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In medicine, there is a clear trend towards individualized
therapies, for cancer and other diseases. Individualized treatment
planning for cancer, particularly in radiotherapy and light
therapies, is a complex optimization problem. As analytical inverse
planning solutions do not exist for light therapies, a large number
of light delivery configurations must be evaluated to find one that
best conforms to the clinical target (e.g., a tumour). An integral
part of this optimization is the accurate computation of light
dose, ideally using Monte Carlo (Me simulations for realistic, 3-D
modelling. This text explores two hardware-accelerated solutions to
overcome the general speed limitation of MC simulations: (1)
designing custom hardware on field-programmable gate arrays, and
(2) creating highly parallel software on graphics processing units
(GPUs). Notably, a speedup of over 1000x was achieved on four GPUs
compared to a state-of-the-art CPU. As the Monte Carlo method is
used in many fields such as radiation medicine, this text also
includes the GPU MC code package and is of interest to scientists,
engineers, and medical professionals exploring real-time treatment
planning solutions.
Single cell analysis has typically been difficult and challenging
to perform in such a way that meaningful statistical information
can be extracted. This thesis documents the development of a
microchip or lab-on-a-chip platform to perform single cell
electrophoresis in parallel separation channels. It describes the
use of optical tweezers to select a cell and transport it to a
desired separation channel. It also describes the use of
electromechanical shearing to lyse a cell. These two techniques are
combined with laser-micromachined separation channels to perform
simple electrohoresis on the fluorescently labeled cellular
contents.
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