by | Sep 18, 2020 | Jha, Abhinav Kumar, Liu, Ziping, Moon, Hae Sol, Rahman, Md Ashequr, Yu, Zitong
— Engineers in Washington University’s Computational Medical Imaging Lab have developed automated, machine-learning techniques to improve nuclear medicine imaging (SPECT and PET). These tools include estimation-based segmentation methods to define boundaries and ASC (attenuation and scatter comp…
by | Apr 30, 2020 | Chung, Charles, Kovacs, Sandor, Shmuylovich, Leonid
— Technology Description
Researchers in Prof. Sandor Kovacs’ laboratory developed a patented, non-invasive, load-independent method to measure the intrinsic diastolic performance of the heart. This load-independent index of filling (LIIF) can be incorporated into the software of echocardiogram…
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…