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The Photonova Spectra, Photonova Spectra Select system is a silicon-based spectral photon counting detector X-ray Computed Tomography scanner.

The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission projection data from the same axial plane taken at different angles.

The system acquires multi-energy data in every scan and natively generates high resolution monochromatic images and material density maps to facilitate visualizing and analyzing information about anatomical and pathological structures.

The system is indicated for head, whole body, cardiac, and vascular CT applications. The system is indicated for patients of all ages. The images can be post-processed to produce additional imaging planes or analysis results.

The system is indicated for lung cancer screening for patients meeting the established inclusion criteria of programs/protocols that have been published by either a governmental body or professional medical society.*

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011;365:395-409) and subsequent literature, for further information.

Photonova Spectra is the next iteration of the predicate, the Revolution Apex platform (K213715), introducing a new Deep Silicon (dSi) photon counting detector for CT imaging. Photonova Spectra aims to realize an improvement in both spatial resolution and spectral imaging performance relative to traditional Energy Integrating Detector (EID) systems for diagnostic CT. With photon-counting detectors that can better discriminate energies, spectral CT imaging can natively provide valuable information about tissue composition and material density without the need for active filtration or kVp modulation by performing material decomposition directly from native multi-energy data.

The Photonova Spectra system is an ultra-premium multi-slice CT scanning system comprised of a gantry, a detector, an x-ray tube, a power distribution unit (PDU), a table, a system cabinet, a scanner desktop computer and user interface, and associated accessories. It is designed as a volumetric CT scanner to provide advanced imaging capability for a range of clinical applications.

Compared to the predicate Revolution Apex, the key differences of the Photonova Spectra System consist of a Deep Silicon (dSi) X-ray detector capable of directly converting X-ray photons to electrical signals, advanced detector data acquisition hardware for managing and processing of large volumes of data, advanced computer hardware and an enhanced image chain for generating High Definition (HD) Spectral and Ultra High Definition (UHD) image series.

The Photonova Spectra image chain is developed to calibrate, pre-process, reconstruct, and post-process images for use in medical imaging applications. Customized for photon counting detection physics and capability, Photonova Spectra does not require user to choose between single kV and dual energy acquisition modes. With Photonova Spectra, all acquisitions are spectral with 8 energy bins over the full high-resolution detector, and the data is stored real-time on the rotating side as the acquisition completes over the full scan sequence.

The system will be offered with either an 80 mm dSi detector and 40 mm dSi detector model configurations, commercialized as Photonova Spectra and Photonova Spectra Select, respectively. The detector size is the key differentiator, but all core technology and functionality are identical.

The provided FDA 510(k) clearance letter and summary for the Photonova Spectra CT System do not contain detailed information about specific acceptance criteria for device performance or the full study design typically expected for such information. The document focuses on regulatory compliance, technological characteristics compared to a predicate, and a general overview of verification and validation testing.

However, based on the information provided, we can infer some aspects and present them to the best of our ability, while noting the missing details.

Missing Information:

• Specific quantitative acceptance criteria: The document describes the types of tests performed (e.g., image quality metrics, LCD studies) but does not provide numerical thresholds that the device had to meet.
• Specific quantitative reported device performance: While it states "substantial equivalence of image quality was demonstrated," it doesn't provide the actual measured values for metrics like CT number accuracy, resolution, or noise texture.
• Detailed sample size for the test set: It mentions a "sample clinical covering a wide range of clinical scenarios" for the reader study but no specific number of cases.
• Data provenance for the test set: The document does not specify the country of origin of the data for the reader study's test set or whether it was retrospective or prospective.
• Detailed qualifications of experts for ground truth: It states "US board-certified Radiologists" but doesn't specify years of experience or subspecialty.
• Adjudication method for the test set.
• Effect size for MRMC study: It implies a reader study was done to compare DL levels, but doesn't quantify improvement with AI assistance.
• Sample size for the training set.
• How ground truth for the training set was established.

Acceptance Criteria and Study for Photonova Spectra CT System

Given the limitations of the provided document, the following is constructed based on the available information and educated inferences regarding CT system clearances.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document states "a reader study of sample clinical covering a wide range of clinical scenarios, including Neuro, Body, and Cardiac/Chest." It also mentions "challenging cases from the above-mentioned reader study."

• Sample Size: Not explicitly stated (e.g., number of cases or images).
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document mentions: "Images were evaluated by US board-certified Radiologists."

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: US board-certified Radiologists. Specific years of experience or subspecialty (e.g., Neuroradiologist, Cardiothoracic Radiologist) are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for the reader study.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Its Effect Size

A reader study was conducted to compare various levels of user-prescribed denoising. It implies a comparative evaluation between the proposed DL reconstruction and FBP-based reconstruction.

• MRMC Study: Yes, a comparative clinical evaluation of challenging cases was performed by "US board certified Radiologists."
• Effect Size: Not quantified. The qualitative finding was: "No reader identified any added, removed, or reduced diagnostic information in any DLIR setting, and all pathologies were consistently visualized across all DL reconstructions." This suggests that the diagnostic interpretability was maintained, implying no negative effect and potential maintenance or improvement in visualization where denoising was effective, though specific metrics of improvement are not provided.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, extensive standalone performance testing was done, referred to as "Image Performance Testing (Verification)" and "Summary of Non-Clinical Testing."

• This included "evaluation of a comprehensive set of image quality metrics" and "acquisitions at varying dose levels and phantom sizes."
• Metrics like "CT number, water accuracy, mean CT number over a range of spectral tasks, in-plane resolution, cross-plane resolution and noise texture (as measured by the noise power spectrum)" were assessed.
• "Low contrast detectability (LCD) studies were conducted incorporating a model observer approach."

7. The Type of Ground Truth Used

Based on the description of the studies:

• For standalone (non-clinical) testing: Phantoms (standard IQ, QA, ACR, anthropomorphic pediatric phantoms) and model observer approaches for objective metrics.
• For clinical (reader) testing: Expert consensus/interpretation by US board-certified Radiologists was used to determine diagnostic utility and whether pathologies were consistently visualized across different reconstructions.

8. The Sample Size for the Training Set

The document states that the "proposed TrueFidelity DL for PCCT is intended for routine clinical use and based on the same framework and training methodology as the reference devices (DLIR and DLIR-GSI)." However, the specific sample size for the training set (e.g., number of images, patients) for the Photonova Spectra's TrueFidelity DL for PCCT is not provided.

9. How the Ground Truth for the Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established for the TrueFidelity DL for PCCT, beyond mentioning it uses the "same framework and training methodology" as previously cleared DLIR products. Typically, for deep learning reconstructions in CT, the "ground truth" during training refers to high-quality, often low-noise or high-dose, reference images from which the algorithm learns to denoise or reconstruct lower-quality/lower-dose inputs. These reference images are usually generated from the CT scanner itself (e.g., by repeating scans at very high doses or using iterative reconstruction techniques to establish a cleaner image for comparison). Specific details are not provided.

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