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High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in

Donghoon Lee1, Sunghoon Choi2, Hee-Joung Kim1,2

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Deep learning effectively reconstructs sparse computed tomography (CT) images, reducing radiation dose. A hybrid domain approach using a fully convolutional network and wavelet transform achieved image quality comparable to fully sampled CT scans.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Sparsely sampled computed tomography (CT) offers reduced radiation dose compared to conventional CT.
  • Iterative reconstruction techniques are typically used for sparse CT but require significant computational power.
  • Deep learning presents a potential alternative for high-quality image reconstruction with reduced computational demands.

Purpose of the Study:

  • To develop a deep learning-based method for high-quality three-dimensional (3D) image reconstruction under sparse sampling conditions.
  • To optimize deep learning reconstruction techniques by evaluating different application domains.

Main Methods:

  • A deep learning model utilizing a fully convolutional network and wavelet transform was employed.
  • Wavelet transform replaced pooling layers to minimize spatial resolution loss.
  • Three domains—sinogram, image, and hybrid—were evaluated for reconstruction optimization using The Cancer Imaging Archive (TCIA) dataset.

Main Results:

  • Deep learning effectively removed streak artifacts common in sparse CT.
  • The hybrid domain approach yielded the highest image quality, comparable to fully sampled images (SSIM index ≥ 0.9).
  • Sinogram domain application reduced artifacts but retained noise; image domain application caused blurring.

Conclusions:

  • A novel deep learning-based sparse CT reconstruction method was proposed, diverging from traditional iterative methods.
  • An optimal deep learning technique for sparse sampling reconstruction was developed and validated through image quality assessment.