Below is a list of the finalists for the 2015 edition of the Minnies, AuntMinnie.com's campaign to recognize the best and brightest in medical imaging.
Winners in each category will be selected by our Expert Panel and announced in October. To see the full list of semifinalists, click here.
Most Influential Radiology Researcher
Dr. David Bluemke, PhD, U.S. National Institutes of Health
Danny Hughes, PhD, Harvey L. Neiman Health Policy Institute
Most Effective Radiology Educator
Dr. Daniel Kopans, Massachusetts General Hospital
Dr. Geraldine McGinty, Weill Cornell Medical College
Most Effective Radiologic Technologist Educator
Germaine Frosolone, Gateway Community College, New Haven, CT
Chad Hensley, University of Nevada, Las Vegas
Most Effective Radiology Administrator/Manager
Mary Lou Lovel, Wake Radiology
Charles Powell, Emory University
Best Radiologist Training Program
Johns Hopkins University, Baltimore, MD
Stanford University, Stanford, CA
University of California, San Francisco, San Francisco, CA
(Three candidates tied for this category)
Best Radiologic Technologist Training Program
Albert Einstein Medical Center, Philadelphia, PA
University of Iowa, Iowa City, IA
Most Significant News Event in Radiology
CMS approves CT lung cancer screening for smokers
New digital breast tomosynthesis systems arrive on U.S. market
Biggest Threat to Radiology
Commoditization of radiology services
Shift of radiology reimbursement away from fee-for-service model
Hottest Clinical Procedure
Cardiac CT angiography
CT lung cancer screening
Scientific Paper of the Year
Gadolinium-based contrast agent accumulates in the brain even in subjects without severe renal dysfunction: Evaluation of autopsy brain specimens with inductively coupled plasma mass spectroscopy. Kanda T et al, Radiology, July 2015. To learn more about this paper, click here.
Intracranial gadolinium deposition after contrast-enhanced MR imaging. McDonald RJ et al, Radiology, June 2015. To learn more about this paper, click here.
Best New Radiology Device
Magnetom Terra 7-tesla MRI scanner, Siemens Healthcare
SenoClaire digital breast tomosynthesis system, GE Healthcare
Best New Radiology Software
DoseWatch Explore, GE Healthcare
FFRCT fractional flow reserve CT software, HeartFlow
Neiman Almanac, Harvey L. Neiman Health Policy Institute
(Three candidates tied for this category)
Best New Radiology Vendor
OncoVision
RadLogics
Click on the above links to learn more about each vendor.
Best Mobile App
CT Anatomy (iOS), iCat Medical Software
Radiology Signs (Android), Androidmedics
Click on the above links to learn more about each app.
![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&fit=crop&h=100&q=70&w=100)
![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)
