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Decoding the Myocardium: Tracer-Aware Deep Learning for Patient-Level Classification in Stress-Rest SPECT Myocardial

Dimitrios Samaras^1,2,3, Dimitra Tsivaka¹, Maria Vakalopoulou^2,4

¹Medical Physics Laboratory, Faculty of Medicine, University of Thessaly, 41500 Larissa, Greece.

Diagnostics (Basel, Switzerland)

|June 26, 2026

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This summary is machine-generated.

A new deep learning framework accurately classifies coronary artery disease using stress-phase SPECT myocardial perfusion imaging (MPI) data from technetium-99m and thallium-201 tracers.

Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is crucial for assessing coronary artery disease.
Existing deep learning models for SPECT MPI often overlook tracer-specific variations.
Automated interpretation of SPECT MPI requires methods that account for different radiotracers.

Purpose of the Study:

To develop and assess a multi-task deep learning framework for patient-level SPECT MPI classification.
To incorporate tracer-specific prediction heads to handle variability between technetium-99m (Tc-99m) and thallium-201 (Tl-201).
To evaluate the performance of stress-only, rest-only, and dual-input models.

Main Methods:

A convolutional neural network with a shared encoder and tracer-specific heads was used.

Keywords:

SPECT MPI coronary artery disease deep learning polar maps

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Polar map representations of SPECT MPI data were utilized.

Transfer learning from ImageNet and patient-stratified cross-validation were employed.

Main Results:

Stress-only models demonstrated high performance for both Tc-99m (test AUC 0.88) and Tl-201 (test AUC 0.80) classification tasks.
Stress-phase information proved highly discriminative for SPECT MPI interpretation.
Performance varied based on tracer and specific classification endpoint.

Conclusions:

Stress-phase SPECT MPI polar maps contain significant discriminative information for AI-based classification.
The developed framework shows promise for automated SPECT MPI interpretation, accounting for tracer differences.
Further external validation is necessary for broad clinical generalization of these AI models.