Photonova Spectra, Photonova Spectra Select

The Photonova Spectra and Photonova Spectra Select are silicon-based spectral photon counting detector CT scanners that produce cross-sectional images of the body. They are indicated for head, whole body, cardiac, and vascular CT applications in patients of all ages, including lung cancer screening when meeting established criteria. The system acquires multi-energy data in every scan and generates high-resolution monochromatic images and material density maps.

Key differences from the predicate Revolution Apex include a new Deep Silicon (dSi) photon counting detector with direct X-ray photon-to-electrical signal conversion (192 rows, 0.2 mm pixel pitch in XY, 0.4 mm in Z) versus the predicate's energy integrating detector (256 rows, 0.625 mm). The Photonova uses 8 discrete energy bins versus predicate's single energy mode, operates at 120 kVp only, includes an extra-small focal spot option, and features TrueFidelity DL for PCCT reconstruction versus predicate's ASiR-V, DLIR, and GSI-DLIR algorithms.

Testing complied with AAMI/ANSI ES 60601-1, IEC 60601-1 Ed. 3.2 and associated collateral and particular standards, 21 CFR Subchapter J, and NEMA standards XR 25 and XR 28. Non-clinical testing included IQ and dose evaluation using standard IQ, QA, ACR and anthropomorphic pediatric phantoms, low contrast detectability studies with model observer approach, and elements of performance testing per IEC 61223-3-5 ed 2.

Photonova Spectra is substantially equivalent to Revolution Apex because it has identical intended use, same patient population, same contraindications, and comparable scan modes and imaging matrices. Design verification, validation, and risk management processes identified no new safety or effectiveness questions. Clinical reader studies by US board-certified radiologists demonstrated that the system's deep learning reconstruction preserves diagnostic interpretability with no added, removed, or reduced diagnostic information compared to FBP-based reconstruction.