- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
Feds appraise the value of nuclear medicine research
The government plans to spend nearly $800,000 to determine the strategic importance of nuclear medicine research, according to National Institutes of Health chief Dr. Elias Zerhouni. Society of Nuclear Medicine president Dr. Peter Conti is encouraged about the initiative.
The government plans to spend nearly $800,000 to determine the strategic importance of nuclear medicine research, according to National Institutes of Health chief Dr. Elias Zerhouni. Society of Nuclear Medicine president Dr. Peter Conti is encouraged about the initiative.
"At least it shows that somebody cares about nuclear medicine research and wants to study it. That's a lot better than cuts made because it is thought to have no value," Conti said.
The Department of Energy and the NIH will spend 13 months investigating topics such as radiopharmaceutical development for diagnosis and therapy, software and hardware development for more precise radiotracer localization, hurdles facing the incorporation of new radiopharmaceutical compounds, and strategies to overcome probable shortages of isotopes and qualified researchers.
Study Reveals Benefits of Photon-Counting CT for Assessing Acute Pulmonary Embolism
April 23rd 2024In comparison to energy-integrating detector CT for the workup of suspected acute pulmonary embolism, the use of photon-counting detector CT reduced radiation dosing by 48 percent, according to newly published research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
AI Adjudication Bolsters Chest CT Assessment of Lung Adenocarcinoma
April 11th 2024The inclusion of simulated adjudication for resolving discordant nodule classifications in a deep learning model for assessing lung adenocarcinoma on chest CT resulted in a 12 percent increase in sensitivity rate.
