Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT
We present a gradient-based method for rigid registration of a patient preoperative computed tomography (CT) to its intraoperative situation with a few fluoroscopic X-ray images obtained with a tracked C-arm. The method is noninvasive, anatomy-based, requires simple user interaction, and includes va...
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description | We present a gradient-based method for rigid registration of a patient preoperative computed tomography (CT) to its intraoperative situation with a few fluoroscopic X-ray images obtained with a tracked C-arm. The method is noninvasive, anatomy-based, requires simple user interaction, and includes validation. It is generic and easily customizable for a variety of routine clinical uses in orthopaedic surgery. Gradient-based registration consists of three steps: 1) initial pose estimation; 2) coarse geometry-based registration on bone contours, and; 3) fine gradient projection registration (GPR) on edge pixels. It optimizes speed, accuracy, and robustness. Its novelty resides in using volume gradients to eliminate outliers and foreign objects in the fluoroscopic X-ray images, in speeding up computation, and in achieving higher accuracy. It overcomes the drawbacks of intensity-based methods, which are slow and have a limited convergence range, and of geometry-based methods, which depend on the image segmentation quality. Our simulated, in vitro, and cadaver experiments on a human pelvis CT, dry vertebra, dry femur, fresh lamb hip, and human pelvis under realistic conditions show a mean 0.5-1.7 mm (0.5-2.6 mm maximum) target registration accuracy. |
doi_str_mv | 10.1109/TMI.2003.819288 |
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The method is noninvasive, anatomy-based, requires simple user interaction, and includes validation. It is generic and easily customizable for a variety of routine clinical uses in orthopaedic surgery. Gradient-based registration consists of three steps: 1) initial pose estimation; 2) coarse geometry-based registration on bone contours, and; 3) fine gradient projection registration (GPR) on edge pixels. It optimizes speed, accuracy, and robustness. Its novelty resides in using volume gradients to eliminate outliers and foreign objects in the fluoroscopic X-ray images, in speeding up computation, and in achieving higher accuracy. It overcomes the drawbacks of intensity-based methods, which are slow and have a limited convergence range, and of geometry-based methods, which depend on the image segmentation quality. Our simulated, in vitro, and cadaver experiments on a human pelvis CT, dry vertebra, dry femur, fresh lamb hip, and human pelvis under realistic conditions show a mean 0.5-1.7 mm (0.5-2.6 mm maximum) target registration accuracy.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2003.819288</identifier><identifier>PMID: 14606673</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Accuracy ; Algorithms ; Animals ; Biological and medical sciences ; Bones ; Cadaver ; Computed tomography ; Convergence ; Femur - diagnostic imaging ; Fluoroscopy - methods ; Ground penetrating radar ; Hip Joint - diagnostic imaging ; Humans ; Image segmentation ; Imaging, Three-Dimensional - methods ; Intraoperative Care - methods ; Medical sciences ; Orthopedic surgery ; Pelvis ; Pelvis - diagnostic imaging ; Preoperative Care - methods ; Radiographic Image Enhancement ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects) ; Reproducibility of Results ; Robustness ; Sensitivity and Specificity ; Sheep ; Subtraction Technique ; Surgery, Computer-Assisted - methods ; Technology. Biomaterials. Equipments. Material. Instrumentation ; Tomography, X-Ray Computed - methods ; X-ray imaging</subject><ispartof>IEEE transactions on medical imaging, 2003-11, Vol.22 (11), p.1395-1406</ispartof><rights>2004 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The method is noninvasive, anatomy-based, requires simple user interaction, and includes validation. It is generic and easily customizable for a variety of routine clinical uses in orthopaedic surgery. Gradient-based registration consists of three steps: 1) initial pose estimation; 2) coarse geometry-based registration on bone contours, and; 3) fine gradient projection registration (GPR) on edge pixels. It optimizes speed, accuracy, and robustness. Its novelty resides in using volume gradients to eliminate outliers and foreign objects in the fluoroscopic X-ray images, in speeding up computation, and in achieving higher accuracy. It overcomes the drawbacks of intensity-based methods, which are slow and have a limited convergence range, and of geometry-based methods, which depend on the image segmentation quality. Our simulated, in vitro, and cadaver experiments on a human pelvis CT, dry vertebra, dry femur, fresh lamb hip, and human pelvis under realistic conditions show a mean 0.5-1.7 mm (0.5-2.6 mm maximum) target registration accuracy.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Animals</subject><subject>Biological and medical sciences</subject><subject>Bones</subject><subject>Cadaver</subject><subject>Computed tomography</subject><subject>Convergence</subject><subject>Femur - diagnostic imaging</subject><subject>Fluoroscopy - methods</subject><subject>Ground penetrating radar</subject><subject>Hip Joint - diagnostic imaging</subject><subject>Humans</subject><subject>Image segmentation</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Intraoperative Care - methods</subject><subject>Medical sciences</subject><subject>Orthopedic surgery</subject><subject>Pelvis</subject><subject>Pelvis - diagnostic imaging</subject><subject>Preoperative Care - methods</subject><subject>Radiographic Image Enhancement</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)</subject><subject>Reproducibility of Results</subject><subject>Robustness</subject><subject>Sensitivity and Specificity</subject><subject>Sheep</subject><subject>Subtraction Technique</subject><subject>Surgery, Computer-Assisted - methods</subject><subject>Technology. Biomaterials. Equipments. Material. Instrumentation</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>X-ray imaging</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0U1rGzEQBmBRUho36bmHQlkCTU-yR58rHYPzCQm5OJDbotVKQWG9cqTdQ_59ZGww9NCAQAc9mtGrQegngTkhoBerh7s5BWBzRTRV6guaESEUpoI_H6EZ0FphAEmP0fecXwEIF6C_oWPCJUhZsxm6ukmmC24YcWuy6yqKLxcMX1YpvISuSu4l5DGZMcShir7y_RRTzDZugq2ecTLv1Rir5eoUffWmz-7Hfj9BT9dXq-Utvn-8uVte3GPLaz5iXXctN6qTXpc3W91qs03BLJPeOyNVx4njlErNuePe18IBc9S02mrmwbAT9HdXd5Pi2-Ty2KxDtq7vzeDilBulGHDBCC_y_L-yJkyABPYppIoqwustPPsHvsYpDSVuacupKl2hoMUO2fJNOTnfbFJYm_TeEGi2WZsysWY7sWY3sXLj977s1K5dd_D7ERXwZw9Mtqb3yQw25IMTVNCSo7hfOxecc4djyikr6wOI56Mz</recordid><startdate>20031101</startdate><enddate>20031101</enddate><creator>Livyatan, H.</creator><creator>Yaniv, Z.</creator><creator>Joskowicz, L.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20031101</creationdate><title>Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT</title><author>Livyatan, H. ; Yaniv, Z. ; Joskowicz, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-97db4a8d6f9003c9b9a11093c36ffea68d41e4226944e4ff75e03e2ab9c93f0a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Animals</topic><topic>Biological and medical sciences</topic><topic>Bones</topic><topic>Cadaver</topic><topic>Computed tomography</topic><topic>Convergence</topic><topic>Femur - diagnostic imaging</topic><topic>Fluoroscopy - methods</topic><topic>Ground penetrating radar</topic><topic>Hip Joint - diagnostic imaging</topic><topic>Humans</topic><topic>Image segmentation</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Intraoperative Care - methods</topic><topic>Medical sciences</topic><topic>Orthopedic surgery</topic><topic>Pelvis</topic><topic>Pelvis - diagnostic imaging</topic><topic>Preoperative Care - methods</topic><topic>Radiographic Image Enhancement</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)</topic><topic>Reproducibility of Results</topic><topic>Robustness</topic><topic>Sensitivity and Specificity</topic><topic>Sheep</topic><topic>Subtraction Technique</topic><topic>Surgery, Computer-Assisted - methods</topic><topic>Technology. Biomaterials. Equipments. Material. Instrumentation</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>X-ray imaging</topic><toplevel>online_resources</toplevel><creatorcontrib>Livyatan, H.</creatorcontrib><creatorcontrib>Yaniv, Z.</creatorcontrib><creatorcontrib>Joskowicz, L.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Livyatan, H.</au><au>Yaniv, Z.</au><au>Joskowicz, L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2003-11-01</date><risdate>2003</risdate><volume>22</volume><issue>11</issue><spage>1395</spage><epage>1406</epage><pages>1395-1406</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>We present a gradient-based method for rigid registration of a patient preoperative computed tomography (CT) to its intraoperative situation with a few fluoroscopic X-ray images obtained with a tracked C-arm. The method is noninvasive, anatomy-based, requires simple user interaction, and includes validation. It is generic and easily customizable for a variety of routine clinical uses in orthopaedic surgery. Gradient-based registration consists of three steps: 1) initial pose estimation; 2) coarse geometry-based registration on bone contours, and; 3) fine gradient projection registration (GPR) on edge pixels. It optimizes speed, accuracy, and robustness. Its novelty resides in using volume gradients to eliminate outliers and foreign objects in the fluoroscopic X-ray images, in speeding up computation, and in achieving higher accuracy. It overcomes the drawbacks of intensity-based methods, which are slow and have a limited convergence range, and of geometry-based methods, which depend on the image segmentation quality. Our simulated, in vitro, and cadaver experiments on a human pelvis CT, dry vertebra, dry femur, fresh lamb hip, and human pelvis under realistic conditions show a mean 0.5-1.7 mm (0.5-2.6 mm maximum) target registration accuracy.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>14606673</pmid><doi>10.1109/TMI.2003.819288</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Algorithms Animals Biological and medical sciences Bones Cadaver Computed tomography Convergence Femur - diagnostic imaging Fluoroscopy - methods Ground penetrating radar Hip Joint - diagnostic imaging Humans Image segmentation Imaging, Three-Dimensional - methods Intraoperative Care - methods Medical sciences Orthopedic surgery Pelvis Pelvis - diagnostic imaging Preoperative Care - methods Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted - methods Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects) Reproducibility of Results Robustness Sensitivity and Specificity Sheep Subtraction Technique Surgery, Computer-Assisted - methods Technology. Biomaterials. Equipments. Material. Instrumentation Tomography, X-Ray Computed - methods X-ray imaging |
title | Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT |
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