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Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.
Kriukova, Elena; LaRochelle, Ethan; Pfefer, T Joshua; Kanniyappan, Udayakumar; Gioux, Sylvain; Pogue, Brian; Ntziachristos, Vasilis; Gorpas, Dimitris.
Affiliation
  • Kriukova E; Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany.
  • LaRochelle E; Technical University of Munich, School of Medicine and Health, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Munich, Germany.
  • Pfefer TJ; QUEL Imaging, White River Junction, Vermont, United States.
  • Kanniyappan U; Thayer School of Engineering at Dartmouth College, Hanover, New Hampshire, United States.
  • Gioux S; Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, United States.
  • Pogue B; Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, United States.
  • Ntziachristos V; Intuitive Surgical, Aubonne, Switzerland.
  • Gorpas D; University of Strasbourg, ICube Laboratory, Strasbourg, France.
J Biomed Opt ; 30(Suppl 1): S13703, 2025 Jan.
Article in En | MEDLINE | ID: mdl-39034959
ABSTRACT

Significance:

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 ∼ 35 dB (SNR), ∼ 8.65 a . u . (contrast), and ∼ 0.67 a . u . (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.
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Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Phantoms, Imaging / Benchmarking / Molecular Imaging / Signal-To-Noise Ratio / Optical Imaging Language: En Journal: J Biomed Opt Journal subject: ENGENHARIA BIOMEDICA / OFTALMOLOGIA Year: 2025 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Phantoms, Imaging / Benchmarking / Molecular Imaging / Signal-To-Noise Ratio / Optical Imaging Language: En Journal: J Biomed Opt Journal subject: ENGENHARIA BIOMEDICA / OFTALMOLOGIA Year: 2025 Document type: Article Affiliation country: Country of publication: