before the treatment. For surgery consideration, an efficient registration method to delineate the clinically critical objects in computed tomography images obtained from the radiation treatment process is needed. Over the last decades, the Deformable Image Registration Model has undergone intensive investigation from researchers in the fields of computer vision, remote sensing, and etc. Despite the significant progress that has been made, deformable registration remains a challenging problem in the field of radiotherapy. Based on the recent development of demons algorithm and kernel-based approximation method, we discuss in this paper a deformation image registration algorithm for the potential development of interactive deformable image registration package for surgery use. The performance in terms of both accuracy and efficiency of the proposed method will be compared and discussed.

关键词： Deformable Image Registration demons algorithm Kernel-based Approximation

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A PROBABILISTIC demons algorithm FOR TEXTURE-RICH IMAGE REGISTRATION

A PROBABILISTIC DEMONS ALGORITHM FOR TEXTURE-RICH IMAGE REGI...

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16th IEEE International Conference on Image Processing

作者： Yang, Di Li, Hongdong Australian Natl Univ RSISE Canberra ACT 0200 Australia

ISBN: (纸本)9781424456536

demons algorithm has attracted considerable attention from the image processing community for registering (i.e., matching/aligning) deformable objects or images. It is observed that this algorithm is particularly successful when it applies to nonrigid object having homogenous region, but often fails when the object of interest is rich in texture. This is mainly because the demons algorithm tends to overfit the many spurious edges inside the texture-rich region, consequently leading to erroneous thermodynamic 'forces'. In this paper, we describe a probabilistic demons algorithm that overcomes this problem. Our key idea is to re-formulate the deformable registration problem in Bayesian statistics framework. The result is a new and more robust demons algorithm able to capture the essence (e.g., the mass) of a deformable image/object even it is rich in texture. This will significantly expand the applicable scopes of the traditional demons algorithm. We give encouraging experimental results on real test images.

关键词： Image Registration demons algorithm Model-based fitting

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Evaluation of the Diffusion-like Accelerated demons algorithm

Evaluation of the Diffusion-like Accelerated Demons Algorith...

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22nd International Symposium on Information Communication and Automation Technologies

作者： Demirovic, Damir Serifovic-Trbalic, Amira Skejic, Emir Prljaca, Naser Univ Tuzla Fac Elect Engn Tuzla Bosnia & Herceg

ISBN: (纸本)9781424442201

In this paper we present a new approach for the smoothing of displacement field in the nonrigid Accelerated demons algorithm for a discontinuities MRI image registration. Accelerated demons algorithm use Gaussian smoothing filter to penalize oscillatory motion in the displacement fields during the registration, which has significant impact on registration quality. Simulated images show that Gaussian smoothing cannot properly handle these discontinuities. For this reason we introduce diffusion-like smoothing which shows promising results, and start as base for our further research on the topic of discontinuous image registration. This significant problem impedes recovering of organ motion in the case for example persons breathing cycle.

关键词： image registration demons algorithm discontinuities

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A unified framework for cross-modality multi-atlas segmentation of brain MRI

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MEDICAL IMAGE ANALYSIS 2013年第8期17卷 1181-1191页

作者： Iglesias, Juan Eugenio Sabuncu, Mert Rory Van Leemput, Koen Harvard Univ Sch Med Massachusetts Gen Hosp Martinos Ctr Biomed Imaging Cambridge MA 02138 USA Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark Aalto Univ Dept Informat & Comp Sci Espoo Finland Aalto Univ Dept Biomed Engn & Computat Sci Espoo Finland

Multi-atlas label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. A standard label fusion algorithm relies on independently computed pairwise registrations between individual atlases and the (target) image to be segmented. These registrations are then used to propagate the atlas labels to the target space and fuse them into a single final segmentation. Such label fusion schemes commonly rely on the similarity between intensity values of the atlases and target scan, which is often problematic in medical imaging - in particular, when the atlases and target images are obtained via different sensor types or imaging protocols. In this paper, we present a generative probabilistic model that yields an algorithm for solving the atlas-to-target registrations and label fusion steps simultaneously. The proposed model does not directly rely on the similarity of image intensities. Instead, it exploits the consistency of voxel intensities within the target scan to drive the registration and label fusion, hence the atlases and target image can be of different modalities. Furthermore, the framework models the joint warp of all the atlases, introducing interdependence between the registrations. We use variational expectation maximization and the demons registration framework in order to efficiently identify the most probable segmentation and registrations. We use two sets of experiments to illustrate the approach, where proton density (PD) MRI atlases are used to segment T1-weighted brain scans and vice versa. Our results clearly demonstrate the accuracy gain due to exploiting within-target intensity consistency and integrating registration into label fusion. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Label fusion Generative model Registration demons algorithm

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Developmental changes in hippocampal shape among preadolescent children

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INTERNATIONAL JOURNAL OF DEVELOPMENTAL NEUROSCIENCE 2013年第7期31卷 473-481页

作者： Lin, Muqing Fwu, Peter T. Buss, Claudia Davis, Elysia P. Head, Kevin Muftuler, L. Tugan Sandman, Curt A. Su, Min-Ying Univ Calif Irvine Dept Radiol Sci Tu & Yuen Ctr Funct Oncoimaging Irvine CA 92697 USA Univ Calif Irvine Women & Childrens Hlth & Well Being Project Dept Psychiat & Human Behav Irvine CA 92697 USA Univ Calif Irvine Dept Pediat Irvine CA 92697 USA Univ Denver Dept Psychol Denver CO 80208 USA Med Coll Wisconsin Dept Neurosurg Milwaukee WI 53226 USA

It is known that the largest developmental changes in the hippocampus take place during the prenatal period and during the first two years of postnatal life. Few studies have been conducted to address the normal developmental trajectory of the hippocampus during childhood. In this study shape analysis was applied to study the normal developing hippocampus in a group of 103 typically developing 6- to 10-year-old preadolescent children. The individual brain was normalized to a template, and then the hippocampus was manually segmented and further divided into the head, body, and tail sub-regions. Three different methods were applied for hippocampal shape analysis: radial distance mapping, surface-based template registration using the robust point matching (RPM) algorithm, and volume-based template registration using the demons algorithm. All three methods show that the older children have bilateral expanded head segments compared to the younger children. The results analyzed based on radial distance to the centerline were consistent with those analyzed using template-based registration methods. In analyses stratified by sex, it was found that the age-associated anatomical changes were similar in boys and girls, but the age-association was strongest in girls. Total hippocampal volume and sub-regional volumes analyzed using manual segmentation did not show a significant age-association. Our results suggest that shape analysis is sensitive to detect sub-regional differences that are not revealed in volumetric analysis. The three methods presented in this study may be applied in future studies to investigate the normal developmental trajectory of the hippocampus in children. They may be further applied to detect early deviations from the normal developmental trajectory in young children for evaluating susceptibility for psychopathological disorders involving hippocampus. (C) 2013 ISDN. Published by Elsevier Ltd. All rights reserved.

关键词： Hippocampal shape analysis Non-rigid registration Radial distance mapping Robust point matching algorithm demons algorithm

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