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This device is indicated to acquire and display cross-sectional volumes of the whole body (abdomen, pelvis, chest, extremities, and head) of adult patients.

TSX-501R has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

CT Scanner TSX-501R/1 V11.1 employs a next-generation X-ray detector unit (photon counting detector unit), which allows images to be obtained based on X-rays with different energy levels. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided 510(k) clearance letter.

It's important to note that a 510(k) summary typically doesn't provide the full, granular detail of a clinical study report. The information often indicates what was tested and the conclusion, but less about the specific methodologies, statistical thresholds for acceptance, or detailed performance metrics.

Understanding the Context: 510(k) Clearance

This document is a 510(k) clearance letter for a new CT scanner (CT Scanner TSX-501R/1 V11.1). The primary goal of a 510(k) submission is to demonstrate "substantial equivalence" to a legally marketed predicate device, not necessarily to prove absolute safety and effectiveness through extensive new clinical trials (which is more typical for a PMA - Premarket Approval). Therefore, the "acceptance criteria" and "study" described here are geared towards demonstrating this equivalence.

The core technology difference is the shift from an Energy Integrating Detector (EID) in the predicate to a Photon Counting Detector in the new device. The testing focuses on ensuring this new detector performs equivalently or better in terms of image quality and safety.

Acceptance Criteria and Reported Device Performance

Given the nature of a 510(k) for a CT scanner's hardware update (new detector), the "acceptance criteria" are implicitly tied to demonstrating equivalent or improved image quality and safety compared to the predicate device. The performance is assessed through bench testing with phantoms and review of clinical images.

Table of Acceptance Criteria and Reported Device Performance:

• Contrast-to-Noise Ratios (CNR)
• CT Number Accuracy
• Uniformity
• Pulse Pile Up
• Slice Sensitivity Profile (SSPz)
• Modulation Transfer Function (MTF)
• Standard Deviation of Noise and Pulse Pile
• Noise Power Spectra (NPS)
• Low Contrast Detectability (LCD) | "It was concluded that the subject device demonstrated equivalent or improved performance, compared to the predicate device, as demonstrated by the results of the above testing." |
  | Fundamental Properties of the Photon Counting Detector (using phantoms) | Effectiveness and equivalent performance compared to expected or predicate device for:
• Detector resolution and noise properties (MTF and DQE)
• Artifact analysis
• Count rate vs. current curve
• Pulse pileup or maximum count rate
• Lag/residual signal levels
• Stability over time
• Bad pixel map | "These bench studies utilized phantom data and achieved results demonstrative of equivalent performance in comparison with the predicate device." |
  | Clinical Image Quality (Human Review) | Reconstructed images using the subject device are of diagnostic quality. | "It was confirmed that the reconstructed images using the subject device were of diagnostic quality." |
  | Safety & Standards Conformance | Conformance to relevant electrical, radiation, software, and cybersecurity standards and regulations. | "This device is in conformance with the applicable parts of the following standards [list provided]... Additionally, this device complies with all applicable requirements of the radiation safety performance standards..." |
  | Risk Analysis & Verification/Validation | Established specifications for the device have been met, and risks are adequately managed. | "Risk analysis and verification/validation activities conducted through bench testing demonstrate that the established specifications for the device have been met." |
  | Software Documentation & Cybersecurity | Adherence to FDA guidance documents for software functions and cybersecurity. | "Software Documentation for a Basic Documentation Level... is included... Cybersecurity documentation... was included..." |

Study Details:

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set (Clinical Images): The specific number of clinical images/cases reviewed is not provided. The text states "Representative chest, abdomen, brain and MSK diagnostic images." This implies a selection of images from various body regions.
   • Data Provenance: The document does not specify the country of origin for the clinical images. It also does not explicitly state whether the data was retrospective or prospective, though for a 510(k) supporting equivalence, retrospective data collection for image review is common.

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

   • Number of Experts: The document states "reviewed by American Board-Certified Radiologists." The specific number is not provided.
   • Qualifications: "American Board-Certified Radiologists." This indicates a high level of qualification and experience in medical imaging interpretation.

3. Adjudication Method for the Test Set:

   • The document does not specify an adjudication method (like 2+1 or 3+1) for the clinical image review. It simply states they were "reviewed by American Board-Certified Radiologists" and "it was confirmed that the reconstructed images using the subject device were of diagnostic quality." This implies a consensus or individual assessment of diagnostic quality, but the process is not detailed.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • Was it done? No, a formal MRMC comparative effectiveness study demonstrating how human readers improve with AI vs. without AI assistance was not conducted or described for this submission. This makes sense as the device is a CT scanner itself, not an AI-assisted diagnostic software. The clinical image review was to confirm diagnostic quality of the images produced by the new scanner, not to assess reader performance with or without an AI helper.

5. Standalone (Algorithm Only) Performance:

   • Was it done? Yes, in a sense. The "bench testing" focusing on Objective Image Quality Evaluations and Fundamental Properties of the Photon Counting Detector can be considered "standalone" performance for the device's imaging capabilities. These tests used phantoms and measured technical specifications without human interpretation as the primary endpoint. The device's stated function is to acquire and display images, so its "standalone" performance is its ability to produce good images.

6. Type of Ground Truth Used:

   • Bench Testing (Phantoms): The ground truth is the physical properties of the phantoms and the expected performance characteristics based on established physics and engineering principles (e.g., a known object size for MTF, known density for CT number accuracy).
   • Clinical Images: The ground truth for confirming "diagnostic quality" is expert consensus/opinion from American Board-Certified Radiologists. It's an assessment of whether the image contains sufficient information and clarity for diagnostic purposes, not necessarily a comparison to a biopsy or long-term outcome.

7. Sample Size for the Training Set:

   • The document does not mention a training set in the context of typical AI/machine learning development. This device is a CT scanner hardware system, not an AI diagnostic algorithm that learns from training data. Therefore, the concept of a "training set" as it relates to AI models is not applicable here.

8. How Ground Truth for the Training Set Was Established:

   • As stated above, the concept of a "training set" as applied to AI/machine learning development does not directly apply to this CT scanner hardware submission. The device's performance is based on its physical design and engineering, not on learning from a large dataset.

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