We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

MedImaging

Download Mobile App
Recent News Radiography MRI Ultrasound Nuclear Medicine General/Advanced Imaging Imaging IT Industry News

Coronary CTA Using DLR and Subtraction Technique Reduces Radiation Exposure, Improves Image Quality

By MedImaging International staff writers
Posted on 12 Aug 2022

Coronary CTA with hybrid iterative reconstruction (HIR) is prone to false-positive results for in-stent restenosis due to stent-related blooming artifact. Now, a new study has found that 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.

The study was conducted by researchers at Peking Union Medical College Hospital (Beijing, China) to assess the impact of DLR, subtraction images, and combination of DLR and subtraction images on diagnostic performance of coronary CTA for detection of in-stent restenosis. Between March 2020 and August 2021, the research 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., non-contrast 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). A simulated assessment of combined conventional and subtraction images was generated, reflecting assessment of conventional and subtraction images in the presence or absence of severe misregistration artifact, respectively. Invasive angiography served as reference standard.


Image: Deep learning, subtraction technique has been found optimal for coronary stent evaluation by CTA (Photo courtesy of Pexels)
Image: Deep learning, subtraction technique has been found optimal for coronary stent evaluation by CTA (Photo courtesy of Pexels)

The researchers found that 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. 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,” according to corresponding author Yi-Ning Wang. Acknowledging that the two-breath-hold subtraction technique requires an additional non-contrast acquisition (and thus a higher radiation dose), “DLR allows a reduction in radiation exposure, while improving image quality,” wrote the authors.

Related Links:
Peking Union Medical College Hospital 


Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Pre-Op Planning Solution
Sectra 3D Trauma
New
Breast Imaging Workstation
SecurView
Ultrasound Needle Guide
Ultra-Pro II

Latest General/Advanced Imaging News

Artificial Intelligence Evaluates Cardiovascular Risk from CT Scans

New AI Method Captures Uncertainty in Medical Images

CT Coronary Angiography Reduces Need for Invasive Tests to Diagnose Coronary Artery Disease