
A coroner in Australia is calling for changes in the standards of practice for radiologists after an 87-year-old woman in Queensland died five days after receiving an epidural injection as part of a radiology procedure.
According to a 28 January report in the Brisbane Times, Dr. Benedict Emechete, a radiologist and owner of Integrated Radiology and Imaging Services at Helensvale, performed the procedure on Maria Aurelia Willersdorf in April 2015. Shortly thereafter, she lost consciousness twice while in a recovery room. After checking her pulse twice and taking her blood pressure, Emechete inserted a cannula for venous access and performed a saline flush.
Paramedics arrived, determined the woman was in cardiac arrest, and began cardiopulmonary resuscitation. Willersdorf was revived but subsequently died several days later. The coroner, James McDougall, ruled the cause of death was hypoxic-ischemic encephalopathy, valvular heart disease, and spinal osteoarthritis, according to the article. Her condition caused a change in her blood pressure, which led to the cardiac arrest.
Given the circumstances of the death, McDougall recommended that electrocardiography monitoring be required for patients undergoing spinal tap, epidural, and spinal nerve root block, particularly for those at high risk. He also said that radiologists should know how to provide contrast and sedation, as well as CPR.
The radiology practice was asked to conduct a six-month review following McDougall's recommendations and was given one year to implement the changes, according to the report.












![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=112&q=70&w=112)




