Journal Article

DKFZ-2025-00014

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy.

Zaffino, P. ; Raggio, C. B. ; Thummerer, A. ; Marmitt, G. G. ; Langendijk, J. A. ; Procopio, A. ; Cosentino, C. ; Seco, J.DKFZ* ; Knopf, A. C. ; Both, S. ; Spadea, M. F.

2024
MDPI Basel

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Please use a persistent id in citations: doi:10.3390/jimaging10120316

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 not affect the clinical decisions made on the basis of the proposed estimation. The workflow exhibited no systematic error in MAE prediction. This work represents a proof of concept about the feasibility of synthetic CT evaluation in daily clinical practice, and it paves the way for future patient-specific quality assessment strategies.

Keyword(s): MR-only adaptive radiotherapy ; conversion prediction ; deep learning ; synthetic CT

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Contributing Institute(s):

1. Med. Physik in der Radioonkologie (E041)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2024

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Database coverage:
; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Web of Science Core Collection

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