Dear AuntMinnie Member,
Up to 10% of women of reproductive age are affected by endometriosis -- and the condition's symptoms can have a significant negative impact on a woman's life. Researchers have found that using deep learning with MRI boosts the modality's ability to identify the condition -- enhancing the performance of radiologists. Click here for more details.
Once you've read that article, check out more of our coverage of MRI's many contributions to patient care, including a study that shows how an MRI-based nomogram predicts breast cancer treatment response, another that demonstrates how timely patient feedback leads to improvements in MRI exam experience, and a third that demonstrates how ultrafast breast MRI can determine malignant from benign features.
We've also reported on a pair of studies regarding Alzheimer's disease. It turns out that having a father with Alzheimer's increases one's risk, and that an MRI technique called deuterium metabolic imaging aligns with F-18 FDG-PET -- a cornerstone of dementia diagnostics.
Don't miss our story on China's retaliation against new tariffs imposed by the Trump administration by limiting export of rare earth elements -- including gadolinium, which is used in MRI contrast agents. Also be sure to read our articles on the American College of Radiology's updated manual on MRI safety, how resting state functional MRI reveals changes in the brain caused by repetitive blast exposure, and the benefits of using MRI radiomics to distinguish low- from high-risk cases of ductal carcinoma in situ.
It's a pleasure to bring our readers up-to-date coverage of MRI's many applications via our MRI content area. Check it out regularly, and as always, if you have MRI topics you'd like us to consider, please contact me.
Kate Madden Yee
Senior Editor
AuntMinnie.com
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=100&q=70&w=100)
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
