by | Feb 27, 2020 | Green, Olga, Mutic, Sasa, Park, Chunjoo (Justin), Zhang, Hao
— Technology Description
A team of researchers at Washington University School of Medicine developed deep learning image processing techniques to improve MRI diagnostics and potentially enable faster, more precise MRI-guided radiation therapy without exposing patients to radiation from CT imaging. S…
by | Feb 24, 2020 | Mostafa, Atahar, Zhu, Quing
— Technology Description
Prof. Quing Zhu and colleagues have pioneered a compact, low-cost ultrasound-guided optical tomography system designed to differentiate between benign and malignant breast lesions and reduce the need for costly and invasive biopsies. This technology enables fast, robust imag…
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 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 | 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…