Vendor source

Reviewed 22 Jul 2026

Completeness Comprehensive (81%)

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.

Supported by 10 papers

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Key Features

  • MRI image enhancement
  • MRI scan acceleration

General Information

Product name

SwiftMR

Modality

MR

Main task

Not specified

Subspeciality

Multiple

Disease targeted

Not applicable

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIa

Verified by Health AI Register

FDA Certification

Pathway:

510(k) cleared

Class:

Class II

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Leveraging a vendor-neutral deep learning reconstruction algorithm to reduce scan time and enhance image quality in T2-weighted liver MRI

Scientific Reports · Jul 2026

Signal-to-noise ratio improvement in postmortem MRI using deep learning reconstruction (SwiftMR)

Forensic Science International · Apr 2026

Examining the Effect of Deep Learning-Based Image Reconstruction on Accelerating Shoulder Magnetic Resonance Imaging (MRI) and Its Impact on Image Quality

Cureus · Oct 2025

A multinational study of deep learning-based image enhancement for multiparametric glioma MRI

Scientific Reports · Sep 2025

Deep learning-based image enhancement for improved black blood imaging in brain metastasis

European Radiology · Aug 2025

Prospective Evaluation of Accelerated Brain MRI Using Deep Learning-Based Reconstruction: Simultaneous Application to 2D Spin-Echo and 3D Gradient-Echo Sequences

Korean Journal of Radiology · Jan 2025

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques

Scientific Reports · Aug 2024

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

Diagnostics · Jun 2024

Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)–based reconstruction: prospective, multi-reader, multi-vendor study

Scientific Reports · Oct 2023

Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI

European Radiology · Jul 2023

Technical Specifications

Patient population age

Not specified

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

Integration

Stand-alone third party application

Deployment

Cloud-based, Hybrid solution, Locally on dedicated hardware

Trigger for analysis

Automatically, right after the image acquisition

Processing time

1 - 10 minutes

Population

All population

Market Presence

On market since

01-2022

AI Platforms

Blackford Analysis, HCKonnect, Incepto, Philips Healthcare

Resellers

Not specified

Countries present

15

Paying clinical customers

175

Research/test users

83

Pricing Information

Pricing model

One-time license fee, Subscription

Based on

Number of analyses, Number of installations

Source: Vendor | First published: Jul 14, 2024 | Last updated: Jul 22, 2026