Deep learning has recently been extensively investigated to remove artifacts in low-dose computed tomography (LDCT). However, the power of transfer learning for medical image denoising tasks has not been fully explored. In this work, we proposed a transfer learning residual convolutional neural network (TLR-CNN) to restore LDCT images at single and blind noise levels. A residual network was implemented to effectively estimate the difference between denoised image and its original map, and a noise-free image was obtained by subtracting the residual map from the LDCT image. The results were compared to competing baseline denoising methods in terms of quantitative metrics including the PSNR, RMSE, SSIM and FSIM. For the single noise level, the proposed method demonstrated better denoising performance than the other algorithms for both simulation data and clinical data. As for the blind denoising, the image qualities were improved for all noise levels for all the quantitative metrics, but such improvements were decreasing as the noise level decrease (higher mAs). Comparative experiments suggested that the proposed network could effectively suppress artifacts and preserve image details with faster converge rate and reduced computational time.
Background: Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging. However, high technical requirements and system costs barrier its step into clinical practice.
Methods:We propose a novel sparse segmental MECT (SSMECT) scheme and corresponding reconstruction method, which is a cost-efficient way to realize MECT on a conventional single-source CT.For the data acquisition, the X-ray source is controlled to maintain an energy within a segmental arc, and then switch alternately to another energy level. This scan only needs to switch tube voltage a few times to acquire multi-energy data, but leads to sparse-view and limited-angle issues in image reconstruction.To solve this problem, we propose a prior image constraint robust principal component analysis (PIC-RPCA) reconstruction method, which introduces structural similarity and spectral correlation into the reconstruction.Results: A numerical simulation and a real phantom experiment were conducted to demonstrate the efficacy and robustness of the scan scheme and reconstruction method. The results showed that our proposed reconstruction method could have achieved better multi-energy images than other competing methods both quantitatively and qualitatively.Conclusions: Our proposed SSMECT scan with PIC-RPCA reconstruction method could lower kVp switching frequency while achieving satisfactory reconstruction accuracy and image quality.
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