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Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

Yuan Chen¹, P Hendrik Pretorius¹, Clifford Lindsay¹

¹Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA.

Medical Physics

|July 31, 2023

Summary

A novel deep learning (DL) method estimates respiratory motion from SPECT projection data, reducing artifacts in cardiac perfusion imaging. This approach achieves similar quantification to external tracking systems, enabling automatic motion correction without extra devices.

Keywords:

cardiac SPECT deep learning respiratory signal

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Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

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Published on: May 30, 2011

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Respiratory motion significantly degrades cardiac perfusion SPECT image quality.
Current respiratory motion correction methods often require external tracking devices, increasing clinical complexity and cost.

Purpose of the Study:

To develop a deep learning (DL) approach for estimating respiratory signals using only SPECT projection data.
To enable automatic respiratory motion correction without external tracking systems.

Main Methods:

A modified U-Net model processed finely sampled SPECT sub-projection data.
The network generated a respiratory motion signal, aggregated from projection angles.
Training utilized a visual tracking system (VTS) as the reference, with comparison to a center-of-mass (CoM) method.

Main Results:

The DL method achieved a high correlation (0.90) with the VTS reference signal, outperforming the CoM method (0.70).
DL-based motion correction reduced image blurring and improved quantification accuracy, with a mean absolute difference of 1.7% compared to VTS for significant motion.
The DL approach demonstrated comparable regional quantification to VTS, outperforming the CoM method and no correction.

Conclusions:

Deep learning effectively estimates respiratory signals from SPECT projection data for cardiac perfusion imaging.
DL-based motion correction reduces artifacts and provides accurate quantification, comparable to external tracking.
This technology facilitates fully automatic, data-driven respiratory motion correction in clinical SPECT imaging.