How accurate is the 'Final Destination Bloodlines' MRI scene? A story published on 17 May on Today.com explores the question, noting that MR imaging is safe for patients, but acknowledging that accidents do happen.
The film "Final Destination Bloodlines" was released on 16 May and includes a scene in which the main characters, Bobby and Erik, get trapped in an MRI scanner -- with horrific results. The article, called "Just how plausible was that 'Final Destination Bloodlines' MRI scene?" describes the technology of MR imaging and outlines its safety concerns -- namely, the introduction of metal into the suite or the system.
MRI machines use magnets to image the body, and that's why any metal in or around the machine can cause problems. Accidents in MRI scanners have occurred regularly over the years, with one of the most distressing being the 2001 death of 6-year-old Michael Colombini in New York. But even last year, a wheelchair was "sucked across the room" by a magnet in California, "narrowly missing the patient."
In an effort to avoid MRI accidents, professional radiology organizations such as the American College of Radiology (ACR) have written and released guidance for MRI safety, the most recent being in April.
In any case, moviegoers should not avoid MR imaging due to safety concerns, according to one of the film's directors, Zach Lipovsky.
"We debated the ethics of putting (the MRI) scene in the movie (for) a long time … but the scene was continuously everyone's favorite, and so we ultimately decided to put it in," he told Today. "But we do want people to go to their MRI appointments. It is actually incredibly safe."
![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.auntminnieeurope.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.auntminnieeurope.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)
