Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction

Abstract

Deep-neural-network-based image reconstruction has demonstrated promising performance in medical imaging for under-sampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especially challenging to train the reconstruction networks for three-dimensional computed tomography (CT) because of the high resolution of CT images. The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstructed images. We unrolled the proximal gradient descent algorithm for iterative image reconstruction to finite iterations and replaced the terms related to the penalty function with trainable convolutional neural networks (CNN). The network was trained greedily iteration by iteration in the image-domain on patches, which requires reasonable amount of memory and time on mainstream graphics processing unit (GPU). To overcome the local-minimum problem caused by greedy learning, we used deep UNet as the CNN and incorporated separable quadratic surrogate with ordered subsets for data fidelity, so that the solution could escape from easy local minimums and achieve better image quality. The proposed method achieved comparable image quality with state-of-the-art neural network for CT image reconstruction on 2D sparse-view and limited-angle problems on the low-dose CT challenge dataset.

arXiv comment33 pages, 14 figures, accepted by Medical Physics

References in corpus (8)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
• Learned Primal-dual Reconstruction
• A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
• CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
• Model based learning for accelerated, limited-view 3D photoacoustic tomography

Cited by in corpus (7)

• Deep learning in biomedical optics
• Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
• Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising
• Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
• Deep Learning for Biomedical Image Reconstruction: A Survey
• End-to-end Memory-Efficient Reconstruction for Cone Beam CT
• Deep learning based dictionary learning and tomographic image reconstruction