Noise reduction in low-dose computed tomography with noise equivalent image and deep learning / 中华放射医学与防护杂志

ABSTRACT

Objective:To investigate the method of simulating low-dose CT (LDCT) images using routine dose level scanning mode to generate LDCT images with correspondence to the routine dose CT (RDCT) images in the training sets for deep learning model, which would be used for LDCT noise reduction.Methods:The CT images reconstructed by different algorithms in Philips CT Big Core had different noise levels, where the noise was larger with iDose 4 algorithm and lower with IMR(knowledge-based iterative model reconstruction)algorithm. A new method of replacing LDCT image with noise equivalent reconstructed image was proposed. The uniform module of CTP712 was scanned with the exposure of 250 mAs for RDCT, 35 mAs for LDCT. The images were reconstructed using IMR algorithm for LDCT images and iDose 4 algorithm at multiple noise reduction levels for RDCT images, respectively. The noise distribution of each image set was analyzed to find the noise equivalent images of LDCT. Then, RDCT images, those selected images were used for training cycle-consistent adversarial networks (CycleGAN)model, and the noise reduction ability of the proposed method on real LDCT images of phantom was tested. Results:The RDCT images generated with iDose 4 level 1 could substitute the LDCT images reconstructed with IMR algorithm. The radiation dose was reduced by 86% in low dose scanning. Using CycleGAN model, the noise reduction degree was 45% for uniform module, and 50%, 13%, 7% for CIRS-SBRT 038 phantom in the specific regions of brain, spinal cord, bone, respectively. Conclusions:Equivalent noise level reconstructed images could potentially serve as the alternative of LDCT images for deep learning network training to avoid additional radiation dose. The generated CT images had substantially reduced noise relative to that of LDCT.