# K260078: Aquilion ServeSP (TSX-307B) V2.0

- Pathway: 510(k) (Traditional)
- Applicant: Canon Medical Systems Corporation
- Decided: Mar 13, 2026 after 60 days
- Product code: JAK (System, X-Ray, Tomography, Computed)
- Device class: 2
- Regulation: 21 CFR 892.1750
- Recalls of this device: none
- Data: FDA decisions to Oct 25, 2026; recalls to Oct 12, 2026

## Lineage
Longest line of descent: 9 generations back to K093891 (2010). 12 ancestors in all.
No later device cites it as a predicate yet.
0 of 13 devices in its family (counting itself) have a Class I recall.

## Predicates on the family tree (from the FDA summary)
- K233334: Aquilion Serve SP (TSX-307B/1) V1.3, Canon Medical Systems Corporation (2023) Summary page 6.

## Indications for use
This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head. The Aquilion Serve SP 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. AiCE (Advanced Intelligent Clear‐IQ Engine) is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Neural Network methods for abdomen, pelvis, lung, cardiac, extremities, head and inner ear applications. PIQE is a Deep Learning Reconstruction method designed to enhance spatial resolution. By incorporating noise reduction into the Deep Convolutional Neural Network (DCNN), it is possible to achieve both spatial resolution improvement and noise reduction for cardiac, abdomen, pelvis, and lung applications, in comparison to FBP and hybrid iterative reconstruction. CLEAR Motion is a Deep Learning Reconstruction (DLR) method designed to reduce motion artifacts. A Deep Convolutional Neural Network (DCNN) is used to estimate the patient's motion. This information is used in the reconstruction process to obtain lung images with less motion artifacts.

Source page: https://510kreport.com/device/K260078
Methodology: https://510kreport.com/methodology