Refinements of the CT hardware, structured reporting and novel reconstruction methods have allowed hospitals to apply resources both in technology and culture to more effectively and safely image patients in the emergency room.
Noise reduction methods have allowed for comparison of conventional X-ray (CXR) to ultra-low-dose CT (ULDCT) at similar radiation doses in both the chest and abdomen for emergency room patients presenting with non-traumatic conditions.
Structured reporting focusses on the 10 key clinical questions in each body area and facilitates MD-MD communication, thus reducing...
Description of activity and work performed
Materials and Methods
The increased dose efficiency in image reconstruction has permitted dose reduction in CT to the level of X-ray doses, thus allowing us to compare conventional X-ray (CXR) to ultra-low-dose CT imaging (ULDCT) in both the chest and abdomen for emergency room patients presenting with nontraumatic acute pain.
Abdominal Imaging (abdominal tomograms)
A new protocol at our institution has replaced abdominal x-ray (AXR) in the ED with abdominal ULDCT. 462 patients underwent a CT of the abdomen (135 kV, 20-40 mA weight-based, 0.5...
Conclusion and recommendations
CXR is often used as a first-step diagnostic tool as it is cheaper, quicker, portable, relatively easier to perform, and has a reduced radiation dose as compared to current CT protocols. Conversely, CXR suffers from reduced sensitivity and increased false-negative rate for many indications. Furthermore, deep learning reconstruction provides superior image quality and is likely to replace the standard-of-care iterative method. With continued improvements in scanner technology, reconstruction and post-processing capabilities, ULDCT has the potential to supplant CXR as the initial imaging study in emergency...
P. Rogalla; Toronto, ON/CA - nothing to disclose S. Kandel; Berlin/CA - nothing to disclose S. Carey; Toronto, ON/CA - nothing to disclose J. Kavanagh; Toronto/CA - nothing to disclose A. Kielar; Barrie, ON/CA - nothing to disclose B. E. Hoppel; Vernon Hills, WI/US - Employee at Canon Medical Systems
Higaki, Toru et al Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics. Academic Radiology, Volume 27, Issue 1, 82 – 87
Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou J, Yu Z, Akino N, Awai K. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol. 2019 Nov; 29(11):6163-6171. Epub 2019 Apr 11.
Self WH, Coutney M, McNaughton CD, Wunderink RG, Kline JA. High Discordance of Chest X-ray and CT for Detection of Pulmonary Opacities in ED...