Biomedical applications of holographic microscopy

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Degani, Ismail

Advisor dc:contributor.advisor

• Ralph Weissleder.

• Engineering and Management Program.
• Integrated Design and Management Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/118494

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/118494