If I had a dollar for every time a patient said, “But they didn’t make me change at the hospital,” I could retire at 33. What’s most disheartening isn’t patient ignorance, but that even though technologists understand the risks of MRI's magnetic field, they take them anyway.
A patient recently looked at me like I had 10 heads when I asked her to remove her sports bra -- the kind with metal clips. “They don’t even make me take off my regular bra at the other place,” she told me.
Kelly Brock
When I explained to her about the MRI's ferromagnetic detector, her reaction was the same. “They don’t have one of those at the other facility, and I’ve been getting scanned there for four years.”
Through years of advocating for MRI safety, a troubling pattern has emerged: Resistance to safety measures doesn’t come from patients, but from within the field. Paradoxically, it’s often the most senior technologists -- those with decades of experience -- who demonstrate the greatest laxity. And that complacency doesn’t remain just with them. When seasoned professionals model relaxed adherence to protocols, newer technologists learn to treat safety standards as optional rather than essential. The culture compounds itself.
After years in this field, disappointment in this laxity isn’t just professional -- it’s personal. We are capable of better. The lack of unity on something as fundamental as basic MRI safety measures is inexcusable. We should be each other’s greatest advocates for higher standards, not the obstacle standing in the way of them.
The inconsistencies between departments -- even within the same corporation -- leave patients frustrated and confused. When a patient is held to one standard at one location and met with complete indifference at another, that frustration is valid. I would feel the same way.
While some technologists may be less concerned about certain MR imaging issues than others, the absence of a universal standard is the core problem. That variation in practice masks a more insidious threat. Risks are multiplying, driven by constantly evolving fashion trends like cat-eye nail polish, copper-beaded hair extensions, and athletic wear containing invisible metallic microfibers. Without a consistent baseline, emerging dangers go unnoticed -- until they don’t.
The fact that an item was safely scanned before provides no guarantee of safety for the next scan. Variables such as pulse sequences, field strengths, specific absorption rates, and the positioning of materials within the bore can dramatically alter risk profiles. Unless a technologist can provide scientific evidence demonstrating that no harm will occur under the specific scanning parameters being used, allowing questionable items compromises both patient safety and professional liability. Past experience is not a scientific justification, confidence is not a substitute for competence, and the cost of prevention will never exceed the cost of an incident.
Let's do better.
Kelly Brock is a radiologic technologist and clinical supervisor of ambulatory imaging at Hackensack Meridian Health in Paramus, NJ.
The comments and observations expressed are those of the author and do not necessarily reflect the opinions of AuntMinnie.com.














![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)


