Iterative reconstruction methods in X-ray CT.

By Marcel Beister, D. Kolditz, Willi A. Kalender. Physica medica (Testo stampato), 2012

777 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/51af00e1f5ffa81a863206a39fee0e61387204ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Jong Chul Ye
2. Paul S. Babyn
3. Xin Yi
4. Jaejun Yoo
5. Yanbo Zhang
6. Hengyong Yu
7. Ge Wang
8. Eunhee Kang

In Radiology, Nuclear Medicine and Imaging

1. Jong Chul Ye
2. Paul S. Babyn
3. Xin Yi
4. Jaejun Yoo
5. Michael T. McCann
6. Hengyong Yu
7. Yanbo Zhang
8. Ge Wang

Citations in the corpus

• Image reconstruction by domain-transform manifold learning (2017, methodology): “…with conventional reconstruction techniques for each acquisition strategy: For Radon projection imaging, the Alge-braic Reconstruction Technique 15,38 with the Kaczmarz method (10 iterations) was used, implemented with MATLAB code available at http://www.imm.dtu.dk/~pcha/ AIRtools/;…”
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss (2017, methodology): “These methods generally fall into three categories: (a) sinogram filtration before reconstruction [3]–[5], (b) iterative reconstruction [6], [7], and (c) image post-processing after reconstruction [8]–[10].”
• Deep Convolutional Neural Network for Inverse Problems in Imaging (2016, background): “X-ray reconstruction has a long history of both direct [40], and iterative methods [41].”
• Medical image processing on the GPU - Past, present and future (2013, background): “We believe that GPUs are one key to practically enable clinical use of iterative reconstruction algorithms in CT, but significant challenges remain (Beister et al., 2012).”
• A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution (2018, background): “This category of reconstruction methods is in explosive growth due to the recent availability of high computing power in personal workstations [20].”
• Multiscale X-ray tomography of cementitious materials: A review (2020, background): “[50] (see also the review by Beister [51]).”
• Laser absorption spectroscopy for combustion diagnosis in reactive flows: A review (2019, background): “In contrast, algebraic reconstruction technique (ART) is more suitable for dealing with incomplete and irregular sampling (140 – 142), since ART uses multiple repetitions in which the current solution converges toward a better solution.”
• Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network (2017, background): “the photon counting statistics and the polychromatic nature of the source x-ray [5].”
• Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network (2017, methodology): “Various model based iterative reconstruction (MBIR) methods [1], [2], [3] have been investigated to obtain a clear reconstructed image.”
• A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction (2016, methodology): “It relies on the physical modeling of projection/backprojection operators and the statistical modeling of the noise in projection measurements.(9)”