Evidence map›Paper›PMID 29343206›Full record

ArticleTechnology in cancer research & treatment2018

Line-Enhanced Deformable Registration of Pulmonary Computed Tomography Images Before and After Radiation Therapy With Radiation-Induced Fibrosis.

Martin King, William F Sensakovic, Peter Maxim, Maximilian Diehn, Billy W Loo, Lei Xing

Abstract read
In one paragraph

Article in Technology in cancer research & treatment, 2018. 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

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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

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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. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Martin King1 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
William F Sensakovic2 Department of Radiology, Florida Hospital, Orlando, FL, USA.
Peter Maxim1 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Maximilian Diehn1 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Billy W Loo1 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Lei Xing1 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.

Funding

Computational Tools for Next Generation Volumetric Cone Beam Computed TomographyR01EB016777 · NIBIB · STANFORD UNIVERSITY · PI XING, LEI · 2013 to 2016
$2.1M
NIBIB NIH HHS R01 EB016777
6 · The paper itself

Abstract

purposeThe deformable registration of pulmonary computed tomography images before and after radiation therapy is challenging due to anatomic changes from radiation fibrosis. We hypothesize that a line-enhanced registration algorithm can reduce landmark error over the entire lung, including the irradiated regions, when compared to an intensity-based deformable registration algorithm. MATERIALS: Two intensity-based B-spline deformable registration algorithms of pre-radiation therapy and post-radiation therapy images were compared. The first was a control intensity-based algorithm that utilized computed tomography images without modification. The second was a line enhancement algorithm that incorporated a Hessian-based line enhancement filter prior to deformable image registration. Registrations were evaluated based on the landmark error between user-identified landmark pairs and the overlap ratio.

resultsTwenty-one patients with pre-radiation therapy and post-radiation therapy scans were included. The median time interval between scans was 1.2 years (range: 0.3-3.3 years). Median landmark errors for the line enhancement algorithm were significantly lower than those for the control algorithm over the entire lung (1.67 vs 1.83 mm; P < .01), as well as within the 0 to 5 Gy (1.40 vs 1.57; P < .01) and >5 Gy (2.25 vs 3.31; P < .01) dose intervals. The median lung mask overlap ratio for the line enhancement algorithm (96.2%) was greater than that for the control algorithm (95.8%; P < .01). Landmark error within the >5 Gy dose interval demonstrated a significant inverse relationship with post-radiation therapy fibrosis enhancement after line enhancement filtration (Pearson correlation coefficient = -0.48; P = .03).

conclusionThe line enhancement registration algorithm is a promising method for registering images before and after radiation therapy.

Indexed as

Radiographic Image EnhancementTomography, X-Ray ComputedAlgorithmsDisease ManagementHumansImage Processing, Computer-AssistedLung NeoplasmsPulmonary FibrosisRadiation PneumonitisRadiotherapyRadiotherapy DosageReproducibility of Resultsdeformable registrationline enhancementlungradiation therapy

Identifiers

PMID29343206
PMCPMC5784562

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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.