Targeted Diagnostic Imaging and Image Post-processing of Colorectal Cancer using Hyperpolarized MRI
| dc.contributor.advisor | Kemere, Caleb | en_US |
| dc.contributor.advisor | Farach-Carson, Mary C | en_US |
| dc.contributor.advisor | Bhattacharya, Pratip | en_US |
| dc.creator | McCowan, Caitlin | en_US |
| dc.date.accessioned | 2022-10-05T21:29:22Z | en_US |
| dc.date.available | 2023-05-01T05:01:12Z | en_US |
| dc.date.created | 2022-05 | en_US |
| dc.date.issued | 2022-04-22 | en_US |
| dc.date.submitted | May 2022 | en_US |
| dc.date.updated | 2022-10-05T21:29:23Z | en_US |
| dc.description.abstract | Colorectal cancer (CRC) is the third leading cause of cancer related deaths. While there are current methods for screening, such as colonoscopy, a number of patients remain undiagnosed until metastasis has occurred, resulting in limited treatment with unfavorable prognosis. Colonoscopy is the current standard of care, but it carries the risk of intestinal perforation and has difficulty detecting small, flat lesions. Patients diagnosed with late-stage cancer have a 5-year survival rate of 14.7%, accounting for approximately 22% of colorectal cancer patients. As early detection is key for favorable patient outcome, an improved screening method is paramount for patients unable to undergo invasive procedures. A less invasive, more quantitative method of diagnosis can be achieved using hyperpolarized magnetic resonance imaging (MRI) with a biomarker targeted imaging agent. MRI is an ideal candidate as it does not utilize ionizing radiation and offers deep tissue imaging. However, low sensitivity and low specificity have hindered its application. Hyperpolarization is a technique that can increase the sensitivity of MRI by over 10,000-fold, through dynamic nuclear polarization. Hyperpolarized MRI is compatible with several nuclear isotopes, including biocompatible elements like silicon and carbon. Additionally, by targeting mucin 1 (MUC1), a transmembrane protein overly expressed by CRC cells on their surfaces, with an imaging agent, increased specificity can be achieved. Here I describe our investigation using two new MRI-based methods to image CRC. In the first, targeted ²⁹Si microparticles were used for in vivo diagnostic imaging of CRC in a humanized MUC1-expressing mouse model. Additionally, I describe a post-processing algorithm I developed that reduces false signal, background noise, and artifacts in hyperpolarized MR images from these mice, and allows for cross-study comparison. In the second method, I developed a MUC1-targeted urease imaging construct that can be detected with hyperpolarized ¹³C-based MRI through a catabolic reaction that converts ¹³C-urea to carbon dioxide and ammonia. The targeting agent uses protein L to bind the targeting antibody and thus presents the opportunity to be used to detect a wide range of cancer-associated targets for which protein L binding antibodies are available. Together, these studies advance the field by providing new methods for non-invasive targeted imaging of CRC and potentially other cancer types. | en_US |
| dc.embargo.terms | 2023-05-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | McCowan, Caitlin. "Targeted Diagnostic Imaging and Image Post-processing of Colorectal Cancer using Hyperpolarized MRI." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/113533">https://hdl.handle.net/1911/113533</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/113533 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | Diagnostic imaging | en_US |
| dc.subject | Colorectal cancer | en_US |
| dc.subject | Hyperpolarized MRI | en_US |
| dc.title | Targeted Diagnostic Imaging and Image Post-processing of Colorectal Cancer using Hyperpolarized MRI | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Electrical and Computer Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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