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000296066 1001_ $$aZaffino, Paolo$$b0
000296066 245__ $$aToward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy.
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000296066 520__ $$aIn 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.
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000296066 650_7 $$2Other$$aMR-only adaptive radiotherapy
000296066 650_7 $$2Other$$aconversion prediction
000296066 650_7 $$2Other$$adeep learning
000296066 650_7 $$2Other$$asynthetic CT
000296066 7001_ $$00009-0008-2443-4029$$aRaggio, Ciro Benito$$b1
000296066 7001_ $$00000-0002-1874-5030$$aThummerer, Adrian$$b2
000296066 7001_ $$00000-0002-8486-7001$$aMarmitt, Gabriel Guterres$$b3
000296066 7001_ $$00000-0003-1083-372X$$aLangendijk, Johannes Albertus$$b4
000296066 7001_ $$00000-0002-3639-8714$$aProcopio, Anna$$b5
000296066 7001_ $$00000-0001-5768-1829$$aCosentino, Carlo$$b6
000296066 7001_ $$0P:(DE-He78)102624aca75cfe987c05343d5fdcf2fe$$aSeco, Joao$$b7$$udkfz
000296066 7001_ $$00000-0001-8212-571X$$aKnopf, Antje Christin$$b8
000296066 7001_ $$aBoth, Stefan$$b9
000296066 7001_ $$00000-0002-5339-9583$$aSpadea, Maria Francesca$$b10
000296066 773__ $$0PERI:(DE-600)2824270-1$$a10.3390/jimaging10120316$$gVol. 10, no. 12, p. 316 -$$n12$$p316$$tJournal of imaging$$v10$$x2313-433X$$y2024
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