One of the biggest stories in MRI for 2004 has been the increasingly rapid adoption of 3-tesla MRI scanners for routine clinical use. Once considered a field strength appropriate for brain research more than anything else, 3-tesla exams are breaking into the mainstream as the new upper end of the high-field clinical MRI market. Some vendors estimate that the 3-tesla segment grew 40% to 45% in 2004, and now makes up 25% to 30% of the total MRI market as measured by dollar value.

Industry observers characterize the MRI market as unsettled -- while some project growth in the 6% to 8% range, others see signs of delayed purchasing decisions, either due to the recent U.S. presidential election or the result of wait-and-see attitudes toward 3-tesla. This could create pent-up demand that will be released as clinicians recognize that 3-tesla scanners are ready for clinical consumption.
Ultra-short-bore systems in the 1.5-tesla segment will be a conference highlight, as vendors push to make cylindrical scanners as patient-friendly as possible. Controversy could erupt, however, if vendors also push the envelope in hyping the open configurations of the systems.
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The open-magnet segment has always been an active market, and things could heat up this year with the introduction of a new 1-tesla open superconducting magnet from Philips Medical Systems of Andover, MA. Other companies with mid-field open supercons will be adding new technologies and applications to make them more competitive with their high-field brethren. Breast imaging seems to be particularly suited to these systems, as their open sides make it possible to easily conduct breast biopsy procedures.
Another hot clinical application is whole-body diffusion-weighted imaging (DWI). By producing PET-like maps of malignant versus benign tissue, vendors hope that DWI will move MRI toward a screening role à la whole-body CT -- only without the radiation concerns.
Cardiac MRI is one promising application that has yet to achieve its full potential. Look for vendors to demonstrate progress in making this technique faster, less complex, and easier for clinicians to conduct -- necessary steps before it can achieve widespread use.




![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)








