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Ralph Weissleder. Degani, Ismail Massachusetts Institute of Technology. Integrated Design and Management Program. Massachusetts Institute of Technology. Engineering and Management Program Massachusetts Institute of Technology. Integrated Design and Management Program. 2018-10-15T18:37:29Z 2018-10-15T18:37:29Z 2018 2018 http://hdl.handle.net/1721.1/118494 1055658584 Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 77-79). Identifying patients with aggressive cancers is a major healthcare challenge in resource-limited settings such as sub-Saharan Africa. Holographic imaging techniques have been shown to perform diagnostic screening at low cost in order to meet this clinical need, however the computational and logistical challenges involved in deploying such systems are manifold. This thesis aims to make two specific contributions to the field of point-of-care diagnostics. First, it documents the design and construction of low-cost holographic imaging hardware which can serve as a template for future research and development. Second, it presents a novel deep-learning architecture that can potentially lower the computational burden of digital holography by replacing existing image reconstruction methods. We demonstrate the effectiveness of the algorithm by reconstructing biological samples and quantifying their structural similarity relative to spatial deconvolution methods. The approaches explored in this work could enable a standalone holographic platform that is capable of efficiently performing diagnostic screening at the point of care. by Ismail Degani. S.M. in Engineering and Management 79 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Engineering and Management Program. Integrated Design and Management Program. Biomedical applications of holographic microscopy Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8e8478a1-d876-4815-8b40-0d036cfa8f13"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Biomedical applications of holographic microscopy</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Degani, Ismail</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Engineering and Management Program.</Keyword> <Keyword>Integrated Design and Management Program.</Keyword> <Abstract>Identifying patients with aggressive cancers is a major healthcare challenge in resource-limited settings such as sub-Saharan Africa. Holographic imaging techniques have been shown to perform diagnostic screening at low cost in order to meet this clinical need, however the computational and logistical challenges involved in deploying such systems are manifold. This thesis aims to make two specific contributions to the field of point-of-care diagnostics. First, it documents the design and construction of low-cost holographic imaging hardware which can serve as a template for future research and development. Second, it presents a novel deep-learning architecture that can potentially lower the computational burden of digital holography by replacing existing image reconstruction methods. We demonstrate the effectiveness of the algorithm by reconstructing biological samples and quantifying their structural similarity relative to spatial deconvolution methods. The approaches explored in this work could enable a standalone holographic platform that is capable of efficiently performing diagnostic screening at the point of care.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>