Sales gains in its biological and drug delivery business contributed to a 62% increase in revenue for MRI Interventions in its third quarter.
For the period (end-September 30), MRI Interventions had record revenue of $2.9 million, up from $1.8 million in the third quarter of 2018. The company had a net loss of $1.1 million, compared with a net loss of $1.4 million a year ago.
In quarterly highlights, functional neurosurgery revenue climbed 28% to reach $1.9 million, while biologics and drug delivery revenue surged 188% to $564,000. In addition, therapy revenue increased to $64,000 and capital equipment sales and related service revenue grew 144% to $395,000. MRI Interventions now has 57 active surgical centers in its client base.
The firm said it also supported a record 233 cases during the third quarter, up 33% from the 175 supported in the third quarter last year.
![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)