Multi-stream Progressive Up-Sampling Network for Dense CT Image Reconstruction

Pulmonary computerized tomography (CT) images with small slice thickness (thin) is very helpful in clinical practice due to its high resolution for precise diagnosis. However, there are still a lot of CT images with large slice thickness (thick) because of the benefits of storage-saving and short taking time. Therefore, it is necessary to build a pipeline to leverage advantages from both thin and thick slices. In this paper, we try to generate thin slices from the thick ones, in order to obtain high quality images with a low storage requirement. Our method is implemented in an encoder-decoder manner with a proposed progressive up-sampling module to exploit enough information for reconstruction. To further lower the difficulty of the task, a multi-stream architecture is established to separately learn the inner- and outer-lung regions. During training, a contrast-aware loss and feature matching loss are designed to capture the appearance of lung markings and reduce the influence of noise. To verify the performance of the proposed method, a total of 880 pairs of CT images with both thin and thick slices are collected. Ablation study demonstrates the effectiveness of each component of our method and higher performance is obtained compared with previous work. Furthermore, three radiologists are required to detect pulmonary nodules in raw thick slices and the generated thin slices independently, the improvement in both sensitivity and precision shows the potential value of the proposed method in clinical applications.

[1]  Yang Chen,et al.  Stereo-Correlation and Noise-Distribution Aware ResVoxGAN for Dense Slices Reconstruction and Noise Reduction in Thick Low-Dose CT , 2019, MICCAI.

[2]  Edgar Simo-Serra,et al.  Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval , 2019, MLMIR@MICCAI.

[3]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[4]  Massimo Bellomi,et al.  Low-dose CT: technique, reading methods and image interpretation , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.

[5]  Takeo Ishigaki,et al.  Solitary pulmonary nodules: optimal slice thickness of high-resolution CT in differentiating malignant from benign. , 2004, Clinical imaging.

[6]  Dr.S.Aruna Mastani K.Silpa.  Comparison Of Image Quality Metrics , 2012 .

[7]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[8]  A. Bankier,et al.  Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. , 2017, Radiology.

[9]  Xiaoou Tang,et al.  Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.

[10]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[11]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[12]  Dwarikanath Mahapatra,et al.  Image super-resolution using progressive generative adversarial networks for medical image analysis , 2019, Comput. Medical Imaging Graph..

[13]  Feng Shi,et al.  Brain MRI super resolution using 3D deep densely connected neural networks , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[14]  Hyunho Park,et al.  Residual CNN-based Image Super-Resolution for CT Slice Thickness Reduction using Paired CT Scans : Preliminary Validation Study , 2018 .

[15]  Wangmeng Zuo,et al.  Blind Super-Resolution With Iterative Kernel Correction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Debiao Li,et al.  Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network , 2018, MICCAI.

[17]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[20]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[21]  Peter B Sachs,et al.  Lung CT Screening Reporting and Data System Speed and Accuracy Are Increased With the Use of a Semiautomated Computer Application. , 2015, Journal of the American College of Radiology : JACR.