
GE HealthCare has launched Sonic DL, a deep-learning software application designed to accelerate MR image acquisition.
The software, which has received U.S. Food and Drug Administration clearance, acquires high-quality MR images up to 12 times faster than conventional methods, enabling cardiac imaging within a single heartbeat, GE HealthCare said in a press release. The risk of motion artifacts is significantly reduced by its speed, according to the company.
Sonic DL's rapid image acquisition functionality is also expected to simplify performing cardiac MRI on patients with arrhythmias, heart failure, or have difficulty holding their breath during the lengthy exam time of this imaging procedure, the vendor said. Although cardiac MRI is the gold standard for assessing the structure and function of the heart in cardiovascular disease, the technology's current acquisition speed isn't fast enough to capture the heart's contraction in real-time, requiring several heartbeats and multiple breath-holds.
Furthermore, its ability to reduce cardiac MRI scan times by up to 83% is expected to help increase MR suite workflow, minimize exam scheduling backlogs, and enhance radiology department productivity, according to GE HealthCare.














![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)

