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ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2014

Hounsfield units variations: impact on CT-density based conversion tables and their effects on dose distribution.

B Zurl, R Tiefling, P Winkler, P Kindl, K S Kapp

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Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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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

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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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  19. In vivo MRI features of spinal muscles in the ovine model.Journal of orthopaedic translation · 2016
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

B ZurlDepartment of Therapeutic Radiotherapy and Oncology, University Medical School of Graz, Comprehensive Cancer Center Graz, Auenbruggerplatz 32, 8036, Graz, Austria, brigitte.zurl@klinikum-graz.at.
R Tiefling
P Winkler
P Kindl
K S Kapp

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDetermination of dose error margins in radiation therapy planning due to variations in Hounsfield Units (HU) values dependent on the use of different CT scanning protocols. PATIENTS AND

methodsBased on a series of different CT scanning protocols used in clinical practice, conversion tables for radiation dose calculations were generated and subsequently tested on a phantom. These tables were then used to recalculate the radiation therapy plans of 28 real patients after an incorrect scanning protocol had inadvertently been used for these patients.

resultsDifferent CT parameter settings resulted in errors of HU values of up to 2.6% for densities of <1.1 g/cm(3), but up to 25.6% for densities of >1.1 g/cm(3). The largest errors were associated with changes in the tube voltage. Tests on a virtual water phantom with layers of variable thickness and density revealed a sawtooth-shaped curve for the increase of dose differences from 0.3 to 0.6% and 1.5% at layer thicknesses of 1, 3, and 7 cm, respectively. Use of a beam hardening filter resulted in a reference dose difference of 0.6% in response to a density change of 5%. The recalculation of data from 28 patients who received radiation therapy to the head revealed an overdose of 1.3 ± 0.4% to the bone and 0.7 ± 0.1% to brain tissue. On average, therefore, one monitor unit (range 0-3 MU) per 100 MU more than the correct dose had been given.

conclusionUse of different CT scanning protocols leads to variations of up to 20% in the HU values. This can result in a mean systematic dose error of 1.5%. Specific conversion tables and automatic CT scanning protocol recognition could reduce dose errors of these types.

Indexed as

ArtifactsRadiation DosageAlgorithmsHumansPhantoms, ImagingRadiographic Image Interpretation, Computer-AssistedRadiotherapy, ConformalRadiotherapy, Image-GuidedRadiotherapy Planning, Computer-AssistedReproducibility of ResultsSensitivity and SpecificityTomography, X-Ray Computed

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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.