glossary

TrueFidelity DLIR (GE Deep-Learning CT Reconstruction)

GE HealthCare's deep-learning image reconstruction stack for CT — the FDA-cleared (2019) trained-neural-network reconstruction algorithm replacing or supplementing iterative reconstruction (ASiR-V) in routine clinical use. TrueFidelity is offered as a license-tier feature across the current Revolution Apex / Revolution CT / Revolution Ascend platforms and is propagated retroactively to selected legacy systems where compute hardware allows.

The deep-learning-reconstruction (DLIR) category as a whole is one of the principal differentiators in current-generation CT. Each major OEM has a competing implementation: GE TrueFidelity, Canon AiCE, Siemens Quantum Iterative Reconstruction (QIR) + Deep Resolve (Deep Resolve is currently MRI-side; Siemens CT uses ADMIRE / QIR). Philips Precise Image (the analog on the Incisive / Incisive CV platforms). Architecturally these are all neural-network-based reconstructions trained on paired low-dose / high-dose data — the differentiation is in training-data scale, network architecture, and validation methodology rather than fundamental physics.

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