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Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood

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This study introduces an interleaved correction (IC) method using deep learning for breast CT image segmentation. The method significantly reduces artifacts and improves Hounsfield unit accuracy in breast imaging.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Imaging

Background:

  • Dedicated breast CT (bCT) imaging is crucial for breast cancer detection.
  • Reconstructed bCT images often suffer from low-frequency shading artifacts due to scatter and beam hardening.
  • Accurate Hounsfield unit (HU) quantification is essential for reliable tissue characterization in bCT.

Purpose of the Study:

  • To develop a robust image segmentation method for bCT volume data.
  • To enhance HU accuracy in bCT by correcting shading artifacts using segmented images.
  • To introduce a postprocessing technique applicable to various bCT systems.

Main Methods:

  • An interleaved correction (IC) method combining image segmentation and polynomial fitting was developed.
  • A deep convolutional neural network (CNN) with a U-Net architecture was used for segmenting adipose voxels.
  • The segmented adipose tissue was fitted to a 3D polynomial for flat fielding correction of artifacts.

Main Results:

  • The CNN achieved high segmentation accuracy (Dice >95%, Precision >97%, Recall >95%, F1 >96%).
  • The IC method significantly reduced cupping (71%) and capping (30%) artifacts in phantom studies.
  • Artifact reduction was validated using metrics like integral nonuniformity and Uniformity Index.

Conclusions:

  • The IC method effectively improves HU accuracy and corrects shading artifacts in bCT.
  • This postprocessing approach is hardware-independent and compatible with existing bCT protocols.
  • The developed CNN model and parameters are available for broader use in bCT research.