GE Medical Systems has incorporated several new applications into its Excite MR platform. The new features, being shown this week at the annual International Society for Magnetic Resonance in Medicine (ISMRM) meeting in Toronto, include the Waukesha, WI-based vendor's Accelerator data management program for additional processing power and faster workflow.
GE has also debuted Propeller, a pulse-sequence technique. Based on fast spin echo, Propeller technology acquires data in radial blades that rotate, making it helpful for stroke cases, pediatric patients, or anyone who can't remain motionless during the exam, according to the vendor. GE said that Propeller produces a 25% to 75% improvement in contrast-to-noise ratio, and leads to reduced tissue-to-air image distortion.
Another new feature, VIBRANT (volume imaging for breast assessment), produces sagittal images of both breasts in the same amount of time used by traditional techniques to image a single breast, GE said. VIBRANT integrates a fat-suppression technology with GE's Asset parallel imaging approach.
TRICKS (time-resolved imaging of contrast kinetics) is GE's application for assessing blood clots and other vascular diseases in the lower extremities. A dynamic scan, TRICKS eliminates the temporal factor of the MRA exam, acquiring all data for an MR angiography procedure, GE said. The vendor is also rolling out new eight-channel surface coils, include neurovascular, torso-pelvic, cardiac, and CTL spine arrays.
GE said that it has installed Excite on more than 300 of its Signa 1.5-tesla MR scanners around the world since last year.
By AuntMinnie.com staff writersJuly 15, 2003
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![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)