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Reduction of SPECT acquisition time using deep learning: A phantom study.

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Deep convolutional neural networks (DCNNs) show promise in reducing single photon emission computed tomography (SPECT) acquisition times by synthesizing missing projection data. However, DCNN performance is better with coarser image data, necessitating careful evaluation methods.

Keywords:
Deep convolutional neural networksOptimizationSPECT acquisition time

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Single photon emission computed tomography (SPECT) procedures require long acquisition times for diagnostic image quality.
  • Reducing SPECT acquisition time is crucial for improving patient comfort and throughput.

Purpose of the Study:

  • To evaluate the feasibility of using a deep convolutional neural network (DCNN) to shorten SPECT acquisition times.
  • To compare DCNN performance against a baseline method for synthesizing missing projection data.

Main Methods:

  • A DCNN was implemented in PyTorch and trained on SPECT phantom data.
  • The DCNN learned to predict missing projections from under-sampled input data.
  • A baseline method using arithmetic means of adjacent projections was used for comparison.

Main Results:

  • The DCNN significantly outperformed the baseline method in synthesizing projections and reconstructing images.
  • Synthesized image data quality was more comparable to under-sampled than fully-sampled data.
  • The DCNN demonstrated a better ability to replicate coarser objects.

Conclusions:

  • DCNNs can effectively reduce SPECT acquisition times by generating missing projection data.
  • The network's performance is influenced by image dataset characteristics (e.g., sampling density, object coarseness).
  • Standardized evaluation protocols, including baseline methods and phantom data, are essential for accurate DCNN assessment in SPECT.