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Full-Text Articles in Medicine and Health Sciences
Inference Of Language Functional Network In Healthy, Cancerous And Bilingual Brains By Fmri And Network Modeling, Qiongge Li
Dissertations, Theses, and Capstone Projects
We study the underlying mechanism by which language processing occurs in the human brain using inference methods on functional magnetic resonance imaging data. The data analyzed stems from several cohorts of subjects; a monolingual group, a bilingual group, a healthy control group and one diseased case. We applied a complex statistical inference pipeline to determine the network structure of brain components involved with language. This healthy network reveals a fully connected triangular relationship between the pre-Supplementary Motor Area (pre-SMA), the Broca's Area (BA), and the ventral Pre-Motor Area (PreMA) in the left hemisphere. This "triangle'' shows consistently in all the …
A Pretargeted Spect Imaging Strategy Employing Technetium-99m Based On Bioorthogonal Diels-Alder Click Chemistry, Justin H. Walsh
A Pretargeted Spect Imaging Strategy Employing Technetium-99m Based On Bioorthogonal Diels-Alder Click Chemistry, Justin H. Walsh
Dissertations, Theses, and Capstone Projects
A series of related N3S 99mTc-peptide complexes were synthesized and tested for use in pretargeting SPECT imaging that utilizes the bioorthogonal Diels-Alder click reaction between tetrazine (Tz) and transcyclooctene (TCO). The objective was to optimize the excretory pathways of the 99mTc-peptide complexes for maximum tumor targeting with the in vivo “click” and minimum non-target uptake. The 99mTc–tetrazine constructs were prepared by reaction of 99mTc-peptide complexes (99mTc-FKC, 99mTc-FKCR, 99mTc-DKC, and 99mTc-SKC) with Tz-NHS or Tz-PEG5-NHS to form 99mTc FK(Tz)C, 99mTc-FK(PEG5-Tz)CR, 99mTc-DK(PEG5-Tz)C, …
Deep Learning Based Medical Image Analysis With Limited Data, Jiaxing Tan
Deep Learning Based Medical Image Analysis With Limited Data, Jiaxing Tan
Dissertations, Theses, and Capstone Projects
Deep Learning Methods have shown its great effort in the area of Computer Vision. However, when solving the problems of medical imaging, deep learning’s power is confined by limited data available. We present a series of novel methodologies for solving medical imaging analysis problems with limited Computed tomography (CT) scans available. Our method, based on deep learning, with different strategies, including using Generative Adversar- ial Networks, two-stage training, infusing the expert knowledge, voting based or converting to other space, solves the data set limitation issue for the cur- rent medical imaging problems, specifically cancer detection and diagnosis, and shows very …