Automatic Registration of MR and SPECT Images for Treatment Planning in Prostate Cancer
To aid in surgical and radiation therapy planning for prostate adenocarcinoma, a general-purpose automatic registration method that is based on mutual information was used to align magnetic resonance (MR) images and single photon emission computed tomographic (SPECT) images of the pelvis and prostat...
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Veröffentlicht in: | Academic radiology 2003-06, Vol.10 (6), p.673-684 |
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description | To aid in surgical and radiation therapy planning for prostate adenocarcinoma, a general-purpose automatic registration method that is based on mutual information was used to align magnetic resonance (MR) images and single photon emission computed tomographic (SPECT) images of the pelvis and prostate.
The authors assessed the effects of various factors on alignment between pairs of MR and SPECT images, including the use of particular pulse sequences in MR imaging, image voxel intensity scaling, the use of different regions on the MR-SPECT histogram, spatial masking of nonoverlapping visual data between images, and multiresolution optimization. A mutual information algorithm was used as the cost function for automatic registration. Automatic registration was deemed acceptable when it resulted in a transformation with less than 2 voxel units (6 mm) difference in translation and less than 2° difference in rotation from that obtained with manual registration performed independently by nuclear medicine radiologists.
Paired sets of MR and SPECT image volumes from four of five patients were successfully registered. For successful registration, MR images must be optimal and registration must be performed at full spatial resolution and at the full intensity range. Masking, cropping, and the normalization of mutual information, used to register partially overlapping MR-SPECT volumes, were not successful. Multiresolution optimization had little effect on the accuracy and speed of the registration.
Automatic registration between MR and SPECT images of the pelvis can be achieved when data acquisition and image processing are performed properly. It should prove useful for prostate cancer diagnosis, staging, and treatment planning. |
doi_str_mv | 10.1016/S1076-6332(03)80088-0 |
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The authors assessed the effects of various factors on alignment between pairs of MR and SPECT images, including the use of particular pulse sequences in MR imaging, image voxel intensity scaling, the use of different regions on the MR-SPECT histogram, spatial masking of nonoverlapping visual data between images, and multiresolution optimization. A mutual information algorithm was used as the cost function for automatic registration. Automatic registration was deemed acceptable when it resulted in a transformation with less than 2 voxel units (6 mm) difference in translation and less than 2° difference in rotation from that obtained with manual registration performed independently by nuclear medicine radiologists.
Paired sets of MR and SPECT image volumes from four of five patients were successfully registered. For successful registration, MR images must be optimal and registration must be performed at full spatial resolution and at the full intensity range. Masking, cropping, and the normalization of mutual information, used to register partially overlapping MR-SPECT volumes, were not successful. Multiresolution optimization had little effect on the accuracy and speed of the registration.
Automatic registration between MR and SPECT images of the pelvis can be achieved when data acquisition and image processing are performed properly. It should prove useful for prostate cancer diagnosis, staging, and treatment planning.</description><identifier>ISSN: 1076-6332</identifier><identifier>EISSN: 1878-4046</identifier><identifier>DOI: 10.1016/S1076-6332(03)80088-0</identifier><identifier>PMID: 12809423</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adenocarcinoma - diagnosis ; Algorithms ; Automatic Data Processing ; Bone Marrow - diagnostic imaging ; Clinical Protocols ; Femur - diagnostic imaging ; Humans ; Image Processing, Computer-Assisted ; Images, processing ; Magnetic Resonance Imaging ; Male ; monoclonal antibodies ; prostate neoplasms, MR ; prostate neoplasms, radionuclide studies ; Prostatic Neoplasms - diagnosis ; Pubic Symphysis - diagnostic imaging ; Radiography ; Registries ; Tomography, Emission-Computed, Single-Photon</subject><ispartof>Academic radiology, 2003-06, Vol.10 (6), p.673-684</ispartof><rights>2003 Acad Radiol</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-d3a9a66ecd76bb23abd7d949fe120dbce3bd8af6665ba21d51b8e94002eb5d203</citedby><cites>FETCH-LOGICAL-c392t-d3a9a66ecd76bb23abd7d949fe120dbce3bd8af6665ba21d51b8e94002eb5d203</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/S1076-6332(03)80088-0$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27922,27923,45993</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/12809423$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Zhenghong</creatorcontrib><creatorcontrib>Nagano, Kenichi K</creatorcontrib><creatorcontrib>Duerk, Jeffrey L</creatorcontrib><creatorcontrib>Sodee, D.Bruce</creatorcontrib><creatorcontrib>Wilson, David L</creatorcontrib><title>Automatic Registration of MR and SPECT Images for Treatment Planning in Prostate Cancer</title><title>Academic radiology</title><addtitle>Acad Radiol</addtitle><description>To aid in surgical and radiation therapy planning for prostate adenocarcinoma, a general-purpose automatic registration method that is based on mutual information was used to align magnetic resonance (MR) images and single photon emission computed tomographic (SPECT) images of the pelvis and prostate.
The authors assessed the effects of various factors on alignment between pairs of MR and SPECT images, including the use of particular pulse sequences in MR imaging, image voxel intensity scaling, the use of different regions on the MR-SPECT histogram, spatial masking of nonoverlapping visual data between images, and multiresolution optimization. A mutual information algorithm was used as the cost function for automatic registration. Automatic registration was deemed acceptable when it resulted in a transformation with less than 2 voxel units (6 mm) difference in translation and less than 2° difference in rotation from that obtained with manual registration performed independently by nuclear medicine radiologists.
Paired sets of MR and SPECT image volumes from four of five patients were successfully registered. For successful registration, MR images must be optimal and registration must be performed at full spatial resolution and at the full intensity range. Masking, cropping, and the normalization of mutual information, used to register partially overlapping MR-SPECT volumes, were not successful. Multiresolution optimization had little effect on the accuracy and speed of the registration.
Automatic registration between MR and SPECT images of the pelvis can be achieved when data acquisition and image processing are performed properly. It should prove useful for prostate cancer diagnosis, staging, and treatment planning.</description><subject>Adenocarcinoma - diagnosis</subject><subject>Algorithms</subject><subject>Automatic Data Processing</subject><subject>Bone Marrow - diagnostic imaging</subject><subject>Clinical Protocols</subject><subject>Femur - diagnostic imaging</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Images, processing</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>monoclonal antibodies</subject><subject>prostate neoplasms, MR</subject><subject>prostate neoplasms, radionuclide studies</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Pubic Symphysis - diagnostic imaging</subject><subject>Radiography</subject><subject>Registries</subject><subject>Tomography, Emission-Computed, Single-Photon</subject><issn>1076-6332</issn><issn>1878-4046</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkEtPGzEURq2qVRNof0KRVxUshl7bMx7PqooiCkhUjUgQS8uPO5FRZobaDlL_fSckiGVW91uc-zqEfGNwyYDJH0sGtSykEPwcxIUCUKqAD2TKVK2KEkr5ccxvyIScpPQEwCqpxGcyYVxBU3IxJY-zbR46k4Oj97gOKccxDz0dWvr7npre0-Xiar6it51ZY6LtEOkqoskd9pkuNqbvQ7-moaeLOKRsMtK56R3GL-RTazYJvx7qKXn4dbWa3xR3f65v57O7womG58IL0xgp0flaWsuFsb72Tdm0yDh461BYr0wrpays4cxXzCpsSgCOtvIcxCn5vp_7HIe_W0xZdyE53IyX4bBNuhalUCU0R0HBWS2aqh7Bag-68aMUsdXPMXQm_tMM9E69flWvd141CP2qXu8uOTss2NoO_XvXwfUI_NwDOPp4CRh1cgFHWT5EdFn7IRxZ8R9iaJMP</recordid><startdate>20030601</startdate><enddate>20030601</enddate><creator>Lee, Zhenghong</creator><creator>Nagano, Kenichi K</creator><creator>Duerk, Jeffrey L</creator><creator>Sodee, D.Bruce</creator><creator>Wilson, David L</creator><general>Elsevier Inc</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>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>7X8</scope></search><sort><creationdate>20030601</creationdate><title>Automatic Registration of MR and SPECT Images for Treatment Planning in Prostate Cancer</title><author>Lee, Zhenghong ; Nagano, Kenichi K ; Duerk, Jeffrey L ; Sodee, D.Bruce ; Wilson, David L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c392t-d3a9a66ecd76bb23abd7d949fe120dbce3bd8af6665ba21d51b8e94002eb5d203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Adenocarcinoma - diagnosis</topic><topic>Algorithms</topic><topic>Automatic Data Processing</topic><topic>Bone Marrow - diagnostic imaging</topic><topic>Clinical Protocols</topic><topic>Femur - diagnostic imaging</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Images, processing</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>monoclonal antibodies</topic><topic>prostate neoplasms, MR</topic><topic>prostate neoplasms, radionuclide studies</topic><topic>Prostatic Neoplasms - diagnosis</topic><topic>Pubic Symphysis - diagnostic imaging</topic><topic>Radiography</topic><topic>Registries</topic><topic>Tomography, Emission-Computed, Single-Photon</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Zhenghong</creatorcontrib><creatorcontrib>Nagano, Kenichi K</creatorcontrib><creatorcontrib>Duerk, Jeffrey L</creatorcontrib><creatorcontrib>Sodee, D.Bruce</creatorcontrib><creatorcontrib>Wilson, David L</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Academic radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Zhenghong</au><au>Nagano, Kenichi K</au><au>Duerk, Jeffrey L</au><au>Sodee, D.Bruce</au><au>Wilson, David L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Registration of MR and SPECT Images for Treatment Planning in Prostate Cancer</atitle><jtitle>Academic radiology</jtitle><addtitle>Acad Radiol</addtitle><date>2003-06-01</date><risdate>2003</risdate><volume>10</volume><issue>6</issue><spage>673</spage><epage>684</epage><pages>673-684</pages><issn>1076-6332</issn><eissn>1878-4046</eissn><abstract>To aid in surgical and radiation therapy planning for prostate adenocarcinoma, a general-purpose automatic registration method that is based on mutual information was used to align magnetic resonance (MR) images and single photon emission computed tomographic (SPECT) images of the pelvis and prostate.
The authors assessed the effects of various factors on alignment between pairs of MR and SPECT images, including the use of particular pulse sequences in MR imaging, image voxel intensity scaling, the use of different regions on the MR-SPECT histogram, spatial masking of nonoverlapping visual data between images, and multiresolution optimization. A mutual information algorithm was used as the cost function for automatic registration. Automatic registration was deemed acceptable when it resulted in a transformation with less than 2 voxel units (6 mm) difference in translation and less than 2° difference in rotation from that obtained with manual registration performed independently by nuclear medicine radiologists.
Paired sets of MR and SPECT image volumes from four of five patients were successfully registered. For successful registration, MR images must be optimal and registration must be performed at full spatial resolution and at the full intensity range. Masking, cropping, and the normalization of mutual information, used to register partially overlapping MR-SPECT volumes, were not successful. Multiresolution optimization had little effect on the accuracy and speed of the registration.
Automatic registration between MR and SPECT images of the pelvis can be achieved when data acquisition and image processing are performed properly. It should prove useful for prostate cancer diagnosis, staging, and treatment planning.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>12809423</pmid><doi>10.1016/S1076-6332(03)80088-0</doi><tpages>12</tpages></addata></record> |
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subjects | Adenocarcinoma - diagnosis Algorithms Automatic Data Processing Bone Marrow - diagnostic imaging Clinical Protocols Femur - diagnostic imaging Humans Image Processing, Computer-Assisted Images, processing Magnetic Resonance Imaging Male monoclonal antibodies prostate neoplasms, MR prostate neoplasms, radionuclide studies Prostatic Neoplasms - diagnosis Pubic Symphysis - diagnostic imaging Radiography Registries Tomography, Emission-Computed, Single-Photon |
title | Automatic Registration of MR and SPECT Images for Treatment Planning in Prostate Cancer |
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