Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems

We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems. Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system.

The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼35dB∼35dB<math><mrow><mo>∼</mo> <mn>35</mn> <mtext> </mtext> <mi>dB</mi></mrow> </math> (SNR), ∼8.65a.u∼8.65a.u<math><mrow><mo>∼</mo> <mn>8.65</mn> <mtext> </mtext> <mi>a</mi> <mo>.</mo> <mi>u</mi></mrow> </math> . (contrast), and ∼0.67a.u∼0.67a.u<math><mrow><mo>∼</mo> <mn>0.67</mn> <mtext> </mtext> <mi>a</mi> <mo>.</mo> <mi>u</mi></mrow> </math> . (BM score). Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed. Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems. Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system. Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼35dB∼35dB<math><mrow><mo>∼</mo> <mn>35</mn> <mtext> </mtext> <mi>dB</mi></mrow> </math> (SNR), ∼8.65a.u∼8.65a.u<math><mrow><mo>∼</mo> <mn>8.65</mn> <mtext> </mtext> <mi>a</mi> <mo>.</mo> <mi>u</mi></mrow> </math> . (contrast), and ∼0.67a.u∼0.67a.u<math><mrow><mo>∼</mo> <mn>0.67</mn> <mtext> </mtext> <mi>a</mi> <mo>.</mo> <mi>u</mi></mrow> </math> . (BM score). Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.