Ultrasound has always been one of imaging's most dynamic modalities -- no pun intended. From the first M-mode devices in the 1960s to today's complex 3-D scanners, ultrasound has shown a remarkable capacity to continually expand and redraw the horizons of medical imaging.
As we click into a new century, advances in scanner technology are helping practitioners develop new and exciting applications, and ultrasound's growth shows no signs of abating. The technology is becoming especially important in developing nations, as it is often the only imaging modality available due to its low cost. Ultrasound's improving spatial resolution will also continue to make it the modality of choice for many new clinical applications in the coming years.
As technology drives ultrasound to new highs, it's also enhancing AuntMinnie.com's ability to deliver information to our members in new and exciting ways. Frontiers in Ultrasound represents the first of what will be many AuntMinnie.com special reports on imaging technology and its clinical applications.
We've divided Frontiers in Ultrasound into several sections, each designed to provide you with a one-stop resource on the Web for ultrasound:
- News articles on the latest clinical developments in ultrasound.
- Product news highlighting the most important recent technology introductions.
- Directory listings of major ultrasound equipment manufacturers.
- Links to ultrasound-focused Web sites.
- Links to ultrasound publications and journals.
- A collection of interactive ultrasound Study Cases.
We hope this interactive resource proves useful. As always, we welcome your comments, which can be sent to us at [email protected].
Brian Casey
Editor in Chief














![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)
