Below is a list of the finalists in each category for the 2011 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 AuntMinnie.com's Expert Panel and announced in October. To see the full list of semifinalist candidates, click here.
Most Influential Radiology Researcher
Dr. Sanjiv "Sam" Gambhir, PhD, Stanford University
Dr. David C. Levin, Thomas Jefferson University
Most Effective Radiology Educator
Dr. Elliot Fishman, Johns Hopkins University
Dr. Eliot Siegel, University of Maryland
Most Effective Radiologic Technologist Educator
Carol Iversen, Johns Hopkins University
Joy Menser, Owensboro Community and Technical College
Most Effective Radiology Administrator/Manager
Patricia Kroken, Healthcare Resource Providers
Jeffrey Palmucci, Children's Hospital at Vanderbilt University
Best Radiologist Training Program
Mallinckrodt Institute of Radiology, St. Louis, MO
Mayo Clinic, Rochester, MN
Best Radiologic Technologist Training Program
City College of San Francisco, San Francisco, CA
Weber State University, Ogden, UT
Most Significant News Event in Radiology
National Lung Screening Trial links CT lung screening to lower cancer death rates
Radiation scare over mammography thyroid shields
Biggest Threat to Radiology
Decline in Medicare and third-party reimbursement rates
Increased use of medical imaging by physicians in other specialties (turf battles)
Hottest Clinical Procedure
Digital breast tomosynthesis
PET for Alzheimer's disease with new radiopharmaceuticals
Scientific Paper of the Year
The effect of self-referral on utilization of advanced diagnostic imaging. Levin DC et al, American Journal of Roentgenology, April 2011. For AuntMinnie.com's coverage of this paper, click here.
Reduced lung-cancer mortality with low-dose computed tomographic screening. Aberle DR et al, New England Journal of Medicine, August 4, 2011. For AuntMinnie.com's coverage of this paper, click here.
Best New Radiology Device
Biograph mMR hybrid PET/MRI scanner, Siemens Healthcare
Ingenuity TF hybrid PET/MRI scanner, Philips Healthcare
Best New Radiology Software
Mobile MIM app for iPads and iPhones, MIM Software
Syngo Webviewer, Siemens Healthcare
Best New Radiology Vendor
Mobisante
Poiesis Informatics
Click on the above links to learn more about each vendor.
Most Effective Philanthropy Program or Campaign
Pulse and Pause: Image Gently in Fluoroscopy, Alliance for Radiation Safety in Pediatric Imaging
Putting Patients First program, AHRA and Toshiba America Medical Systems










![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)




