2D-3D registration for prostate radiation therapy based on a statistical model of transmission images
Purpose: In external beam radiation therapy of pelvic sites, patient setup errors can be quantified by registering 2D projection radiographs acquired during treatment to a 3D planning computed tomograph (CT). We present a 2D-3D registration framework based on a statistical model of the intensity val...
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| Veröffentlicht in: | Medical physics (Lancaster) 2009-10, Vol.36 (10), p.4555-4568 |
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| creator | Munbodh, Reshma Tagare, Hemant D. Chen, Zhe Jaffray, David A. Moseley, Douglas J. Knisely, Jonathan P. S. Duncan, James S. |
| description | Purpose:
In external beam radiation therapy of pelvic sites, patient setup errors can be quantified by registering 2D projection radiographs acquired during treatment to a 3D planning computed tomograph (CT). We present a 2D-3D registration framework based on a statistical model of the intensity values in the two imaging modalities.
Methods:
The model assumes that intensity values in projection radiographs are independently but not identically distributed due to the nonstationary nature of photon counting noise. Two probability distributions are considered for the intensity values: Poisson and Gaussian. Using maximum likelihood estimation, two similarity measures, maximum likelihood with a Poisson (MLP) and maximum likelihood with Gaussian (MLG), distribution are derived. Further, we investigate the merit of the model-based registration approach for data obtained with current imaging equipment and doses by comparing the performance of the similarity measures derived to that of the Pearson correlation coefficient (ICC) on accurately collected data of an anthropomorphic phantom of the pelvis and on patient data.
Results:
Registration accuracy was similar for all three similarity measures and surpassed current clinical requirements of
3
mm
for pelvic sites. For pose determination experiments with a kilovoltage (kV) cone-beam CT (CBCT) and kV projection radiographs of the phantom in the anterior-posterior (AP) view, registration accuracies were
0.42
mm
(MLP),
0.29
mm
(MLG), and
0.29
mm
(ICC). For kV CBCT and megavoltage (MV) AP portal images of the same phantom, registration accuracies were
1.15
mm
(MLP),
0.90
mm
(MLG), and
0.69
mm
(ICC). Registration of a kV CT and MV AP portal images of a patient was successful in all instances.
Conclusions:
The results indicate that high registration accuracy is achievable with multiple methods including methods that are based on a statistical model of a 3D CT and 2D projection images. |
| doi_str_mv | 10.1118/1.3213531 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_osti_</sourceid><recordid>TN_cdi_osti_scitechconnect_22102109</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>734144332</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5411-1e20354aa3b6446119610e7f8ea8393e5c5883b608aed5a679996d07afcde72c3</originalsourceid><addsrcrecordid>eNqNkU1LxDAQhoMoun4c_AMS8CAK1UySfuQiiOsXKHrQc8imU4102zXpKvvvzdqCXhQhEEieeZh5h5BdYMcAUJzAseAgUgErZMRlLhLJmVolI8aUTLhk6QbZDOGVMZaJlK2TDVCKF6zIRwT5OBFj6vHZhc6bzrUNrVpPZ74NnemQelO6_rl7QW9mCzoxAUsaHwxdIrHOWVPTaVtiTduKRk0Tpi6EZZGbmmcM22StMnXAneHeIk-XF4_n18nt_dXN-dltYlMJkAByJlJpjJhkUmYAKgOGeVWgKYQSmNq0KOIfKwyWqclypVRWstxUtsScW7FF9ntvbN7pYF2H9sW2TYO205wDi0dF6qCn4pBvcwydjt1arGvTYDsPOhcSpBSCR_KwJ22MI3is9MzHifxCA9PL6DXoIfrI7g3W-WSK5Tc5ZB2BpAc-XI2L30367mEQnvb8co6vFfxew8dajPXPLeoqCo7-LfgLfm_9j-5mZSU-AfJ7u8I</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>734144332</pqid></control><display><type>article</type><title>2D-3D registration for prostate radiation therapy based on a statistical model of transmission images</title><source>Access via Wiley Online Library</source><source>MEDLINE</source><source>Alma/SFX Local Collection</source><creator>Munbodh, Reshma ; Tagare, Hemant D. ; Chen, Zhe ; Jaffray, David A. ; Moseley, Douglas J. ; Knisely, Jonathan P. S. ; Duncan, James S.</creator><creatorcontrib>Munbodh, Reshma ; Tagare, Hemant D. ; Chen, Zhe ; Jaffray, David A. ; Moseley, Douglas J. ; Knisely, Jonathan P. S. ; Duncan, James S.</creatorcontrib><description>Purpose:
In external beam radiation therapy of pelvic sites, patient setup errors can be quantified by registering 2D projection radiographs acquired during treatment to a 3D planning computed tomograph (CT). We present a 2D-3D registration framework based on a statistical model of the intensity values in the two imaging modalities.
Methods:
The model assumes that intensity values in projection radiographs are independently but not identically distributed due to the nonstationary nature of photon counting noise. Two probability distributions are considered for the intensity values: Poisson and Gaussian. Using maximum likelihood estimation, two similarity measures, maximum likelihood with a Poisson (MLP) and maximum likelihood with Gaussian (MLG), distribution are derived. Further, we investigate the merit of the model-based registration approach for data obtained with current imaging equipment and doses by comparing the performance of the similarity measures derived to that of the Pearson correlation coefficient (ICC) on accurately collected data of an anthropomorphic phantom of the pelvis and on patient data.
Results:
Registration accuracy was similar for all three similarity measures and surpassed current clinical requirements of
3
mm
for pelvic sites. For pose determination experiments with a kilovoltage (kV) cone-beam CT (CBCT) and kV projection radiographs of the phantom in the anterior-posterior (AP) view, registration accuracies were
0.42
mm
(MLP),
0.29
mm
(MLG), and
0.29
mm
(ICC). For kV CBCT and megavoltage (MV) AP portal images of the same phantom, registration accuracies were
1.15
mm
(MLP),
0.90
mm
(MLG), and
0.69
mm
(ICC). Registration of a kV CT and MV AP portal images of a patient was successful in all instances.
Conclusions:
The results indicate that high registration accuracy is achievable with multiple methods including methods that are based on a statistical model of a 3D CT and 2D projection images.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1118/1.3213531</identifier><identifier>PMID: 19928087</identifier><identifier>CODEN: MPHYA6</identifier><language>eng</language><publisher>United States: American Association of Physicists in Medicine</publisher><subject>2D-3D registration ; ACCURACY ; Algorithms ; biological organs ; Computed radiography ; Computed tomography ; computerised tomography ; COMPUTERIZED TOMOGRAPHY ; Cone beam computed tomography ; cone-beam CT ; correlation methods ; Data Interpretation, Statistical ; diagnostic radiography ; Digital radiography ; Distribution theory and Monte Carlo studies ; Gaussian distribution ; Humans ; Image Interpretation, Computer-Assisted - methods ; IMAGE PROCESSING ; image registration ; Imaging, Three-Dimensional - methods ; Information Storage and Retrieval - methods ; Male ; MATHEMATICAL METHODS AND COMPUTING ; maximum likelihood ; maximum likelihood estimation ; MAXIMUM-LIKELIHOOD FIT ; Medical image noise ; medical image processing ; Medical imaging ; Medical X‐ray imaging ; Pattern Recognition, Automated - methods ; PHANTOMS ; Phantoms, Imaging ; photon counting ; PHOTONS ; Poisson distribution ; portal images ; Probability theory ; PROSTATE ; Prostatic Neoplasms - diagnostic imaging ; Prostatic Neoplasms - radiotherapy ; radiation therapy ; Radiographic Image Enhancement - methods ; Radiography ; RADIOLOGY AND NUCLEAR MEDICINE ; RADIOTHERAPY ; Radiotherapy, Conformal - methods ; Registration ; Reproducibility of Results ; Sensitivity and Specificity ; setup verification ; Statistical model calculations ; STATISTICAL MODELS ; Subtraction Technique ; Therapeutic applications, including brachytherapy</subject><ispartof>Medical physics (Lancaster), 2009-10, Vol.36 (10), p.4555-4568</ispartof><rights>American Association of Physicists in Medicine</rights><rights>2009 American Association of Physicists in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5411-1e20354aa3b6446119610e7f8ea8393e5c5883b608aed5a679996d07afcde72c3</citedby><cites>FETCH-LOGICAL-c5411-1e20354aa3b6446119610e7f8ea8393e5c5883b608aed5a679996d07afcde72c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1118%2F1.3213531$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1118%2F1.3213531$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,315,782,786,887,1419,27933,27934,45583,45584</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19928087$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/22102109$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Munbodh, Reshma</creatorcontrib><creatorcontrib>Tagare, Hemant D.</creatorcontrib><creatorcontrib>Chen, Zhe</creatorcontrib><creatorcontrib>Jaffray, David A.</creatorcontrib><creatorcontrib>Moseley, Douglas J.</creatorcontrib><creatorcontrib>Knisely, Jonathan P. S.</creatorcontrib><creatorcontrib>Duncan, James S.</creatorcontrib><title>2D-3D registration for prostate radiation therapy based on a statistical model of transmission images</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose:
In external beam radiation therapy of pelvic sites, patient setup errors can be quantified by registering 2D projection radiographs acquired during treatment to a 3D planning computed tomograph (CT). We present a 2D-3D registration framework based on a statistical model of the intensity values in the two imaging modalities.
Methods:
The model assumes that intensity values in projection radiographs are independently but not identically distributed due to the nonstationary nature of photon counting noise. Two probability distributions are considered for the intensity values: Poisson and Gaussian. Using maximum likelihood estimation, two similarity measures, maximum likelihood with a Poisson (MLP) and maximum likelihood with Gaussian (MLG), distribution are derived. Further, we investigate the merit of the model-based registration approach for data obtained with current imaging equipment and doses by comparing the performance of the similarity measures derived to that of the Pearson correlation coefficient (ICC) on accurately collected data of an anthropomorphic phantom of the pelvis and on patient data.
Results:
Registration accuracy was similar for all three similarity measures and surpassed current clinical requirements of
3
mm
for pelvic sites. For pose determination experiments with a kilovoltage (kV) cone-beam CT (CBCT) and kV projection radiographs of the phantom in the anterior-posterior (AP) view, registration accuracies were
0.42
mm
(MLP),
0.29
mm
(MLG), and
0.29
mm
(ICC). For kV CBCT and megavoltage (MV) AP portal images of the same phantom, registration accuracies were
1.15
mm
(MLP),
0.90
mm
(MLG), and
0.69
mm
(ICC). Registration of a kV CT and MV AP portal images of a patient was successful in all instances.
Conclusions:
The results indicate that high registration accuracy is achievable with multiple methods including methods that are based on a statistical model of a 3D CT and 2D projection images.</description><subject>2D-3D registration</subject><subject>ACCURACY</subject><subject>Algorithms</subject><subject>biological organs</subject><subject>Computed radiography</subject><subject>Computed tomography</subject><subject>computerised tomography</subject><subject>COMPUTERIZED TOMOGRAPHY</subject><subject>Cone beam computed tomography</subject><subject>cone-beam CT</subject><subject>correlation methods</subject><subject>Data Interpretation, Statistical</subject><subject>diagnostic radiography</subject><subject>Digital radiography</subject><subject>Distribution theory and Monte Carlo studies</subject><subject>Gaussian distribution</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>IMAGE PROCESSING</subject><subject>image registration</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Male</subject><subject>MATHEMATICAL METHODS AND COMPUTING</subject><subject>maximum likelihood</subject><subject>maximum likelihood estimation</subject><subject>MAXIMUM-LIKELIHOOD FIT</subject><subject>Medical image noise</subject><subject>medical image processing</subject><subject>Medical imaging</subject><subject>Medical X‐ray imaging</subject><subject>Pattern Recognition, Automated - methods</subject><subject>PHANTOMS</subject><subject>Phantoms, Imaging</subject><subject>photon counting</subject><subject>PHOTONS</subject><subject>Poisson distribution</subject><subject>portal images</subject><subject>Probability theory</subject><subject>PROSTATE</subject><subject>Prostatic Neoplasms - diagnostic imaging</subject><subject>Prostatic Neoplasms - radiotherapy</subject><subject>radiation therapy</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiography</subject><subject>RADIOLOGY AND NUCLEAR MEDICINE</subject><subject>RADIOTHERAPY</subject><subject>Radiotherapy, Conformal - methods</subject><subject>Registration</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>setup verification</subject><subject>Statistical model calculations</subject><subject>STATISTICAL MODELS</subject><subject>Subtraction Technique</subject><subject>Therapeutic applications, including brachytherapy</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU1LxDAQhoMoun4c_AMS8CAK1UySfuQiiOsXKHrQc8imU4102zXpKvvvzdqCXhQhEEieeZh5h5BdYMcAUJzAseAgUgErZMRlLhLJmVolI8aUTLhk6QbZDOGVMZaJlK2TDVCKF6zIRwT5OBFj6vHZhc6bzrUNrVpPZ74NnemQelO6_rl7QW9mCzoxAUsaHwxdIrHOWVPTaVtiTduKRk0Tpi6EZZGbmmcM22StMnXAneHeIk-XF4_n18nt_dXN-dltYlMJkAByJlJpjJhkUmYAKgOGeVWgKYQSmNq0KOIfKwyWqclypVRWstxUtsScW7FF9ntvbN7pYF2H9sW2TYO205wDi0dF6qCn4pBvcwydjt1arGvTYDsPOhcSpBSCR_KwJ22MI3is9MzHifxCA9PL6DXoIfrI7g3W-WSK5Tc5ZB2BpAc-XI2L30367mEQnvb8co6vFfxew8dajPXPLeoqCo7-LfgLfm_9j-5mZSU-AfJ7u8I</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Munbodh, Reshma</creator><creator>Tagare, Hemant D.</creator><creator>Chen, Zhe</creator><creator>Jaffray, David A.</creator><creator>Moseley, Douglas J.</creator><creator>Knisely, Jonathan P. S.</creator><creator>Duncan, James S.</creator><general>American Association of Physicists in Medicine</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>7X8</scope><scope>OTOTI</scope></search><sort><creationdate>200910</creationdate><title>2D-3D registration for prostate radiation therapy based on a statistical model of transmission images</title><author>Munbodh, Reshma ; Tagare, Hemant D. ; Chen, Zhe ; Jaffray, David A. ; Moseley, Douglas J. ; Knisely, Jonathan P. S. ; Duncan, James S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5411-1e20354aa3b6446119610e7f8ea8393e5c5883b608aed5a679996d07afcde72c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>2D-3D registration</topic><topic>ACCURACY</topic><topic>Algorithms</topic><topic>biological organs</topic><topic>Computed radiography</topic><topic>Computed tomography</topic><topic>computerised tomography</topic><topic>COMPUTERIZED TOMOGRAPHY</topic><topic>Cone beam computed tomography</topic><topic>cone-beam CT</topic><topic>correlation methods</topic><topic>Data Interpretation, Statistical</topic><topic>diagnostic radiography</topic><topic>Digital radiography</topic><topic>Distribution theory and Monte Carlo studies</topic><topic>Gaussian distribution</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>IMAGE PROCESSING</topic><topic>image registration</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Information Storage and Retrieval - methods</topic><topic>Male</topic><topic>MATHEMATICAL METHODS AND COMPUTING</topic><topic>maximum likelihood</topic><topic>maximum likelihood estimation</topic><topic>MAXIMUM-LIKELIHOOD FIT</topic><topic>Medical image noise</topic><topic>medical image processing</topic><topic>Medical imaging</topic><topic>Medical X‐ray imaging</topic><topic>Pattern Recognition, Automated - methods</topic><topic>PHANTOMS</topic><topic>Phantoms, Imaging</topic><topic>photon counting</topic><topic>PHOTONS</topic><topic>Poisson distribution</topic><topic>portal images</topic><topic>Probability theory</topic><topic>PROSTATE</topic><topic>Prostatic Neoplasms - diagnostic imaging</topic><topic>Prostatic Neoplasms - radiotherapy</topic><topic>radiation therapy</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiography</topic><topic>RADIOLOGY AND NUCLEAR MEDICINE</topic><topic>RADIOTHERAPY</topic><topic>Radiotherapy, Conformal - methods</topic><topic>Registration</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>setup verification</topic><topic>Statistical model calculations</topic><topic>STATISTICAL MODELS</topic><topic>Subtraction Technique</topic><topic>Therapeutic applications, including brachytherapy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Munbodh, Reshma</creatorcontrib><creatorcontrib>Tagare, Hemant D.</creatorcontrib><creatorcontrib>Chen, Zhe</creatorcontrib><creatorcontrib>Jaffray, David A.</creatorcontrib><creatorcontrib>Moseley, Douglas J.</creatorcontrib><creatorcontrib>Knisely, Jonathan P. S.</creatorcontrib><creatorcontrib>Duncan, James S.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Munbodh, Reshma</au><au>Tagare, Hemant D.</au><au>Chen, Zhe</au><au>Jaffray, David A.</au><au>Moseley, Douglas J.</au><au>Knisely, Jonathan P. S.</au><au>Duncan, James S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>2D-3D registration for prostate radiation therapy based on a statistical model of transmission images</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2009-10</date><risdate>2009</risdate><volume>36</volume><issue>10</issue><spage>4555</spage><epage>4568</epage><pages>4555-4568</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>Purpose:
In external beam radiation therapy of pelvic sites, patient setup errors can be quantified by registering 2D projection radiographs acquired during treatment to a 3D planning computed tomograph (CT). We present a 2D-3D registration framework based on a statistical model of the intensity values in the two imaging modalities.
Methods:
The model assumes that intensity values in projection radiographs are independently but not identically distributed due to the nonstationary nature of photon counting noise. Two probability distributions are considered for the intensity values: Poisson and Gaussian. Using maximum likelihood estimation, two similarity measures, maximum likelihood with a Poisson (MLP) and maximum likelihood with Gaussian (MLG), distribution are derived. Further, we investigate the merit of the model-based registration approach for data obtained with current imaging equipment and doses by comparing the performance of the similarity measures derived to that of the Pearson correlation coefficient (ICC) on accurately collected data of an anthropomorphic phantom of the pelvis and on patient data.
Results:
Registration accuracy was similar for all three similarity measures and surpassed current clinical requirements of
3
mm
for pelvic sites. For pose determination experiments with a kilovoltage (kV) cone-beam CT (CBCT) and kV projection radiographs of the phantom in the anterior-posterior (AP) view, registration accuracies were
0.42
mm
(MLP),
0.29
mm
(MLG), and
0.29
mm
(ICC). For kV CBCT and megavoltage (MV) AP portal images of the same phantom, registration accuracies were
1.15
mm
(MLP),
0.90
mm
(MLG), and
0.69
mm
(ICC). Registration of a kV CT and MV AP portal images of a patient was successful in all instances.
Conclusions:
The results indicate that high registration accuracy is achievable with multiple methods including methods that are based on a statistical model of a 3D CT and 2D projection images.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>19928087</pmid><doi>10.1118/1.3213531</doi><tpages>14</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-2405 |
| ispartof | Medical physics (Lancaster), 2009-10, Vol.36 (10), p.4555-4568 |
| issn | 0094-2405 2473-4209 |
| language | eng |
| recordid | cdi_osti_scitechconnect_22102109 |
| source | Access via Wiley Online Library; MEDLINE; Alma/SFX Local Collection |
| subjects | 2D-3D registration ACCURACY Algorithms biological organs Computed radiography Computed tomography computerised tomography COMPUTERIZED TOMOGRAPHY Cone beam computed tomography cone-beam CT correlation methods Data Interpretation, Statistical diagnostic radiography Digital radiography Distribution theory and Monte Carlo studies Gaussian distribution Humans Image Interpretation, Computer-Assisted - methods IMAGE PROCESSING image registration Imaging, Three-Dimensional - methods Information Storage and Retrieval - methods Male MATHEMATICAL METHODS AND COMPUTING maximum likelihood maximum likelihood estimation MAXIMUM-LIKELIHOOD FIT Medical image noise medical image processing Medical imaging Medical X‐ray imaging Pattern Recognition, Automated - methods PHANTOMS Phantoms, Imaging photon counting PHOTONS Poisson distribution portal images Probability theory PROSTATE Prostatic Neoplasms - diagnostic imaging Prostatic Neoplasms - radiotherapy radiation therapy Radiographic Image Enhancement - methods Radiography RADIOLOGY AND NUCLEAR MEDICINE RADIOTHERAPY Radiotherapy, Conformal - methods Registration Reproducibility of Results Sensitivity and Specificity setup verification Statistical model calculations STATISTICAL MODELS Subtraction Technique Therapeutic applications, including brachytherapy |
| title | 2D-3D registration for prostate radiation therapy based on a statistical model of transmission images |
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