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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning.

Kyounghyoun Kwon1,2, Donghwi Hwang3,4, Dongkyu Oh2,5

  • 1Department of Health Science and Technology, The Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea.

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Summary
This summary is machine-generated.

Deep learning enables CT-free quantitative thyroid SPECT by generating synthetic attenuation maps and automatically segmenting the thyroid. This approach accurately measures %thyroid uptake without CT, simplifying quantitative SPECT imaging.

Keywords:
Quantification; Single-photon emission computed tomography; Deep-learning; Attenuation correction; Segmentation

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Quantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) traditionally relies on CT for attenuation correction and thyroid segmentation.
  • Manual segmentation of the thyroid on CT is time-consuming and can introduce variability in %thyroid uptake measurements.
  • Developing a CT-free approach for quantitative SPECT could streamline the imaging process and improve efficiency.

Purpose of the Study:

  • To develop a deep-learning-based method for CT-free quantitative thyroid SPECT.
  • To generate synthetic attenuation maps (μ-maps) using deep learning.
  • To achieve automatic segmentation of the thyroid gland for accurate %thyroid uptake quantification.

Main Methods:

  • Retrospective analysis of 650 quantitative thyroid SPECT/CT datasets.
  • Utilized 3D U-Nets for synthetic μ-map generation from SPECT data and for automatic thyroid segmentation.
  • Validated the generated μ-maps and segmentations against CT-derived ground truth and manual segmentations, respectively, using independent datasets.

Main Results:

  • Synthetic μ-maps showed high correlation (R²=0.972) and minimal error compared to CT-derived μ-maps.
  • Automatic thyroid segmentation achieved excellent results with a Dice similarity coefficient of 0.767 and minimal volume difference.
  • %Thyroid uptake measurements using the CT-free method were comparable to the conventional SPECT/CT approach (p=0.1090).

Conclusions:

  • Deep learning can effectively generate synthetic attenuation maps and perform automatic thyroid segmentation for quantitative SPECT.
  • CT-free quantitative thyroid SPECT is feasible, enabling accurate %thyroid uptake evaluation.
  • This AI-driven approach has the potential to simplify quantitative SPECT imaging workflows.