Evidence map›Paper›PMID 35920116›Full record

ArticleJournal of applied clinical medical physics2022

Abdominopelvic MR to CT registration using a synthetic CT intermediate.

Jin Uk Heo, Feifei Zhou, Robert Jones, Jiamin Zheng, Xin Song, Pengjiang Qian, Atallah Baydoun, Melanie S Traughber, Jung-Wen Kuo, Rose Al Helo and 21 more

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Abdominopelvic MR to CT registration using a synthetic CT intermediate.Journal of applied clinical medical physics · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

31 authors.

Jin Uk HeoDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Feifei ZhouDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Robert JonesDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Jiamin ZhengSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China.
Xin SongSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China.
Pengjiang QianSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China.
Atallah BaydounDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Melanie S TraughberDepartment of Radiation Oncology, Penn State University, Hershey, Pennsylvania, USA.
Jung-Wen KuoDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Rose Al HeloDepartment of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Cheryl ThompsonDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
Norbert AvrilDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Daniel DeVincentDepartment of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Harold HuntDepartment of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Amit GuptaDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Navid FarajiDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Michael Z KharoutaDepartment of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Arash KardanDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
David BitonteDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Christian B LangmackDepartment of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Aaron NelsonMIM Software Inc., Cleveland, Ohio, USA.
Alexandria KruzerMIM Software Inc., Cleveland, Ohio, USA.
Min YaoDepartment of Radiation Oncology, Penn State University, Hershey, Pennsylvania, USA.
Jennifer DorthDepartment of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
John NakayamaDepartment of Obstetrics and Gynecology, Allegheny Health Network, Pittsburgh, Pennsylvania, USA.
Steven E WaggonerDepartment of Obstetrics and Gynecology, Cleveland Clinic, Cleveland, Ohio, USA.
Tithi BiswasDepartment of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Eleanor HarrisDepartment of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Susan SandstromDepartment of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
Bryan J TraughberDepartment of Radiation Oncology, Penn State University, Hershey, Pennsylvania, USA.
Raymond F MuzicDepartment of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.

Funding

Accurate MR-based PET Attenuation Correction for Quantitative Clinical TrialsR01CA196687 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MUZIC, RAYMOND F · 2015 to 2019
$2.4M
Fundamental Research Funds for the Central Universities JUSRP51614ANational Cancer Institute of the National Institutes of Health R01CA196687National Natural Science Foundation of China 61702225National Natural Science Foundation of China 61772241Natural Science Foundation of Jiangsu Province BK20160187NCI NIH HHS R01 CA196687Science and Technology Demonstration Project of Social Development of Wuxi WX18IVJN002
6 · The paper itself

Abstract

Accurate coregistration of computed tomography (CT) and magnetic resonance (MR) imaging can provide clinically relevant and complementary information and can serve to facilitate multiple clinical tasks including surgical and radiation treatment planning, and generating a virtual Positron Emission Tomography (PET)/MR for the sites that do not have a PET/MR system available. Despite the long-standing interest in multimodality co-registration, a robust, routine clinical solution remains an unmet need. Part of the challenge may be the use of mutual information (MI) maximization and local phase difference (LPD) as similarity metrics, which have limited robustness, efficiency, and are difficult to optimize. Accordingly, we propose registering MR to CT by mapping the MR to a synthetic CT intermediate (sCT) and further using it in a sCT-CT deformable image registration (DIR) that minimizes the sum of squared differences. The resultant deformation field of a sCT-CT DIR is applied to the MRI to register it with the CT. Twenty-five sets of abdominopelvic imaging data are used for evaluation. The proposed method is compared to standard MI- and LPD-based methods, and the multimodality DIR provided by a state of the art, commercially available FDA-cleared clinical software package. The results are compared using global similarity metrics, Modified Hausdorff Distance, and Dice Similarity Index on six structures. Further, four physicians visually assessed and scored registered images for their registration accuracy. As evident from both quantitative and qualitative evaluation, the proposed method achieved registration accuracy superior to LPD- and MI-based methods and can refine the results of the commercial package DIR when using its results as a starting point. Supported by these, this manuscript concludes the proposed registration method is more robust, accurate, and efficient than the MI- and LPD-based methods.

Indexed as

Magnetic Resonance ImagingTomography, X-Ray ComputedAlgorithmsHumansImage Processing, Computer-AssistedMultimodal ImagingPositron-Emission Tomographylocal phase differencemultimodality deformable image registrationmutual informationsynthetic CT

Identifiers

PMID35920116
PMCPMC9512351

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.