基于图像边缘约束cGAN的稀疏角度锥束乳腺CT重建

Sparse-view Cone-beam Breast CT Reconstruction via cGAN Constrained by Image Edges

锥束乳腺CT(CBBCT)相对高的辐射剂量和相对长的扫描时间阻碍了它的临床应用。该研究提出稀疏角度CBBCT解决上述问题。为了去除用滤波反投影(FBP)重建稀疏角度CT带来的伪影,该研究提出带有图像边缘约束的条件生成对抗网络—ECGAN。ECGAN的生成器是改进后的U-net,判别器是patch GAN和LSGAN的结合,这样的设计是为了提高训练效果,保留高频信息。为了进一步保留细微结构,图像边缘提取后被同时加入生成器和判别器中。之后采用20组临床原始数据进行训练验证。结果显示,ECGAN能实质性地提高稀疏角度CBBCT的图像质量,效果也优于基于全变分的迭代重建和基于FBPConv Net的深度学习后处理。在投影数从300降低到100时,ECGAN将峰值信噪比(PSNR)和结构相似度(SSIM)从FBP的24.26、0.812提高到37.78、0.963。结果表明ECGAN在保证图像质量基本不变的前提下,能将CBBCT的扫描时间和剂量降低为原来的三分之一。

Clinical applications of cone-beam breast CT(CBBCT) are hindered by relatively higher radiation dose and longer scan time.This study proposes sparse-view CBBCT,i.e.with a small number of projections,to overcome the above bottlenecks.A deep learning method – conditional generative adversarial network constrained by image edges(ECGAN) – is proposed to suppress artifacts on sparse-view CBBCT images reconstructed by filtered backprojection(FBP).The discriminator of the ECGAN is the combination of patch GAN and LSGAN for preserving high frequency information,with a modified U-net as the generator.To further preserve subtle structures and micro calcifications which are particularly important for breast cancer screening and diagnosis,edge images of CBBCT are added to both the generator and the discriminator to guide the learning.The proposed algorithm has been evaluated on 20 clinical raw datasets of CBBCT.ECGAN substantially improves the image qualities of sparse-view CBBCT,with a performance superior to those of total variation(TV) based iterative reconstruction and FBPConv Net based post-processing.On one CBBCT case with the projection number reduced from 300 to 100,ECGAN enhances peak-signal-to-noise ratio(PSNR) and structural similarity(SSIM) on FBP reconstruction from 24.26 and 0.812 to 37.78 and 0.963,respectively.These results indicate that ECGAN successfully reduces radiation dose and scan time of CBBCT by 1/3 with only small image degradations.