- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
3D PET/CT wins accolades
The ability to simultaneously display 3D anatomic and molecular information to evaluate lung cancer earned recognition from the Society of Nuclear Medicine in its choice of image of the year.
The ability to simultaneously display 3D anatomic and molecular information to evaluate lung cancer earned recognition from the Society of Nuclear Medicine in its choice of image of the year.
Dr. Henry Wagner, who has performed this ritual for decades, singled out an example of 3D FDG-PET/CT bronchoscopy produced by Dr. Andrew Quon, an assistant professor of radiology at the Institute for Molecular Imaging at Stanford University.
The image displays a 3D primary lung lesion and an avid mediastinal lymph node partially obscured by the bronchus. Three-D-rendered multislice CT contributes a stunning view of surrounding anatomy and allows the physician to rule out endobronchial involvement.
The Stanford researcher stretched volume imaging and produced an exquisite structural and biochemical image, said Wagner, a professor of environmental health sciences at Johns Hopkins University and past president of the SNM.
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.
