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        "datetime_modified":"20220822T165404",
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        "event_title":"Data-driven Methods for Automated Assessment of Coronary Artery Disease",
        "occurrence_title":"",
        "combined_title":"Data-driven Methods for Automated Assessment of Coronary Artery Disease: BME Ph.D. Defense: Kritika Iyer",
        "event_subtitle":"BME Ph.D. Defense: Kritika Iyer",
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        "description":"Abstract:\r\n\r\nThe current gold standard for Coronary Artery Disease (CAD) diagnosis is X-ray angiography. Visual estimation can be subjective, therefore semi-automated software tools such as Quantitative Coronary Angiography (QCA) have been developed to quantify disease severity. Alternatively, functional metrics such as Fractional Flow Reserve (FFR) have demonstrated better diagnostic outcomes than anatomical assessment, but they are not widely used due to cost and risk. Ideally, quantitative and functional information could be derived directly from X-ray angiography images without the additional risks, time, and cost associated with performing FFR or QCA.\r\n\r\nThe goal of this project is to develop automated data-driven approaches for anatomical and functional quantification of disease severity using X-ray angiography images. To this end, we have developed algorithms for 1) automated coronary vessel segmentation, 2) stenosis detection and characterization, 3) 3D reconstruction of coronary anatomy, and 4) image-based flow extraction. These algorithms can be used in conjunction with computational fluid dynamics (CFD) modeling to assess the functional significance of disease.\r\n\r\nWe first present AngioNet, a neural network for coronary segmentation from X-ray angiography images. Conventional algorithms relying on thresholding or filtering cannot distinguish between the coronary vessels and the catheter used to inject the dye. AngioNet\u2019s key innovation is an Angiographic Processing Network, or APN, which learns the best possible combination of pre-processing filters to improve segmentation performance. AngioNet demonstrates state-of-the-art segmentation accuracy (Dice score = 0.864) and does not segment the catheter in challenging cases where other neural networks fail.\r\n\r\nBuilding upon AngioNet, we developed combination of neural networks and image processing algorithms to automatically localize, segment, and measure stenoses. This pipeline was able to measure stenosis diameter within 0.206\u00b10.155mm or approximately 1 pixel of ground truth measurements from QCA. It is also the first automated pipeline to quantify rather than categorize disease severity.\r\n\r\nAlthough measuring stenosis diameter in 2D images is useful, a more robust approach would be to measure diameters in the 3D coronary anatomy. Another advantage of the 3D coronary anatomy is that it can be used to perform CFD simulations of blood flow and compute functional metrics such as FFR. To this end, we developed a machine learning approach for automated 3D vessel reconstruction from a series of uncalibrated 2D X-ray angiography images. This approach is superior to projective geometry methods for 3D reconstruction due to their semi-automatic nature and reliance on accurate knowledge of input image acquisition angles. Our machine learning approach has demonstrated sub-pixel error in radius reconstruction (0.16\u00b10.07mm) and 1% error in FFR computed in a reconstructed coronary tree.\r\n\r\nIn addition to the 3D coronary geometry, information about patient-specific flow or pressure is required to perform a hemodynamics simulation and compute FFR. We developed an algorithm that tracks vessel area in sequential frames of a segmented angiography series to estimate relative flow in each branch. We validated the algorithm in the simplest possible case, using a simulation of dye transport under steady flow conditions as the ground truth. On average, the difference in relative flow per branch was 5.15% for a healthy coronary tree and 3.68% in a coronary tree with stenosis.\r\n\r\nWe finally demonstrated the successes and limitations of the methods developed in this thesis by comparing computational FFR derived using the above algorithms against clinically measured FFR. The error between the calculated and clinically measured FFR was 0.1, corresponding to an 11% error.\r\n\r\nCommittee Chair(s):\r\nDr. C. Alberto Figueroa and Dr. Brahmajee K. Nallamothu\r\n\r\nZoom Link: https:\/\/umich.zoom.us\/j\/94364657250, Passcode: 390041 *Registration is required",
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        "event_title":"Point B Consulting: Resume Workshop (University of Michigan)",
        "occurrence_title":"",
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        "event_type":"Careers \/ Jobs",
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        "description":"Interested in receiving resume tips from one of Point B's Recruiters and get live feedback on your resume? In the session you will:  \n\n \n\nReceive key tips\/tricks for creating a stellar resume that won't getlost in the black hole of applications. Ask any other resume related questions live.  \n\n \n\nGet an opportunity to have your resume reviewed live. We will randomly select 5 resumes to be reviewed live (we will mask the names during the presentation) and will send those selected a marked up copy for your review.  \n\n \n\nRegister below and our team will follow up with the meeting information for the session. Our team will follow up with more instructions on how to submit your resume to be selected for review,if interested.  \n\n \n\nWhile you register for this session, be sure tocheck out other professional development workshops:  \n\n\u2022 Consulting 101  \n\n\u2022 Case Study Interview Preparation  \n\n\u2022 Point B Information Session \n\n\u2022 Office Hours \n\n \n\n \n\nPlease click the link to register and complete the form.\u202f You will receive a confirmation email right away. One day before the event, you will receive a reminder email that contains the meeting link to login. Be sure to check your junk\/spam folder sometimes the confirmation email is filtered.  \n\n \n\nIf you have any questions, please reach out to campusrecruiting@pointb.com - we look forward to seeing you there!",
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        "event_title":"Poster Sale",
        "occurrence_title":"",
        "combined_title":"Poster Sale",
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        "event_type":"Other",
        "event_type_id":"22",
        "description":"Check out the biggest and newest back-to-school poster sale!\r\n\r\nSunday, 8\/28-Friday, 9\/2\r\nSunday-Thursday 10:00am-6:00pm\r\nFriday-10:00am-5:00pm\r\n\r\nMichigan Union, First Floor",
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        "event_title":"Transfer Student Library Orientation",
        "occurrence_title":"",
        "combined_title":"Transfer Student Library Orientation",
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        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"There are almost 20 libraries at the University of Michigan. Join U-M librarians to learn where to get research help, how to find resources, and how to access technology assistance. Learn strategies for efficiently finding information for your research projects. And bring any questions you have about the library!\r\n\r\nRegister here: https:\/\/ttc.iss.lsa.umich.edu\/ttc\/sessions\/tag\/transfer-students\/\r\n\r\nWe're offering online and onsite options. Onsite sessions include a tour of the Shapiro and Hatcher buildings.\r\n\r\nAugust 30, 10-11:30am \u2014 onsite\r\nSeptember 1, 2-3:0pm \u2014 onsite\r\nSeptember 6, 6-7pm \u2014 online (registration required)\r\nSeptember 7, 6-7pm \u2014 online (registration required)",
        "occurrence_notes":"Please register: https:\/\/ttc.iss.lsa.umich.edu\/ttc\/sessions\/tag\/transfer-students\/",
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        "building_id":"1000227",
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        "room":"Library Learning Lab 1, 4059 Shapiro",
        "location_name":"Shapiro Library",
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                "group_id":"1186",
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        "datetime_modified":"20220825T114137",
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        "event_title":"A Novel Bioelastomer Platform with Tailorable Design Parameters for Cartilage Regeneration",
        "occurrence_title":"",
        "combined_title":"A Novel Bioelastomer Platform with Tailorable Design Parameters for Cartilage Regeneration: BME Ph.D. Defense:\u00a0Yue Qin",
        "event_subtitle":"BME Ph.D. Defense:\u00a0Yue Qin",
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        "description":"Abstract:\r\n\r\nArticular cartilage has limited ability to self-repair, which often causes focal defects to progress into post traumatic osteoarthritis. Autologous chondrocyte implantation, a process in which chondrocytes are harvested from the patient, expanded in monolayer culture, and injected into the defect site, is one of the most common approaches to treat cartilage defect. However, chondrocyte dedifferentiation during this process reduces their ability to durably restore cartilage function. Chondrocyte-based cartilage tissue engineering offers alternative approaches for cartilage repair to overcome the limitations of current clinical options by developing environments that combines cues from synthetic scaffold and biological factors to enhance chondrocyte function. However, the translation to the clinic has been limited by our incomplete understanding of how scaffold design parameters interact together to control cell function. Therefore, this dissertation focuses on designing a chondrocyte-based biomaterial platform made with a novel elastomeric synthetic scaffold, poly(glycerol dodecanedioate) (PGD), to investigate the combinatory effects of design parameters on chondrocytes behavior in vitro.\r\n\r\nFirst, this thesis evaluates the effects of surface modification of PGD on the shape and extracellular matrix (ECM) production of chondrocyte, both of which are crucial for robust cartilage formation. I investigated two different strategies to generate a biomaterial surface with high cell affinity: 1) coating with various concentration of collagen type I or hyaluronic acid individually or in combination, or 2) altering the surface charge and roughness using various level of alkaline hydrolysis. Our results revealed the combinatorial effects of ligand composition and density or surface charge and roughness on human articular chondrocyte function.\r\n\r\nLastly, I used finite element analysis to determine if the local strain fields that developed inside the pores under load could be tuned to be within the range shown to have an anabolic effect on chondrocyte function. The tensile strains that develop along 31% \u2013 71% pore surfaces inside of porous PGD scaffolds, according to varying pore size and porosity, were at levels shown to stimulate chondrocyte ECM production, indicating that the pore structural parameters could be tuned to optimize cellular-level strain profiles. These results suggest that porous PGD scaffolds have the potential to guide cartilage regeneration.\r\n\r\nOverall, this dissertation produces a platform for cartilage tissue engineering using a novel bioelastomer PGD, in which the scaffold design parameters, such as surface modification and cellular strain, can be modified to enhance chondrocyte function.\r\n\r\nCommittee Chair(s):\r\nDr. Rhima Coleman\r\n\r\nZoom Link: https:\/\/umich.zoom.us\/j\/91391389305, Passcode: PGD",
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