The Surgical Management and Magnetic Resonance Imaging of Trigeminal Neuralgia
2022-08
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The Surgical Management and Magnetic Resonance Imaging of Trigeminal Neuralgia
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2022-08
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Despite a lengthy history of medical treatment and research, trigeminal neuralgia remains a vexing clinical problem. Those who are not well managed with medications undergo surgical procedures, and of those most can expect only a few years of pain relief. Even further, significant disagreement between clinicians in the field muddles every step of the diagnosis and treatment process for trigeminal neuralgia patients. This dissertation reports the results from three lines of investigation into trigeminal neuralgia in an attempt to understand these disagreements and provide evidence-based recommendations for moving the field forward. The first section contains the results of the analysis of a large, assembled database of trigeminal neuralgia patients treated surgically at the University of Minnesota. Survival analysis was used to determine patient, disease, and surgical characteristics that impact the durability of surgical treatments targeting trigeminal neuralgia. The major finding of this line of research is that contrary to conventional teaching, lower lesioning temperatures in percutaneous radiofrequency rhizotomy procedures appear to result in better long-term outcomes for patients. Additionally, for patients who have the fitness to undergo it, the microvascular decompression provides the best long-term outcomes among all surgical procedures. The second line of investigation in this document concerns the clinical use of magnetic resonance imaging to diagnose and classify trigeminal neuralgia along with the potential for machine learning methods to improve these processes. By using radiomics features in a machine learning classifier trained to distinguish symptomatic vs. asymptomatic trigeminal nerves, we show that there are anatomical variations in symptomatic nerves that can be detected from imaging data. Additionally, we demonstrate that expert readers do not strongly agree on the presence or absence of neurovascular conflict on high quality magnetic resonance images. Finally, we provide recommendations for the optimal set of image acquisition parameters to maximize the reliability and reproducibility of neurovascular conflict grading. Radiographic evidence of neurovascular conflict is the accepted diagnostic criteria for distinguishing the sub types of trigeminal neuralgia, which further determines the optimal treatment strategy. These results highlight the importance of obtaining clear, high-resolution imaging data for use in determining the optimal treatment and suggest that implementation of artificial intelligence methods may help improve the robustness of these classifications. The third and final section details the results of applying two advanced magnetic resonance imaging techniques to visualize the trigeminal nerve. One, we present a method for reliably tracking the peripheral branches of the trigeminal nerve from the pons to the face using diffusion tensor imaging. Our method consistently produces tracts that extend to distal anatomical landmarks in each peripheral branch, at the expense of long scanning times. Tracts of the peripheral nerves could be used in surgical planning, or to assess peripheral nerve injury before or after surgery. Two, we demonstrate that targeted ultra-high field imaging has the potential to visualize the small thermocoagulative lesions produced during a radiofrequency rhizotomy. Combined, these results suggest that magnetic resonance imaging can play an important role in the surgical planning and treatment of trigeminal neuralgia and facial pain disorders, beyond anatomical classification. Taken as a whole, the collection of results in this document challenges some conventional thinking in trigeminal neuralgia, particularly in surgical management and the reading of clinical images. But these results also suggest that innovative applications of imaging and machine learning can help improve our understanding of this insidious condition.
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University of Minnesota Ph.D. dissertation. August 2022. Major: Biomedical Engineering. Advisor: Pierre-Francois Van de Moortele. 1 computer file (PDF); vi, 136 pages.
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Mulford, Kellen. (2022). The Surgical Management and Magnetic Resonance Imaging of Trigeminal Neuralgia. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/243137.
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