Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps.

Area of Science:

• Nuclear Medicine
• Medical Imaging
• Signal Processing

Background:

• Respiratory motion significantly degrades image quality in Single-Photon Emission Computed Tomography (SPECT).
• Accurate respiratory signal extraction is crucial for motion correction in SPECT.
• Existing methods for respiratory artifact correction in SPECT are limited.

Purpose of the Study:

• To develop and validate a fully-automatic, data-driven method for extracting a respiratory surrogate signal from SPECT list-mode data.
• To assess the performance of the proposed method across various SPECT acquisition types.
• To compare the method's accuracy against a gold standard respiratory monitoring device.

Main Methods:

• Utilized dimensionality reduction with Laplacian Eigenmaps for signal extraction from SPECT list-mode data.
• Implemented adaptive scale parameter setting and post-processing steps for automatic operation.
• Validated the method on 67 patient scans from myocardial perfusion, liver shunt, and lung imaging studies.
• Used an Anzai pressure belt as the gold standard for respiratory monitoring.

Main Results:

• The proposed method achieved a high mean correlation of 0.81 ± 0.17 (median 0.89) with the Anzai pressure belt.
• Performance was characterized concerning count rates, and a predictor for insufficient statistics was developed.
• The method demonstrated robust performance across diverse SPECT acquisition types.

Conclusions:

• This study presents the first large-scale validation of a data-driven respiratory signal extraction method for SPECT.
• The developed technique offers a fully-automatic and accurate solution for respiratory motion management in SPECT imaging.
• Results indicate that this method is comparable to techniques used in other modalities like MRI and PET.