It's no secret that radiology is becoming more specialized every day. But what happens when you're faced with a case that's outside your area of expertise? Learn about the growing move toward radiology subspecialization and the tools that are available to help you succeed in this October 14 webinar.
Our guests at this one-hour event starting at 1 p.m. EDT on Wednesday, October 14, will include the following radiology key opinion leaders who will speak on subspecialization in medical imaging:
- Dr. Mark Awh, a board-certified radiologist and president of Radsource, a leading provider of innovative solutions in medical imaging. Radsource's orthopedic and neurological MRI interpretation service is provided by a nationally recognized team of subspecialized radiologists.
- Dr. Michael Brown, a board-certified radiologist with Carolina Radiology Associates (CRA), which provides radiology coverage for hospitals, imaging centers, urgent care centers, and doctors' offices throughout South Carolina, North Carolina, Tennessee, and Georgia.
- Dr. Vikram Krishnasetty, a musculoskeletal radiologist and associate chief medical officer, clinical technology, and data at Radiology Partners of Columbus, OH.
Dr. Mark Awh.
Dr. Michael Brown
Dr. Vikram Krishnasetty.
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
