Quincy Huynh

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-281

December 15, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-281.pdf

Magnetic Particle Imaging (MPI) is an emerging medical imaging modality that detects the strong magnetization of superparamagnetic iron oxide nanoparticle tracers. MPI has proven applications in angiography, stroke, stem cell tracking, white blood cell tracking, lung perfusion, traumatic brain injury, and gastrointestinal bleed imaging among many other highly critical medical imaging applications. However, MPI receive frontend hardware suffers from direct feedthrough interference as a result of simultaneous transmit and receive and is currently not optimized for higher resolution tracers that require a ten-fold wider bandwidth. In this dissertation I will discuss my PhD work that introduces methods to optimize for signal-to-noise ratio and suppress feedthrough interference for inductive sensors used in MPI, specifically for benchtop magnetic particle sensing systems.

The first part will discuss design methodologies for the preamplifier and receive coil. The preamplifier can be designed for another ten-fold lower noise over a five-fold wider bandwidth despite the challenge of broadband noise matching for inductive sensors. The receive coil can be designed for a ten-fold higher sensitivity per volume and lower inductance with microcoils.

The second part will discuss methods to suppress feedthrough interference with passive and active cancellation. Passive cancellation is done using gradiometric coils designed with linear programming to achieve better mechanical shimming tolerance, cancelling feedthrough by three orders of magnitude. Active cancellation using an adaptive feedforward scheme reduces feedthrough by another two orders of magnitude.

Advisors: Steven Conolly

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BibTeX citation:

@phdthesis{Huynh:EECS-2023-281,
    Author= {Huynh, Quincy},
    Title= {Optimized Receive Frontend Hardware for Magnetic Particle Imaging, Characterization Tools, and Biosensors},
    School= {EECS Department, University of California, Berkeley},
    Year= {2023},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-281.html},
    Number= {UCB/EECS-2023-281},
    Abstract= {Magnetic Particle Imaging (MPI) is an emerging medical imaging modality that detects the strong magnetization of superparamagnetic iron oxide nanoparticle tracers. MPI has proven applications in angiography, stroke, stem cell tracking, white blood cell tracking, lung perfusion, traumatic brain injury, and gastrointestinal bleed imaging among many other highly critical medical imaging applications. However, MPI receive frontend hardware suffers from direct feedthrough interference as a result of simultaneous transmit and receive and is currently not optimized for higher resolution tracers that require a ten-fold wider bandwidth. In this dissertation I will discuss my PhD work that introduces methods to  optimize for signal-to-noise ratio and suppress feedthrough interference for inductive sensors used in MPI, specifically for benchtop magnetic particle sensing systems.

The first part will discuss design methodologies for the preamplifier and receive coil. The preamplifier can be designed for another ten-fold lower noise over a five-fold wider bandwidth despite the challenge of broadband noise matching for inductive sensors. The receive coil can be designed for a ten-fold higher sensitivity per volume and lower inductance with microcoils. 

The second part will discuss methods to suppress feedthrough interference with passive and active cancellation. Passive cancellation is done using gradiometric coils designed with linear programming to achieve better mechanical shimming tolerance, cancelling feedthrough by three orders of magnitude. Active cancellation using an adaptive feedforward scheme reduces feedthrough by another two orders of magnitude.},
}

EndNote citation:

%0 Thesis
%A Huynh, Quincy 
%T Optimized Receive Frontend Hardware for Magnetic Particle Imaging, Characterization Tools, and Biosensors
%I EECS Department, University of California, Berkeley
%D 2023
%8 December 15
%@ UCB/EECS-2023-281
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-281.html
%F Huynh:EECS-2023-281