• Corpus ID: 203951457

A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT

@article{Zheng2019ACD,
  title={A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT},
  author={Ao Zheng and Hewei Gao and Li Zhang and Yuxiang Xing},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.03746}
}
Helical CT has been widely used in clinical diagnosis. Sparsely spaced multidetector in z direction can increase the coverage of the detector provided limited detector rows. It can speed up volumetric CT scan, lower the radiation dose and reduce motion artifacts. However, it leads to insufficient data for reconstruction. That means reconstructions from general analytical methods will have severe artifacts. Iterative reconstruction methods might be able to deal with this situation but with the… 

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References

SHOWING 1-10 OF 24 REFERENCES

Comparision of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction

TLDR
Deep learning networks can effectively reconstruct rich high frequency structural information without streaking artefact commonly seen in sparse view CT reconstruction with projectiondomain network, image domain network, and comprehensive network combining projection and image domains.

Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction

TLDR
Deep learning networks can effectively reconstruct rich high frequency structural information without streaking artefact commonly seen in sparse view CT reconstruction with projectiondomain network, image domain network, and comprehensive network combining projection and image domains.

Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network

Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT (CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction

SparseCT: System concept and design of multislit collimators.

TLDR
The purpose of this work is to design the spacing and width of the M SC slits and the MSC motion patterns based on beam separation, undersampling efficiency, and image quality, and compare the initially chosen MSC designs in terms of their reconstruction image quality.

Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions

TLDR
Deep learning method with a common network architecture, termed iCT-Net, was developed and trained to accurately reconstruct images for previously solved and unsolved CT reconstruction problems with high quantitative accuracy, and accurate reconstructions were achieved for the case when the sparse view reconstruction problem is entangled with the classical interior tomographic problems.

A three-dimensional statistical approach to improved image quality for multislice helical CT.

TLDR
Enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies and clinical results illustrate the capabilities of the algorithm on real patient data.

Statistical model based iterative reconstruction (MBIR) in clinical CT systems: experimental assessment of noise performance.

TLDR
Clinical CT scan protocols that had been optimized based on the classical CT noise properties need to be carefully re-evaluated for systems equipped with MBIR in order to maximize the method's potential clinical benefits in dose reduction and/or in CT image quality improvement.

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance.

TLDR
A systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d'.

Helical-scan computed tomography using cone-beam projections

  • H. KudoTsuneo Saito
  • Physics
    Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference
  • 1991
TLDR
An approximate convolution backprojection image reconstruction algorithm for the helical-scan is developed by extending L.A. Feldkamp's (1984) cone-beam reconstruction algorithm, for the circular-scan, and the performance is analyzed by evaluating the point spread function of the reconstruction algorithm.

Theoretically Exact Filtered Backprojection-Type Inversion Algorithm for Spiral CT

TLDR
This work proposes a theoretically exact formula for inversion of data obtained by a spiral computed tomography (CT) scan with a two-dimensional detector array that can be implemented in a truly filtered backprojection fashion.