- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
Melanoma patients can skip brain, extremity scans
Imaging the brain and uninvolved extremities during routine whole-body PET/CT of melanoma wastes scanner time and exposes patients to unnecessary radiation, according to a study presented at the 2006 Academy of Molecular Imaging meeting.
Imaging the brain and uninvolved extremities during routine whole-body PET/CT of melanoma wastes scanner time and exposes patients to unnecessary radiation, according to a study presented at the 2006 Academy of Molecular Imaging meeting.
At the University of California, Los Angeles, melanoma patients used to undergo routine whole-body scanning. A dedicated PET study could take up to 75 minutes; newer PET/CT exams could be acquired in 30 minutes. A retrospective review of 300 cases found that only a negligible number of patients developed metastases in initially uninvolved extremities and/or the brain. Melanoma patients should be scanned the same way as other oncology patients, a protocol that would improve scanner efficiency without compromising diagnostic power or patient safety, said lead investigator Dr. Esther Choi.
Four Strategies to Address the Tipping Point in Radiology
January 17th 2025In order to flip the script on the impact of the radiology workforce shortage, radiology groups and practices need to make sound investments in technologies and leverage partnerships to mitigate gaps in coverage and maximize workflow efficiencies.
Can Generative AI Facilitate Simulated Contrast Enhancement for Prostate MRI?
January 14th 2025Deep learning synthesis of contrast-enhanced MRI from non-contrast prostate MRI sequences provided an average multiscale structural similarity index of 70 percent with actual contrast-enhanced prostate MRI in external validation testing from newly published research.
Can MRI-Based AI Enhance Risk Stratification in Prostate Cancer?
January 13th 2025Employing baseline MRI and clinical data, an emerging deep learning model was 32 percent more likely to predict the progression of low-risk prostate cancer (PCa) to clinically significant prostate cancer (csPCa), according to new research.
