Browsing by Subject "Geant4"

Proposed is an investigation into the effect of lower-energy source on the tumor representation of an image acquired by a neutron-based spectroscopic imaging modality, Neutron Stimulated Emission Computed Tomography (NSECT).

The NSECT experiments were performed at a shielded neutron source of the Triangle Universities Nuclear Laboratory (TUNL), which was proficient at creating neutron beams of energy up to 20MeV. However, this neutron generator is not feasible for clinical use due to its large size. Smaller compact sources such as deuterium-deuterium (DD) neutron generators are attractive alternatives that can produce neutrons of sufficient energy to stimulate isotopes of interest in the human body. However, DD generator is not competent at producing neutrons of high energy. Thus, the focus of this work is to evaluate the effect of lower-energy neutrons, such as 2.5MeV and 3.2MeV, on the NSECT images.

The experiments were modeled and simulated in this work using a Monte Carlo toolkit, Geant4. In Geant4 space, an anthropomorphic phantom of cancerous tissue was scanned by a simulated neutron source. During scanning, the phantom was translated to cover the whole field of view (FOV) and rotated over 180 degrees for the purpose of tomographic imaging. Neutrons and gammas emitted were captured by a virtual detector, which could identify the energy and position of each particle. Information of position and energy of gammas detected resulted in a sinogram for an array of energies, created by selecting the energy characteristic to a specific element. Using the sinograms, two-dimensional maps of the spatial concentration of the element could be reconstructed through a reconstruction algorithm and the elemental concentration revealed the internal geometry of the phantom.

Images were generated when the phantom was scanned by 5MeV, 3.2MeV and 2.5MeV neutron sources. Comparison of tumor parameters in these images indicates that a neutron source of lower energy could degrade the tumor representation in a NSECT image on the aspects of concentration, brightness and underestimation of the tumor size. Then further investigations with 50,000, 100,000, 150,000 and 200,000 neutron events were performed respectively in the 3.2MeV-source case and 2.5MeV-source case in order to test whether the number of neutrons is correlated to the quality of the reconstructed images. Improvement of tumor representation, for example, a clearer tumor region and more accurate tumor size information, shows that increase in the number of incident neutrons has a positive effect on the reconstructed image. This work demonstrates the effect that low energy neutrons have on the image and verifies the feasibility of using low-energy neutrons as the source in NSECT breast imaging.

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.

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.

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.

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.

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.