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Modeling Contrast-Imaging-Assisted Optimal Targeted Drug Delivery: A Touchable Communication Channel Estimation and Waveform Design Perspective

Research output: Contribution to journalArticle

Yifan Chen, Yu Zhou, Ross Murch, Panagiotis Kosmas

Original languageEnglish
Article number7857098
Pages (from-to)203-215
Number of pages13
Issue number3
Early online date15 Feb 2017
Publication statusPublished - Apr 2017


  • Modeling Contrast-Imaging-Assisted_CHEN_Published15February2017_GREEN AMM

    Review_Manuscript_R1.pdf, 4.17 MB, application/pdf


    Accepted author manuscript

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King's Authors


To maximize the effect of treatment and minimize the adverse effect on patients, we propose to optimize nanorobots-assisted targeted drug delivery (TDD) for locoregional treatment of tumor from the perspective of touchable communication channel estimation and waveform design. The drug particles are the information molecules; the loading/injection and unloading of the drug correspond to the transmitting and receiving processes; the concentration-time profile of the drug particles administered corresponds to the signaling pulse. Given this analogy, we first propose to use contrast-enhanced microwave imaging (CMI) as a pretherapeutic evaluation technique to determine the pharmacokinetic model of nanorobots-assisted TDD. The CMI system applies an information-theoretic-criteria-based algorithm to estimate drug accumulation in tumor, which is analogous to the estimation of channel impulse response in the communication context. Subsequently, we present three strategies for optimal targeted therapies from the communication waveform design perspective, which are based on minimization of residual drug molecules at the end of each therapeutic session (i.e., inter-symbol interference), maximization of duration when the drug intensity is above a prespecified threshold during each therapeutic session (i.e., non-fade duration), and minimization of average rate that a therapeutic operation is not received correctly at tumor (i.e., bit error rate). Finally, numerical examples are applied to demonstrate the effectiveness of the proposed analytical framework.

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