This research demonstrates improved image quality in radiotherapy on MR-Linac, suggesting faster contouring and enhanced adaptive treatment workflows.
Motivation: Patients undergoing online-adaptive radiotherapy on an MR-Linac are imaged throughout treatment, but insufficient image quality can lead to long contouring times for pancreatic tumours. Goal(s): We aim to improve image quality by exploiting the accumulated MRI raw data across treatment fractions, to decrease contouring times and provide high-quality reference images for real-time algorithms. Approach: Deformation vector fields estimated from non-rigid image registration of daily images were used to reconstruct a midposition reference image combining the data across multiple treatment fractions. Results: Reference images presented fewer streaking artefacts, but new artefacts appeared in areas with large daily changes (stomach, bowels). Impact: Image quality on an MR-Linac can lead to slow contouring of abdominal tumours, limiting the potential of adaptive MR-guided radiotherapy. Combining raw data across fractions can improve image quality, potentially reducing clinical workloads and enabling novel real-time adaptive treatment workflows.
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Lecoeur et al. (2025) studied this question.
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