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Intelligently Efficient | MR & CT with Deep Learning Reconstruction


GE Healthcare’s deep learning tools like TrueFidelity™ and AIR™ Recon DL are designed to work for you. These intelligent solutions help save you time, increase your productivity, reduce eye strain, and enable you to achieve higher patient throughput without sacrificing image quality.

AIR™ Recon DL, an innovative new reconstruction technology from GE Healthcare based on deep learning, offers a fundamental shift in the balance between image quality and scan time, resulting in TrueFidelity™ MR images that elevate the science of image reconstruction for clinical excellence without conventional compromises. TrueFidelity™ is GE Healthcare’s proprietary DLIR training that reflects an unmatched understanding of what successful DLIR requires in body imaging and is the gold standard for image quality. The artificial intelligence that powers this Deep Learning Image Reconstruction allows you to achieve never-before-possible clarity at low dose.





The Latest in MR AI Recon & CT Detector Development with Siemens Healthineers


Please join the team from Siemens as we share new innovations in MR & CT.


Acquiring sharper images, faster with Deep Resolve via the AI-based reconstruction is the next chapter in the digital future of MRI. The convolutional neural network at the core of Deep Resolve was trained by comparing a vast amount of high and low resolution data. Within the reconstruction pipeline, Deep Resolve generates a high-resolution image from low-resolution input. By including the raw data in the reconstruction process, clinically robust results are achieved, with sharper images and higher SNR across the MRI exam.


For the last 20 years, CT development has been driven largely by the change from xenon gas to solid-state scintillator detectors.  This has enabled innovations such as spiral CT, multi-slice and dual source CT.  The next era of CT will be defined by a new detector innovation, photon-counting.  Join us to see how this exciting technology will shape the future of CT.