by | Feb 19, 2020 | Lew, Matthew, Mazidisharfabadi, Hesamaldin, Nehorai, Arye
— Engineers at Washington University have devised an automated system to enhance super-resolution microscopy images by detecting and quantifying image artifacts using no a priori information. This project stems from the advanced imaging research in Prof. Matthew Lew’s laboratory that includes op…
by | Jan 21, 2020 | Liu, Hui, Rosenberg, Adam, Tu, Zhude "Will", Yue, Xuyi
— Technology Description
Researchers at Washington University in St. Louis have developed sphingosine 1-phosphate receptor 1 (S1P1)-specific PET tracers. Sphingosine 1-phosphate receptors are G-protein coupled receptors, with five subtypes denoted S1P1-5, that have key functions in immune, inflammat…
by | Jan 6, 2020 | Cahill, Alison, Cuculich, Phillip, Schwartz, Alan, Wang, Yong
— Technology Description
Researchers at Washington University in St. Louis have developed an electromyometrial imaging (EMMI) method to non-invasively monitor uterine contractions. During pregnancy, many women experience preterm contractions. Sometimes these contractions progress to pre-term labor a…
by | Dec 17, 2019 | Yang, Deshan
— Technology Description
Prof. Deshan Yang and colleagues have developed automated systems and software toolkits to provide more accurate deformable image registration (DIR) for adaptive radiotherapy and other clinical applications. In particular, they have automated tedious and labor-intensive DIR …
by | Nov 18, 2019 | Altman, Michael, Green, Olga, Kavanaugh, James, Li, Hua, Mutic, Sasa, Wooten, Hasani
— Technology Description
A team of researchers at Washington University has created a machine learning method to quickly and reliably validate patient contours in digital medical images for radiation therapy and computer aided image analysis.
Currently, automated contouring tools for delineating tu…