Evidence map›Paper›PMID 31257709›Full record

ArticleJournal of applied clinical medical physics2019

Tissue segmentation-based electron density mapping for MR-only radiotherapy treatment planning of brain using conventional T1-weighted MR images.

Huan Yu, Michael Oliver, Konrad Leszczynski, Young Lee, Irene Karam, Arjun Sahgal

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 22% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Huan YuDepartment of Medical Physics, Northeast Cancer Centre, Health Sciences North, Medical Sciences Division, Northern Ontario School of Medicine, Faculty of Medicine, Laurentian University, Lakehead University, Sudbury, ON, Canada.
Michael OliverDepartment of Medical Physics, Northeast Cancer Centre, Health Sciences North, Medical Sciences Division, Northern Ontario School of Medicine, Faculty of Medicine, Laurentian University, Lakehead University, Sudbury, ON, Canada.
Konrad LeszczynskiDepartment of Medical Physics, Northeast Cancer Centre, Health Sciences North, Medical Sciences Division, Northern Ontario School of Medicine, Faculty of Medicine, Laurentian University, Lakehead University, Sudbury, ON, Canada.
Young LeeDepartment of Medical Physics, Odette Cancer Centre, Sunnybrook Health Science Center, Toronto, ON, Canada.
Irene KaramDepartment of Radiation Oncology, University of Toronto, Toronto, ON, Canada.
Arjun SahgalDepartment of Radiation Oncology, University of Toronto, Toronto, ON, Canada.
Health Sciences North · CASunnybrook Health Science Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeMagnetic resonance imaging (MRI) is the primary modality for targeting brain tumors in radiotherapy treatment planning (RTP). MRI is not directly used for dose calculation since image voxel intensities of MRI are not associated with EDs of tissues as those of computed tomography (CT). The purpose of the present study is to develop and evaluate a tissue segmentation-based method to generate a synthetic-CT (sCT) by mapping EDs to corresponding tissues using only T1-weighted MR images for MR-only RTP.

methodsAir regions were contoured in several slices. Then, air, bone, brain, cerebrospinal fluid (CSF), and other soft tissues were automatically segmented with an in-house algorithm based on edge detection and anatomical information and relative intensity distribution. The intensities of voxels in each segmented tissue were mapped into their CT number range to generate a sCT. Twenty-five stereotactic radiosurgery and stereotactic ablative radiotherapy patients' T1-weighted MRI and coregistered CT images from two centers were retrospectively evaluated. The CT was used as ground truth. Distances between bone contours of the external skull of sCT and CT were measured. The mean error (ME) and mean absolute error (MAE) of electron density represented by standardized CT number was calculated in HU.

resultsThe average distance between the contour of the external skull in sCT and the contour in coregistered CT is 1.0 ± 0.2 mm (mean ± 1SD). The ME and MAE differences for air, soft tissue and whole body voxels within external body contours are -4 HU/24 HU, 2 HU/26 HU, and -2 HU/125 HU, respectively.

conclusionsA MR-sCT generation technique was developed based on tissue segmentation and voxel-based tissue ED mapping. The generated sCT is comparable to real CT in terms of anatomical position of tissues and similarity to the ED assignment. This method provides a feasible method to generate sCT for MR-only radiotherapy treatment planning.

Indexed as

AlgorithmsBrain NeoplasmsHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingOrgans at RiskRadiosurgeryRadiotherapy DosageRadiotherapy Planning, Computer-AssistedbrainMR-linacMR-only treatment planningMR pseudo CTMR synthetic CT

Identifiers

PMID31257709
PMCPMC6698944
OpenAlexW2953522270

What OpenQuestion holds

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LicenceCC BY
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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.