Beam-hardening-free synthetic images with absolute CT numbers that radiologists are used to can be constructed from spectral CT data by forming 'dichromatic" images after basis decomposition. The CT numbers are accurate for all tissues and the method does not require additional reconstruction. This method prevents radiologists from having to relearn new rules-of-thumb regarding absolute CT numbers for various organs and conditions as conventional CT is replaced by spectral CT. Displaying the synthetic Hounsfield unit images side-by-side with images reconstructed for optimal detectability for a certain task can ease the transition from conventional to spectral CT.
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http://dx.doi.org/10.1088/0031-9155/57/7/N83 | DOI Listing |
J Imaging
December 2024
Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
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).
View Article and Find Full Text PDFMed Phys
December 2024
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Phys Imaging Radiat Oncol
October 2024
Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.
Background And Purpose: Poor image quality of cone-beam computed tomography (CBCT) images can hinder proton dose calculation to assess the influence of anatomy changes. The aim of this study was to evaluate image quality and proton dose calculation accuracy of synthetic CTs generated from CBCT using unsupervised 3D deep-learning networks.
Materials And Methods: A total of 102 head-and-neck cancer patients were used to train (N=82) and test (N=20) i) a cycle-consistent generative adversarial network, ii) a contrastive unpaired translation, and iii) a fusion of the two (CycleCUT).
J Imaging Inform Med
November 2024
Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan.
Cone-beam computed tomography (CBCT) is widely utilized in image-guided radiation therapy; however, its image quality is poor compared to planning CT (pCT), thus restricting its utility for adaptive radiotherapy (ART). Our objective was to enhance CBCT image quality utilizing a transformer-based deep learning model, SwinUNETR, which we compared with a conventional convolutional neural network (CNN) model, U-net. This retrospective study involved 260 patients undergoing prostate radiotherapy, with 245 patients used for training and 15 patients reserved as an independent hold-out test dataset.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
April 2024
Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich 81377, Germany.
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