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A pre-log correction method based on dynamic approximation to reduce photon-starved deterioration.

Jianhong Liu1,2, Wei Chen2, Haochuan Jiang1,2

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|December 18, 2025
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Summary
This summary is machine-generated.

Photon starvation in computed tomography (CT) causes image noise. A new pre-correction method effectively suppresses electronic noise in sinograms, significantly improving low-dose CT image quality and reducing artifacts.

Keywords:
ArtifactsCTLow-doseLow-signalNoisePhoton starvation

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Radiological Physics

Background:

  • Photon starvation in computed tomography (CT) arises from insufficient photon counts, leading to electronic noise dominating the signal.
  • This noise severely degrades the quality of reconstructed CT images, particularly in low-dose scans.
  • Existing denoising methods often struggle to effectively address noise and artifacts under severe photon starvation.

Purpose of the Study:

  • To propose and evaluate a novel pre-correction method for mitigating photon starvation effects in CT.
  • To suppress electronic noise in the sinogram before logarithmic transformation, thereby improving image quality.
  • To compare the proposed method against existing sinogram-based denoising techniques.

Main Methods:

  • A pre-correction method combining a negative feedback mechanism with an adaptive diffusion filter was developed.
  • The method suppresses electronic noise in the sinogram prior to logarithmic transformation.
  • Evaluation involved ultra-low-dose scans of an anthropomorphic torso phantom and clinical patient data, with comparisons to other denoising methods.

Main Results:

  • The proposed method yielded reconstructed images with the lowest noise levels.
  • It achieved the highest structural similarity and superior spatial resolution compared to other methods.
  • Significantly reduced streaking and bias artifacts were observed, especially in low-signal anatomical regions.

Conclusions:

  • The developed pre-correction method effectively suppresses noise and artifacts in low-dose CT under photon starvation.
  • It maintains acceptable spatial resolution while significantly improving image quality.
  • This approach offers a promising solution for enhancing diagnostic accuracy in ultra-low-dose CT imaging.