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21 June 2018 Correcting for targeted and control agent signal differences in paired-agent molecular imaging of cancer cell-surface receptors
Negar Sadeghipour, Scott C. Davis, Kenneth M. Tichauer
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Journal of Biomedical Optics, Vol. 23, Issue 6, 066004 (June 2018). https://doi.org/10.1117/1.JBO.23.6.066004
Abstract
Paired-agent kinetic modeling protocols provide one means of estimating cancer cell-surface receptors with in vivo molecular imaging. The protocols employ the coadministration of a control imaging agent with one or more targeted imaging agent to account for the nonspecific uptake and retention of the targeted agent. These methods require the targeted and control agent data be converted to equivalent units of concentration, typically requiring specialized equipment and calibration, and/or complex algorithms that raise the barrier to adoption. This work evaluates a kinetic model capable of correcting for targeted and control agent signal differences. This approach was compared with an existing simplified paired-agent model (SPAM), and modified SPAM that accounts for signal differences by early time point normalization of targeted and control signals (SPAMPN). The scaling factor model (SPAMSF) outperformed both SPAM and SPAMPN in terms of accuracy and precision when the scale differences between targeted and imaging agent signals (α) were not equal to 1, and it matched the performance of SPAM for α  =  1. This model could have wide-reaching implications for quantitative cancer receptor imaging using any imaging modalities, or combinations of imaging modalities, capable of concurrent detection of at least two distinct imaging agents (e.g., SPECT, optical, and PET/MR).
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2018/$25.00 © 2018 SPIE
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Negar Sadeghipour, Scott C. Davis, and Kenneth M. Tichauer "Correcting for targeted and control agent signal differences in paired-agent molecular imaging of cancer cell-surface receptors," Journal of Biomedical Optics 23(6), 066004 (21 June 2018). https://doi.org/10.1117/1.JBO.23.6.066004
Received: 25 January 2018; Accepted: 31 May 2018; Published: 21 June 2018
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Cited by 5 scholarly publications.
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KEYWORDS
Tissues
Receptors
Tumors
Cancer
Imaging systems
Error analysis
Data modeling
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