Abstract
The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix with a 3D framework, which is a conditional generative adversarial network, to map the MRI data domain into the CT data domain of our dataset. The original dataset contains paired MRI and CT images of 31 subjects; 26 pairs were used for model training and 5 were used for model validation. To identify the correctness of the synthetic CT of models, all of the synthetic CTs were calculated by the quantized image similarity formulas: cosine angle distance, Euclidean distance, mean square error, peak signal-to-noise ratio, and mean structural similarity. Two radiologists independently evaluated the satisfaction score, including spatial, detail, contrast, noise, and artifacts, for each imaging attribute. The mean (±standard deviation) of the structural similarity indices (CAD, L2 norm, MSE, PSNR, and MSSIM) between five real CT scans and the synthetic CT scans were 0.96 ± 0.015, 76.83 ± 12.06, 0.00118 ± 0.00037, 29.47 ± 1.35, and 0.84 ± 0.036, respectively. For synthetic CT, radiologists rated the results as evincing excellent satisfaction in spatial geometry and noise level, good satisfaction in contrast and artifacts, and fair imaging details. The similarity index and clinical evaluation results between synthetic CT and original CT guarantee the usability of the proposed method.
Highlights
Computed tomography (CT) simulation is a necessary procedure performed after mold customization for every patient who will undergo radiation therapy
Radiotherapy treatment planning, image reconstruction, and daily treatment guidance are all based on electron density and geometric information provided by CT
We propose an extensible solution for 3D image translation with a high-resolution dataset and demonstrate the effectiveness of data augmentation under the circumstance of insufficient training data
Summary
Computed tomography (CT) simulation is a necessary procedure performed after mold customization for every patient who will undergo radiation therapy. It provides information on electron density and geometry. Radiotherapy treatment planning, image reconstruction, and daily treatment guidance are all based on electron density and geometric information provided by CT. Treatment is based on the CT simulation image for image guidance and positioning error correction. The most critical issue of MR-only simulation workflows is retrieving this information only through MRI. It can be done by rigid or deformable registration, but errors are inevitable, so high-quality results cannot be expected. A CT-independent method should be developed as a better solution
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