Sinogram interpolation for sparse-view micro-CT with deep learning neural network

Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction

Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled pr…

Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography

The quality of the reconstructed photoacoustic image largely depends on the amount of photoacoustic (PA) boundary data available, which in turn is proportional to the number of detectors employed. In case of limited data (owing to less number of detectors due to cost/instrumentation constraints), the reconstructed PA images suffer from artifacts and are often noisy. In this work, for the first time, a deep learning based model was developed to super resolve and denoise the photoacoustic sinogram data. The proposed method was compared with existing nearest neighbor interpolation and wavelet bas…

Learning from our neighbours: a novel approach on sinogram completion using bin-sharing and deep learning to reconstruct high quality 4DCBCT

Inspired by the success of deep learning applications on restoration of low-dose and sparse CT images, we propose a novel method to reconstruct high-quality 4D cone-beam CT (4DCBCT) images from sparse datasets. Our approach combines the idea of 'bin-sharing' with a deep convolutional neural network (CNN) model. More specifically, for each respiratory bin, an initial estimate of the patient sinogram is obtained by taking projections from adjacent bins and performing linear interpolation. Subsequently, the estimated sinogram is propagated through a CNN that predicts a full, high-quality sinogram…

利用残差U-Net填充稀疏PET探测器配置产生的不完整正弦图

Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net

针对长轴向视野PET扫描仪因密集探测器成本高限制临床应用的问题，提出基于改进残差U-Net的深度正弦图恢复网络，在模拟移除50%探测器（保留25%响应线）的场景下恢复缺失数据，效果优于2D插值，为低成本全身PET设计提供可行方案。

PND-Net：基于物理的非局部双域网络用于金属伪影消除

PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction

本文提出基于物理的非局部双域网络PND-Net，通过NSD-Net分解正弦图伪影分量、IR-Net恢复图像域残余伪影，并结合F-Net实现非配对学习，在仿真和临床CT数据上优于现有金属伪影消除方法。

临床CT中拉东变换局部相关方程的几何一致离散化

Geometry-Consistent Discretization of Local Correlation Equations for the Radon Transform in Clinical CT

将二维拉东变换局部相关方程框架扩展到临床CT几何，提出几何一致离散化方案，用有限差分法离散，评估其数值有效性并用于稀疏视图正弦图插值，提升重建质量。