The cost of providing a rapid appendicitis MRI exam for children is significantly less than other MRI examinations billed with current procedural technology (CPT) codes typically used for appendicitis MRI, researchers have reported.
The findings underscore the need for a specific CPT code for abbreviated MRI used for this indication, wrote a team led by Pradipta Debnath, MD, of Cincinnati Children’s Hospital Medical Center in Ohio. The results were published June 14 in the Journal of the American College of Radiology.
"Our analysis shows that the provider cost of an abbreviated MRI appendicitis protocol is significantly less than the cost of other abdominal and pelvic MRI examinations billed using the same billing codes," the group noted. "This suggests that abbreviated MRI for appendicitis is a unique examination and likely warrants a unique billing code to account for the significantly lower cost of performing this examination.
Acute appendicitis is the most common cause of urgent abdominal surgery in children and young adults, and imaging plays a key role in diagnosis to reduce the rate of negative appendectomies and surgical complications, the authors explained.
Although ultrasound is considered "usually appropriate" as first-line imaging for children with intermediate clinical risk of appendicitis, less literature exists to clarify whether MRI should be used with or without contrast for this indication and whether there is an appropriate abbreviated protocol.
Debnath and colleagues assessed the cost of a range of MRI exams for pediatric appendicitis via research that included data from 20 MRI studies. The group used time driven activity-based costing to evaluate provider cost of a rapid MRI exam (n = 10) for appendicitis compared with other MR imaging commonly used for appendicitis and billed with current procedural technology (CPT) codes (i.e., MRI pelvis without intravenous contrast, [n = 2] and MRI abdomen/pelvis with intravenous contrast [n = 8]). The rapid MRI is an abbreviated three-sequence examination that includes coronal and axial T2 single shot fast spin-echo sequences and an axial fat-saturated T2 single shot fast spin-echo sequence; it does not include sedation or contrast.
The research found the following:
| Estimated time and costs for MR imaging for appendicitis by type of exam | ||
|---|---|---|
| Type of exam | Exam duration in minutes (mean) | Exam cost (mean) |
| Rapid MRI appendicitis |
11 |
$20.03 |
| MRI pelvis without contrast |
55 |
$105.99 |
| MRI abdomen/pelvis without contrast |
65 |
$144.83 |
| MRI abdomen/pelvis with contrast |
128 |
$236.99 |
"Mechanisms to appropriately bill rapid MRI examinations with limited sequences are needed to improve cost efficiency for the patient, and to enable wider use of limited MRI examinations in the pediatric population," the researchers concluded.
The complete study can be found here.


![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)









