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Journal articles on the topic '2D/2D registration'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zhang, Yue, Yue Wu, Wenping Ma, Maoguo Gong, Hao Li, and Biao Hou. "Partial Point Cloud Registration with Multi-view 2D Image Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10329–37. https://doi.org/10.1609/aaai.v39i10.33121.

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Learning representations from numerous 2D image data has shown promising performance, yet very few works apply this representations to point cloud registration. In this paper, we explore how to leverage the 2D information to assist the point cloud registration, and propose IAPReg, an Image-Assisted Partial 3D point cloud Registration framework with the multi-view images generated by the input point cloud. It is expected to enrich 3D information with 2D knowledge, and leverage 2D knowledge to assist with point cloud registration. Specifically, we create multi-view depth maps by projecting the i

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Sun, Haoran. "A Review of 3D-2D Registration Methods and Applications based on Medical Images." Highlights in Science, Engineering and Technology 35 (April 11, 2023): 200–224. http://dx.doi.org/10.54097/hset.v35i.7055.

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The registration of preoperative three-dimensional (3D) medical images with intraoperative two-dimensional (2D) data is a key technology for image-guided radiotherapy, minimally invasive surgery, and interventional procedures. In this paper, we review 3D-2D registration methods using computed tomography (CT) and magnetic resonance imaging (MRI) as preoperative 3D images and ultrasound, X-ray, and visible light images as intraoperative 2D images. The 3D-2D registration techniques are classified into intensity-based, structure-based, and gradient-based according to the different registration fea

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Edsfeldt, Andreas, Björn Sonesson, Helena Rosén, et al. "Validation of a New Method for 2D Fusion Imaging Registration in a System Prepared Only for 3D." Journal of Endovascular Therapy 27, no. 3 (2020): 468–72. http://dx.doi.org/10.1177/1526602820912223.

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Purpose: To validate a new 2D-3D registration method of fusion imaging during aortic repair in a system prepared only for 3D-3D registration and to compare radiation doses and accuracy. Materials and Methods: The study involved 189 patients, including 94 patients (median age 70 years; 85 men) who underwent abdominal endovascular aneurysm repair (EVAR) with 2D-3D fusion on an Artis zee imaging system and 95 EVAR patients (median age 70 years; 81 men) from a prior study who had 3D-3D registration done using cone beam computed tomography (CBCT). For the 2D-3D registration, an offline CBCT of the

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He, Yifeng, Jingui Zou, Ruoming Zhai, et al. "An Automatic Solution for Registration Between Single-Image and Point Cloud in Manhattan World Using Line Primitives." Remote Sensing 16, no. 23 (2024): 4382. http://dx.doi.org/10.3390/rs16234382.

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2D-3D registration is increasingly being applied in various scientific and engineering scenarios. However, due to appearance differences and cross-modal discrepancies, it is demanding for image and point cloud registration methods to establish correspondences, making 2D-3D registration highly challenging. To handle these problems, we propose a novel and automatic solution for 2D-3D registration in Manhattan world based on line primitives, which we denote as VPPnL. Firstly, we derive the rotation matrix candidates by establishing the vanishing point coordinate system as the link of point cloud

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Plattard, Delphine, Marine Soret, Jocelyne Troccaz, et al. "Patient Set-Up Using Portal Images: 2D/2D Image Registration Using Mutual Information." Computer Aided Surgery 5, no. 4 (2000): 246–62. http://dx.doi.org/10.3109/10929080009148893.

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Palaniswaamy, G., S. Goddu, M. Mamalui-Hunter, and D. Low. "SU-FF-J-74: Validation of Bony Anatomy Based (2D-2D) Image Registration." Medical Physics 36, no. 6Part6 (2009): 2493. http://dx.doi.org/10.1118/1.3181366.

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Gong, Ren Hui, Özgür Güler, Mustafa Kürklüoglu, John Lovejoy, and Ziv Yaniv. "Interactive initialization of 2D/3D rigid registration." Medical Physics 40, no. 12 (2013): 121911. http://dx.doi.org/10.1118/1.4830428.

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Lucas, Charles-Gérard, and Jérôme Gilles. "Demon Registration for 2D Empirical Wavelet Transforms." Foundations 4, no. 4 (2024): 690–703. https://doi.org/10.3390/foundations4040043.

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The empirical wavelet transform is a fully adaptive time-scale representation that has been widely used in the last decade. Inspired by the empirical mode decomposition, it consists of filter banks based on harmonic mode supports. Recently, it has been generalized to build the filter banks from any generating function using mappings. In practice, the harmonic mode supports can have a low-constrained shape in 2D, leading to numerical difficulties to estimate mappings adapted to the construction of empirical wavelet filters. This work aims to propose an efficient numerical scheme to compute empi

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Kubias, A., F. Deinzer, T. Feldmann, D. Paulus, B. Schreiber, and Th Brunner. "2D/3D image registration on the GPU." Pattern Recognition and Image Analysis 18, no. 3 (2008): 381–89. http://dx.doi.org/10.1134/s1054661808030048.

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Varnavas, Andreas, Tom Carrell, and Graeme Penney. "Fully automated 2D–3D registration and verification." Medical Image Analysis 26, no. 1 (2015): 108–19. http://dx.doi.org/10.1016/j.media.2015.08.005.

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Chou, Chen-Rui, Brandon Frederick, Gig Mageras, Sha Chang, and Stephen Pizer. "2D/3D image registration using regression learning." Computer Vision and Image Understanding 117, no. 9 (2013): 1095–106. http://dx.doi.org/10.1016/j.cviu.2013.02.009.

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Zvonarev, P. S., T. J. Farrell, R. Hunter, M. Wierzbicki, J. E. Hayward, and R. K. Sur. "2D/3D registration algorithm for lung brachytherapy." Medical Physics 40, no. 2 (2013): 021913. http://dx.doi.org/10.1118/1.4788663.

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Darzi, Fatemehzahra, and Thomas Bocklitz. "A Review of Medical Image Registration for Different Modalities." Bioengineering 11, no. 8 (2024): 786. http://dx.doi.org/10.3390/bioengineering11080786.

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Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interp

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Alizadeh Naeini, A., A. Ahmad, M. M. Sheikholeslami, P. Claudio, and G. Sohn. "AN UNSUPERVISED REGISTRATION OF 3D POINT CLOUDS TO 2D CAD MODEL: A CASE STUDY OF FLOOR PLAN." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 9–13. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-9-2020.

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Abstract. Thanks to the proliferation of commodity 3D devices such as HoloLens, one can have easy access to the 3D model of indoor building objects. However, this model does not match 2D available computer-aided design (CAD) models as the as-built model. To address this problem, in this study, a 3-step registration method is proposed. First, binary images, including walls and background, are generated for the 3D point cloud (PC) and the 2D CAD model. Then, 2D-to-2D corresponding pixels (CPs) are extracted based on the intersection of walls in each binary image of PC (BIPC) and binary CAD (BCAD

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Winz, M. L., K. Rohr, and S. Wörz. "Geometric Alignment of 2D Gel Electrophoresis Images." Methods of Information in Medicine 48, no. 04 (2009): 320–23. http://dx.doi.org/10.3414/me9229.

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Summary Objectives: 2D gel electrophoresis (2-DE) is the method of choice for analyzing protein expression in the field of proteomics, for example, comparing a reference with a test population. However, due to complex physical and chemical processes the locations of proteins generally vary in different 2-DE images. To cope with these variations, accurate geometric alignment of 2-DE images is important. Methods: We introduce a new elastic registration approach for 2-DE images, which is based on an analytic solution of the Navier equation using Gaussian elastic body splines (GEBS). With this app

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Baek, Jang-Hyun. "Analyzing Distance-Based Registration with Two Location Areas: A Semi-Markov Process Approach." Electronics 13, no. 1 (2024): 233. http://dx.doi.org/10.3390/electronics13010233.

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In order to connect an incoming call to the user equipment (UE) in a mobile communication network, the location information of the UE must be always kept in the network database. Therefore, the efficiency of the location registration method of reporting new location information to a mobile communication network whenever the location information of the UE changes directly affects the performance of the radio channel, which is a limited resource in a mobile communication network. This study deals with distance-based registration (DBR). DBR does not cause the ping-pong phenomenon known to be a ma

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WANG Lei, 王雷, 高欣 GAO Xin, 崔学理 CUI Xue-li, and 梁志远 LIANG Zhi-yuan. "2D/3D rigid registration by integrating intensity distance." Optics and Precision Engineering 22, no. 10 (2014): 2815–24. http://dx.doi.org/10.3788/ope.20142210.2815.

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Tümer, Nazlı, Aimee Kok, Frans Vos, et al. "Three-Dimensional Registration of Freehand-Tracked Ultrasound to CT Images of the Talocrural Joint." Sensors 18, no. 7 (2018): 2375. http://dx.doi.org/10.3390/s18072375.

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A rigid surface–volume registration scheme is presented in this study to register computed tomography (CT) and free-hand tracked ultrasound (US) images of the talocrural joint. Prior to registration, bone surfaces expected to be visible in US are extracted from the CT volume and bone contours in 2D US data are enhanced based on monogenic signal representation of 2D US images. A 3D monogenic signal data is reconstructed from the 2D data using the position of the US probe recorded with an optical tracking system. When registering the surface extracted from the CT scan to the monogenic signal fea

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HU, ZHENCHENG, and KEIICHI UCHIMURA. "SOLUTION OF CAMERA REGISTRATION PROBLEM VIA 3D-2D PARAMETERIZED MODEL MATCHING FOR ON-ROAD NAVIGATION." International Journal of Image and Graphics 04, no. 01 (2004): 3–20. http://dx.doi.org/10.1142/s0219467804001269.

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This paper presents a dynamical solution of camera registration problem for on-road navigation applications via a 3D-2D parameterized model matching algorithm. The traditional camera'fs three dimensional (3D) position and pose estimation algorithms have always employed fixed and known-structure models as well as the depth information to obtain the 3D-2D correlations, which is however unavailable for on-road navigation applications since there are no fixed models in the general road scene. With the constraints of road structure and on-road navigation features, this paper presents a 2D digital r

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Huang, R., Z. Ye, R. Boerner, W. Yao, Y. Xu, and U. Stilla. "FAST PAIRWISE COARSE REGISTRATION BETWEEN POINT CLOUDS OF CONSTRUCTION SITES USING 2D PROJECTION BASED PHASE CORRELATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1015–20. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1015-2019.

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<p><strong>Abstract.</strong> For conducting change detection using 3D scans of a construction site, the registration between point clouds at different acquisition times is normally necessary. However, due to the complexity of constructing areas, the automatic registration of temporal scans is a challenging problem. In this work, we propose a fast and maker-free method for coarse registration between point clouds by converting the 3D matching problem into a 2D correlation problem, taking the special properties of building structures into consideration. Our proposed method con

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Deepa, M. "Automatic Image Registration using 2D-Discrete Wavelet Transform." Indian Journal of Science and Technology 9, no. 1 (2016): 1–3. http://dx.doi.org/10.17485/ijst/2016/v9i5/83183.

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Jung, Sukwoo, Seunghyun Song, Minho Chang, and Sangchul Park. "Range image registration based on 2D synthetic images." Computer-Aided Design 94 (January 2018): 16–27. http://dx.doi.org/10.1016/j.cad.2017.08.001.

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Arganda-Carreras, I., C. O. S. Sorzano, P. Thévenaz, et al. "Non-rigid consistent registration of 2D image sequences." Physics in Medicine and Biology 55, no. 20 (2010): 6215–42. http://dx.doi.org/10.1088/0031-9155/55/20/012.

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Fitzgibbon, Andrew W. "Robust registration of 2D and 3D point sets." Image and Vision Computing 21, no. 13-14 (2003): 1145–53. http://dx.doi.org/10.1016/j.imavis.2003.09.004.

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25

Demirci, Stefanie, Maximilian Baust, Oliver Kutter, Frode Manstad-Hulaas, Hans-Henning Eckstein, and Nassir Navab. "Disocclusion-based 2D–3D registration for aortic interventions." Computers in Biology and Medicine 43, no. 4 (2013): 312–22. http://dx.doi.org/10.1016/j.compbiomed.2013.01.012.

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26

Alvarez-Gomez, Julio C., Gerardo Jimenez Palavicini, Hubert Roth, and Jürgen Wahrburg. "An Ultra-Fast Digitally Reconstructed Radiograph (DRR) Implementation of the Siddon-Jacobs Algorithm using Parallel Computing: Runtime Improvement of an Intensity-Based 2D/3D Registration." Current Directions in Biomedical Engineering 7, no. 2 (2021): 25–28. http://dx.doi.org/10.1515/cdbme-2021-2007.

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Abstract A key component of an intensity-based 2D/3D registration is the digitally reconstructed radiograph (DRR) module, which creates 2D projections from pre-operative 3D data, e.g., CT and MRI scans. On average, an intensity-based 2D/3D registration requires ten iterations and the rendering of twelve DRR images per iteration. In a typical DRR implementation, the rendering time is about two seconds, and the registration runtime is four minutes. We present an implementation of the Siddon-Jacobs algorithm that uses a novel pixel-step approach to determine the pixel location of the rendering pl

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Ye, Wentao, Jianghong Wu, Wei Zhang, Liyang Sun, Xue Dong, and Shuogui Xu. "A Robust Method for Real Time Intraoperative 2D and Preoperative 3D X-Ray Image Registration Based on an Enhanced Swin Transformer Framework." Bioengineering 12, no. 2 (2025): 114. https://doi.org/10.3390/bioengineering12020114.

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In image-guided surgery (IGS) practice, combining intraoperative 2D X-ray images with preoperative 3D X-ray images from computed tomography (CT) enables the rapid and accurate localization of lesions, which allows for a more minimally invasive and efficient surgery, and also reduces the risk of secondary injuries to nerves and vessels. Conventional optimization-based methods for 2D X-ray and 3D CT matching are limited in speed and precision due to non-convex optimization spaces and a constrained searching range. Recently, deep learning (DL) approaches have demonstrated remarkable proficiency i

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Rha, In Chan, Jae Hwan Bong, and Shin Suk Park. "Development of 2D-3D Image Registration Techniques for Corrective Osteotomy for Lower Limbs." Journal of the Korean Society for Precision Engineering 30, no. 9 (2013): 991–99. http://dx.doi.org/10.7736/kspe.2013.30.9.991.

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Ban, Yuxi, Yang Wang, Shan Liu, et al. "2D/3D Multimode Medical Image Alignment Based on Spatial Histograms." Applied Sciences 12, no. 16 (2022): 8261. http://dx.doi.org/10.3390/app12168261.

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The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial histogram of gradient directions to extract statistical features, overcome the algorithm’s limitations, and expand the applicable scenarios under the premise of ensuring accuracy. The proposed algorithm was tested on CT and synthetic X-ray images, and compared with existing algori

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Zhao, Liya, and Kebin Jia. "Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations." Computational and Mathematical Methods in Medicine 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/836202.

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This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D) images, the two-layer deep adaptive registr

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Zhong, Zichun, Xiaohu Guo, Yiqi Cai, et al. "3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes." BioMed Research International 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/4382854.

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By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at th

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Zhang, S., D. D. Lichti, J. C. Küpper, and J. L. Ronsky. "AN AUTOMATIC ICP-BASED 2D-3D REGISTRATION METHOD FOR A HIGH-SPEED BIPLANAR VIDEORADIOGRAPHY IMAGING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 805–12. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-805-2020.

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Abstract. High-Speed Biplanar Videoradiography (HSBV) is an X-ray based non-invasive imaging system that can be used to derive dynamic bony translations and rotations. The 2D-3D registration process matches a 3D bone model acquired from magnetic resonance imaging (MRI) or computed tomography (CT) scans with the 2D X-ray image pairs. This study focuses on the registration of MRI data as it can acquire detailed soft tissue contrast that cannot be easily discerned in CT scans. A novel 2D-3D registration method is reported in this paper that is suitable for the MRI-based bone models with high prec

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Wei, Wei, Wei Lin, Liang Liu, and Zhong Qin Hu. "2D-3D Medical Image Registration Based on Ant Colony Algorithm." Applied Mechanics and Materials 462-463 (November 2013): 267–73. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.267.

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Object: To optimize the rigidity registration algorithm between X-ray fluoroscopy and CT, and improve the accuracy of registration. Method: By changing the transmission parameters of the ray tracing, it can obtain the original DRR images and the float DRR image for registration. In trials, it uses ant colony algorithm as the optimized search strategy and Mutual information as the similarity measure. Result: ant colony algorithm and the improved ant colony algorithm compared to the classic Powell algorithm to improve the accuracy of registration about 10% and 20%, achieved good results. Conclus

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Kim, Gang Yeon, and Seong Min Son. "Realistic 3D model generation of a real product based on 2D-3D registration." Journal of the Korea Academia-Industrial cooperation Society 14, no. 11 (2013): 5385–91. http://dx.doi.org/10.5762/kais.2013.14.11.5385.

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Eberharter, Johannes K., and Bahram Ravani. "Kinematic Registration in 3D Using the 2D Reuleaux Method." Journal of Mechanical Design 128, no. 2 (2005): 349–55. http://dx.doi.org/10.1115/1.2159027.

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This paper presents a method for kinematic registration in three dimensions using a classical technique from two-dimensional kinematics, namely the Reuleaux method. In three dimensions the kinematic registration problem involves reconstruction of a spatial displacement from data on a minimum of three homologous points at two finitely separated positions of a rigid body. When more than the minimum number of homologous points are specified or when errors in specification of these points are considered, the problem becomes an over determined approximation problem. A computational geometric method

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Visutsak, Porawat, Xiabi Liu, Chalothon Choothong, and Fuangfar Pensiri. "SIFT-Based Depth Estimation for Accurate 3D Reconstruction in Cultural Heritage Preservation." Applied System Innovation 8, no. 2 (2025): 43. https://doi.org/10.3390/asi8020043.

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This paper describes a proposed method for preserving tangible cultural heritage by reconstructing a 3D model of cultural heritage using 2D captured images. The input data represent a set of multiple 2D images captured using different views around the object. An image registration technique is applied to configure the overlapping images with the depth of images computed to construct the 3D model. The automatic 3D reconstruction system consists of three steps: (1) Image registration for managing the overlapping of 2D input images; (2) Depth computation for managing image orientation and calibra

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Tao, Wuyong, Xianghong Hua, Zhiping Chen, and Pengju Tian. "Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features." Remote Sensing 12, no. 8 (2020): 1283. http://dx.doi.org/10.3390/rs12081283.

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Point cloud registration, as the first step for the use of point cloud data, has attracted increasing attention. In order to obtain the entire point cloud of a scene, the registration of point clouds from multiple views is necessary. In this paper, we propose an automatic method for the coarse registration of point clouds. The 2D lines are first extracted from the two point clouds being matched. Then, the line correspondences are established and the 2D transformation is calculated. Finally, a method is developed to calculate the displacement along the z-axis. With the 2D transformation and dis

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Tian, Yingzhong, Xining Liu, Long Li, and Wenbin Wang. "Intensity-Assisted ICP for Fast Registration of 2D-LIDAR." Sensors 19, no. 9 (2019): 2124. http://dx.doi.org/10.3390/s19092124.

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Iterative closest point (ICP) is a method commonly used to perform scan-matching and registration. To be a simple and robust algorithm, it is still computationally expensive, and it has been regarded as having a crucial challenge especially in a real-time application as used for the simultaneous localization and mapping (SLAM) problem. For these reasons, this paper presents a new method for the acceleration of ICP with an assisted intensity. Unlike the conventional ICP, this method is proposed to reduce the computational cost and avoid divergences. An initial transformation guess is computed w

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Ben-Haim, G., S. Dalyot, and Y. Doytsher. "Triangulation based topology approach for 2D point sets registration." Survey Review 46, no. 338 (2014): 355–65. http://dx.doi.org/10.1179/1752270614y.0000000115.

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Gendrin, C., C. Weber, M. Figl, D. Georg, H. Bergmann, and W. Birkfellner. "FAST 2D/3D IMAGE REGISTRATION FOR ONLINE TUMOR TRACKING." Radiotherapy and Oncology 92 (August 2009): S172. http://dx.doi.org/10.1016/s0167-8140(12)73039-6.

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Zvonarev, P., T. Farrell, R. Hunter, J. Hayward, M. Wierzbicki, and R. Sur. "1284 poster AUTOMATED 2D/3D REGISTRATION FOR LUNG BRACHYTHERAPY." Radiotherapy and Oncology 99 (May 2011): S479. http://dx.doi.org/10.1016/s0167-8140(11)71406-2.

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Weese, J., T. M. Buzug, G. P. Penney, and P. Desmedt. "2D/3D registration and motion tracking for surgical interventions." Philips Journal of Research 51, no. 2 (1998): 299–316. http://dx.doi.org/10.1016/s0165-5817(98)00007-2.

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Bae, Kitae, and Hyoungjin Kim. "Optimal Point Correspondence for Image Registration in 2D Images." International Journal of Multimedia and Ubiquitous Engineering 8, no. 6 (2013): 127–40. http://dx.doi.org/10.14257/ijmue.2013.8.6.13.

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Varnavas, Andreas, Tom Carrell, and Graeme Penney. "Increasing the Automation of a 2D-3D Registration System." IEEE Transactions on Medical Imaging 32, no. 2 (2013): 387–99. http://dx.doi.org/10.1109/tmi.2012.2227337.

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Gao, Cong, Amirhossein Farvardin, Robert B. Grupp, et al. "Fiducial-Free 2D/3D Registration for Robot-Assisted Femoroplasty." IEEE Transactions on Medical Robotics and Bionics 2, no. 3 (2020): 437–46. http://dx.doi.org/10.1109/tmrb.2020.3012460.

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Han, Huan, Qi Jin, and Yimin Zhang. "A 2D diffeomorphic image registration model with inequality constraint." Computers & Mathematics with Applications 132 (February 2023): 135–44. http://dx.doi.org/10.1016/j.camwa.2022.12.012.

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Ruijters, Daniel, Bart M. ter Haar Romeny, and Paul Suetens. "Vesselness-based 2D–3D registration of the coronary arteries." International Journal of Computer Assisted Radiology and Surgery 4, no. 4 (2009): 391–97. http://dx.doi.org/10.1007/s11548-009-0316-z.

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Benseghir, Thomas, Grégoire Malandain, and Régis Vaillant. "A tree-topology preserving pairing for 3D/2D registration." International Journal of Computer Assisted Radiology and Surgery 10, no. 6 (2015): 913–23. http://dx.doi.org/10.1007/s11548-015-1207-0.

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Avventi, Enrico, Henric Ryden, Ola Norbeck, Johan Berglund, Tim Sprenger, and Stefan Skare. "Projection‐based 3D/2D registration for prospective motion correction." Magnetic Resonance in Medicine 84, no. 3 (2020): 1534–42. http://dx.doi.org/10.1002/mrm.28225.

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Selmi, Sonia-Yuki, Emmanuel Promayon, and Jocelyne Troccaz. "Hybrid 2D–3D ultrasound registration for navigated prostate biopsy." International Journal of Computer Assisted Radiology and Surgery 13, no. 7 (2018): 987–95. http://dx.doi.org/10.1007/s11548-018-1736-4.

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