MRI Interventions announced a plan to change its name to ClearPoint Neuro, effective February 14. The company also reported growth in revenue for its fiscal 2019 year and fourth quarter.
The company's preliminary results indicate that revenue for the year is expected to reach approximately $11.2 million, an increase of 53% from $7.4 million in 2018. Quarterly revenue (end-December 31, 2019) is expected to be approximately $3.2 million, up 43% from $2.3 million in the prior year's fourth quarter. The increases stemmed largely from the completion of 801 cases with the company's ClearPoint Neuro navigation system, compared with 670 completed cases in 2018, the company said.
MRI Interventions also announced that pharmaceutical company PTC Therapeutics and Petrichor Healthcare Capital Management agreed to invest $17.5 million in the company. The transaction is scheduled to close on or before February 29.
![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)
