Dual-energy CT accurately identifies anterior cruciate ligament (ACL) tears in the emergency setting, clearing a path to faster diagnosis of this common knee injury, investigators reported at this week's American Roentgen Ray Society (ARRS) meeting in Washington, DC.
In more than 50 patients, dual-energy CT showed greater than 90% sensitivity and similar specificity for detecting ACL tears in the emergency room (ER), said lead author Dr. Katrina Glazebrook from the Mayo Clinic in Rochester, MN.
Using dual-energy CT to identify significant internal derangement of the knee early can facilitate treatment planning for patients with knee trauma, she said in a statement.
ACL tears are a common ligamentous injury of the knee, but they are rarely diagnosed in the emergency department because they are not seen on plain x-rays. MRI is the gold standard, but the exams can take upward of 40 minutes, and scanners are rarely sited in the emergency department; at the same time, CT scanners are more common in the ER setting.
The team examined the knees of 27 patients using dual-energy CT in three planes: axial, sagittal, and oblique sagittal images to which a bone-removal algorithm and tendon-specific color mapping had been applied. The results showed that 16 patients had confirmed ACL tears, while 11 others had no history of trauma.
Two experienced readers, a musculoskeletal radiologist and a senior radiology resident, reviewed all the images. The radiologist achieved a 94% accuracy rate in identifying tears versus 87% for the resident.
This is a new use for dual-energy CT, but the images had sufficient spatial resolution and diagnostic quality to be interpreted by a physician not specializing in musculoskeletal trauma, the researchers wrote.


















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