- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
R2 signs licensing agreement
The University of Chicago is collaborating with R2 Technology on the development of a computer-aided detection mammography workstation, leveraging an investigational CAD reference library that contains a database of both malignant and benign lesions. The
The University of Chicago is collaborating with R2 Technology on the development of a computer-aided detection mammography workstation, leveraging an investigational CAD reference library that contains a database of both malignant and benign lesions. The reference library, which radiologists will be able to access for clinical comparison and relevancy, is being made available to R2 under an exclusive licensing agreement with the University of Chicago. This agreement is a natural extension of R2's existing collaboration with the university. The library may be the means for developing the next generation of CAD tools, according to Michael Klein, president and CEO of R2 Technology. The reference library workstation is designed to display and compare lesions the way most radiologists do in their own minds.
MRI-Based AI Radiomics Model Offers 'Robust' Prediction of Perineural Invasion in Prostate Cancer
July 26th 2024A model that combines MRI-based deep learning radiomics and clinical factors demonstrated an 84.8 percent ROC AUC and a 92.6 percent precision-recall AUC for predicting perineural invasion in prostate cancer cases.
Breast MRI Study Examines Common Factors with False Negatives and False Positives
July 24th 2024The absence of ipsilateral breast hypervascularity is three times more likely to be associated with false-negative findings on breast MRI and non-mass enhancement lesions have a 4.5-fold likelihood of being linked to false-positive results, according to new research.
Can Polyenergetic Reconstruction Help Resolve Streak Artifacts in Photon Counting CT?
July 22nd 2024New research looking at photon-counting computed tomography (PCCT) demonstrated significantly reduced variation and tracheal air density attenuation with polyenergetic reconstruction in contrast to monoenergetic reconstruction on chest CT.
