(201 days)
This device is indicated to acquire and display cross sectional volumes of the whole the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc. The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software, of the whole organ by a trained and qualified physician.
FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.
AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, brain, extremities, head, and inner ear applications.
The spectral imaging system allows the system to acquire two nearly simultaneous CT images of an anatomical location using distinct tube voltages and/or tube currents by rapid KV switching. The X-ray dose will be the sum of the dose at each respective tube voltage and current in a rotation.
Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in X-ray attenuation between these distinct energies. When used by a qualified physician, a potential application is to determine the course of treatment.
PIQE is a Deep Learning Reconstruction method designed to enhance spatial resolution. By incorporating noise reduction into the Deep Convolutional Network (DCNN), it is possible to achieve both spatial resolution improvement and noise reduction for cardiac, abdomen, and pelvis applications, in comparison to FBP and hybrid iterative reconstruction.
Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System is a whole body multi-sice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. 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.
Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System is equipped with PIQE, a deep learning reconstruction technology designed to fully utilize the maximum resolution of the detector, intended to improve spatial resolution. Original image data is available to end users when PIQE images are used for diagnosis.
The provided text describes the regulatory clearance of a medical device, the Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System, and its performance evaluation. However, it does not explicitly detail a separate "acceptance criteria" table with specific thresholds or the complete results of a study designed solely to prove the device meets these criteria in a quantitative, acceptance-testing style format.
Instead, the document details "Performance Testing - Bench" and "Performance Testing - Clinical Images" that demonstrate the device's substantial equivalence to a predicate device and its overall image quality.
Here's an attempt to structure the information based on your request, inferring acceptance implied by demonstrating equivalence and diagnostic quality:
Acceptance Criteria and Performance Study for Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System
The acceptance criteria for the Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System are implicitly derived from its claim of substantial equivalence to the predicate device (Aquilion ONE (TSX-306A/3) V10.12 with Spectral Imaging System) and its ability to produce diagnostic quality images. The study supporting this involved a combination of phantom-based bench testing and clinical image reviews.
1. Table of Implied Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| General Image Quality | The subject device demonstrated equivalent or improved performance compared to the predicate device regarding: |
- Contrast-to-Noise Ratios
- CT Number Accuracy
- Uniformity
- Slice Sensitivity Profile
- Modulation Transfer Function
- Standard Deviation of Noise Power Spectra
- Low Contrast Detectability. |
| PIQE Performance | PIQE (Deep Learning Reconstruction method) is designed to enhance spatial resolution by incorporating noise reduction into the Deep Convolutional Network (DCNN). This achieves both spatial resolution improvement and noise reduction for cardiac, abdomen, and pelvis applications, in comparison to FBP (Filtered Back Projection) and hybrid iterative reconstruction. Its performance was evaluated as part of the overall "General Image Quality" assessment, contributing to the "equivalent or improved" statement. |
| SilverBeam Dose Reduction | A phantom study confirmed that DR-mode (with SilverBeam Filter) resulted in dose reduction in Head/Body modes compared to normal scan mode. |
| Low Contrast Detectability| A phantom study supported the following claims: - 2 mm (0.3% contrast) detectability at 15.3 mGy CTDIvol using AIDR3D.
- 2 mm (0.3% contrast) detectability at 14.7 mGy CTDIvol using AiCE.
- 3 mm (0.3% contrast) detectability at 5.7 mGy CTDIvol using AiCE. |
| Diagnostic Quality | Representative body, cardiac, chest, head, and extremity clinical images reviewed by American Board-Certified Radiologists confirmed that the reconstructed images using the subject device were of diagnostic quality. |
2. Sample Size Used for the Test Set and Data Provenance
- Bench Testing (Phantom Studies): The document does not specify a numerical sample size (e.g., number of phantom scans) but indicates that various phantoms were used for comprehensive image quality assessments, low contrast detectability, and dose reduction studies. The provenance type is retrospective, as it involves controlled phantom experiments. The geographical provenance is not specified but implicitly linked to Canon Medical Systems' testing facilities.
- Clinical Images: The document mentions "Representative body, cardiac, chest, head, and extremity diagnostic images." The exact number of clinical images or cases used for the review is not specified. The provenance is retrospective, as these were "obtained" and then reviewed. The country of origin for the clinical data is not explicitly stated.
3. Number of Experts Used and Their Qualifications
For the clinical image review:
- Number of Experts: The document states "reviewed by American Board-Certified Radiologists." The exact number of radiologists involved is not specified.
- Qualifications: "American Board-Certified Radiologists." No further details on years of experience are provided.
4. Adjudication Method
The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the clinical image review to establish ground truth or determine diagnostic quality. It only states that images were "reviewed" and "confirmed" to be of diagnostic quality.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
An MRMC study was not explicitly detailed in the provided text as a an evaluation comparing human readers with AI assistance versus human readers without AI assistance. The clinical image review confirmed diagnostic quality of images produced by the device, but not the effect of AI assistance on human readers.
6. Standalone (Algorithm Only) Performance
The bench testing on phantoms and the evaluation of reconstruction algorithms (PIQE, AiCE, AIDR 3D) are indicative of standalone (algorithm only) performance regarding technical image quality metrics (CNR, MTF, noise, resolution, low contrast detectability). These tests were performed without human interaction for interpretation within the evaluation itself.
7. Type of Ground Truth Used
- Bench Testing (Phantom Studies): The ground truth for metrics like CT Number Accuracy, Uniformity, and specific low contrast targets is inherent to the known physical properties of the phantoms used. This is a form of phantom-derived ground truth.
- Clinical Images: The ground truth for "diagnostic quality" of clinical images was established by the consensus/judgment of American Board-Certified Radiologists. This is a form of expert consensus ground truth.
8. Sample Size for the Training Set
The document describes PIQE and AiCE as Deep Learning Reconstruction methods. Therefore, they would have been trained on large datasets. However, the sample size for the training set is not provided in this document.
9. How the Ground Truth for the Training Set Was Established
For the deep learning algorithms (PIQE and AiCE) used in the device, the method for establishing the ground truth for their training set is not described in the provided text. Typically, this would involve extensive curated datasets with expert annotations or high-quality reference images.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.