Evidence map›Paper›PMID 30007075›Full record

ArticleMedical physics2018

Synthetic head and neck and phantom images for determining deformable image registration accuracy in magnetic resonance imaging.

Rachel B Ger, Jinzhong Yang, Yao Ding, Megan C Jacobsen, Carlos E Cardenas, Clifton D Fuller, Rebecca M Howell, Heng Li, R Jason Stafford, Shouhao Zhou and 1 more

Abstract read
In one paragraph

Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

11 authors.

Rachel B GerDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Jinzhong YangDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Yao DingDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Megan C JacobsenThe University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
Carlos E CardenasDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Clifton D FullerThe University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
Rebecca M HowellDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Heng LiDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
R Jason StaffordThe University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
Shouhao ZhouThe University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
Laurence E CourtDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Targeting the p53 pathway for reversal of oral premalignancyP50CA097007 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MYERS, JEFFREY NICHOLAS · 2002 to 2012
$26.1M
SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer TreatmentR01CA214825 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CANAHUATE, GUADALUPE, FULLER, CLIFTON DAVID · 2017 to 2019
$1.1M
imaging Radiation-Associated Dysphagia (iRAD)R01CA218148 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI FULLER, CLIFTON DAVID, HUTCHESON, KATHERINE ARNOLD · 2017 to 2019
$810k
Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Establish Objective Clinical Outcome Measures for Mandibular OsteoradionecrosisR56DE025248 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAI, STEPHEN Y · 2015 to 2015
$470k
NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA097007NCI NIH HHS R01 CA214825NCI NIH HHS R01 CA218148NIDCR NIH HHS R56 DE025248
6 · The paper itself

Abstract

purposeMagnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images. ACQUISITION AND VALIDATION

methodsTwo sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model. DATA FORMAT AND USAGE NOTES: The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR. POTENTIAL APPLICATIONS: The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.

Indexed as

deformable image registrationdeformationMRIregistration

Identifiers

PMID30007075
PMCPMC6331282

What OpenQuestion holds

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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.