Deep learning image reconstruction decreases radiation dose 43% on coronary CTA scans
Deep learning reduces radiation dosage for patients undergoing coronary computed tomography angiography (CCTA) by up to 43% without skewing diagnostic image quality, according to new research published in European Radiology.
This study compared a deep learning image reconstruction (DLIR) tool against the conventional Adaptives Statistical Iterative Reconstruction-Veo (ASiR-V) algorithm when processing CCTA exams from 50 patients.
While dose reduction techniques have been tested in other CT scans—abdominal and CT urography—the efficacy in CCTA, in terms of maintaining the same diagnostic quality as traditional dosages and reconstructions, requires further investigation.
“The present study aimed at assessing more clinically relevant endpoints such as stenosis severity, plaque composition, and quantitative plaque volume to test the utility of DLIR-H for radiation dose reduction,” Ronny R. Buechel, with the Department of Nuclear Medicine and Cardiac Imaging at University Hospital Zurich, and co-authors explained.
For this study, patients underwent two CCTA scans—one at a normal dose with voltage and current adapted to BMI and one at a dose that had been decreased by 40%. Normal dose scans were reconstructed with ASiR-V, while the low-dose scans utilized DLIR. Image noise and plaque volumes were subsequently compared.
The research suggested image noise was not impacted by the reduction in radiation. Experts also noted the “excellent” reliability of DLIR for determining stenosis severity, plaque composition and quantitative plaque volume.
“Our results increase our confidence that the DLIR algorithm does neither add nor lose any image information relevant for coronary plaque assessment,” the authors noted.
They added that a dose reduction of 40% for this common exam could be quite beneficial for those at risk of coronary artery disease, especially those who will need follow-up imaging to monitor disease progression.
You can read the full study in European Radiology.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She joined Innovate Healthcare in 2021 and has since put her unique expertise to use in her editorial role with Health Imaging.