ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy
Ricardo Coimbra Brioso, Leonardo Crespi, Andrea Seghetto, Damiano Dei,, Nicola Lambri, Pietro Mancosu, Marta Scorsetti, Daniele Loiacono

TL;DR
ARTInp is a deep-learning framework that improves CBCT images by filling anatomical gaps and translating them into high-quality synthetic CT images, enhancing accuracy in adaptive radiotherapy workflows.
Contribution
It introduces a dual-network deep learning approach combining inpainting and translation for CBCT enhancement in radiotherapy.
Findings
Achieved high-quality synthetic CT generation from CBCT data.
Effectively filled anatomical gaps in CBCT volumes.
Demonstrated potential for improved treatment validation.
Abstract
A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of the patient's anatomy at treatment time to ensure the accuracy of the delivery. To this end, Cone Beam Computerized Tomography (CBCT) is widely used being cost-effective and easy to integrate into the treatment process. Nonetheless, CBCT images have lower resolution and more artifacts than CT scans, making them less reliable for precise treatment validation. Moreover, in complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), where full-body visualization of the patient is critical for accurate dose delivery, the CBCT images are often discontinuous, leaving gaps that could contain relevant anatomical information. To address these limitations, we propose ARTInp (Adaptive Radiation Therapy Inpainting), a novel deep-learning framework combining image inpainting and CBCT-to-CT translation.…
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Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · Advanced Radiotherapy Techniques · Medical Imaging Techniques and Applications
MethodsInpainting · Sparse Evolutionary Training
