Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention
The fusion of tracked ultrasound with CT has benefits for a variety of clinical applications, however extensive manual effort is usually required for correct registration. We developed new methods that allow one to simulate medical ultrasound from CT in real-time, reproducing the majority of ultraso...
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Veröffentlicht in: | Medical image analysis 2008-10, Vol.12 (5), p.577-585 |
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creator | Wein, Wolfgang Brunke, Shelby Khamene, Ali Callstrom, Matthew R. Navab, Nassir |
description | The fusion of tracked ultrasound with CT has benefits for a variety of clinical applications, however extensive manual effort is usually required for correct registration. We developed new methods that allow one to simulate medical ultrasound from CT in real-time, reproducing the majority of ultrasonic imaging effects. They are combined with a robust similarity measure that assesses the correlation of a combination of signals extracted from CT with ultrasound, without knowing the influence of each signal. This serves as the foundation of a fully automatic registration, that aligns a 3D ultrasound sweep with the corresponding tomographic modality using a rigid or an affine transformation model, without any manual interaction.
These techniques were evaluated in a study involving 25 patients with indeterminate lesions in liver and kidney. The clinical setup, acquisition and registration workflow is described, along with the evaluation of the registration accuracy with respect to physician-defined Ground Truth. Our new algorithm correctly registers without any manual interaction in 76% of the cases, the average RMS TRE over multiple target lesions throughout the liver is 8.1
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doi_str_mv | 10.1016/j.media.2008.06.006 |
format | Article |
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These techniques were evaluated in a study involving 25 patients with indeterminate lesions in liver and kidney. The clinical setup, acquisition and registration workflow is described, along with the evaluation of the registration accuracy with respect to physician-defined Ground Truth. Our new algorithm correctly registers without any manual interaction in 76% of the cases, the average RMS TRE over multiple target lesions throughout the liver is 8.1
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These techniques were evaluated in a study involving 25 patients with indeterminate lesions in liver and kidney. The clinical setup, acquisition and registration workflow is described, along with the evaluation of the registration accuracy with respect to physician-defined Ground Truth. Our new algorithm correctly registers without any manual interaction in 76% of the cases, the average RMS TRE over multiple target lesions throughout the liver is 8.1
mm.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Fusion</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image-guided Intervention</subject><subject>Kidney Neoplasms - diagnosis</subject><subject>Kidney Neoplasms - surgery</subject><subject>Liver Neoplasms - diagnosis</subject><subject>Liver Neoplasms - surgery</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Registration</subject><subject>Surgery, Computer-Assisted - methods</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Ultrasonography - methods</subject><subject>Ultrasound</subject><issn>1361-8415</issn><issn>1361-8423</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkD1v2zAQhomgRb7aXxAg0JRN6h1JUfLQwTDyBRjo4gyZCIo6CTRs0SWlAP33pWKj2dKJd-Dz3uEexm4QCgRUP7bFnlpnCg5QF6AKAHXGLlEozGvJxZd_NZYX7CrGLQBUUsI5u8BalYAcL9nrchr93ozOZqtNPu3GYKKfhjYL1LuYutH5Iet8yNKqfvBxJt3e9G7oM5O4uaa8n1xLqRlGCm80zKFv7GtndpG-n95r9vJwv1k95etfj8-r5Tq3spRjTrYRXPGmk6bFljopK2ga3qEwFVbISbYSxEKRrWujeFWVXY21VCm24GLRiWt2d5x7CP73RHHUexct7XZmID9FrRYSVVXDf0GOIMqkJYHiCNrgYwzU6UNIZ4Y_GkHP6vVWv6vXs3oNSif1KXV7Gj816fcjc3KdgJ9HgJKNN0dBR-tosGlSIDvq1rtPF_wF_4SWdQ</recordid><startdate>20081001</startdate><enddate>20081001</enddate><creator>Wein, Wolfgang</creator><creator>Brunke, Shelby</creator><creator>Khamene, Ali</creator><creator>Callstrom, Matthew R.</creator><creator>Navab, Nassir</creator><general>Elsevier B.V</general><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>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20081001</creationdate><title>Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention</title><author>Wein, Wolfgang ; Brunke, Shelby ; Khamene, Ali ; Callstrom, Matthew R. ; Navab, Nassir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c454t-ecb3262bf4ad1def4470bb2f13a71712e4d40396ec88a62775f81846cb39239f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Fusion</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image-guided Intervention</topic><topic>Kidney Neoplasms - diagnosis</topic><topic>Kidney Neoplasms - surgery</topic><topic>Liver Neoplasms - diagnosis</topic><topic>Liver Neoplasms - surgery</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Registration</topic><topic>Surgery, Computer-Assisted - methods</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>Ultrasonography - methods</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wein, Wolfgang</creatorcontrib><creatorcontrib>Brunke, Shelby</creatorcontrib><creatorcontrib>Khamene, Ali</creatorcontrib><creatorcontrib>Callstrom, Matthew R.</creatorcontrib><creatorcontrib>Navab, Nassir</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Medical image analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wein, Wolfgang</au><au>Brunke, Shelby</au><au>Khamene, Ali</au><au>Callstrom, Matthew R.</au><au>Navab, Nassir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention</atitle><jtitle>Medical image analysis</jtitle><addtitle>Med Image Anal</addtitle><date>2008-10-01</date><risdate>2008</risdate><volume>12</volume><issue>5</issue><spage>577</spage><epage>585</epage><pages>577-585</pages><issn>1361-8415</issn><eissn>1361-8423</eissn><abstract>The fusion of tracked ultrasound with CT has benefits for a variety of clinical applications, however extensive manual effort is usually required for correct registration. We developed new methods that allow one to simulate medical ultrasound from CT in real-time, reproducing the majority of ultrasonic imaging effects. They are combined with a robust similarity measure that assesses the correlation of a combination of signals extracted from CT with ultrasound, without knowing the influence of each signal. This serves as the foundation of a fully automatic registration, that aligns a 3D ultrasound sweep with the corresponding tomographic modality using a rigid or an affine transformation model, without any manual interaction.
These techniques were evaluated in a study involving 25 patients with indeterminate lesions in liver and kidney. The clinical setup, acquisition and registration workflow is described, along with the evaluation of the registration accuracy with respect to physician-defined Ground Truth. Our new algorithm correctly registers without any manual interaction in 76% of the cases, the average RMS TRE over multiple target lesions throughout the liver is 8.1
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subjects | Algorithms Artificial Intelligence Fusion Humans Image Interpretation, Computer-Assisted - methods Image-guided Intervention Kidney Neoplasms - diagnosis Kidney Neoplasms - surgery Liver Neoplasms - diagnosis Liver Neoplasms - surgery Pattern Recognition, Automated - methods Registration Surgery, Computer-Assisted - methods Tomography, X-Ray Computed - methods Ultrasonography - methods Ultrasound |
title | Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention |
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