Hounsfield units variations

Zurl, B.; Tiefling, R.; Winkler, Peter; Kindl, Peter; Kapp, Karin S.

doi:10.1007/s00066-013-0464-5

Cited by 53 publications

(42 citation statements)

References 20 publications

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“…A recent study by Zurl et al . [ 43 ] indicated that even though the CT number variation can be significant, its dosimetric impact is limited to only 1.5% concluded from study based on 28 real patients. Compared with the above studies, we observed similar variation in CT number among different scanners.…”

Section: Discussionmentioning

confidence: 99%

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Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning

et al. 2016

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Modern treatment planning systems provide accurate dosimetry in heterogeneous media (such as a patient' body) with the help of tissue characterization based on computed tomography (CT) number. However, CT number depends on the type of scanner, tube voltage, field of view (FOV), reconstruction algorithm including artifact reduction and processing filters. The impact of these parameters on CT to electron density (ED) conversion had been subject of investigation for treatment planning in various clinical situations. This is usually performed with a tissue characterization phantom with various density plugs acquired with different tube voltages (kilovoltage peak), FOV reconstruction and different scanners to generate CT number to ED tables. This article provides an overview of inhomogeneity correction in the context of CT scanning and a new evaluation tool, difference volume dose-volume histogram (DVH), dV-DVH. It has been concluded that scanner and CT parameters are important for tissue characterizations, but changes in ED are minimal and only pronounced for higher density materials. For lungs, changes in CT number are minimal among scanners and CT parameters. Dosimetric differences for lung and prostate cases are usually insignificant (<2%) in three-dimensional conformal radiation therapy and < 5% for intensity-modulated radiation therapy (IMRT) with CT parameters. It could be concluded that CT number variability is dependent on acquisition parameters, but its dosimetric impact is pronounced only in high-density media and possibly in IMRT. In view of such small dosimetric changes in low-density medium, the acquisition of additional CT data for financially difficult clinics and countries may not be warranted.

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Section: Discussionmentioning

confidence: 99%

“…Recently Zurl et al . [ 43 ] compared CT parameters and showed that variation up to 20% in HU could be noted; however the impact on dose is limited to only 1.5%. Thus, effect of CT number for photon and electron beam Monte Carlo calculations has been noted to be different and needs attention.…”

Section: Introductionmentioning

confidence: 99%

Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning

et al. 2016

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“…There is, however, for in vivo studies, some overlap between fat and mammary tissue or fat and lung tissue on one side of the HU scale, and bone or muscle, as well as internal organs such as liver, tumor tissue and blood on the upper side of the HU scale. It has to be considered, additionally, that differences in CT protocols may lead to variations of up to 20% in the HU values, especially for (bone containing) tissues with densities >1.1 g/cm 3 (Zurl et al , 2014 ). As described above, tissue segmentation, for example, by threshold setting is based on assumptions of specific mass attenuation coefficients for different body or carcass tissues, which are calculated as HU.…”

Section: Non-invasive Techniques For Body/carcass Composition Measurementioning

confidence: 99%

Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review

Scholz

Bünger

Kongsro

et al. 2015

Animal

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The ability to accurately measure body or carcass composition is important for performance testing, grading and finally selection or payment of meat-producing animals. Advances especially in non-invasive techniques are mainly based on the development of electronic and computer-driven methods in order to provide objective phenotypic data. The preference for a specific technique depends on the target animal species or carcass, combined with technical and practical aspects such as accuracy, reliability, cost, portability, speed, ease of use, safety and for in vivo measurements the need for fixation or sedation. The techniques rely on specific device-driven signals, which interact with tissues in the body or carcass at the atomic or molecular level, resulting in secondary or attenuated signals detected by the instruments and analyzed quantitatively. The electromagnetic signal produced by the instrument may originate from mechanical energy such as sound waves (ultrasound – US), ‘photon’ radiation (X-ray-computed tomography – CT, dual-energy X-ray absorptiometry – DXA) or radio frequency waves (magnetic resonance imaging – MRI). The signals detected by the corresponding instruments are processed to measure, for example, tissue depths, areas, volumes or distributions of fat, muscle (water, protein) and partly bone or bone mineral. Among the above techniques, CT is the most accurate one followed by MRI and DXA, whereas US can be used for all sizes of farm animal species even under field conditions. CT, MRI and US can provide volume data, whereas only DXA delivers immediate whole-body composition results without (2D) image manipulation. A combination of simple US and more expensive CT, MRI or DXA might be applied for farm animal selection programs in a stepwise approach.

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“…The variability of CT number and the dependence of Monte Carlo https://doi.org/10.1016/j.phro.2018.10.002 dose calculation on mass density prompted the question of whether variations in CT number measurements from factors such as scanning technique and scanner model have any clinically significant impact on calculated dose. Many studies have investigated the sensitivity of dose calculation for photon and proton beams to conversion between CT number and electron density [17][18][19][20][21][22]; however, the impact of conversion between CT number and density has not been rigorously investigated. A study by Verhaegen and Devic observed dose errors of up to 10% for photon beams and more than 30% for 18 MeV electron beams when using different assignments between CT number and mass density in Monte Carlo simulations of phantoms using DOSXYZnrc [23].…”

Section: Introductionmentioning

confidence: 99%

The impact of mass density variations on an electron Monte Carlo algorithm for radiotherapy dose calculations

Fang

Mazur

Mutic

et al. 2018

Physics and Imaging in Radiation Oncology

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Background and Purpose: A key step in electron Monte Carlo dose calculation requires converting Computed Tomography (CT) numbers from a tomographic acquisition to a mass density. This study investigates the dosimetric consequences of perturbations applied to a calibration table between CT number and mass density. Materials and Methods: A literature search was performed to define lower and upper bounds for physically reasonable perturbations to a reference CT number to mass density calibration table. Electron beam dose was calculated for ten patients using these variations and the results were compared to clinical plans originally derived with a reference calibration table. Dose differences both globally and in the Planning Target Volume (PTV) were assessed using dose-and volume-based metrics and 3-dimensional gamma analysis for each patient. Results: Small but statistically significant differences were observed between perturbations and reference data for certain metrics including volume of the 50% prescription isodose. Upper and lower variations in CT number to mass density calibration yielded mean values of V50% that were 4.4% larger and 2.1% smaller than reference values respectively. Gamma analysis using 3%/3mm criteria indicated > 99% passing rate for the PTV for all patients. Global gamma analysis for some patients showed larger discrepancies possibly due to large electron path lengths through inhomogeneities. Conclusions: In most patients, physically reasonable perturbations in CT number to mass density curves will not induce clinically significant impact on calculated target dose distributions. Strong dependence of electron transport on voxel material may produce dose speckle throughout the volume. Care should be taken in evaluating critical structures at depths beyond the target volume in highly heterogeneous regions.

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Hounsfield units variations

Abstract: Use 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.

Cited by 53 publications

References 20 publications

Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning

Computed tomography imaging parameters for inhomogeneity correction in radiation treatment planning

Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review

The impact of mass density variations on an electron Monte Carlo algorithm for radiotherapy dose calculations

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