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Xiao H, Xue X, Zhu M, Jiang X, Xia Q, Chen K, Li H, Long L, Peng K. Deep learning-based lung image registration: A review. Comput Biol Med 2023; 165:107434. [PMID: 37696177 DOI: 10.1016/j.compbiomed.2023.107434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/13/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.
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Affiliation(s)
- Hanguang Xiao
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Xufeng Xue
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Mi Zhu
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
| | - Xin Jiang
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Qingling Xia
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Kai Chen
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Huanqi Li
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Li Long
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Ke Peng
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
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2
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Alscher T, Erleben K, Darkner S. Collision-constrained deformable image registration framework for discontinuity management. PLoS One 2023; 18:e0290243. [PMID: 37594943 PMCID: PMC10437794 DOI: 10.1371/journal.pone.0290243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 08/03/2023] [Indexed: 08/20/2023] Open
Abstract
Topological changes like sliding motion, sources and sinks are a significant challenge in image registration. This work proposes the use of the alternating direction method of multipliers as a general framework for constraining the registration of separate objects with individual deformation fields from overlapping in image registration. This constraint is enforced by introducing a collision detection algorithm from the field of computer graphics which results in a robust divide and conquer optimization strategy using Free-Form Deformations. A series of experiments demonstrate that the proposed framework performs superior with regards to the combination of intersection prevention and image registration including synthetic examples containing complex displacement patterns. The results show compliance with the non-intersection constraints while simultaneously preventing a decrease in registration accuracy. Furthermore, the application of the proposed algorithm to the DIR-Lab data set demonstrates that the framework generalizes to real data by validating it on a lung registration problem.
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Affiliation(s)
- Thomas Alscher
- Department of Computer Science, University of Copenhagen, Copenhagen, Region Hovedstaden, Denmark
| | - Kenny Erleben
- Department of Computer Science, University of Copenhagen, Copenhagen, Region Hovedstaden, Denmark
| | - Sune Darkner
- Department of Computer Science, University of Copenhagen, Copenhagen, Region Hovedstaden, Denmark
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3
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Xue P, Fu Y, Zhang J, Ma L, Ren M, Zhang Z, Dong E. Effective lung ventilation estimation based on 4D CT image registration and supervoxels. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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4
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He Y, Wang A, Li S, Hao A. Hierarchical anatomical structure-aware based thoracic CT images registration. Comput Biol Med 2022; 148:105876. [PMID: 35863247 DOI: 10.1016/j.compbiomed.2022.105876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 06/17/2022] [Accepted: 07/09/2022] [Indexed: 11/25/2022]
Abstract
Accurate thoracic CT image registration remains challenging due to complex joint deformations and different motion patterns in multiple organs/tissues during breathing. To combat this, we devise a hierarchical anatomical structure-aware based registration framework. It affords a coordination scheme necessary for constraining a general free-form deformation (FFD) during thoracic CT registration. The key is to integrate the deformations of different anatomical structures in a divide-and-conquer way. Specifically, a deformation ability-aware dissimilarity metric is proposed for complex joint deformations containing large-scale flexible deformation of the lung region, rigid displacement of the bone region, and small-scale flexible deformation of the rest region. Furthermore, a motion pattern-aware regularization is devised to handle different motion patterns, which contain sliding motion along the lung surface, almost no displacement of the spine and smooth deformation of other regions. Moreover, to accommodate large-scale deformation, a novel hierarchical strategy, wherein different anatomical structures are fused on the same control lattice, registers images from coarse to fine via elaborate Gaussian pyramids. Extensive experiments and comprehensive evaluations have been executed on the 4D-CT DIR and 3D DIR COPD datasets. It confirms that this newly proposed method is locally comparable to state-of-the-art registration methods specializing in local deformations, while guaranteeing overall accuracy. Additionally, in contrast to the current popular learning-based methods that typically require dozens of hours or more pre-training with powerful graphics cards, our method only takes an average of 63 s to register a case with an ordinary graphics card of RTX2080 SUPER, making our method still worth promoting. Our code is available at https://github.com/heluxixue/Structure_Aware_Registration/tree/master.
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Affiliation(s)
- Yuanbo He
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China; Peng Cheng Laboratory, Shenzhen, 518055, China.
| | - Aoyu Wang
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China.
| | - Shuai Li
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering,Beihang University, Beijing, 100191, China; Peng Cheng Laboratory, Shenzhen, 518055, China.
| | - Aimin Hao
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering,Beihang University, Beijing, 100191, China; Peng Cheng Laboratory, Shenzhen, 518055, China.
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5
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Symmetric Diffeomorphic Image Registration with Multi-Label Segmentation Masks. MATHEMATICS 2022. [DOI: 10.3390/math10111946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Image registration aims to align two images through a spatial transformation. It plays a significant role in brain imaging analysis. In this research, we propose a symmetric diffeomorphic image registration model based on multi-label segmentation masks to solve the problems in brain MRI registration. We first introduce the similarity metric of the multi-label masks to the energy function, which improves the alignment of the brain region boundaries and the robustness to the noise. Next, we establish the model on the diffeomorphism group through the relaxation method and the inverse consistent constraint. The algorithm is designed through the local linearization and least-squares method. We then give spatially adaptive parameters to coordinate the descent of the energy function in different regions. The results show that our approach, compared with the mainstream methods, has better accuracy and noise resistance, and the transformations are more smooth and more reasonable.
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6
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Yang J, Wu Y, Zhang D, Cui W, Yue X, Du S, Zhang H. LDVoxelMorph: A precise loss function and cascaded architecture for unsupervised diffeomorphic large displacement registration. Med Phys 2022; 49:2427-2441. [PMID: 35106787 DOI: 10.1002/mp.15515] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 01/11/2022] [Accepted: 01/22/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The traditional learning-based non-rigid registration methods for medical images are trained by an invariant smoothness regularization parameter, which cannot satisfy the registration accuracy and diffeomorphic property simultaneously. The diffeomorphic property reflects the credibility of the registration results. METHOD To improve the diffeomorphic property in 3D medical image registration, we propose a diffeomorphic cascaded network based on the compressed loss, named LDVoxelMorph. the proposed network has several constituent U-Nets and is trained with deep supervision, which uses a different spatial smoothness regularization parameter in each constituent U-Nets for training. This cascade-variant smoothness regularization parameter can maintain the diffeomorphic property in top cascades with large displacement and achieves precise registration in bottom cascades. Besides, we develop the compressed loss (CL) as a penalty for the velocity field, which can accurately limit the velocity field that causes the deformation field overlap after the velocity field integration. RESULTS In our registration experiments, the dice scores of our method were 0.892±0.040 on liver CT datasets SLIVER37 , 0.848±0.044 on liver CT datasets LiTS38 , 0.689±0.014 on brain MRI datasets LPBA38 , and the number of overlapping voxels of deformation field were 325, 159 and 0, respectively. Ablation study shows the compressed loss improves the diffeomorphic property more effectively than increases. CONCLUSION Experiments results show that our method can achieve higher registration accuracy assessed by dice scores and overlapping voxels while maintaining the diffeomorphic property for large deformation. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Jing Yang
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.,Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yinghao Wu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Dong Zhang
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.,Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Wenting Cui
- Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xiaoli Yue
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shaoyi Du
- Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, 710049, China
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Keelson B, Buzzatti L, Ceranka J, Gutiérrez A, Battista S, Scheerlinck T, Van Gompel G, De Mey J, Cattrysse E, Buls N, Vandemeulebroucke J. Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach. Diagnostics (Basel) 2021; 11:diagnostics11112062. [PMID: 34829409 PMCID: PMC8621122 DOI: 10.3390/diagnostics11112062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/27/2021] [Accepted: 11/02/2021] [Indexed: 11/16/2022] Open
Abstract
Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.
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Affiliation(s)
- Benyameen Keelson
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium;
- IMEC, Kapeldreef 75, B-3002 Leuven, Belgium
- Correspondence:
| | - Luca Buzzatti
- Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Vrije Universiteit Brussel (VUB), Vrije Universiteit, 1090 Brussel, Belgium; (L.B.); (E.C.)
| | - Jakub Ceranka
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium;
- IMEC, Kapeldreef 75, B-3002 Leuven, Belgium
| | - Adrián Gutiérrez
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Simone Battista
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Campus of Savona, University of Genova, 17100 Savona, Italy;
| | - Thierry Scheerlinck
- Department of Orthopaedic Surgery and Traumatology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium;
| | - Gert Van Gompel
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Johan De Mey
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Erik Cattrysse
- Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Vrije Universiteit Brussel (VUB), Vrije Universiteit, 1090 Brussel, Belgium; (L.B.); (E.C.)
| | - Nico Buls
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
| | - Jef Vandemeulebroucke
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), 1090 Brussels, Belgium; (A.G.); (G.V.G.); (J.D.M.); (N.B.); (J.V.)
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium;
- IMEC, Kapeldreef 75, B-3002 Leuven, Belgium
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8
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Beekman C, Schaake E, Sonke JJ, Remeijer P. Deformation trajectory prediction using a neural network trained on finite element data-application to library of CTVs creation for cervical cancer. Phys Med Biol 2021; 66. [PMID: 34607325 DOI: 10.1088/1361-6560/ac2c9b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022]
Abstract
Purpose. We propose a neural network for fast prediction of realistic, time-parametrized deformations between pairs of input segmentations. The proposed method was used to generate a library of planning CTVs for cervical cancer radiotherapy.Methods.A 3D convolutional neural network (CNN) was introduced to predict a stationary velocity field given the distance maps of the cervix CTV in empty and full bladder anatomy. Diffeomorphic deformation trajectories between the two states were obtained by time integration. Intermediate deformation states were used to populate a library of cervix CTVs. The network was trained on cervix CTV deformations of 20 patients generated by finite element modeling (FEM). Validation was performed on FEM data of 9 healthy volunteers. Additionally, for these subjects, CTV deformations were observed in a series of repeat MR scans as the bladder filled from empty to full. Predicted and FEM libraries were compared, and benchmarked against the observed deformations. Finally, for an independent test set of 20 patients the predicted libraries were evaluated clinically, and compared to the current method.Results.The median Dice score over the validation subjects between the predicted and FEM libraries was >0.95 throughout the deformation, with a median 90 percentile surface distance of <3 mm. The ability to cover observed CTVs was similar for both the FEM-based and the proposed method, with residual offsets being about twice as large as the difference between the two methods. Clinical evaluation showed improved library properties over the method currently used in clinic.Conclusions.We proposed a CNN trained on FEM deformations, which predicts the deformation trajectory between two input states of the cervix CTV in one forward pass. We applied this to CTV library prediction for cervical cancer. The network is able to mimic FEM deformations well, while being much faster and simpler in use.
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Affiliation(s)
- Chris Beekman
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Eva Schaake
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Peter Remeijer
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Shao W, Pan Y, Durumeric OC, Reinhardt JM, Bayouth JE, Rusu M, Christensen GE. Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts. Med Image Anal 2021; 72:102140. [PMID: 34214957 DOI: 10.1016/j.media.2021.102140] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 06/12/2021] [Accepted: 06/17/2021] [Indexed: 11/25/2022]
Abstract
Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.
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Affiliation(s)
- Wei Shao
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA; Department of Radiology, Stanford University, Stanford, CA 94305 USA.
| | - Yue Pan
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA
| | - Oguz C Durumeric
- Department of Mathematics, University of Iowa, Iowa City, IA 52242 USA
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242 USA
| | - John E Bayouth
- Department of Human Oncology, University of Wisconsin - Madison, Madison, WI 53792 USA
| | - Mirabela Rusu
- Department of Radiology, Stanford University, Stanford, CA 94305 USA.
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA; Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242 USA.
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10
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Nie X, Huang K, Deasy J, Rimner A, Li G. Enhanced super-resolution reconstruction of T1w time-resolved 4DMRI in low-contrast tissue using 2-step hybrid deformable image registration. J Appl Clin Med Phys 2020; 21:25-39. [PMID: 32961002 PMCID: PMC7592986 DOI: 10.1002/acm2.12988] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 06/22/2019] [Accepted: 06/23/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose Deformable image registration (DIR) in low‐contrast tissues is often suboptimal because of low visibility of landmarks, low driving‐force to deform, and low penalty for misalignment. We aim to overcome the shortcomings for improved reconstruction of time‐resolved four‐dimensional magnetic resonance imaging (TR‐4DMRI). Methods and Materials Super‐resolution TR‐4DMRI reconstruction utilizes DIR to combine high‐resolution (highR:2x2x2mm3) breath‐hold (BH) and low‐resolution (lowR:5x5x5mm3) free‐breathing (FB) 3D cine (2Hz) images to achieve clinically acceptable spatiotemporal resolution. A 2‐step hybrid DIR approach was developed to segment low‐dynamic‐range (LDR) regions: low‐intensity lungs and high‐intensity “bodyshell” (=body‐lungs) for DIR refinement after conventional DIR. The intensity in LDR regions was renormalized to the full dynamic range (FDR) to enhance local tissue contrast. A T1‐mapped 4D XCAT digital phantom was created, and seven volunteers and five lung cancer patients were scanned with two BH and one 3D cine series per subject to compare the 1‐step conventional and 2‐step hybrid DIR using: (a) the ground truth in the phantom, (b) highR‐BH references, which were used to simulate 3D cine images by down‐sampling and Rayleigh‐noise‐adding, and (c) cross‐verification between two TR‐4DMRI images reconstructed from two BHs. To assess DIR improvement, 8‐17 blood vessel bifurcations were used in volunteers, and lung tumor position, size, and shape were used in phantom and patients, together with the voxel intensity correlation (VIC), structural similarity (SSIM), and cross‐consistency check (CCC). Results The 2‐step hybrid DIR improves contrast and DIR accuracy. In volunteers, it improves low‐contrast alignment from 6.5 ± 1.8 mm to 3.3 ± 1.0 mm. In phantom, it improves tumor center of mass alignment (COM = 1.3 ± 0.2 mm) and minimizes DIR directional difference. In patients, it produces almost‐identical tumor COM, size, and shape (dice> 0.85) as the reference. The VIC and SSIM are significantly increased and the number of CCC outliers are reduced by half. Conclusion The 2‐step hybrid DIR improves low‐contrast‐tissue alignment and increases lung tumor fidelity. It is recommended to adopt the 2‐step hybrid DIR for TR‐4DMRI reconstruction.
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Affiliation(s)
- Xingyu Nie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Kirk Huang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joseph Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Guang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Niethammer M, Kwitt R, Vialard FX. Metric Learning for Image Registration. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2020; 2019:8455-8464. [PMID: 32523327 DOI: 10.1109/cvpr.2019.00866] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself. Source code is publicly-available at https://github.com/uncbiag/registration.
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12
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Lee BC, Lin MK, Fu Y, Hata J, Miller MI, Mitra PP. Multimodal cross-registration and quantification of metric distortions in marmoset whole brain histology using diffeomorphic mappings. J Comp Neurol 2020; 529:281-295. [PMID: 32406083 DOI: 10.1002/cne.24946] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 03/23/2020] [Accepted: 04/30/2020] [Indexed: 11/08/2022]
Abstract
Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified in-vivo to ex-vivo distortions in brain geometry from tissue processing. Further, existing approaches focus on registering unimodal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research and the importance of addressing individual brain architecture variations, new algorithms are necessary to cross-register multimodal data sets including MRIs and multiple histological series. Here we present a computational approach for same-subject multimodal MRI-guided reconstruction of a series of consecutive histological sections, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during different stages of brain histological processing using the Jacobian determinant of the diffeomorphic transformations involved. By mapping the final image stacks to the ex-vivo post-fixation MRI, we show that (a) tape-transfer assisted histological sections can be reassembled accurately into 3D volumes with a local scale change of 2.0 ± 0.4% per axis dimension; in contrast, (b) tissue perfusion/fixation as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs shows a larger median absolute scale change of 6.9 ± 2.1% per axis dimension. This is the first systematic quantification of local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species. These local scale changes will be important for computing local properties to create reference brain maps.
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Affiliation(s)
- Brian C Lee
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Meng K Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | - Yan Fu
- Shanghai Jiaotong University, Shanghai, China
| | | | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
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13
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Velázquez-Durán MJ, Campos-Delgado DU, Arce-Santana ER, Mejía-Rodríguez AR. Multimodal 3D rigid image registration based on expectation maximization. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-019-00353-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Gong L, Duan L, Dai Y, He Q, Zuo S, Fu T, Yang X, Zheng J. Locally Adaptive Total p-Variation Regularization for Non-Rigid Image Registration With Sliding Motion. IEEE Trans Biomed Eng 2020; 67:2560-2571. [PMID: 31940514 DOI: 10.1109/tbme.2020.2964695] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Due to the complicated thoracic movements which contain both sliding motion occurring at lung surfaces and smooth motion within individual organs, respiratory estimation is still an intrinsically challenging task. In this paper, we propose a novel regularization term called locally adaptive total p-variation (LaTpV) and embed it into a parametric registration framework to accurately recover lung motion. LaTpV originates from a modified Lp-norm constraint (1 < p < 2), where a prior distribution of p modeled by the Dirac-shaped function is constructed to specifically assign different values to voxels. LaTpV adaptively balances the smoothness and discontinuity of the displacement field to encourage an expected sliding interface. Additionally, we also analytically deduce the gradient of the cost function with respect to transformation parameters. To validate the performance of LaTpV, we not only test it on two mono-modal databases including synthetic images and pulmonary computed tomography (CT) images, but also on a more difficult thoracic CT and positron emission tomography (PET) dataset for the first time. For all experiments, both the quantitative and qualitative results indicate that LaTpV significantly surpasses some existing regularizers such as bending energy and parametric total variation. The proposed LaTpV based registration scheme might be more superior for sliding motion correction and more potential for clinical applications such as the diagnosis of pleural mesothelioma and the adjustment of radiotherapy plans.
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15
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S P, A ET. A Study on Robustness of Various Deformable Image Registration Algorithms on Image Reconstruction Using 4DCT Thoracic Images. J Biomed Phys Eng 2019; 9:559-568. [PMID: 31750270 PMCID: PMC6820026 DOI: 10.31661/jbpe.v0i0.377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 07/16/2015] [Indexed: 11/16/2022]
Abstract
Background Medical image interpolation is recently introduced as a helpful tool to obtain further information via initial available images taken by tomography systems. To do this, deformable image registration algorithms are mainly utilized to perform image interpolation using tomography images. Materials and Methods In this work, 4DCT thoracic images of five real patients provided by DIR-lab group were utilized. Four implemented registration algorithms as 1) Original Horn-Schunck, 2) Inverse consistent Horn-Schunck, 3) Original Demons and 4) Fast Demons were implemented by means of DIRART software packages. Then, the calculated vector fields are processed to reconstruct 4DCT images at any desired time using optical flow based on interpolation method. As a comparative study, the accuracy of interpolated image obtained by each strategy is measured by calculating mean square error between the interpolated image and real middle image as ground truth dataset. Results Final results represent the ability to accomplish image interpolation among given two-paired images. Among them, Inverse Consistent Horn-Schunck algorithm has the best performance to reconstruct interpolated image with the highest accuracy while Demons method had the worst performance. Conclusion Since image interpolation is affected by increasing the distance between two given available images, the performance accuracy of four different registration algorithms is investigated concerning this issue. As a result, Inverse Consistent Horn-Schunck does not essentially have the best performance especially in facing large displacements happened due to distance increment.
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Affiliation(s)
- Parande S
- MSc, Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Haftbagh St. Kerman Iran
| | - Esmaili Torshabi A
- PhD, Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Haftbagh St. Kerman Iran
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16
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Ahmad S, Fan J, Dong P, Cao X, Yap PT, Shen D. Deep Learning Deformation Initialization for Rapid Groupwise Registration of Inhomogeneous Image Populations. Front Neuroinform 2019; 13:34. [PMID: 32760265 PMCID: PMC7373822 DOI: 10.3389/fninf.2019.00034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 04/23/2019] [Indexed: 12/22/2022] Open
Abstract
Groupwise image registration tackles biases that can potentially arise from inappropriate template selection. It typically involves simultaneous registration of a cohort of images to a common space that is not specified a priori. Existing groupwise registration methods are computationally complex and are only effective for image populations without large anatomical variations. In this paper, we propose a deep learning framework to rapidly estimate large deformations between images to significantly reduce structural variability. Specifically, we employ a multi-level graph coarsening method to agglomerate similar images into clusters, each represented by an exemplar image. We then use a deep learning framework to predict the initial deformations between images. Warping with the estimated deformations brings the images closer in the image manifold and their alignment can be further refined using conventional groupwise registration algorithms. We evaluated the effectiveness of our method in groupwise registration of MR brain images and compared it against state-of-the-art groupwise registration methods. Experimental results indicate that deformation initialization enables groupwise registration to converge significantly faster with competitive accuracy, therefore facilitates large-scale imaging studies.
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Affiliation(s)
- Sahar Ahmad
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Jingfan Fan
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Pei Dong
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Xiaohuan Cao
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States.,School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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17
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Szmul A, Matin T, Gleeson FV, Schnabel JA, Grau V, Papież BW. Patch-based lung ventilation estimation using multi-layer supervoxels. Comput Med Imaging Graph 2019; 74:49-60. [PMID: 31009928 DOI: 10.1016/j.compmedimag.2019.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 03/31/2019] [Accepted: 04/02/2019] [Indexed: 01/03/2023]
Abstract
Patch-based approaches have received substantial attention over the recent years in medical imaging. One of their potential applications may be to provide more anatomically consistent ventilation maps estimated on dynamic lung CT. An assessment of regional lung function may act as a guide for radiotherapy, ensuring a more accurate treatment plan. This in turn, could spare well-functioning parts of the lungs. We present a novel method for lung ventilation estimation from dynamic lung CT imaging, combining a supervoxel-based image representation with deformations estimated during deformable image registration, performed between peak breathing phases. For this we propose a method that tracks changes of the intensity of previously extracted supervoxels. For the evaluation of the method we calculate correlation of the estimated ventilation maps with static ventilation images acquired from hyperpolarized Xenon129 MRI. We also investigate the influence of different image registration methods used to estimate deformations between the peak breathing phases in the dynamic CT imaging. We show that our method performs favorably to other ventilation estimation methods commonly used in the field, independently of the image registration method applied to dynamic CT. Due to the patch-based approach of our method, it may be physiologically more consistent with lung anatomy than previous methods relying on voxel-wise relationships. In our method the ventilation is estimated for supervoxels, which tend to group spatially close voxels with similar intensity values. The proposed method was evaluated on a dataset consisting of three lung cancer patients undergoing radiotherapy treatment, and this resulted in a correlation of 0.485 with XeMRI ventilation images, compared with 0.393 for the intensity-based approach, 0.231 for the Jacobian-based method and 0.386 for the Hounsfield units averaging method, on average. Within the limitation of the small number of cases analyzed, results suggest that the presented technique may be advantageous for CT-based ventilation estimation. The results showing higher values of correlation of the proposed method demonstrate the potential of our method to more accurately mimic the lung physiology.
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Affiliation(s)
- Adam Szmul
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.
| | - Tahreema Matin
- Department of Radiology, Oxford University Hospitals NHS FT, Oxford, UK
| | - Fergus V Gleeson
- Department of Oncology, University of Oxford, UK; Department of Radiology, Oxford University Hospitals NHS FT, Oxford, UK
| | - Julia A Schnabel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Bartłomiej W Papież
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
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18
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Li D, Zhong W, Deh KM, Nguyen TD, Prince MR, Wang Y, Spincemaille P. Discontinuity Preserving Liver MR Registration with 3D Active Contour Motion Segmentation. IEEE Trans Biomed Eng 2018; 66:10.1109/TBME.2018.2880733. [PMID: 30418878 PMCID: PMC6565504 DOI: 10.1109/tbme.2018.2880733] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE The sliding motion of the liver during respiration violates the homogeneous motion smoothness assumption in conventional non-rigid image registration and commonly results in compromised registration accuracy. This paper presents a novel approach, registration with 3D active contour motion segmentation (RAMS), to improve registration accuracy with discontinuity-aware motion regularization. METHODS A Markov random field-based discrete optimization with dense displacement sampling and self-similarity context metric is used for registration, while a graph cuts-based 3D active contour approach is applied to segment the sliding interface. In the first registration pass, a mask-free L1 regularization on an image-derived minimum spanning tree is performed to allow motion discontinuity. Based on the motion field estimates, a coarse segmentation finds the motion boundaries. Next, based on MR signal intensity, a fine segmentation aligns the motion boundaries with anatomical boundaries. In the second registration pass, smoothness constraints across the segmented sliding interface are removed by masked regularization on a minimum spanning forest and masked interpolation of the motion field. RESULTS For in vivo breath-hold abdominal MRI data, the motion masks calculated by RAMS are highly consistent with manual segmentations in terms of Dice similarity and bidirectional local distance measure. These automatically obtained masks are shown to substantially improve registration accuracy for both the proposed discrete registration as well as conventional continuous non-rigid algorithms. CONCLUSION/SIGNIFICANCE The presented results demonstrated the feasibility of automated segmentation of the respiratory sliding motion interface in liver MR images and the effectiveness of using the derived motion masks to preserve motion discontinuity.
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Affiliation(s)
- Dongxiao Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
| | - Wenxiong Zhong
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
| | - Kofi M. Deh
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA., Department of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, NY 10021, USA
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19
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Gori P, Colliot O, Kacem LM, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2033-2043. [PMID: 29993599 DOI: 10.1109/tmi.2018.2813062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modeled as 3-D surfaces and curves, respectively. Neurodevelopmental disorders involve morphological and organizational alterations which cannot be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3-D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify, and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.
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20
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Papież BW, Franklin JM, Heinrich MP, Gleeson FV, Brady M, Schnabel JA. GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications. J Med Imaging (Bellingham) 2018; 5:024001. [PMID: 29662918 PMCID: PMC5886381 DOI: 10.1117/1.jmi.5.2.024001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 03/13/2018] [Indexed: 11/14/2022] Open
Abstract
Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset.
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Affiliation(s)
- Bartłomiej W Papież
- University of Oxford, Institute of Biomedical Engineering, Department of Engineering Science, Oxford, United Kingdom
| | - James M Franklin
- University of Oxford, Department of Oncology, Oxford, United Kingdom
| | | | - Fergus V Gleeson
- Oxford University Hospitals NHS Trust, Churchill Hospital, Department of Radiology, Oxford, United Kingdom
| | - Michael Brady
- University of Oxford, Department of Oncology, Oxford, United Kingdom
| | - Julia A Schnabel
- University of Oxford, Institute of Biomedical Engineering, Department of Engineering Science, Oxford, United Kingdom.,King's College London, School of Biomedical Engineering and Imaging Sciences, London, United Kingdom
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21
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Cryo-Imaging and Software Platform for Analysis of Molecular MR Imaging of Micrometastases. Int J Biomed Imaging 2018; 2018:9780349. [PMID: 29805438 PMCID: PMC5899875 DOI: 10.1155/2018/9780349] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/24/2018] [Indexed: 11/25/2022] Open
Abstract
We created and evaluated a preclinical, multimodality imaging, and software platform to assess molecular imaging of small metastases. This included experimental methods (e.g., GFP-labeled tumor and high resolution multispectral cryo-imaging), nonrigid image registration, and interactive visualization of imaging agent targeting. We describe technological details earlier applied to GFP-labeled metastatic tumor targeting by molecular MR (CREKA-Gd) and red fluorescent (CREKA-Cy5) imaging agents. Optimized nonrigid cryo-MRI registration enabled nonambiguous association of MR signals to GFP tumors. Interactive visualization of out-of-RAM volumetric image data allowed one to zoom to a GFP-labeled micrometastasis, determine its anatomical location from color cryo-images, and establish the presence/absence of targeted CREKA-Gd and CREKA-Cy5. In a mouse with >160 GFP-labeled tumors, we determined that in the MR images every tumor in the lung >0.3 mm2 had visible signal and that some metastases as small as 0.1 mm2 were also visible. More tumors were visible in CREKA-Cy5 than in CREKA-Gd MRI. Tape transfer method and nonrigid registration allowed accurate (<11 μm error) registration of whole mouse histology to corresponding cryo-images. Histology showed inflammation and necrotic regions not labeled by imaging agents. This mouse-to-cells multiscale and multimodality platform should uniquely enable more informative and accurate studies of metastatic cancer imaging and therapy.
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22
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Wen D, Nye K, Zhou B, Gilkeson RC, Gupta A, Ranim S, Couturier S, Wilson DL. Enhanced coronary calcium visualization and detection from dual energy chest x-rays with sliding organ registration. Comput Med Imaging Graph 2018; 64:12-21. [PMID: 29397274 DOI: 10.1016/j.compmedimag.2018.01.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 11/06/2017] [Accepted: 01/09/2018] [Indexed: 12/01/2022]
Abstract
We have developed a technique to image coronary calcium, an excellent biomarker for atherosclerotic disease, using low cost, low radiation dual energy (DE) chest radiography, with potential for widespread screening from an already ordered exam. Our dual energy coronary calcium (DECC) processing method included automatic heart silhouette segmentation, sliding organ registration and scatter removal to create a bone-image-like, coronary calcium image with significant reduction in motion artifacts and improved calcium conspicuity compared to standard, clinically available DE processing. Experiments with a physical dynamic cardiac phantom showed that DECC processing reduced 73% of misregistration error caused by cardiac motion over a wide range of heart rates and x-ray radiation exposures. Using the functional measurement test (FMT), we determined significant image quality improvement in clinical images with DECC processing (p < 0.0001), where DECC images were chosen best in 94% of human readings. Comparing DECC images to registered and projected CT calcium images, we found good correspondence between the size and location of calcification signals. In a very preliminary coronary calcium ROC study, we used CT Agatston calcium score >50 as the gold standard for an actual positive test result. AUC performance was significantly improved from 0.73 ± 0.14 with standard DE to 0.87 ± 0.10 with DECC (p = 0.0095) for this limited set of surgical patient data biased towards heavy calcifications. The proposed DECC processing shows good potential for coronary calcium detection in DE chest radiography, giving impetus for a larger clinical evaluation.
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Affiliation(s)
- Di Wen
- Case Western Reserve University, Department of Biomedical Engineering, 10900 Euclid Ave, Cleveland, OH, 44106, United States
| | - Katelyn Nye
- GE Healthcare, Waukesha, WI, 53188, United States
| | - Bo Zhou
- Case Western Reserve University, Department of Biomedical Engineering, 10900 Euclid Ave, Cleveland, OH, 44106, United States
| | - Robert C Gilkeson
- University Hospitals Cleveland Medical Center, Department of Radiology, 11100 Euclid Ave, Cleveland, OH, 44106, United States
| | - Amit Gupta
- University Hospitals Cleveland Medical Center, Department of Radiology, 11100 Euclid Ave, Cleveland, OH, 44106, United States
| | - Shiraz Ranim
- University Hospitals Cleveland Medical Center, Department of Radiology, 11100 Euclid Ave, Cleveland, OH, 44106, United States
| | - Spencer Couturier
- University Hospitals Cleveland Medical Center, Department of Radiology, 11100 Euclid Ave, Cleveland, OH, 44106, United States
| | - David L Wilson
- Case Western Reserve University, Department of Biomedical Engineering, 10900 Euclid Ave, Cleveland, OH, 44106, United States; University Hospitals Cleveland Medical Center, Department of Radiology, 11100 Euclid Ave, Cleveland, OH, 44106, United States.
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23
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Szmul A, Papież BW, Hallack A, Grau V, Schnabel JA. Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering. JOURNAL OF ELECTRONIC IMAGING 2017; 26:061607. [PMID: 29225433 PMCID: PMC5722202 DOI: 10.1117/1.jei.26.6.061607] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model 'sliding motion'. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark.
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Affiliation(s)
- Adam Szmul
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Bartłomiej W. Papież
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Andre Hallack
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Julia A. Schnabel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, UK
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24
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Sugawara Y, Tachibana H, Kadoya N, Kitamura N, Sawant A, Jingu K. Prognostic factors associated with the accuracy of deformable image registration in lung cancer patients treated with stereotactic body radiotherapy. Med Dosim 2017; 42:326-333. [PMID: 28802976 DOI: 10.1016/j.meddos.2017.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Revised: 03/28/2017] [Accepted: 07/03/2017] [Indexed: 11/19/2022]
Abstract
We evaluated the accuracy of an in-house program in lung stereotactic body radiation therapy (SBRT) cancer patients, and explored the prognostic factors associated with the accuracy of deformable image registrations (DIRs). The accuracy of the 3 programs which implement the free-form deformation and the B-spline algorithm was compared regarding the structures on 4-dimensional computed tomography (4DCT) image datasets between the peak-inhale and peak-exhale phases. The dice similarity coefficient (DSC) and normalized DSC (NDSC) were measured for the gross tumor volumes from 19 lung SBRT patients. We evaluated the accuracy of DIR using gross tumor volume, magnitude of displacement from 0% phase to 50% phase, whole lung volume in the 50% phase image, and status of tumor pleural attachment. The median NDSC values using the NiftyReg, MIM Maestro and Velocity AI programs were 1.027, 1.005, and 0.946, respectively, indicating that NiftyReg and MIM Maestro programs had similar accuracy with an uncertainty of < 1 mm. Larger uncertainty of 1 to 2 mm was observed using the Velocity AI program. The NiftyReg and the MIM programs provided higher NDSC values than the median values when the gross tumor volume was attached to the pleura (p <0.05). However, it showed a different trend in using the Velocity AI program. All software programs provided unexpected results, and there is a possibility that such results would reduce the accuracy of 4D treatment planning and adaptive radiotherapy. The unexpected results may be because the tumors are surrounded by other tissues, and there are differences regarding the region of interest for rigid and nonrigid registration. Furthermore, our results indicated that the pleural attachment status might be an important predictor of DIR accuracy for thoracic images, indicating that there is a potentially large dose distribution discrepancy concerning 4D treatment planning and adaptive radiotherapy.
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Affiliation(s)
- Yasuharu Sugawara
- Department of Radiology, National Center for Global Health and Medicine, Tokyo, Japan; Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Hidenobu Tachibana
- Particle Therapy Division, Research Center for Innovative Oncology, National Cancer Center Hospital East, Chiba, Japan.
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Nozomi Kitamura
- Department of Radiation Oncology, Cancer Institute Hospital of the Japanese Foundation of Cancer Research, Tokyo, Japan
| | - Amit Sawant
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Texas, USA
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Miyagi, Japan
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25
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Ruhaak J, Polzin T, Heldmann S, Simpson IJA, Handels H, Modersitzki J, Heinrich MP. Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1746-1757. [PMID: 28391192 DOI: 10.1109/tmi.2017.2691259] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework. The detection of keypoint correspondences enables robustness against large deformations by jointly optimizing over a large number of potential discrete displacements, whereas the dense continuous registration achieves subvoxel alignment with smooth transformations. Both steps are driven by the same normalized gradient fields data term. We employ curvature regularization and a volume change control mechanism to prevent foldings of the deformation grid and restrict the determinant of the Jacobian to physiologically meaningful values. Keypoint correspondences are integrated into the dense registration by a quadratic penalty with adaptively determined weight. Using a parallel matrix-free derivative calculation scheme, a runtime of about 5 min was realized on a standard PC. The proposed algorithm ranks first in the EMPIRE10 challenge on pulmonary image registration. Moreover, it achieves an average landmark distance of 0.82 mm on the DIR-Lab COPD database, thereby improving upon the state of the art in accuracy by 15%. Our algorithm is the first to reach the inter-observer variability in landmark annotation on this dataset.
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26
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Hart V, Burrow D, Allen Li X. A graphical approach to optimizing variable-kernel smoothing parameters for improved deformable registration of CT and cone beam CT images. Phys Med Biol 2017; 62:6246-6260. [PMID: 28714458 DOI: 10.1088/1361-6560/aa7ccb] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A systematic method is presented for determining optimal parameters in variable-kernel deformable image registration of cone beam CT and CT images, in order to improve accuracy and convergence for potential use in online adaptive radiotherapy. Assessed conditions included the noise constant (symmetric force demons), the kernel reduction rate, the kernel reduction percentage, and the kernel adjustment criteria. Four such parameters were tested in conjunction with reductions of 5, 10, 15, 20, 30, and 40%. Noise constants ranged from 1.0 to 1.9 for pelvic images in ten prostate cancer patients. A total of 516 tests were performed and assessed using the structural similarity index. Registration accuracy was plotted as a function of iteration number and a least-squares regression line was calculated, which implied an average improvement of 0.0236% per iteration. This baseline was used to determine if a given set of parameters under- or over-performed. The most accurate parameters within this range were applied to contoured images. The mean Dice similarity coefficient was calculated for bladder, prostate, and rectum with mean values of 98.26%, 97.58%, and 96.73%, respectively; corresponding to improvements of 2.3%, 9.8%, and 1.2% over previously reported values for the same organ contours. This graphical approach to registration analysis could aid in determining optimal parameters for Demons-based algorithms. It also establishes expectation values for convergence rates and could serve as an indicator of non-physical warping, which often occurred in cases >0.6% from the regression line.
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Affiliation(s)
- Vern Hart
- Department of Radiation Oncology, Medical College of Wisconsin, 8701 W Watertown Plank Road, Milwaukee, WI 53226, United States of America. Department of Physics, Utah Valley University, 800 W University Parkway, Orem, UT 84058, United States of America
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Han L, Dong H, McClelland JR, Han L, Hawkes DJ, Barratt DC. A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs. Med Image Anal 2017; 39:87-100. [PMID: 28458088 DOI: 10.1016/j.media.2017.04.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 01/24/2017] [Accepted: 04/11/2017] [Indexed: 11/20/2022]
Abstract
This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson's ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated.
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Affiliation(s)
- Lianghao Han
- Shanghai East Hospital, School of Medicine, Tongji University, 1239 Siping Road, Shanghai, PR China.
| | - Hua Dong
- College of Design and Innovation, Tongji University, 1239 Siping Road, Shanghai, PR China.
| | - Jamie R McClelland
- Centre for Medical Image Computing, University College London, Gower Street, London, WC1E 6BT, UK
| | - Liangxiu Han
- School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Chester Street, Manchester M1 5GD, UK.
| | - David J Hawkes
- Centre for Medical Image Computing, University College London, Gower Street, London, WC1E 6BT, UK.
| | - Dean C Barratt
- Centre for Medical Image Computing, University College London, Gower Street, London, WC1E 6BT, UK.
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Dang J, Yin FF, You T, Dai C, Chen D, Wang J. Simultaneous 4D-CBCT reconstruction with sliding motion constraint. Med Phys 2017; 43:5453. [PMID: 27782722 DOI: 10.1118/1.4959998] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Current approaches using deformable vector field (DVF) for motion-compensated 4D-cone beam CT (CBCT) reconstruction typically utilize an isotropically smoothed DVF between different respiration phases. Such isotropically smoothed DVF does not work well if sliding motion exists between neighboring organs. This study investigated an anisotropic motion modeling scheme by extracting organ boundary local motions (e.g., sliding) and incorporated them into 4D-CBCT reconstruction to optimize the motion modeling and reconstruction methods. METHODS Initially, a modified simultaneous algebraic reconstruction technique (mSART) was applied to reconstruct high quality reference phase CBCT using all phase projections. The initial DVFs were precalculated and subsequently updated to achieve the optimized solution. During the DVF update, sliding motion estimation was performed by matching the measured projections to the forward projection of the deformed reference phase CBCT. In this process, each moving organ boundary was first segmented. The normal vectors of the boundary DVF were then extracted and incorporated for further DVF optimization. The regularization term in the objective function adaptively regularizes the DVF by (1) isotopically smoothing the DVF within each organ; (2) smoothing the DVF at boundary along the normal direction; and (3) leaving the tangent direction of boundary DVF unsmoothed (i.e., allowing for sliding motion). A nonlinear conjugate gradient optimizer was used. The algorithm was validated on a digital cubic tube phantom with sliding motion, nonuniform rotational B-spline based cardiac-torso (NCAT) phantom, and two anonymized patient data. The relative reconstruction error (RE), the motion trajectory's root mean square error (RMSE) together with its maximum error (MaxE), and the Dice coefficient of the lung boundary were calculated to evaluate the algorithm performance. RESULTS For the cubic tube and NCAT phantom tests, the REs are 10.2% and 7.4% with sliding motion compensation, compared to 13.4% and 8.9% without sliding modeling. The motion trajectory's RMSE and MaxE for NCAT phantom tests are 0.5 and 0.8 mm with sliding motion constraint compared to 3.5 and 7.3 mm without sliding motion modeling. The Dice coefficients for both NCAT phantom and the patients show a consistent trend that sliding motion constraint achieves better similarity for segmented lung boundary compared with the ground truth or patient reference. CONCLUSIONS A sliding motion-compensated 4D-CBCT reconstruction and the motion modeling scheme was developed. Both phantom and patient study demonstrated the improved accuracy and motion modeling accuracy in reconstructed 4D-CBCT.
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Affiliation(s)
- Jun Dang
- Department of Radiation Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, China
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27705 and Department of Medical Physics, Duke Kunshan University, Kunshan 215316, China
| | - Tao You
- Department of Radiation Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, China
| | - Chunhua Dai
- Department of Radiation Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, China
| | - Deyu Chen
- Department of Radiation Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, China
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390
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Hua R, Pozo JM, Taylor ZA, Frangi AF. Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms. Med Image Anal 2017; 36:113-122. [PMID: 27894001 DOI: 10.1016/j.media.2016.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 10/18/2016] [Accepted: 10/26/2016] [Indexed: 10/20/2022]
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Vishnevskiy V, Gass T, Szekely G, Tanner C, Goksel O. Isotropic Total Variation Regularization of Displacements in Parametric Image Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:385-395. [PMID: 27654322 DOI: 10.1109/tmi.2016.2610583] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Spatial regularization is essential in image registration, which is an ill-posed problem. Regularization can help to avoid both physically implausible displacement fields and local minima during optimization. Tikhonov regularization (squared l2 -norm) is unable to correctly represent non-smooth displacement fields, that can, for example, occur at sliding interfaces in the thorax and abdomen in image time-series during respiration. In this paper, isotropic Total Variation (TV) regularization is used to enable accurate registration near such interfaces. We further develop the TV-regularization for parametric displacement fields and provide an efficient numerical solution scheme using the Alternating Directions Method of Multipliers (ADMM). The proposed method was successfully applied to four clinical databases which capture breathing motion, including CT lung and MR liver images. It provided accurate registration results for the whole volume. A key strength of our proposed method is that it does not depend on organ masks that are conventionally required by many algorithms to avoid errors at sliding interfaces. Furthermore, our method is robust to parameter selection, allowing the use of the same parameters for all tested databases. The average target registration error (TRE) of our method is superior (10% to 40%) to other techniques in the literature. It provides precise motion quantification and sliding detection with sub-pixel accuracy on the publicly available breathing motion databases (mean TREs of 0.95 mm for DIR 4D CT, 0.96 mm for DIR COPDgene, 0.91 mm for POPI databases).
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Yi J, Yang H, Yang X, Chen G. Lung motion estimation by robust point matching and spatiotemporal tracking for 4D CT. Comput Biol Med 2016; 78:107-119. [PMID: 27684323 DOI: 10.1016/j.compbiomed.2016.09.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 09/10/2016] [Accepted: 09/16/2016] [Indexed: 10/21/2022]
Abstract
We propose a deformable registration approach to estimate patient-specific lung motion during free breathing for four-dimensional (4D) computed tomography (CT) based on point matching and tracking between images in different phases. First, a robust point matching (RPM) algorithm coarsely aligns the source phase image onto all other target phase images of 4D CT. Scale-invariant feature transform (SIFT) is introduced into the cost function in order to accelerate and stabilize the convergence of the point matching. Next, the temporal consistency of the estimated lung motion model is preserved by fitting the trajectories of the points in the respiratory phase using L1 norm regularization. Then, the fitted positions of a point along the trajectory are used as the initial positions for the point tracking. Spatial mean-shift iteration is employed to track points in all phase images. The tracked positions in all phases are used to perform RPM again. These steps are repeated until the number of updated points is smaller than a given threshold σ. With this method, the correspondence between the source phase image and other target phase image is established more accurately. Trajectory fitting ensures the estimated trajectory does not fluctuate violently. We evaluated our method by using the public DIR-lab, POPI-model, CREATIS and COPDgene lung datasets. In the experimental results, the proposed method achieved satisfied accuracy for image registration. Our method also preserved the topology of the deformation fields well for image registration with large deformation.
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Affiliation(s)
- Jianbing Yi
- National High Performance Computing Center at Shenzhen, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China; College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China
| | - Hao Yang
- Xi'an Electric Power College, Changle West Road 180, Xi'an, Shaanxi, China
| | - Xuan Yang
- National High Performance Computing Center at Shenzhen, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China.
| | - Guoliang Chen
- National High Performance Computing Center at Shenzhen, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
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Schnabel JA, Heinrich MP, Papież BW, Brady SJM. Advances and challenges in deformable image registration: From image fusion to complex motion modelling. Med Image Anal 2016; 33:145-148. [DOI: 10.1016/j.media.2016.06.031] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 06/17/2016] [Accepted: 06/17/2016] [Indexed: 10/21/2022]
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Mang A, Biros G. Constrained H1-regularization schemes for diffeomorphic image registration. SIAM JOURNAL ON IMAGING SCIENCES 2016; 9:1154-1194. [PMID: 29075361 PMCID: PMC5654641 DOI: 10.1137/15m1010919] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We propose regularization schemes for deformable registration and efficient algorithms for their numerical approximation. We treat image registration as a variational optimal control problem. The deformation map is parametrized by its velocity. Tikhonov regularization ensures well-posedness. Our scheme augments standard smoothness regularization operators based on H1- and H2-seminorms with a constraint on the divergence of the velocity field, which resembles variational formulations for Stokes incompressible flows. In our formulation, we invert for a stationary velocity field and a mass source map. This allows us to explicitly control the compressibility of the deformation map and by that the determinant of the deformation gradient. We also introduce a new regularization scheme that allows us to control shear. We use a globalized, preconditioned, matrix-free, reduced space (Gauss-)Newton-Krylov scheme for numerical optimization. We exploit variable elimination techniques to reduce the number of unknowns of our system; we only iterate on the reduced space of the velocity field. Our current implementation is limited to the two-dimensional case. The numerical experiments demonstrate that we can control the determinant of the deformation gradient without compromising registration quality. This additional control allows us to avoid oversmoothing of the deformation map. We also demonstrate that we can promote or penalize shear whilst controlling the determinant of the deformation gradient.
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Affiliation(s)
- Andreas Mang
- The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, 78712-0027, US
| | - George Biros
- The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, 78712-0027, US
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Manber R, Thielemans K, Hutton BF, Wan S, McClelland J, Barnes A, Arridge S, Ourselin S, Atkinson D. Joint PET-MR respiratory motion models for clinical PET motion correction. Phys Med Biol 2016; 61:6515-30. [DOI: 10.1088/0031-9155/61/17/6515] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Yi J, Yang X, Chen G, Li YR. Lung motion estimation using dynamic point shifting: An innovative model based on a robust point matching algorithm. Med Phys 2016; 42:5616-32. [PMID: 26429236 DOI: 10.1118/1.4929556] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. METHODS An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered at points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. RESULTS The performances of the authors' method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors' method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the 3000 landmark points of ten cases by the authors' method are 1.21 and 1.04 mm. In the EMPIRE10 lung registration challenge, the authors' method ranks 24 of 39. According to the index of the maximum shear stretch, the authors' method is also efficient to describe the discontinuous motion at the lung boundaries. CONCLUSIONS By establishing the correspondence of the landmark points in the source phase and the other target phases combining shape matching and image intensity matching together, the mismatching issue in the robust point matching algorithm is adequately addressed. The target registration errors are statistically reduced by shifting the virtual target points and target points. The authors' method with consideration of sliding conditions can effectively estimate the discontinuous motion, and the estimated motion is natural. The primary limitation of the proposed method is that the temporal constraints of the trajectories of voxels are not introduced into the motion model. However, the proposed method provides satisfactory motion information, which results in precise tumor coverage by the radiation dose during radiotherapy.
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Affiliation(s)
- Jianbing Yi
- College of Information Engineering, Shenzhen University, Shenzhen, Guangdong 518000, China and College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
| | - Xuan Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518000, China
| | - Guoliang Chen
- National High Performance Computing Center at Shenzhen, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518000, China
| | - Yan-Ran Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518000, China
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Cazoulat G, Owen D, Matuszak MM, Balter JM, Brock KK. Biomechanical deformable image registration of longitudinal lung CT images using vessel information. Phys Med Biol 2016; 61:4826-39. [PMID: 27273115 DOI: 10.1088/0031-9155/61/13/4826] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: an initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix's eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute target registration errors (TRE). The TRE was: [Formula: see text], [Formula: see text] and [Formula: see text] mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable future plan adaptation strategies.
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Affiliation(s)
- Guillaume Cazoulat
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA
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Xu Z, Panjwani SA, Lee CP, Burke RP, Baucom RB, Poulose BK, Abramson RG, Landman BA. Evaluation of Body-Wise and Organ-Wise Registrations For Abdominal Organs. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9784. [PMID: 27127329 DOI: 10.1117/12.2217082] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Identifying cross-sectional and longitudinal correspondence in the abdomen on computed tomography (CT) scans is necessary for quantitatively tracking change and understanding population characteristics, yet abdominal image registration is a challenging problem. The key difficulty in solving this problem is huge variations in organ dimensions and shapes across subjects. The current standard registration method uses the global or body-wise registration technique, which is based on the global topology for alignment. This method (although producing decent results) has substantial influence of outliers, thus leaving room for significant improvement. Here, we study a new image registration approach using local (organ-wise registration) by first creating organ-specific bounding boxes and then using these regions of interest (ROIs) for aligning references to target. Based on Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD) and Hausdorff Distance (HD), the organ-wise approach is demonstrated to have significantly better results by minimizing the distorting effects of organ variations. This paper compares exclusively the two registration methods by providing novel quantitative and qualitative comparison data and is a subset of the more comprehensive problem of improving the multi-atlas segmentation by using organ normalization.
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Affiliation(s)
- Zhoubing Xu
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235
| | - Sahil A Panjwani
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235
| | - Christopher P Lee
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235
| | - Ryan P Burke
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235
| | | | | | - Richard G Abramson
- Radiology and Radiological Science, Vanderbilt University, Nashville, TN 37235
| | - Bennett A Landman
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235; Radiology and Radiological Science, Vanderbilt University, Nashville, TN 37235
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Heinrich MP, Simpson IJ, Papież BW, Brady SM, Schnabel JA. Deformable image registration by combining uncertainty estimates from supervoxel belief propagation. Med Image Anal 2016; 27:57-71. [DOI: 10.1016/j.media.2015.09.005] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Revised: 09/20/2015] [Accepted: 09/22/2015] [Indexed: 11/26/2022]
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Ahmad S, Khan MF. Topology preserving non-rigid image registration using time-varying elasticity model for MRI brain volumes. Comput Biol Med 2015; 67:21-8. [PMID: 26492319 DOI: 10.1016/j.compbiomed.2015.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 09/29/2015] [Indexed: 10/22/2022]
Abstract
In this paper, we present a new non-rigid image registration method that imposes a topology preservation constraint on the deformation. We propose to incorporate the time varying elasticity model into the deformable image matching procedure and constrain the Jacobian determinant of the transformation over the entire image domain. The motion of elastic bodies is governed by a hyperbolic partial differential equation, generally termed as elastodynamics wave equation, which we propose to use as a deformation model. We carried out clinical image registration experiments on 3D magnetic resonance brain scans from IBSR database. The results of the proposed registration approach in terms of Kappa index and relative overlap computed over the subcortical structures were compared against the existing topology preserving non-rigid image registration methods and non topology preserving variant of our proposed registration scheme. The Jacobian determinant maps obtained with our proposed registration method were qualitatively and quantitatively analyzed. The results demonstrated that the proposed scheme provides good registration accuracy with smooth transformations, thereby guaranteeing the preservation of topology.
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Affiliation(s)
- Sahar Ahmad
- National University of Sciences and Technology (NUST), Military College of Signals, Islamabad, Pakistan.
| | - Muhammad Faisal Khan
- National University of Sciences and Technology (NUST), Military College of Signals, Islamabad, Pakistan.
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Vásquez Osorio EM, Kolkman-Deurloo IKK, Schuring-Pereira M, Zolnay A, Heijmen BJM, Hoogeman MS. Improving anatomical mapping of complexly deformed anatomy for external beam radiotherapy and brachytherapy dose accumulation in cervical cancer. Med Phys 2015; 42:206-220. [PMID: 25563261 DOI: 10.1118/1.4903300] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In the treatment of cervical cancer, large anatomical deformations, caused by, e.g., tumor shrinkage, bladder and rectum filling changes, organ sliding, and the presence of the brachytherapy (BT) applicator, prohibit the accumulation of external beam radiotherapy (EBRT) and BT dose distributions. This work proposes a structure-wise registration with vector field integration (SW+VF) to map the largely deformed anatomies between EBRT and BT, paving the way for 3D dose accumulation between EBRT and BT. METHODS T2w-MRIs acquired before EBRT and as a part of the MRI-guided BT procedure for 12 cervical cancer patients, along with the manual delineations of the bladder, cervix-uterus, and rectum-sigmoid, were used for this study. A rigid transformation was used to align the bony anatomy in the MRIs. The proposed SW+VF method starts by automatically segmenting features in the area surrounding the delineated organs. Then, each organ and feature pair is registered independently using a feature-based nonrigid registration algorithm developed in-house. Additionally, a background transformation is calculated to account for areas far from all organs and features. In order to obtain one transformation that can be used for dose accumulation, the organ-based, feature-based, and the background transformations are combined into one vector field using a weighted sum, where the contribution of each transformation can be directly controlled by its extent of influence (scope size). The optimal scope sizes for organ-based and feature-based transformations were found by an exhaustive analysis. The anatomical correctness of the mapping was independently validated by measuring the residual distances after transformation for delineated structures inside the cervix-uterus (inner anatomical correctness), and for anatomical landmarks outside the organs in the surrounding region (outer anatomical correctness). The results of the proposed method were compared with the results of the rigid transformation and nonrigid registration of all structures together (AST). RESULTS The rigid transformation achieved a good global alignment (mean outer anatomical correctness of 4.3 mm) but failed to align the deformed organs (mean inner anatomical correctness of 22.4 mm). Conversely, the AST registration produced a reasonable alignment for the organs (6.3 mm) but not for the surrounding region (16.9 mm). SW+VF registration achieved the best results for both regions (3.5 and 3.4 mm for the inner and outer anatomical correctness, respectively). All differences were significant (p < 0.02, Wilcoxon rank sum test). Additionally, optimization of the scope sizes determined that the method was robust for a large range of scope size values. CONCLUSIONS The novel SW+VF method improved the mapping of large and complex deformations observed between EBRT and BT for cervical cancer patients. Future studies that quantify the mapping error in terms of dose errors are required to test the clinical applicability of dose accumulation by the SW+VF method.
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Affiliation(s)
- Eliana M Vásquez Osorio
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam 3075, The Netherlands
| | | | - Monica Schuring-Pereira
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam 3075, The Netherlands
| | - András Zolnay
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam 3075, The Netherlands
| | - Ben J M Heijmen
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam 3075, The Netherlands
| | - Mischa S Hoogeman
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam 3075, The Netherlands
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Fuerst B, Mansi T, Carnis F, Salzle M, Zhang J, Declerck J, Boettger T, Bayouth J, Navab N, Kamen A. Patient-specific biomechanical model for the prediction of lung motion from 4-D CT images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:599-607. [PMID: 25343757 DOI: 10.1109/tmi.2014.2363611] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40 ±1.0 mm over the entire respiratory cycle. The estimated 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis.
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Weistrand O, Svensson S. The ANACONDA algorithm for deformable image registration in radiotherapy. Med Phys 2014; 42:40-53. [PMID: 25563246 DOI: 10.1118/1.4894702] [Citation(s) in RCA: 175] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Affiliation(s)
- Ola Weistrand
- RaySearch Laboratories AB, Sveavägen 44, SE-11134 Stockholm, Sweden
| | - Stina Svensson
- RaySearch Laboratories AB, Sveavägen 44, SE-11134 Stockholm, Sweden
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Papież BW, Heinrich MP, Fehrenbach J, Risser L, Schnabel JA. An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration. Med Image Anal 2014; 18:1299-311. [PMID: 24968741 DOI: 10.1016/j.media.2014.05.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 03/24/2014] [Accepted: 05/15/2014] [Indexed: 12/27/2022]
Abstract
Several biomedical applications require accurate image registration that can cope effectively with complex organ deformations. This paper addresses this problem by introducing a generic deformable registration algorithm with a new regularization scheme, which is performed through bilateral filtering of the deformation field. The proposed approach is primarily designed to handle smooth deformations both between and within body structures, and also more challenging deformation discontinuities exhibited by sliding organs. The conventional Gaussian smoothing of deformation fields is replaced by a bilateral filtering procedure, which compromises between the spatial smoothness and local intensity similarity kernels, and is further supported by a deformation field similarity kernel. Moreover, the presented framework does not require any explicit prior knowledge about the organ motion properties (e.g. segmentation) and therefore forms a fully automated registration technique. Validation was performed using synthetic phantom data and publicly available clinical 4D CT lung data sets. In both cases, the quantitative analysis shows improved accuracy when compared to conventional Gaussian smoothing. In addition, we provide experimental evidence that masking the lungs in order to avoid the problem of sliding motion during registration performs similarly in terms of the target registration error when compared to the proposed approach, however it requires accurate lung segmentation. Finally, quantification of the level and location of detected sliding motion yields visually plausible results by demonstrating noticeable sliding at the pleural cavity boundaries.
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Affiliation(s)
- Bartłomiej W Papież
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.
| | | | - Jérome Fehrenbach
- Institut de Mathématiques de Toulouse (UMR 5219), Université Paul Sabatier, France
| | - Laurent Risser
- Institut de Mathématiques de Toulouse (UMR 5219), CNRS, France
| | - Julia A Schnabel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
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Arif O, Sundaramoorthi G, Hong BW, Yezzi A. Tracking using motion estimation with physically motivated inter-region constraints. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1875-1889. [PMID: 24846558 DOI: 10.1109/tmi.2014.2325040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
We propose a method for tracking structures (e.g., ventricles and myocardium) in cardiac images (e.g., magnetic resonance) by propagating forward in time a previous estimate of the structures using a new physically motivated motion estimation scheme. Our method estimates motion by regularizing only within structures so that differing motions among different structures are not mixed. It simultaneously satisfies the physical constraints at the interface between a fluid and a medium that the normal component of the fluid's motion must match the normal component of the medium's motion and the No-Slip condition, which states that the tangential velocity approaches zero near the interface. We show that these conditions lead to partial differential equations with Robin boundary conditions at the interface, which couple the motion between structures. We show that propagating a segmentation across frames using our motion estimation scheme leads to more accurate segmentation than traditional motion estimation that does not use physical constraints. Our method is suited to interactive segmentation, prominently used in commercial applications for cardiac analysis, where segmentation propagation is used to predict a segmentation in the next frame. We show that our method leads to more accurate predictions than a popular and recent interactive method used in cardiac segmentation.
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Papiez BW, Heinrich MP, Risser L, Schnabel JA. Complex lung motion estimation via adaptive bilateral filtering of the deformation field. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2014; 16:25-32. [PMID: 24505740 DOI: 10.1007/978-3-642-40760-4_4] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Estimation of physiologically plausible deformations is critical for several medical applications. For example, lung cancer diagnosis and treatment requires accurate image registration which preserves sliding motion in the pleural cavity, and the rigidity of chest bones. This paper addresses these challenges by introducing a novel approach for regularisation of non-linear transformations derived from a bilateral filter. For this purpose, the classic Gaussian kernel is replaced by a new kernel that smoothes the estimated deformation field with respect to the spatial position, intensity and deformation dissimilarity. The proposed regularisation is a spatially adaptive filter that is able to preserve discontinuity between the lungs and the pleura and reduces any rigid structures deformations in volumes. Moreover, the presented framework is fully automatic and no prior knowledge of the underlying anatomy is required. The performance of our novel regularisation technique is demonstrated on phantom data for a proof of concept as well as 3D inhale and exhale pairs of clinical CT lung volumes. The results of the quantitative evaluation exhibit a significant improvement when compared to the corresponding state-of-the-art method using classic Gaussian smoothing.
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Affiliation(s)
- Bartlomiej W Papiez
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Mattias Paul Heinrich
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Laurent Risser
- CNRS, Institut de Mathématiques de Toulouse (UMR5219), France
| | - Julia A Schnabel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
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Vishnevskiy V, Gass T, Székely G, Goksel O. Total Variation Regularization of Displacements in Parametric Image Registration. LECTURE NOTES IN COMPUTER SCIENCE 2014. [DOI: 10.1007/978-3-319-13692-9_20] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Pace DF, Aylward SR, Niethammer M. A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:2114-26. [PMID: 23899632 PMCID: PMC4112204 DOI: 10.1109/tmi.2013.2274777] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall.
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Left-Invariant Metrics for Diffeomorphic Image Registration with Spatially-Varying Regularisation. ADVANCED INFORMATION SYSTEMS ENGINEERING 2013; 16:203-10. [DOI: 10.1007/978-3-642-40811-3_26] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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