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<p>Neutron stimulated emission computed tomography (NSECT) is a non-invasive, tomographic
imaging technique with the ability to locate and quantify elemental concentration
in a tissue sample. Previous studies have shown that NSECT has the ability to differentiate
between benign and malignant tissue and diagnose liver iron overload while using a
neutron beam tomographic acquisition protocol followed by iterative image reconstruction.
These studies have shown that moderate concentrations of iron can be detected in the
liver with moderate dose levels and long scan times. However, a low-dose, reduced
scan time technique to differentiate various liver diseases has not been tested. As
with other imaging modalities, the performance of NSECT in detecting different diseases
while reducing dose and scan time will depend on the acquisition techniques and parameters
that are used to scan the patients. In order to optimize a clinical liver imaging
system based on NSECT, it is important to implement low-dose techniques and evaluate
their feasibility, sensitivity, specificity and accuracy by analyzing the generated
liver images from a patient population. This research work proposes to use Monte-Carlo
simulations to optimize a clinical NSECT system for detection, localization, quantification
and classification of liver diseases. This project has been divided into three parts;
(a) implement two novel acquisition techniques for dose reduction, (b) modify MLEM
iterative image reconstruction algorithm to incorporate the new acquisition techniques
and (c) evaluate the performance of this combined technique on a simulated patient
population. </p><p>The two dose-reduction, acquisition techniques that have been implemented
are; (i) use of a single angle scanning, multi-detector acquisition system and (ii)
the neutron-time resolved imaging (n-TRI) technique. In n-TRI, the NSECT signal has
been resolved in time by a function of the speed of the incident neutron beam and
this information has been used to locate the liver lesions in the tissue. These changes
in the acquisition system have been incorporated and used to modify MLEM iterative
image reconstruction algorithm to generate liver images. The liver images are generated
from sinograms acquired by the simulated n-TRI based NSECT scanner from a simulated
patient population.</p><p>The simulated patient population has patients of different
sizes, with different liver diseases, multiple lesions with different sizes and locations
in the liver. The NSECT images generated from this population have been used to validate
the liver imaging system developed in this project. Statistical tests such as ROC
and student t-tests have been used to evaluate this system. The overall improvement
in dose and scan time as compared to the NSECT tomographic system have been calculated
to verify the improvement in the imaging system. The patient dose was calculated by
measuring the energy deposited by the neutron beam in the liver and surrounding body
tissue. The scan time was calculated by measuring the time required by a neutron source
to produce the neutron fluence required to generate a clinically viable NSECT image.</p><p>Simulation
studies indicate that this NSECT system can detect, locate, quantify and classify
liver lesions in different sized patients. The n-TRI imaging technique can detect
lesions with wet iron concentration of 0.5 mg/g or higher in liver tissue in patients
with 30 cm torso and can quantify lesions at 0.3 ns timing resolution with errors
≤ 17.8%. The NSECT system can localize and classify liver lesions of hemochromatosis,
hepatocellular carcinoma, fatty liver tissue and cirrhotic liver tissue based on bulk
and trace element concentrations. In a small patient with a torso major axis of 30
cm, the n-TRI based liver imaging technique can localize 91.67% of all lesions and
classify lesions with an accuracy of 88.23%. The dose to the small patient is 0.37
mSv a reduction of 39.9% as compared to the NSECT tomographic system and scan times
are comparable to that of an abdominal MRI scan. In a bigger patient with a torso
major axis of 50cm, the n-TRI based technique can detect 75% of the lesions, while
localizing 66.67% of the lesions, the accuracy of classification is 76.47%. The effective
dose equivalent delivered to the larger patient is 1.57 mSv for a 68.8% decrease in
dose as compared to a tomographic NSECT system.</p><p>The research performed for this
dissertation has two important outcomes. First, it demonstrates that NSECT has the
clinical potential for detection, localization and classification of liver diseases
in patients. Second, it provides a validation of the simulation of a novel low-dose
liver imaging technique which can be used to guide future development and experimental
implementation of the technique.</p>
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