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- Creators: Chopin, Frédéric, 1810-1849
Description
During the past five decades neurosurgery has made great progress, with marked improvements in patient outcomes. These noticeable improvements of morbidity and mortality can be attributed to the advances in innovative technologies used in neurosurgery. Cutting-edge technologies are essential in most neurosurgical procedures, and there is no doubt that neurosurgery has become heavily technology dependent. With the introduction of any new modalities, surgeons must adapt, train, and become thoroughly familiar with the capabilities and the extent of application of these new innovations. Within the past decade, endoscopy has become more widely used in neurosurgery, and this newly adopted technology is being recognized as the new minimally invasive future of neurosurgery. The use of endoscopy has allowed neurosurgeons to overcome common challenges, such as limited illumination and visualization in a very narrow surgical corridor; however, it introduces other challenges, such as instrument "sword fighting" and limited maneuverability (surgical freedom). The newly introduced concept of surgical freedom is very essential in surgical planning and approach selection and can play a role in determining outcome of the procedure, since limited surgical freedom can cause fatigue or limit the extent of lesion resection. In my thesis, we develop a consistent objective methodology to quantify and evaluate surgical freedom, which has been previously evaluated subjectively, and apply this model to the analysis of various endoscopic techniques. This model is crucial for evaluating different endoscopic surgical approaches before they are applied in a clinical setting, for identifying surgical maneuvers that can improve surgical freedom, and for developing endoscopic training simulators that accurately model the surgical freedom of various approaches. Quantifying the extent of endoscopic surgical freedom will also provide developers with valuable data that will help them design improved endoscopes and endoscopic instrumentation.
ContributorsElhadi, Ali M. (Author) / Preul, Mark C (Thesis advisor) / Towe, Bruce (Thesis advisor) / Little, Andrew S. (Committee member) / Nakaji, Peter (Committee member) / Vu, Eric T (Committee member) / Arizona State University (Publisher)
Created2014
ContributorsChopin, Frédéric, 1810-1849 (Composer)
Description
Current treatment methods for cerebral aneurysms are providing life-saving measures for patients suffering from these blood vessel wall protrusions; however, the drawbacks present unfortunate circumstances in the invasive procedure or with efficient occlusion of the aneurysms. With the advancement of medical devices, liquid-to-solid gelling materials that could be delivered endovascularly have gained interest. The development of these systems stems from the need to circumvent surgical methods and the requirement for improved occlusion of aneurysms to prevent recanalization and potential complications. The work presented herein reports on a liquid-to-solid gelling material, which undergoes gelation via dual mechanisms. Using a temperature-responsive polymer, poly(N-isopropylacrylamide) (poly(NIPAAm), the gelling system can transition from a solution at low temperatures to a gel at body temperature (physical gelation). Additionally, by conjugating reactive functional groups onto the polymers, covalent cross-links can be formed via chemical reaction between the two moieties (chemical gelation). The advantage of this gelling system comprises of its water-based properties as well as the ability of the physical and chemical gelation to occur within physiological conditions. By developing the polymer gelling system in a ground-up approach via synthesis, its added benefit is the capability of modifying the properties of the system as needed for particular applications, in this case for embolization of cerebral aneurysms. The studies provided in this doctoral work highlight the synthesis, characterization and testing of these polymer gelling systems for occlusion of aneurysms. Conducted experiments include thermal, mechanical, structural and chemical characterization, as well as analysis of swelling, degradation, kinetics, cytotoxicity, in vitro glass models and in vivo swine study. Data on thermoresponsive poly(NIPAAm) indicated that the phase transition it undertakes comes as a result of the polymer chains associating as temperature is increased. Poly(NIPAAm) was functionalized with thiols and vinyls to provide for added chemical cross-linking. By combining both modes of gelation, physical and chemical, a gel with reduced creep flow and increased strength was developed. Being waterborne, the gels demonstrated excellent biocompatibility and were easily delivered via catheters and injected within aneurysms, without undergoing degradation. The dual gelling polymer systems demonstrated potential in use as embolic agents for cerebral aneurysm embolization.
ContributorsBearat, Hanin H (Author) / Vernon, Brent L (Thesis advisor) / Frakes, David (Committee member) / Massia, Stephen (Committee member) / Pauken, Christine (Committee member) / Preul, Mark (Committee member) / Solis, Francisco (Committee member) / Arizona State University (Publisher)
Created2012
Description
Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
ContributorsIzady Yazdanabadi, Mohammadhassan (Author) / Preul, Mark (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Nakaji, Peter (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2019
ContributorsChopin, Frédéric, 1810-1849 (Composer)
Description
Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.
ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
ContributorsChopin, Frédéric, 1810-1849 (Composer)
ContributorsChopin, Frédéric, 1810-1849 (Composer)
ContributorsChopin, Frédéric, 1810-1849 (Composer)