Application of single- and dual-energy CT brain tissue segmentation to PET monitoring of proton therapy

• Verfasst von: Berndt, Bianca [VerfasserIn]
• Verfasst von: Tessonnier, Thomas [VerfasserIn]
• Verfasst von: Debus, Jürgen [VerfasserIn]
• Verfasst von: Bauer, Julia [VerfasserIn]
• Verfasst von: Parodi, Katia [VerfasserIn]
• Titel: Application of single- and dual-energy CT brain tissue segmentation to PET monitoring of proton therapy
• Verf.angabe: Bianca Berndt, Guillaume Landry, Florian Schwarz, Thomas Tessonnier, Florian Kamp, George Dedes, Christian Thieke, Matthias Würl, Christopher Kurz, Ute Ganswindt, Frank Verhaegen, Jürgen Debus, Claus Belka, Wieland Sommer, Maximilian Reiser, Julia Bauer, Katia Parodi
• E-Jahr: 2017
• Jahr: 27 February 2017
• Umfang: 22 S.
• Fussnoten: Gesehen am 22.05.2018
• Titel Quelle: Enthalten in: Physics in medicine and biology
• Ort Quelle: Bristol : IOP Publ., 1956
• Jahr Quelle: 2017
• Band/Heft Quelle: 62(2017), 6, Seite 2427-2448
• ISSN Quelle: 1361-6560
• DOI: doi:10.1088/1361-6560/aa5f9f
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1575375753

Abstract

The purpose of this work was to evaluate the ability of single and dual energy computed tomography (SECT, DECT) to estimate tissue composition and density for usage in Monte Carlo (MC) simulations of irradiation induced β + activity distributions. This was done to assess the impact on positron emission tomography (PET) range verification in proton therapy. A DECT-based brain tissue segmentation method was developed for white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). The elemental composition of reference tissues was assigned to closest CT numbers in DECT space (DECT dist ). The method was also applied to SECT data (SECT dist ). In a validation experiment, the proton irradiation induced PET activity of three brain equivalent solutions (BES) was compared to simulations based on different tissue segmentations. Five patients scanned with a dual source DECT scanner were analyzed to compare the different segmentation methods. A single magnetic resonance (MR) scan was used for comparison with an established segmentation toolkit. Additionally, one patient with SECT and post-treatment PET scans was investigated. For BES, DECT dist and SECT dist reduced differences to the reference simulation by up to 62% when compared to the conventional stoichiometric segmentation (SECT Schneider ). In comparison to MR brain segmentation, Dice similarity coefficients for WM, GM and CSF were 0.61, 0.67 and 0.66 for DECT dist and 0.54, 0.41 and 0.66 for SECT dist . MC simulations of PET treatment verification in patients showed important differences between DECT dist /SECT dist and SECT Schneider for patients with large CSF areas within the treatment field but not in WM and GM. Differences could be misinterpreted as PET derived range shifts of up to 4 mm. DECT dist and SECT dist yielded comparable activity distributions, and comparison of SECT dist to a measured patient PET scan showed improved agreement when compared to SECT Schneider . The agreement between predicted and measured PET activity distributions was improved by employing a brain specific segmentation applicable to both DECT and SECT data.