| Popis: |
X-ray computed tomography (CT) is widely used in diagnostic imaging. Due to the growing number of CT scans worldwide, the consequent increase in populational dose is of concern. Therefore, strategies for dose reduction are investigated. One strategy is to perform interior computed tomography (iCT), where X-ray attenuation data are collected only from an internal region-of-interest. The resulting incomplete measurement is called a truncated sinogram (TS). Missing data from the surrounding structures results in reconstruction artifacts with traditional methods. In this work, a deep learning framework for iCT is presented. TS is extended with a U-net convolutional neural network, and the extended sinogram is reconstructed with filtered backprojection (FBP). U-net was trained for 300 epochs with L1 loss. Truncated and full sinograms were simulated from CT angiography slice images for training data.1097/193/152 sinograms from 500 patients were used in the training, validation, and test sets, respectively. Our method was compared with FBP applied to TS (TS-FBP), adaptive sinogram detruncation followed by FBP (ADT-FBP), total variation regularization applied to TS, and FBPConvNet using TS-FBP as input. The best root-mean-square error (0.04±0.01, mean±SD) and peak signal-to-noise-ratio (29.5±2.9) dB in the test set were observed with the proposed method. However, slightly higher structural similarity indices were observed with FBPConvNet (0.97±0.01) and ADT-FBP (0.97±0.01) than with our method (0.96 ± 0.01). This work suggests that extension of truncated sinogram data with U-Net is a feasible way to reconstruct iCT data without artifacts that render image quality undesirable for medical diagnostics. |
