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Reducing scan time of paediatric 99mTc-DMSA SPECT via deep learning.

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Deep learning (DL) can significantly reduce pediatric technetium 99m (99mTc) dimercaptosuccinic acid (DMSA) SPECT scan times. This method maintains diagnostic accuracy, improving efficiency in pediatric kidney imaging.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pediatric Radiology

Background:

  • Technetium 99m (99mTc) dimercaptosuccinic acid (DMSA) SPECT is crucial for pediatric renal imaging.
  • Long scan times can be challenging for pediatric patients.
  • Deep learning (DL) offers potential solutions for optimizing imaging protocols.

Purpose of the Study:

  • To assess the feasibility of reducing pediatric 99mTc-DMSA SPECT scan duration using a DL approach.
  • To evaluate if shortened scan times impact diagnostic image quality and accuracy.

Main Methods:

  • Retrospective analysis of 112 pediatric 99mTc-DMSA SPECT scans.
  • Training a DL model on 88 scans to generate full-time SPECT images from half-time acquisitions.
  • Validating the DL model's performance on the remaining 24 scans.

Main Results:

  • DL-generated SPECT images from half-time acquisition demonstrated comparable image quality to standard full-time acquisition images.
  • The DL-based method achieved high diagnostic performance: 91.7% accuracy, 83.3% sensitivity, and 100% specificity for detecting affected kidneys.

Conclusions:

  • Deep learning shows promise in reducing pediatric 99mTc-DMSA SPECT scan times.
  • This approach can maintain diagnostic accuracy, potentially improving patient comfort and workflow efficiency.