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, Siti; Szkitsak, Juliane; Grigo, Johanna; Fietkau, Rainer; Putz, Florian; Bert, Christoph

doi:10.1016/j.phro.2022.10.002

Cited by 8 publications

(1 citation statement)

References 47 publications

(63 reference statements)

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“…Han demonstrated the superiority of deep learning (DL) for this task [12] . Several investigations followed, aiming to generate sCT from MR imaging for RT applications [13] for several sites [14] , [15] , [16] . Dosimetric deviations between CT and sCT generated with DL are generally lower than 1 % [13] , which satisfies the 2 % requirement for clinical applicability [17] .…”

Section: Introductionmentioning

confidence: 99%

Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen

Lapaeva

Saint-Esteven

Wallimann

et al. 2022

Physics and Imaging in Radiation Oncology

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Section: Introductionmentioning

confidence: 99%

Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen

Lapaeva

Saint-Esteven

Wallimann

et al. 2022

Physics and Imaging in Radiation Oncology

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sCT and Dose Calculation

Alvarez-Andres,

Villegas,

Barateau

et al. 2024

A Practical Guide to MR-Linac

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Synthetic CTs for MRI-only brain RT treatment: integration of immobilization systems

et al. 2023

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Purpose Auxiliary devices such as immobilization systems should be considered in synthetic CT (sCT)-based treatment planning (TP) for MRI-only brain radiotherapy (RT). A method for auxiliary device definition in the sCT is introduced, and its dosimetric impact on the sCT-based TP is addressed. Methods T1-VIBE DIXON was acquired in an RT setup. Ten datasets were retrospectively used for sCT generation. Silicone markers were used to determine the auxiliary devices’ relative position. An auxiliary structure template (AST) was created in the TP system and placed manually on the MRI. Various RT mask characteristics were simulated in the sCT and investigated by recalculating the CT-based clinical plan on the sCT. The influence of auxiliary devices was investigated by creating static fields aimed at artificial planning target volumes (PTVs) in the CT and recalculated in the sCT. The dose covering 50% of the PTV (D50) deviation percentage between CT-based/recalculated plan (∆D50[%]) was evaluated. Results Defining an optimal RT mask yielded a ∆D50[%] of 0.2 ± 1.03% for the PTV and between −1.6 ± 3.4% and 1.1 ± 2.0% for OARs. Evaluating each static field, the largest ∆D50[%] was delivered by AST positioning inaccuracy (max: 3.5 ± 2.4%), followed by the RT table (max: 3.6 ± 1.2%) and the RT mask (max: 3.0 ± 0.8% [anterior], 1.6 ± 0.4% [rest]). No correlation between ∆D50[%] and beam depth was found for the sum of opposing beams, except for (45° + 315°). Conclusion This study evaluated the integration of auxiliary devices and their dosimetric influence on sCT-based TP. The AST can be easily integrated into the sCT-based TP. Further, we found that the dosimetric impact was within an acceptable range for an MRI-only workflow.

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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

Cited by 8 publications

References 47 publications

Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen

Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen

sCT and Dose Calculation

Synthetic CTs for MRI-only brain RT treatment: integration of immobilization systems

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