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Updated: May 23, 2025

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Shorter SPECT scans using self-supervised coordinate learning to synthesize skipped projection views.

Zongyu Li1,2, Yixuan Jia3,4, Xiaojian Xu1

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109-2122, USA.

EJNMMI Physics
|May 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SpeRF, a self-supervised learning method that synthesizes SPECT projection views to reduce imaging time for Lu-177 SPECT scans. SpeRF significantly shortens scan durations while maintaining quantitative accuracy, benefiting low-count and whole-body imaging protocols.

Keywords:
Lu-177 imagingSPECTSelf-supervised learningSparse projection views

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Extended SPECT imaging duration, especially under low-count conditions like Lu-177 SPECT, poses clinical challenges.
  • Reducing scan times is crucial for patient comfort and improving throughput in nuclear medicine.
  • Current methods for reducing SPECT acquisition time may compromise image quality and quantitative accuracy.

Purpose of the Study:

  • To develop and evaluate a self-supervised learning approach (SpeRF) for synthesizing SPECT projection views.
  • To shorten SPECT scan times in clinical settings, particularly for Lu-177 imaging, by reducing the number of acquired projections.
  • To maintain quantitative accuracy and image quality despite reduced acquisition duration.

Main Methods:

  • Developed SpeRF, a SPECT reconstruction pipeline using a self-supervised, coordinate-based learning framework inspired by Neural Radiance Fields (NeRF).
  • SpeRF independently trains a multi-layer perceptron (MLP) to estimate skipped SPECT projection views for each scan.
  • Tested SpeRF with down-sampling factors (DFs=2, 4, 8) on Lu-177 phantom and clinical SPECT/CT datasets ([177Lu]Lu-DOTATATE and [177Lu]Lu-PSMA-617), comparing reconstructions against 'Full', 'Partial', and 'LinInt' methods.

Main Results:

  • SpeRF projections showed lower Normalized Root Mean Squared Difference (NRMSD) compared to linear interpolation (LinInt) projections across phantom and patient studies.
  • At DF=4, SpeRF reconstructions outperformed LinInt and Partial methods in Contrast-to-Noise Ratio (CNR) for lesions and organs in both DOTATATE and PSMA-617 studies.
  • SpeRF demonstrated good quantitative accuracy, with count recovery close to 'Full' acquisition, outperforming LinInt, especially for organs.

Conclusions:

  • SpeRF enables significant reduction in SPECT acquisition time (up to 4x) while preserving quantitative accuracy in clinical protocols.
  • The self-supervised nature of SpeRF eliminates the need for extensive training datasets, processing data independently per patient.
  • This method is particularly beneficial for low-count SPECT imaging and protocols requiring multiple bed positions, such as whole-body imaging.