Evidence map›Paper›PMID 28291745›Full record

ArticlePhysics in medicine and biology2017

Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning.

Ninon Burgos, Filipa Guerreiro, Jamie McClelland, Benoît Presles, Marc Modat, Simeon Nill, David Dearnaley, Nandita deSouza, Uwe Oelfke, Antje-Christin Knopf and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
4.9field-weighted citation impact, top 5% of its field
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

8 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
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  4. Anomaly detection for the individual analysis of brain PET images.Journal of medical imaging (Bellingham, Wash.) · 2021
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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

12 authors at 8 institutions in 3 countries.

Ninon BurgosTranslational Imaging Group, CMIC, University College London, London, United Kingdom.
Filipa Guerreiro
Jamie McClelland
Benoît Presles
Marc Modat
Simeon Nill
David Dearnaley
Nandita deSouza
Uwe Oelfke
Antje-Christin Knopf
Sébastien Ourselin
M Jorge Cardoso
University College London · GBInstitute of Cancer Research · CAInstitute of Cancer Research · GBRoyal Marsden NHS Foundation Trust · GBSutton Hospital · GBUK Dementia Research Institute · GBUniversity Medical Center Utrecht · NLUniversity of Groningen · NL

Funding

Cancer Research UK 10588Cancer Research UK 12518Cancer Research UK 16464Cancer Research UK 19727Cancer Research UK 19908Cancer Research UK 21993Cancer Research UK C33589/A19727Medical Research Council
6 · The paper itself

Abstract

To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average [Formula: see text] HU and the ME [Formula: see text] HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of [Formula: see text] in the PTV for [Formula: see text], and between [Formula: see text] and 0.05% in the PTV, bladder, rectum and femur heads for D

Indexed as

Magnetic Resonance ImagingTomography, X-Ray ComputedImage Processing, Computer-AssistedJointsRadiometryRadiotherapy Planning, Computer-Assisted

Identifiers

PMID28291745
PMCPMC5423555
OpenAlexW2597156741

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.