MRI accessories firm IRadimed said it expects third-quarter earnings to come in well below previous guidance.
For the third quarter (end-September 30), IRadimed now expects revenues will range between $7.5 million and $7.6 million, down from previous guidance of $9.9 million to $10 million. Diluted earnings per share based on nongenerally accepted accounting principles (non-GAAP) are expected to reach only 9¢ to 10¢, compared with previous guidance of 23¢ to 24¢.
For 2016, IRadimed now expects revenue of approximately $32.9 million to $33.2 million, compared with previous guidance of $39 million to $40 million. Non-GAAP diluted earnings per share are expected to reach approximately 60¢ to 63¢, compared with previous guidance of 91¢ to 93¢.
In a statement, President and CEO Roger Susi said that the lowered estimates were due to a combination of underestimating sales cycle times in its new sales strategy and achieving its long-stated goal of reducing backlog. Despite the disappointing news, Susi said he remains encouraged by the higher volume and dollar value of recent quoting activity.
Susi said the new sales strategy of calling upon hospital departments outside of radiology and anesthesiology has done well in creating initial interest in its MRI-compatible IV pump. However, these other departments have never budgeted for MRI-compatible pumps, resulting in an extended sales cycle, he said. IRadimed is focusing on continuing to develop the abilities of its sales team and implementing better sales techniques aimed at closing deals more quickly, according to Susi.
![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=100&q=70&w=100)
![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)
