Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning

Masitho S, Szkitsak J, Grigo J, Fietkau R, Putz F, Bert C (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Physics and Imaging in Radiation Oncology Elsevier

Book Volume: 24

Pages Range: 111-117

DOI: 10.1016/j.phro.2022.10.002

Abstract

Background and purpose: Magnetic Resonance Imaging (MRI)-only workflow eliminates the MRI-computed tomography (CT) registration inaccuracy, which degrades radiotherapy (RT) treatment accuracy. For an MRI-only workflow MRI sequences need to be converted to synthetic-CT (sCT). The purpose of this study was to evaluate a commercially available artificial intelligence (AI)-based sCT generation for dose calculation and 2D/2D kV-image daily positioning for brain RT workflow. Materials and methods: T1-VIBE DIXON was acquired at the 1.5 T MRI for 26 patients in RT setup for sCTs generation. For each patient, a volumetric modulated arc therapy (VMAT) plan was optimized on the CT, then recalculated on the sCT; and vice versa. sCT-based digitally reconstructed radiographs (DRRs) were fused with stereoscopic X-ray images recorded as image guidance for clinical treatments. Dosimetric differences between planned/recalculated doses and the differences between the calculated and recorded clinical couch shift/rotation were evaluated. Results: Mean ΔD50 between planned/recalculated doses for target volumes ranged between −0.2 % and 0.2 %; mean ΔD50 and ΔD0.01ccm were −0.6 % and 1.6 % and −1.4 % and 1.0 % for organ-at-risks, respectively. Differences were tested for clinical equivalence using intervals ±2 % (dose), ±1mm (translation), and ±1° (rotation). Dose equivalence was found using ±2 % interval (p < 0.001). The median differences between lat./long./vert. couch shift between CT-based/sCT-based DRRs were 0.3 mm/0.2 mm/0.3 mm (p < 0.05); median differences between lat./long./vert. couch rotation were −1.5°/0.1°/0.1° (after improvement of RT setup: −0.4°/−0.1°/−0.4°, p < 0.05). Conclusions: This in-silico study showed that the AI-based sCT provided equivalent results to the CT for dose calculation and daily stereoscopic X-ray positioning when using an optimal RT setup during MRI acquisition.

Authors with CRIS profile

Siti Masitho Department of Radiation Oncology Juliane Szkitsak Department of Radiation Oncology Johanna Grigo Department of Radiation Oncology Rainer Fietkau Lehrstuhl für Strahlentherapie Florian Putz Department of Radiation Oncology Christoph Bert Professur für Med. Strahlenphysik

How to cite

APA:

Masitho, S., Szkitsak, J., Grigo, J., Fietkau, R., Putz, F., & Bert, C. (2022). Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning. Physics and Imaging in Radiation Oncology, 24, 111-117. https://doi.org/10.1016/j.phro.2022.10.002

MLA:

Masitho, Siti, et al. "Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning." Physics and Imaging in Radiation Oncology 24 (2022): 111-117.

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