{"id":1871,"date":"2022-03-31T21:08:25","date_gmt":"2022-04-01T01:08:25","guid":{"rendered":"https:\/\/sites.bu.edu\/tianlab\/?p=1871"},"modified":"2022-03-31T21:09:14","modified_gmt":"2022-04-01T01:09:14","slug":"yujia-defended-phd","status":"publish","type":"post","link":"https:\/\/sites.bu.edu\/tianlab\/2022\/03\/31\/yujia-defended-phd\/","title":{"rendered":"Yujia defended PhD Dissertation!"},"content":{"rendered":"

\"\" \"\"<\/p>\n

Title:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Computational\u00a0Miniature Mesoscope for Large-Scale 3D Fluorescence Imaging<\/strong><\/p>\n

Presenter:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span>Yujia<\/span>\u00a0<\/span>Xue<\/span><\/p>\n

Date:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/strong>Thursday, March 31, 2022
\nTime:<\/strong>\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a011:00am to 1:00pm
\nLocation:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a08 Saint Mary’s Street, Room 339
\nAdvisor:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Professor Lei Tian, ECE
\nChair:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Professor Abdoulaye Ndao, ECE
\nCommittee:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0 Professor David A. Boas, BME\/ECE; Professor Vivek K. Goyal, ECE; Professor Jerome C. Mertz, BME\/ECE.<\/p>\n

Abstract:<\/strong><\/p>\n

Fluorescence imaging is indispensable to biology and neuroscience. The need for large-scale imaging in freely behaving animals has further driven the development in miniature microscopes (miniscopes). However, conventional microscopes and miniscopes are inherently constrained by their limited space-bandwidth-product, shallow depth-of-field, and inability to resolve 3D distributed emitters. In this dissertation, I present a Computational Miniature Mesoscope (CM2<\/sup>)\u00a0leveraged by two computation frameworks that overcomes these bottlenecks and enables single-shot 3D imaging across a wide imaging field-of-view (7~8 mm) and an extended depth-of-field (0.8~2 mm) with a high lateral (7 \u03bcm) and axial resolution (25 \u03bcm).\u00a0The\u00a0CM2<\/sup>\u00a0is\u00a0a novel fluorescence imaging device that achieves large-scale illumination and single-shot 3D imaging on a compact platform. Its expanded imaging capability is enabled by computational imaging that jointly designs optics and algorithms. I present two versions of\u00a0CM2<\/sup>\u00a0platforms and two 3D reconstruction algorithms in the dissertation. In addition, pilot studies of in vivo imaging experiments using a wearable\u00a0CM2<\/sup>\u00a0prototype are conducted to demonstrate the\u00a0CM2<\/sup>‘s potential applications in neural imaging.<\/p>\n

First, I present the\u00a0CM2<\/sup>\u00a0V1 platform and a model-based 3D reconstruction algorithm which performs volumetric reconstructions from single-shot measurements. The mesoscale 3D imaging capability is validated on various fluorescent samples and analyzed under bulk scattering and background fluorescence in phantom experiments.\u00a0Next, I present and demonstrate an upgraded\u00a0CM2<\/sup>\u00a0V2 platform augmented with a deep learning-based 3D reconstruction framework, termed\u00a0CM2<\/sup>Net, to enable fast 3D reconstruction with higher axial resolution.The\u00a0CM2<\/sup>\u00a0V2 design features an array of freeform illuminators and hybrid emission filters to achieve high excitation efficiency and better suppression of background fluorescence.The\u00a0CM2<\/sup>Net combines ideas from view demixing, lightfield refocusing and view synthesis to achieve reliable 3D reconstruction with high axial resolution. Trained purely on simulated data, I show that the\u00a0CM2<\/sup>Net can generalize to experimental measurements. The key element of\u00a0CM2<\/sup>Net’s generalizability is a 3D Linear Shift Variant model of\u00a0CM2<\/sup>\u00a0that simulates realistic training data with field varying aberrations. The\u00a0CM2<\/sup>Net achieves a 10 times better axial resolution at 1400 times faster reconstruction speed compared to the model-based reconstruction.<\/p>\n

Built from off-the-shelf and 3D printed components, I envision the low-cost and compact\u00a0<\/span>CM2<\/sup>\u00a0<\/span>can be adopted in various biomedical and neuroscience research. The\u00a0<\/span>CM2<\/sup>\u00a0<\/span>and the developed computational tools may bring impact to a wide range of large-scale 3D fluorescence imaging applications.<\/p>\n","protected":false},"excerpt":{"rendered":"

Title:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Computational\u00a0Miniature Mesoscope for Large-Scale 3D Fluorescence Imaging Presenter:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Yujia\u00a0Xue Date:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Thursday, March 31, 2022 Time:\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a011:00am to 1:00pm Location:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a08 Saint Mary’s Street, Room 339 Advisor:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Professor Lei Tian, ECE Chair:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Professor Abdoulaye Ndao, ECE Committee:\u00a0\u00a0\u00a0\u00a0\u00a0 Professor David A. Boas, BME\/ECE; Professor Vivek K. Goyal, ECE; Professor Jerome C. Mertz, BME\/ECE. Abstract: Fluorescence imaging is indispensable to […]<\/p>\n","protected":false},"author":12228,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[16],"tags":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/1871"}],"collection":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/users\/12228"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/comments?post=1871"}],"version-history":[{"count":2,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/1871\/revisions"}],"predecessor-version":[{"id":1875,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/posts\/1871\/revisions\/1875"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/media?parent=1871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/categories?post=1871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.bu.edu\/tianlab\/wp-json\/wp\/v2\/tags?post=1871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}