The University of Wisconsin-Madison CT Protocol Optimization Team has been keeping very busy. Not only have we continued to see increased distribution of the UW CT protocols (1,175 GE scanners were shipped with our protocols installed through September 2017), but more importantly, we’re seeing increased utilization. It is still a bit slower than we like, but it’s accelerating.
We are proud to announce that this month a major health care consortium in the South has decided to convert their entire CT operation to the UW protocols. This may be the break we have been waiting for as other large providers will certainly take notice. In this era of cost containment and standardization, our protocols deliver just that, so we hope more organizations will adopt them.
Radiologists, physicists, and technologists, both inside and outside of our organization, have provided their CT expertise and collaboration to identify areas for improvement, and we are happy to declare that many of those improvements were implemented in Version 3.0 of the UW CT protocols, which were submitted to GE in December after extensive validation.
Congratulations to all of us — the CT protocol optimization team; the UW Radiologists (both academic and community), especially the CT section leads; the medical physicists; all of our hardworking technologists and nurses; and the IT support staff. We thank all of you for your constant surveillance of protocol and CT image quality and helping make our protocols so robust. There is nothing else like this on the planet.
It has been a wonderfully fulfilling adventure so far and promises to just keep getting more interesting.
- Myron Pozniak MD
Each month David Bluemke, MD, PhD, Professor in the Department of Radiology and editor of the journal Radiology, releases a podcast which highlights key articles and topics included in that months edition of Radiology. The podcasts begin with an introduction where Bluemke notes and briefly summarizes a few interesting stories before moving into discussing the three or so key articles included in the issue.
In the most recent podcast this included articles on patient experience in CT colonography and flexible sigmoidoscopy screening, the effectiveness of staged ultrasonography and unenhanced MR imaging in diagnosing pediatric appendicitis, and a look at white matter microstructure and functional task-related neural activity in former football players in relation to career duration, concussion history, and playing position. Transcripts of the podcasts are also available each month.
To view the current month’s podcast as well as those from previous issues, visit the Radiology Podcasts webpage
By sparing the radiation required for one examination, more studies can be performed without surpassing the radiation exposure from previous methods, allowing for closer monitoring of disease activity. Conversely, more frequent scans may indicate remission achievement, thereby allowing the reduction or cessation of drug intervention, which can also have significant side effects and substantial financial cost. Prompt detection of inflammation, which can occur in the absence of clinical symptoms such as pain or nausea, may facilitate rapid treatment and support prevention of serious complications. Therefore, with the development of new low dose imaging techniques, imaging techniques that were previously reserved for diagnosis or extent of disease evaluation can now be used as screening examinations, providing valuable information to better guide and inform future care.
In radiology, Artificial Intelligence (AI), and more specifically Deep Learning, has been of significant interest, as evidenced by the rapid growth of research and incredible amount of investment by corporations and institutions. AI adaptations for a multitude of different applications across radiology are being developed, from computer aided diagnosis (CAD) to improving PACS to EMR crosstalk. Ultimately, continued AI research will accelerate progress in the field of radiology and result in improved diagnosis and treatment for patients. In this project, AI is being used to reduce radiation exposure, by training an algorithm to recognize the features of a full-dose image from an acquired low-dose image thereby allowing a low-dose image to be used in place of the conventional full-dose images.
“It is an honor for me to receive this grant from the RSNA so that I have the opportunity to pursue my research interests in Artificial Intelligence with Dr. McMillan at the University of Wisconsin,” said Park. “Having recently matched into an academic radiology residency program, I hope to continue my research throughout my career and contribute to the field of radiology. AI has enormous potential and only through continued progress and efforts can we discover all the ways in which we can improve the lives of patients.”
The full article can be read at appliedradiology.com
Weibo Cai – Professor
Chris Francois – Professor (CHS)
Allison Grayev – Associate Professor (CHS)
Emily Lewis – Clinical Professor
John Park – Clinical Professor
Jason Pinchot – Associate Professor (CHS)
Conrad Pun – Clinical Associate Professor
Elizabeth Sadowski – Professor (CHS)
Frank Thornton – Professor (CHS)
Congratulations to all on this momentous achievement!