Abstract 6911: Frequency-domain signal separation network improves dual-tracer detection of aneurysm regions in magnetic particle imaging.

Abstract Aneurysms are vascular abnormalities characterized by marked structuralheterogeneity, and their rupture risk is closely associated with regional dilation, focalweakening, and inflammatory activity. Therefore, reliably distinguishing normalvessels from aneurysm-affected regions is essential for aneurysm detection, regionalvisualization, and lesion assessment. Compared with existing medical imagingtechniques such as magnetic resonance imaging (MRI) and computed tomography(CT), Magnetic Particle Imaging (MPI) offers unique advantages including zero tissuebackground, high sensitivity, real-time imaging, and excellent quantitative capability,making it well suited for multi-tracer vascular imaging. Different superparamagneticiron oxide (SPIO) tracers exhibit inherent differences in magnetic properties andmagnetization dynamics, providing the physical basis for multi-tracer MPI; however,noise, cross-talk, and signal imbalance in practical measurements can obscure thesedifferences and hinder accurate dual-tracer separation. To enhance the reliability ofmulti-tracer MPI, we propose a frequency-domain dual-tracer separation framework,the Frequency-Domain Signal Separation Network (FSS-Net).FSS-Net maps raw MPI signals into a two-dimensional harmonic-frequencyrepresentation, predicts tracer-specific masks through a separation module, andreconstructs interference-free dual-channel harmonic signals using afrequency-domain decoder. To validate its performance, we constructed a dual-traceraneurysm phantom in which normal vessel regions and aneurysm lesions were labeledwith different SPIO tracers, simulating heterogeneous nanoparticle distribution indiseased versus healthy vasculature. FSS-Net was compared with two establishedmulti-color MPI methods—System Matrix Concatenation (SM Cat) and MKZ.Quantitative evaluation using PSNR and SSIM showed that FSS-Net significantlyoutperformed both methods in signal fidelity and structural preservation, effectivelyreducing cross-talk and improving visualization of normal vessels andaneurysm-affected regions.Overall, FSS-Net enables high-quality dual-tracer MPI signal separation and offers areliable approach for aneurysm region detection, regional visualization, andnanoparticle distribution analysis, demonstrating strong potential for vascularpathology imaging and nanoparticle-based biomedical research. Citation Format: Ziwei Chen, Yimeng Li, Gen Shi, Jian'an Ye, Zeyu Zhang, Xin Feng, Yu An, Jie Tian. Frequency-domain signal separation network improves dual-tracer detection of aneurysm regions in magnetic particle imaging abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6911.