Fast Intra- and Intermodal Deformable Registration Based on Local Subvolume Matching Matthias Söhn 1, Verena Scheel 2, Markus Alber 1 (1) Radiooncological.

Slides:

Advertisements

Similar presentations

Computerized Craniofacial Reconstruction using CT-derived Implicit Surface Representations D. Vandermeulen, P. Claes, D. Loeckx, P. Suetens Medical Image.

Advertisements

In the past few years the usage of conformal and IMRT treatments has been increasing rapidly. These treatments employ the use of tighter margins around.

Validation of Blood Flow Simulations in Intracranial Aneurysms Yue Yu Brown University.

Towards synthetic 3D dynamic cardiac Imaging Isabelle E. Magnin CREATIS-LRMN CNRS UMR 5220, Inserm U 630, Université Lyon 1, Insa-Lyon

Intensity-based deformable registration of 2D fluoroscopic X- ray images to a 3D CT model Aviv Hurvitz Advisor: Prof. Leo Joskowicz.

Xiaofen Zheng, Jayaram Udupa, Xinjian Chen Medical Image Processing Group Department of Radiology University of Pennsylvania Feb 10, 2008 (4:30 – 4:50pm)

1 Head and Neck Lymph Node Region Delineation with Auto-segmentation and Image Registration Chia-Chi Teng Department of Electrical Engineering University.

Non-Rigid Registration. Why Non-Rigid Registration  In many applications a rigid transformation is sufficient. (Brain)  Other applications: Intra-subject:

Estimating 3D Respiratory Motion from Orbiting Views Rongping Zeng, Jeffrey A. Fessler, James M. Balter The University of Michigan Oct Funding provided.

Medical Image Synthesis via Monte Carlo Simulation James Z. Chen, Stephen M. Pizer, Edward L. Chaney, Sarang Joshi Medical Image Display & Analysis Group,

Yujun Guo Kent State University August PRESENTATION A Binarization Approach for CT-MR Registration Using Normalized Mutual Information.

Image Registration Narendhran Vijayakumar (Naren) 12/17/2007 Department of Electrical and Computer Engineering 1.

Medical Image Registration

Multimodal Visualization for neurosurgical planning CMPS 261 June 8 th 2010 Uliana Popov.

MSc project Janneke Ansems Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.

Improving the MI registration metric Narendhran Vijayakumar 04/25/2008.

July 2010 Image Registration Techniques, Benchmarking, Strategy Surgical Planning Laboratory Center for Neurological Imaging July 2010 Lidwien Veugen Supervision.

An Integrated Pose and Correspondence Approach to Image Matching Anand Rangarajan Image Processing and Analysis Group Departments of Electrical Engineering.

Model-based Automatic AC/PC Detection on Three-dimensional MRI Scans Babak A. Ardekani, Ph.D., Alvin H. Bachman, Ph.D., Ali Tabesh, Ph.D. The Nathan S.

National Alliance for Medical Image Computing Registration in Slicer3 Julien Jomier Kitware Inc.

Quality Assurance for a modern treatment planning system

Navigation in Robotic Radiosurgery Achim Schweikard.

Quantitative Brain Structure Analysis on MR Images

Multimodal Interaction Dr. Mike Spann

Image Segmentation and Registration Rachel Jiang Department of Computer Science Ryerson University 2006.

Dinggang Shen Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB ) Department of Radiology and BRIC UNC-Chapel Hill IDEA.

DTU Medical Visionday May 27, 2009 Generative models for automated brain MRI segmentation Koen Van Leemput Athinoula A. Martinos Center for Biomedical.

4D Stereotactic Lung IMRT Planning using Monte-Carlo Dose Calculations on Multiple RCCT-based Deformable Geometries Matthias Söhn1, Di Yan2 and Markus.

1 4D: Adaptive Radiotherapy & Tomotherapy Bhudatt Paliwal, PhD Professor Departments of Human Oncology & Medical Physics University of Wisconsin Madison.

Introduction Surgical training environments as well as pre- and intra-operative planning environments require physics-based simulation systems to achieve.

Semi-Automatic Central-Chest Lymph-Node Definition from 3D MDCT Images Kongkuo Lu and William E. Higgins Penn State University University Park and Hershey,

Registration of functional PET and structural MR images PVEOut satellite meeting Budapest, June 11 th 2004 Peter Willendrup & Claus Svarer Neurobiology.

Simulated vascular reconstruction in a virtual operating theatre Robert G. Belleman, Peter M.A. Sloot Section Computational Science, University of Amsterdam,

Protein Folding Programs By Asım OKUR CSE 549 November 14, 2002.

Yen Le Computation Biomedicine Lab Advisor: Dr. Kakadiaris 1 Automatic Multi-Region Segmentation Applied to Gene Expression Image from Mouse Brain.

MEDICAL IMAGE REGISTRATION BY MAXIMIZATION OF MUTUAL INFORMATION Dissertation Defense by Chi-hsiang Lo June 27, 2003 PRESENTATION.

A Frequency-Domain Approach to Registration Estimation in 3-D Space Phillip Curtis Pierre Payeur Vision, Imaging, Video and Autonomous Systems Research.

Medical Image Analysis Image Registration Figures come from the textbook: Medical Image Analysis, by Atam P. Dhawan, IEEE Press, 2003.

Feature based deformable registration of neuroimages using interest point and feature selection Leonid Teverovskiy Center for Automated Learning and Discovery.

Danny Ruijters 17 April D-3D intra-interventional registration of coronary arteries.

Generalized Tensor-Based Morphometry (TBM) for the analysis of brain MRI and DTI Natasha Leporé, Laboratory of Neuro Imaging at UCLA.

QIBA CT Volumetrics - Cross-Platform Study (Group 1C) March 18, 2009 Interclinic Comparison of CT Volumetry Quantitative Imaging Biomarker Alliance.

Spatio-Temporal Free-Form Registration of Cardiac MR Image Sequences Antonios Perperidis s /02/2006.

Algorithms for Image Registration: Advanced Normalization Tools (ANTS) Brian Avants, Nick Tustison, Gang Song, James C. Gee Penn Image Computing and Science.

Conclusions The success rate of proposed method is higher than that of traditional MI MI based on GVFI is robust to noise GVFI based on f1 performs better.

GPU Brainstorming What Classes to focus on. Top Priorities Level Sets – (1) ParallelSparseFieldSolver (look at link from Paul) – (?) NarrowBandLevelSet.

Adaptive Rigid Multi-region Selection for 3D face recognition K. Chang, K. Bowyer, P. Flynn Paper presentation Kin-chung (Ryan) Wong 2006/7/27.

A B C D E F A ABSTRACT A novel, efficient, robust, feature-based algorithm is presented for intramodality and multimodality medical image registration.

Statistical Parametric Mapping Lecture 11 - Chapter 13 Head motion and correction Textbook: Functional MRI an introduction to methods, Peter Jezzard, Paul.

Conclusions Simulated fMRI phantoms with real motion and realistic susceptibility artifacts have been generated and tested using SPM2. Image distortion.

Skuller: A volumetric shape registration algorithm for modeling skull deformities Yusuf Sahillioğlu 1 and Ladislav Kavan 2 Medical Image Analysis 2015.

Declaration of Conflict of Interest or Relationship Research partially supported by a grant from GE Healthcare. THOMAS: T HALAMUS O PTIMIZED M ULTI -A.

Reformatting registration Fiducials segmentation registration dataset1 dataset2 dataset1 dataset2 transformation reformatted dataset transformation reformatted.

A 2D/3D correspondence building method for reconstruction of a 3D bone surface model Longwei Fang

Development of elements of 3D planning program for radiotherapy Graphical editor options  automated enclose of contour  correction of intersections 

1 LIRIS Laboratory, Lyon, France 2 Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA 3 Radiotherapy Department, Léon Bérard Cancer Center,

E. Mezzenga 1, E. Cagni 1, A. Botti 1, M. Orlandi 1, W.D. Renner 2, M. Iori 1 1. Medical Physics Unit, ASMN-IRCCS of Reggio Emilia, Italy 2. MathResolution.

Automated airway evaluation system for multi-slice computed tomography using airway lumen diameter, airway wall thickness and broncho-arterial ratio B.

Physica Medica 32 (2016) 1570–1574 報告人：王俊淵

You Zhang, Jeffrey Meyer, Joubin Nasehi Tehrani, Jing Wang

Yuanke Zhang1,2, Hongbing Lu1, Junyan Rong1, Yuxiang Xing3, Jing Meng2

The use of 4DCT images to optimize the Internal Target Volume in Radiotherapy Nikos Giakoumakis, Brian Winey, Joseph Killoran, Tania Lingos, Laurence.

CNRS applications in medical imaging

Comparison of carina- versus bony anatomy-based registration for setup verification in esophageal cancer image-guided radiotherapy Melanie Machiels* 1,

CT-based surrogates of pulmonary ventilation in lung cancer:

Multi-modality image registration using mutual information based on gradient vector flow Yujun Guo May 1,2006.

Iterative Optimization

Mashup Service Recommendation based on User Interest and Service Network Buqing Cao ICWS2013, IJWSR.

Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis Areas of marked lung shrinkage.

GHG meeting at ESTRO36 May, 2017

Presentation transcript:

Fast Intra- and Intermodal Deformable Registration Based on Local Subvolume Matching Matthias Söhn 1, Verena Scheel 2, Markus Alber 1 (1) Radiooncological Clinic, Section for Biomedical Physics, University of Tübingen, Germany (2) Laboratory for Preclinical Imaging and Imaging Technology, Department of Radiology, University of Tübingen, Germany Forschungszentrum für Hochpräzisionsbetrahlung

2 ESTRO 2007 Barcelona – Söhn et al. UKTübingen Deformable Registration for Radiotherapy Requirements & Challenges: accuracy fast no or little user interaction versatility 4D-CT CT-ConeBeamCT CT-MRI Featurelet-based deformable registration our approach…

3 ESTRO 2007 Barcelona – Söhn et al. UKTübingen Algorithmic Implementation 1 Cover region of interest in reference image with regular 3D-grid of featurelets typical size: 1.5x1.5x1.5 cm

4 ESTRO 2007 Barcelona – Söhn et al. UKTübingen reference image (exhale) target image (inhale) 2 for each featurelet Individual rigid registration of each featurelet maximization of local normalized mutual information (NMI) allowing 3D-shifts within local search region  fast & parallelizable!

5 ESTRO 2007 Barcelona – Söhn et al. UKTübingen reference image (exhale) target image (inhale) 2 regions with mismatched featurelets Individual rigid registration of each featurelet for each featurelet maximization of local normalized mutual information (NMI) allowing 3D-shifts within local search region  fast & parallelizable!

6 ESTRO 2007 Barcelona – Söhn et al. UKTübingen Automatic assessment of local registration quality 3 reference featurelet registered target featurelet local similarity measure field (NMI) accept position => shift to position with minimal local deformation energy => …shift to position with minimal local deformation energy within NMI-optimum =>

7 ESTRO 2007 Barcelona – Söhn et al. UKTübingen Automatic assessment of local registration quality -- Result 3

8 ESTRO 2007 Barcelona – Söhn et al. UKTübingen Relaxation Step: Iterative Minimization of Deformation Energy for mismatched Featurelets 4

9 ESTRO 2007 Barcelona – Söhn et al. UKTübingen B-Spline Interpolation of Featurelet shift vectors 5 target image, final featurelet positions interpolation of shift vectors => continuous deformation field!

10 ESTRO 2007 Barcelona – Söhn et al. UKTübingenResults RCCT Inhale-Exhale deformable registration: Visual evaluation before……after registration

11 ESTRO 2007 Barcelona – Söhn et al. UKTübingenResults CT-ConeBeamCT deformable registration: Visual evaluation Elekta XVI ConeBeam-CT data, courtesy D. Yan, Y. Chi (Beaumont) before… …after registration

12 ESTRO 2007 Barcelona – Söhn et al. UKTübingenResults CT-MRI deformable registration: Visual evaluation before… …after registration CT MRIMRI (backtransformed)

13 ESTRO 2007 Barcelona – Söhn et al. UKTübingenResults Quantitative evaluation: Anatomical landmarks N 3D-residuals [mm]… before registration after registration pat ±4.61.3±0.9 pat ±1.51.5±1.0 pat ±5.51.8±0.7 pat ±5.71.8±1.3 avg. 7.8±5.1 (max. 21.3) 1.6±1.0 (max. 4.6) N=55 landmarks altogether marked in inhale and exhale CTs of 4 patients [Siemens Somatom Sensation Open RCCT 1x1x3mm voxelsize]

14 ESTRO 2007 Barcelona – Söhn et al. UKTübingenResults Quantitative evaluation: Virtual phantom courtesy D. Yan, Y. Chi (Beaumont Hospital) Virtual thorax phantom: known deformation field used to to deform real lung CT dataset [based on ~ voxels] before: 2.9±2.8mm after: 1.1±1.2mm Residuals of featurelet algorithm based on thorax phantom:

15 ESTRO 2007 Barcelona – Söhn et al. UKTübingenResults Computational performance test case registered region [voxels] calculation time (dual-core Xeon PC, 2x2.66GHz) Thorax (CT-CT)360x270x1202min 12sec H&N (CT-CBCT)225x225x11549sec H&N (CT-MRI)378x210x701min 23sec calculation time mainly depends on… size of registered region size of local search region featurelet size

16 ESTRO 2007 Barcelona – Söhn et al. UKTübingenResults Computational performance test case registered region [voxels] calculation time (dual-quadcore Xeon PC, 8x2.66GHz) Thorax (CT-CT)360x270x12038sec H&N (CT-CBCT)225x225x11514sec H&N (CT-MRI)378x210x7019sec “online” deformable registration!

17 ESTRO 2007 Barcelona – Söhn et al. UKTübingenConclusions Featurelet-based deformable registration: fast, parallizable model-independent, fully automatic enables multi-modality registration due to use of mutual information sub-voxel registration accuracy as shown by landmark-based evaluation and virtual thorax phantom ‘online’ multi-modality deformable registration within reach!