SwiftMR

AIRS Medical

SwiftMR aims to accelerate MRI scans by up to 50% by enhancing low-quality initial outputs from accelerated scans. The deep-learning model improves SNR and resolution of MRI inputs, irrespective of body part or vendor type.
Operating in DICOM format, SwiftMR receives assigned scans from connected MRIs and dispatches results to PACS. Installable via cloud or on-premise.
*Product coverage and supported types may vary by region depending on regulatory status.

Information source: Vendor
Last updated: September 4, 2024

General Information

General
Product name	SwiftMR
Company	AIRS Medical
Subspeciality	Neuro, Cardiac, MSK, Chest, Abdomen, Breast
Modality	MR
Disease targeted	Not applicable
Key-features	MRI scan acceleration, MRI image enhancement
Suggested use

Technical Specifications

Data characteristics
Population	All population
Input	2D, 3D, T1WI, T2WI, PDWI, FLAIR, T2*WI, contrast, MR Angiography, MR Arthrography, MR Myelography
Input format	DICOM
Output	Enhanced MR images
Output format	DICOM
Technology
Integration	Stand-alone third party application
Deployment	Locally on dedicated hardware, Cloud-based, Hybrid solution
Trigger for analysis	Automatically, right after the image acquisition
Processing time	1 - 10 minutes

Regulatory

Certification
CE	Certified, Class IIa , MDR
FDA	510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)	SwiftMR is a stand-alone software solution intended to be used for acceptance, enhancement and transfer of brain, spine, knee, ankle, shoulder, and hip MR images in DICOM format. It can be used for noise reduction and increasing image sharpness for MR images. SwiftMR is not intended for use on mobile devices.

Market

Market presence
On market since	01-2022
Distribution channels
Countries present (clinical, non-research use)	15
Paying clinical customers (institutes)	175
Research/test users (institutes)	83
Pricing
Pricing model	Pay-per-use, Subscription, One-time license fee
Based on	Number of installations, Number of analyses

Evidence

Evidence
Peer reviewed papers on performance	Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques (read) Deep Learning-Based High-Resolution Magnetic Resonance Angiography (MRA) Generation Model for 4D Time-Resolved Angiography with Interleaved Stochastic Trajectories (TWIST) MRA in Fast Stroke Imaging (read) Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI (read) Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)–based reconstruction: prospective, multi-reader, multi-vendor study (read)
Non-peer reviewed papers on performance
Other relevant papers