Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System
K232835 · Canon Medical Systems Corporation · JAK · Apr 2, 2024 · Radiology
Device Facts
Record ID
K232835
Device Name
Aquilion ONE (TSX-308A/3) V1.4 with PIQE Reconstruction System
Applicant
Canon Medical Systems Corporation
Product Code
JAK · Radiology
Decision Date
Apr 2, 2024
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.1750
Device Class
Class 2
Attributes
AI/ML
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Image Noise Reduction
Deep Convolutional Network
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Spatial Resolution Enhancement
Deep Convolutional Network
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Indications for Use
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.
Device Story
Aquilion ONE (TSX-308A/3) V1.4 is a whole-body multi-slice helical CT scanner comprising a gantry, couch, and console. It acquires cross-sectional volume data via X-ray attenuation, processed by reconstruction algorithms (FIRST, AiCE, PIQE) to produce diagnostic images. Operated by trained physicians in clinical settings, the device supports specialized studies of whole organs. PIQE, a Deep Learning Reconstruction (DCNN) method, enhances spatial resolution and reduces noise for cardiac, abdomen, and pelvis applications. Spectral imaging enables material composition analysis via rapid KV switching. Output images assist physicians in clinical decision-making and treatment planning. Benefits include improved image quality, reduced noise, and potential dose reduction through iterative and deep learning reconstruction techniques.
Clinical Evidence
Bench testing only. Phantom studies evaluated image quality (Contrast-to-Noise Ratio, CT Number Accuracy, Uniformity, MTF, Low Contrast Detectability) and dose reduction (SilverBeam filter). Low contrast detectability validated at 0.3%/2mm and 0.3%/3mm. Clinical image quality confirmed by American Board-Certified Radiologists for body, cardiac, chest, head, and extremity applications.
Technological Characteristics
Multi-slice helical CT scanner; 800mm gantry aperture; spectral imaging via rapid KV switching; reconstruction algorithms include FIRST (iterative), AiCE (DCNN), and PIQE (DCNN). Conforms to IEC 60601-1, IEC 60601-2-44, NEMA XR-26, NEMA XR-29. Connectivity includes DICOM. Software includes DCNN-based reconstruction.
Indications for Use
Indicated for patients requiring cross-sectional volume imaging of the whole body, including head and whole organs (e.g., brain, heart, pancreas). Used by trained physicians for specialized diagnostic studies.
Regulatory Classification
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.
Predicate Devices
Aquilion ONE (TSX-306A/3) V10.12 with Spectral Imaging System (K213504)
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1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
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Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
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Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
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Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
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Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
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Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
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The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.