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Harmonic Ratio Analysis in Magnetic Particle Imaging Enables Differentiation of Malignant and Benign Human Breast Tissues: A Feasibility Study

Year: 2026
Type: article
Abstract: Accurate intraoperative differentiation between malignant and benign breast tissues, particularly the assessment of lymph node status and tumor margins, is critical for surgical decision-making and prognosis. Traditional histopathological methods, such as frozen section analysis, are time-consuming and labor-intensive. Magnetic Particle Imaging (MPI) is a novel, radiation-free modality that senses the microenvironmental properties of tissues through the dynamic response of magnetic tracers. In this study, we propose a diagnostic method utilizing the higher-order harmonic response of magnetic nanoparticles. Various ex vivo breast tissue samples were immersed in Synomag-50 nanoparticles. Using a custom-built MPI spectrometer (5 kHz excitation, 9 mT amplitude) operating in spectroscopic mode, we implemented a rapid acquisition protocol in which each sample was measured 10 times, with 0.1 s per cycle. We analyzed the magnetic response spectrum and calculated the ratio of the third to the fifth harmonic (H3/H5). Histological analysis confirmed the effective infiltration of MNPs into the interstitial spaces. The repeated measurement data demonstrated high stability. A distinct stepwise increase in harmonic ratios was observed from normal tissue to tumor-adjacent tissue and finally to malignant tumors. Specifically, malignant samples showed ratios that generally exceeded 2.2, whereas benign samples remained below 2.0. These preliminary findings suggest that the harmonic ratio could serve as a sensitive biomarker reflecting the microenvironmental constraints associated with malignancy. This study validates the feasibility of utilizing MPI signal harmonics as a quantitative metric with rapid signal acquisition capabilities for differentiating benign and malignant lymph nodes.
Language: English
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7127637571
FWCI:
Cites: 23
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