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Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study.

Jaewon Yang¹, Luyao Shi², Rui Wang^3,4

¹Physics Research Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California; jaewon.yang@ucsf.edu.

Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine

|February 27, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning (DL) method for direct attenuation correction (AC) in cardiac myocardial perfusion imaging (MPI) using SPECT. The DL approach significantly reduces artifacts, improving image quality in dedicated SPECT systems.

Keywords:

MPI attenuation correction cardiac SPECT deep learning

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Area of Science:

Nuclear Medicine
Medical Imaging
Artificial Intelligence in Healthcare

Background:

Dedicated cardiac SPECT scanners offer advantages like reduced scan times and radiation doses but often lack integrated CT for attenuation correction (AC).
This limitation leads to challenges in image quantification and frequent artifacts in clinical practice.
Accurate AC is crucial for reliable myocardial perfusion imaging (MPI) interpretation.

Purpose of the Study:

To demonstrate a novel deep learning (DL) technique for direct attenuation correction (AC) in cardiac myocardial perfusion imaging (MPI).
To evaluate the accuracy and effectiveness of DL-based AC compared to traditional CT-based AC.
To assess the potential of DL for improving image quality in dedicated SPECT systems.

Main Methods:

A retrospective study involving 100 cardiac SPECT/CT datasets acquired with 99mTc-tetrofosmin on a dedicated small field-of-view scanner.
A convolutional neural network was developed to generate DL-based attenuation-corrected SPECT (SPECT_DL) directly from non-corrected SPECT (SPECT_NC).
Accuracy was assessed using voxelwise and segmentwise analyses against CT-based AC (SPECT_CTAC) and visual comparison of polar maps.

Main Results:

SPECT_DL showed significantly improved voxelwise correlation with SPECT_CTAC (97.7% ± 1.8%) compared to SPECT_NC (92.2% ± 3.7%).
Segmental errors were substantially reduced with DL, staying within ±10% for SPECT_DL versus -35% to 21% for SPECT_NC.
DL effectively reduced attenuation artifacts, though consistency varied with individual patient attenuation levels and uptake patterns.

Conclusions:

Direct attenuation correction using deep learning is feasible for SPECT MPI.
The DL approach significantly reduces attenuation artifacts compared to non-corrected SPECT.
Further research is warranted to establish the safety and consistency of this DL method for clinical use in stand-alone SPECT systems.