
Image-guided radiation therapy vendor ViewRay has signed a nonbinding memorandum of understanding to collaborate with radiation oncology vendor Elekta and device manufacturer Medtronic on MR-guided radiation therapy.
Elekta has committed to invest capital for up to a 9.9% minority interest in ViewRay, subject to the terms and conditions set forth in a commitment agreement. The two plan to study the effect of MR-guided therapy in oncology and work to expand the potential role of MR-guided therapy into other areas. The companies could also form a cooperative group and work on healthcare policy, they said.
With Medtronic, the firms will explore the clinical benefits of the MRIdian MR-guided radiation therapy system. Similar to Elekta, Medtronic will also invest a minority interest in ViewRay, subject to the terms and conditions set forth in a commitment agreement.
ViewRay's largest shareholder, Fosun International, committed capital up to an amount that would allow it to maintain its current beneficial ownership percentage in ViewRay, subject to the terms and conditions set forth in a commitment agreement.
The investments from Elekta and Medtronic are conditioned upon an equity capital raise of at least $75 million.
In other ViewRay news, the vendor commenced an underwritten public offering of $75 million of shares of its common stock. ViewRay expects to grant the underwriters a 30-day option to purchase up to an additional $11.25 million of shares of common stock at the public offering price, minus underwriting discounts and commissions.
The offering is subject to market and other conditions, and ViewRay cannot estimate whether or when the offering may be completed or the actual size or terms of the offering.
ViewRay intends to use the net proceeds for working capital and general corporate purposes, such as capital expenditures, research and development expenses, investments, commercial expenses, clinical data generation costs, and infrastructure expenses, the firm said.

![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&fit=crop&h=112&q=70&w=112)










