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Home NEWS Science News Biology

Deep Learning, Subtraction Technique Optimal for Coronary Stent Evaluation by CTA

Bioengineer by Bioengineer
August 10, 2022
in Biology
Reading Time: 2 mins read
0
57-year-old man who underwent coronary CTA using two-breath-hold subtraction technique to assess patency of coronary artery stent.
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Leesburg, VA, August 10, 2022—According to ARRS’ American Journal of Roentgenology (AJR), the combination of deep-learning reconstruction (DLR) and a subtraction technique yielded optimal diagnostic performance for the detection of in-stent restenosis by coronary CTA.

57-year-old man who underwent coronary CTA using two-breath-hold subtraction technique to assess patency of coronary artery stent.

Credit: ARRS and AJR

Leesburg, VA, August 10, 2022—According to ARRS’ American Journal of Roentgenology (AJR), the combination of deep-learning reconstruction (DLR) and a subtraction technique yielded optimal diagnostic performance for the detection of in-stent restenosis by coronary CTA.

Noting that these findings could guide patient selection for invasive coronary stent evaluation, combining DLR with a two-breath-hold subtraction technique “may help overcome challenges related to stent-related blooming artifact,” added corresponding author Yi-Ning Wang from the State Key Laboratory of Complex Severe and Rare Diseases at China’s Peking Union Medical College Hospital.

Between March 2020 and August 2021, Wang and team studied 30 patients (22 men, 8 women; mean age, 63.6 years) with a total of 59 coronary stents who underwent coronary CTA using the two-breath-hold technique (i.e., noncontrast and contrast-enhanced acquisitions). Conventional and subtraction images were reconstructed for hybrid iterative reconstruction (HIR) and DLR, while maximum visible in-stent lumen diameter was measured. Two readers independently evaluated images for in-stent restenosis (≥50% stenosis).

Ultimately, coronary CTA using DLR and subtraction technique—with a combined (conventional and subtraction images) interpretation—yielded PPV, NPV, and accuracy for in-stent restenosis for reader 1 of 73.3%, 93.2%, and 88.1%, and for reader 2 of 75.0%, 84.3%, and 83.1%, respectively.

Acknowledging that the two-breath-hold subtraction technique requires an additional noncontrast acquisition (and thus a higher radiation dose), “DLR allows a reduction in radiation exposure, while improving image quality,” the authors of this AJR article pointed out.


North America’s first radiological society, the American Roentgen Ray Society (ARRS) remains dedicated to the advancement of medicine through the profession of medical imaging and its allied sciences. An international forum for progress in radiology since the discovery of the x-ray, ARRS maintains its mission of improving health through a community committed to advancing knowledge and skills with the world’s longest continuously published radiology journal—American Journal of Roentgenology—the ARRS Annual Meeting, InPractice magazine, topical symposia, myriad multimedia educational materials, as well as awarding scholarships via The Roentgen Fund®.

MEDIA CONTACT:

Logan K. Young, PIO

44211 Slatestone Court

Leesburg, VA 20176

703-858-4332

[email protected]



Journal

American Journal of Roentgenology

DOI

10.2214/AJR.22.27983

Method of Research

Imaging analysis

Subject of Research

People

Article Title

Coronary Artery Stent Evaluation by CTA: Impact of Deep Learning Reconstruction and Subtraction Technique

Article Publication Date

10-Aug-2022

COI Statement

All other authors have no conflict of interest to disclose.

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