GE HealthCare announces U.S, FDA 510(k) clearance of Precision DL, a deep learning-based image processing software included in GE HealthCare’s Effortless Recon DL portfolio. Precision DL provides the image quality performance benefits typically associated with hardware-based Time-of-Flight (ToF) reconstruction, including improved contrast-to-noise ratio, contrast recovery, and quantitative accuracy. The AI-based technology is available on the company’s Omni Legend PET/CT scanner.

“We can’t treat what we don’t see, which is why we require precise image quality to help diagnose, plan treatment for, and monitor disease,” says Prof. Flavio Forrer, MD, PhD, chairman of nuclear medicine in the division of radiology and nuclear medicine at Kantonsspital St. Gallen in Switzerland. 

“Precision DL enhances image quality—enabling us to spot small lesions, including on images obtained with very low dose injections and short bedtimes, to potentially start treatment and monitoring early, which might result in improved patient outcomes,” Forrer adds. “Additionally, Omni Legend offers a streamlined, simple solution that helps enable technologists to increase efficiency, enhance patient care, and reduce potential radiation exposure to medical staff.”

A subset of AI and machine learning, deep learning utilizes deep neural networks, which consist of layers of mathematical equations and millions of connections and parameters that are trained and strengthened based on the desired output. In doing so, deep learning is a significant leap forward in efficacy compared to previous processes that require more human intervention, handling complex models and vast numbers of parameters with ease to help provide clinicians the time and insights they need to more confidently diagnose and care for patients.

Precision DL processes patient images for enhanced image quality, including:

  • 11% improvement on average in contrast recovery
  • 23% improvement on average in contrast-to-noise ratio
  • 42% increase on average in small, low contrast lesion detectability
  • 14% improvement feature quantification accuracyi

Altogether, a study published in the European Journal of Nuclear Medicine and Molecular Imaging, demonstrated improvement in feature quantitation, overall image sharpness, and overall diagnostic value, particularly in terms of lesion detectability and diagnostic confidence of PET/CT images reconstructed without ToF using deep learning models trained for ToF image enhancement.