- A type of artificial intelligence (AI), Deep Learning (DL)
technology can advance medical imaging capabilities by providing
more robust, accurate, and data-driven information as well as
support more efficient workflows and exams
- Precision DL for PET/CT is the latest addition to GE
HealthCare’s Effortless Recon DL portfolio of deep learning-based
solutions – which also includes AIR Recon DL for MRI, TrueFidelity
for CT, and Helix DL for X-ray – to significantly improve image
quality and help better inform clinical decision-making for
improved patient outcomes
GE HealthCare (Nasdaq: GEHC) today announced US FDA 510(k)
clearance of Precision DL – a new, revolutionary deep
learning-based image processing software included in GE
HealthCare’s growing 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 ratioi, contrast recoveryi,
and quantitative accuracyii. The AI-based technology is available
on the company’s fastest-ever-selling PET/CTiii, Omni Legend, which
already boasts more than two times the sensitivity of prior digital
scannersiv, enabling faster scan timesv and impressive small lesion
detectabilityvi.
Together, the availability of Precision DL with Omni Legend’s
ultra-high sensitivity, third generation digital detector
technology marks a new era for PET/CT performance and outcomes,
transitioning from ToF technology to the next generation of PET/CT
performance and enabling clinicians to decode coincidence events at
exceptionally fine resolutions for informed diagnoses and treatment
planning.
“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,” explains Prof. Flavio Forrer, MD, PhD, Chairman
of Nuclear Medicine in the Division of Radiology and Nuclear
Medicine at Kantonsspital St. Gallen in Switzerlandvii. “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.
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 vii.”
Medical imaging is a crucial tool for diagnosing disease,
identifying a course of treatment, and determining whether therapy
is successful for millions of patients around the world. Image
quality matters – to the clinician and the patient – making the
difference between finding a small lesion early or in its later
stages, potentially affecting patient outcomes and disease
management. For this reason, clinicians are increasingly adopting
AI-based solutions for enhanced image quality compared to that of
standard care.
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.
“One of the main advantages of moving fully into the future of
AI and deep learning is making state-of-the-art imaging accessible
to more practices, across more care areas than ever before,” shares
Jan Makela, President & CEO, Imaging, GE HealthCare.
“Clinicians are seeing the value of applying deep learning
technology to enhance image quality with our multi-modality family
of Effortless Recon DL applications, which already includes AIR
Recon DL for MR, TrueFidelity for CT, and Helix DL for X-ray. Now
we are proud to add Precision DL for PET/CT, enabling more precise
and personalized care across healthcare systems’ imaging
departments.”
More than a new imaging processing technique, Precision DL was
engineered with a sophisticated deep neural network trained on
thousands of images created with multiple reconstruction methods,
including ToF reconstruction, to provide the image quality
performance benefits typically associated with hardware-based ToF
reconstruction, such as improved contrast-to-noise ratio and
contrast recoveryi.
Precision DL processes patient images for enhanced image
quality, including:
- 11% improvement on average in contrast recoveryi,
- 23% improvement on average in contrast-to-noise ratioi,
- 42% increase on average in small, low contrast lesion
detectabilityviii, and
- 14% improvement feature quantification accuracyii.
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 enhancementix.
GE HealthCare’s deep-learning-enabled software is
revolutionizing image acquisition and reconstruction in MR, CT,
X-ray and now PET/CT, empowering clinicians and helping improve
patient outcomes.
For more information about GE HealthCare’s Precision DL, Omni
Legend PET/CT, or Effortless Workflow DL, please visit
gehealthcare.com.
___________________________________
i Precision DL with Omni Legend 32cm data improves Contrast
Recovery (CR) by 11% on average and Contrast-to-Noise Ratio (CNR)
by average of 23% as compared to non-ToF reconstruction. CR and CNR
demonstrated using clinical data with inserted lesions of known
size, location, and contrast. Using data from Omni Legend 32 cm, CR
and CNR were measured using High Precision DL and QCHD.
ii Precision DL with Omni Legend 32cm improves feature
quantitation accuracy by 14% as compared to Discovery MI with ToF
reconstruction, at comparable noise level. Quantitation accuracy
demonstrated using clinical data with inserted lesions of known
size, location, and contrast (ground truth). Feature SUVmean from
Omni Legend 32 cm with High Precision DL compared to SUVmean from
Discovery MI 25 cm with QCFX.
iii Based on orders data of GE HealthCare PET/CT systems since
2010.
iv Omni Legend 32 cm has up to 2.2 increase in system
sensitivity as compared to Discovery MI 25 cm. Measurement follows
NEMA NU 2-2018.
v Up to 53% reduction of PET scan time on Omni Legend 32 cm
compared to Discovery MI 25 cm, as demonstrated in phantom
testing.
vi Omni Legend 32 cm increases small lesion detectability 16% on
average and up to 20%, as compared to Discovery MI 25 cm with
matched scan time/injected dose, as demonstrated in phantom testing
using a model observer with 4 mm lesions; average of different
reconstruction methods.
vii Not a consultant for GEHC: The statements by GE’s customers
described here are based on their own opinions and on results that
were achieved in the customer’s unique setting. Since there is no
“typical” hospital and many variables exist, i.e. hospital size,
case mix, etc. there can be no guarantee that other customers will
achieve the same results.
viii Omni Legend 32cm as compared to Discovery MI 25cm, at
matched scan time and injected dose. Detectability using clinical
data with an inserted 8 mm diameter liver lesion of known location
and 2:1 contrast using a CHO model observer, comparing SNR from
Omni Legend 32 cm with QCHD and Precision DL to SNR from Discovery™
MI 25 cm with QCFX.
ix Mehranian, A., Wollenweber, S.D., Walker, M.D. et al. Deep
learning–based time-of-flight (ToF) image enhancement of non-ToF
PET scans. Eur J of Nucl Med Mol Imaging 49, 3740–3749 (2022).
https://doi.org/10.1007/s00259-022-05824-7
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version on businesswire.com: https://www.businesswire.com/news/home/20230530005497/en/
Margaret Steinhafel M +1 608 381 8829
margaret.steinhafel@ge.com
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