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Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy

Zaffino, Paolo ; Raggio, Ciro Benito 1; Thummerer, Adrian; Marmitt, Gabriel Guterres; Langendijk, Johannes Albertus; Procopio, Anna; Cosentino, Carlo; Seco, Joao; Knopf, Antje Christin; Both, Stefan; Spadea, Maria Francesca 1
1 Institut für Biomedizinische Technik (IBT), Karlsruher Institut für Technologie (KIT)

Abstract:

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic
Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be
comparable to real CT images in terms of dose computation for radiotherapy simulation. However,
until now, there has been no independent strategy to assess the quality of each synthetic image in the
absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict
the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground
truth (GT). The proposed algorithm generates a volumetric map as an output, informing clinicians of
the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the
complexity of the MAE prediction task. The workflow was trained and tested on two cohorts of head
and neck cancer patients with different imaging modalities: 27 MR scans and 33 CBCT. The algorithm
evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4
HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do
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Zugehörige Institution(en) am KIT Institut für Biomedizinische Technik (IBT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 2313-433X
KITopen-ID: 1000178510
Erschienen in Journal of Imaging
Verlag MDPI
Band 10
Heft 12
Seiten Art.-Nr. 316
Vorab online veröffentlicht am 10.12.2024
Nachgewiesen in Dimensions
Scopus

Verlagsausgabe §
DOI: 10.5445/IR/1000178510
Veröffentlicht am 30.01.2025
Seitenaufrufe: 14
seit 30.01.2025
Downloads: 5
seit 02.02.2025
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