Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2015 Dec;191(12):970-8.
doi: 10.1007/s00066-015-0890-7. Epub 2015 Sep 24.

Comparison of CT number calibration techniques for CBCT-based dose calculation

Alex Dunlop  1   2 , Dualta McQuaid  3 , Simeon Nill  3 , Julia Murray  4   5 , Gavin Poludniowski  6 , Vibeke N Hansen  3 , Shreerang Bhide  4   5 , Christopher Nutting  5 , Kevin Harrington  4   5 , Kate Newbold  4 , Uwe Oelfke  3
Affiliations
Comparative Study

Comparison of CT number calibration techniques for CBCT-based dose calculation

Alex Dunlop et al. Strahlenther Onkol. 2015 Dec.

Abstract
in English, German

Purpose: The aim of this work was to compare and validate various computed tomography (CT) number calibration techniques with respect to cone beam CT (CBCT) dose calculation accuracy.

Methods: CBCT dose calculation accuracy was assessed for pelvic, lung, and head and neck (H&N) treatment sites for two approaches: (1) physics-based scatter correction methods (CBCTr); (2) density override approaches including assigning water density to the entire CBCT (W), assignment of either water or bone density (WB), and assignment of either water or lung density (WL). Methods for CBCT density assignment within a commercially available treatment planning system (RSauto), where CBCT voxels are binned into six density levels, were assessed and validated. Dose-difference maps and dose-volume statistics were used to compare the CBCT dose distributions with the ground truth of a planning CT acquired the same day as the CBCT.

Results: For pelvic cases, all CTN calibration methods resulted in average dose-volume deviations below 1.5 %. RSauto provided larger than average errors for pelvic treatments for patients with large amounts of adipose tissue. For H&N cases, all CTN calibration methods resulted in average dose-volume differences below 1.0 % with CBCTr (0.5 %) and RSauto (0.6 %) performing best. For lung cases, WL and RSauto methods generated dose distributions most similar to the ground truth.

Conclusion: The RSauto density override approach is an attractive option for CTN adjustments for a variety of anatomical sites. RSauto methods were validated, resulting in dose calculations that were consistent with those calculated on diagnostic-quality CT images, for CBCT images acquired of the lung, for patients receiving pelvic RT in cases without excess adipose tissue, and for H&N cases.

Ziel: Ziel dieser Arbeit ist der Vergleich und die Validierung mehrerer CT-Kalibrierungsmethoden zur Dosisberechnung auf der Grundlage von Kegelstrahlcomputertomographie(CBCT)-Aufnahmen.

Methoden: Bei 4 Becken-, 3 Lungen- und 4 Kopf-Hals-Patienten wurde die Genauigkeit der Dosisberechnung auf der Basis von CBCT-Aufnahmen für die folgenden Ansätze untersucht: einerseits Rekonstruktion der CBCT-Aufnahmen mithilfe eines Streukorrekturalgorithmus (CBCTr) und andererseits 3 verschiedene Methoden zur expliziten Zuweisung der Elektronendichten auf Basis des CBCT-Datensatzes (W: Zuordnung von Wasserdichte auf dem gesamten CBCT, WB: Zuordnung von entweder Wasser- oder Knochendichte, WL: Zuordnung von entweder Wasser- oder Lungendichte) sowie der in einem kommerziellen Planungssystem implementierten Methode der automatischen Zuordnung von 6 Dichtestufen auf Basis der CBCT-Hounsfield-Werte (RSauto). Als Grundlage zur Evaluierung der Methoden diente die Dosisverteilung, welche anhand einer am gleichen Tag wie die CBCT aufgenommenen Planungs-CT berechnet wurde. Die Genauigkeit der einzelnen Ansätze wurde anhand von Dosis-Volumen-Statistiken und lokalen Dosisunterschieden beurteilt.

Ergebnisse: Bei den Datensätzen mit Tumoren im Beckenbereich ist die mittlere Dosisabweichung für alle Kalibrierungsmethoden unter 1,5 %, wobei RSauto in einer überdurchschnittlichen Abweichung für Patienten mit einem höheren Anteil von Fettgewebe resultiert. Die mittlere Abweichung für Kopf-Hals-Patienten beträgt unter 1,0 %, wobei CBCTr (0,5 %) und RSauto (0,6 %) am besten abschneiden. WL und RSauto resultieren für die Patienten mit Lungentumoren in Dosisverteilungen, welche der Referenzdosisverteilung entsprechen.

Schlussfolgerung: RSauto zur Kalibrierung von CBCT-Aufnahmen zur Dosisberechnung ist eine aussichtsreiche Methode für die untersuchten Indikationen. Es wurde gezeigt, dass mithilfe von RSauto die Dosisberechnung auf der Basis von CBCT-Aufnahmen von Lungen- und Kopf-Hals-Erkrankungen sowie für Tumoren im Beckenbereich bei Patienten ohne ein Übermaß an Fettgewebe in einer akkuraten Dosisverteilung resultiert.

Keywords: Adaptive radiation therapy, ART; Cone beam computed tomography; Density; Dose calculation; Hounsfield units.

PubMed Disclaimer

Conflict of interest statement

A. Dunlop reports grants from NIHR Biomedical Research Centre and grants from Cancer Research UK C7224/A13407. D. McQuaid reports grants from NIHR Biomedical Research Centre and grants from Cancer Research UK C7224/A13407. J. Murray states that there are no conflicts of interest. K. Newbold has received honoraria for advisory roles for Genzyme, Astra Zeneca, and Eisai. S. Nill reports grants from NIHR Biomedical Research Centre, grants from Cancer Research UK Programme Grant C33589/A19727, during the conduct of the study. V.N. Hansen states that there are no conflicts of interest. C. Nutting reports grants from Cancer Research UK C7224/A13407. G. Poludniowski states that there are no conflicts of interest. K. Harrington reports grants from NIHR Biomedical Research Centre, grants from Cancer Research UK C7224/A13407, during the study, and outside the submitted work, grants from Merck, from Cellgene, from Oncolytics Biotech, and from Genelux Corporation. S. Bhide states that there are no conflicts of interest. U. Oelfke reports grants from NIHR Biomedical Research Centre, grants from Cancer Research UK Programme Grant C33589/A19727, and grants from EPSRC Platform Grant EP/H046526/1, during the study.

Figures

Fig. 1
Fig. 1
The RayStation treatment planning system (TPS) cone beam computed tomography (CBCT) CT number (CTN) adjustment method. Top row from left: Sagittal slice of a CBCT image of an H&N cancer patient viewed within the TPS; the CBCT after density assignment by the TPS (regions assigned as bone are shown as yellow, for example); CTN-density table generated for the CBCT image. Bottom row, left: Typical CBCT acquisition of a patient with a tumor in their right lung and (right) the same CBCT image but with the field of view (red contour), external (green contour), and left lung (orange contour) regions of interest displayed
Fig. 2
Fig. 2
Dose difference maps for between doses calculated on CBCT images and the ground truth (PCTCBCT). From top row to bottom: (1) a pelvic case with anterior-posterior distance (DAP) = 23 cm; (2) a pelvic case with DAP = 32 cm; (3) an H&N case; and (4) a lung case. All dose difference maps are presented as a percentage of the prescribed dose
Fig. 3
Fig. 3
Left: Sagittal images of a prostate RT treatment plan with dose calculated on (top) the PCTorig; (middle) PCTCBCT; and (bottom) the CBCT. The rectum is shown as an orange contour and the dose is shown in color wash relative to the prescribed dose. Right: dose–volume histogram of the rectum for the dose calculated on the PCTorig scan (orange line); the PCTCBCT (blue line); and the CBCT (red dashed line). The RSauto method was used for CTN adjustment of the CBCT image. The rectum OAR was similar in size and shape on the CBCT and the PCTCBCT but was very different to that seen on the PCTorig scan. PCT planning computed tomography, CBCT cone beam CT, CTN CT number, OAR organ at risk
Fig. 4
Fig. 4
Sagittal (top row) and coronal slices (bottom row) of a pelvic case with a high proportion of adipose tissue. From left to right: the PCTCBCT with tissue density < 0.95 g/cm3 colored purple, and CBCT with RSauto CTN adjustment. In the CBCT images purple, turquoise, and yellow represent adipose (0.95 g/cm3), connective tissue (1.05 g/cm3), and bone (1.6 g/cm3), respectively. PCT planning computed tomography, CBCT cone beam CT, CTN CT number
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Smitsmans MH, de Bois J, Sonke JJ, et al. Automatic prostate localization on cone-beam CT scans for high precision image-guided radiotherapy. Int J Radiat Oncol Biol Phys. 2005;63:975–984. doi: 10.1016/j.ijrobp.2005.07.973. - DOI - PubMed
    1. Purdie TG, Bissonnette JP, Franks K, et al. Cone-beam computed tomography for on-line image guidance of lung stereotactic radiotherapy: localization, verification, and intrafraction tumor position. Int J Radiat Oncol Biol Phys. 2007;68:243–252. doi: 10.1016/j.ijrobp.2006.12.022. - DOI - PubMed
    1. Moseley DJ, White EA, Wiltshire KL, et al. Comparison of localization performance with implanted fiducial markers and cone-beam computed tomography for on-line image-guided radiotherapy of the prostate. Int J Radiat Oncol Biol Phys. 2007;67:942–953. doi: 10.1016/j.ijrobp.2006.10.039. - DOI - PMC - PubMed
    1. Jaffray D, Siewerdsen J, Wong J, et al. Flat-panel cone-beam computed tomography for image-guided radiation therapy. Int J Radiat Oncol Biol Phys. 2001;53:1337–1349. doi: 10.1016/S0360-3016(02)02884-5. - DOI - PubMed
    1. Vestergaars A, Muren L, Sondergaard J, et al. Adaptive plan selection vs. Re-optimisation in radiotherapy for bladder cancer: a dose accumulation comparison. Radiother Oncol. 2013;109:457–462. doi: 10.1016/j.radonc.2013.08.045. - DOI - PubMed

Publication types

MeSH terms