Dear AuntMinnie Member,
The SalaryScan Survey is back! Yes, AuntMinnie.com's annual review of compensation and benefits packages for imaging professionals returns for a 2007 run.
Like last year's survey, the new edition asks you to enter your salary information into a short survey that covers multiple professions, areas of specialization, and other variables. Then we'll take all the data submitted by thousands of AuntMinnie members and compile it into an interactive online tool that you can use to research compensation packages by profession, region, or modality specialization.
But unlike last year's survey, this year we're offering all SalaryScan Survey participants the chance to win one of 10 iPod Shuffle MP3 players that we're giving away to thank you for your participation. All you have to do is fill out a completed survey, which you can reach by going to salaryscansurvey.auntminnie.com.
You can fill out the survey anonymously if you wish, but to be entered into the drawing you'll have to give us your name and contact information. As always, your answers are completely confidential, and we won't share any of the information you give us with any third party.
To look at the data from the 2006 SalaryScan Survey, just go to www.salaryscan.com. And if you're thinking of greener pastures, check out the hundreds of job listings available in our Job Boards, at jobs.auntminnie.com.
When you're finished, check out the latest news on the pending sale of Rochester, NY-based Eastman Kodak's Health Group to Canadian investment firm Onex Healthcare Holdings. Onex has announced the new corporate name for the business -- find out more by clicking here.
![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=100&q=70&w=100)
![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&dpr=2&fit=crop&h=167&q=70&w=250)
