Clinical Evidence

SwiftMR® improves MR images across four dimensions: reduced noise, sharper resolution, accelerated scan time, and reduced artifacts. View before/after comparisons and peer-reviewed studies from our clinical team.

Denoising

Improve SNR with well-preserved tissue contrast

Super-resolution

Sharper lesion and structural conspicuity

Acceleration

Shorter scan time, less motion artifact

Artifact reduction

Cleaner truncation and susceptibility artifacts

Improved SNR with well-preserved tissue contrast, revealing structures that noise obscures, without changing your scan protocol.

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

SwiftMR provided significant improvement in SNR and spatial resolution in vessel wall imaging, leading to detection of vessel wall lesions and intraplaque hemorrhage compared to conventional image.

Seo et al.. Scientific Reports. Aug 2024.

Improved lesion and structural conspicuity, enabling thin-slice imaging that would otherwise be limited by SNR constraints.

Improving diagnostic performance of MRI for temporal lobe epilepsy with deep learning-based image reconstruction in patients with suspected focal epilepsy

SwiftMR resulted in significantly improved resolution and structural conspicuity in hippocampal imaging compared to routine imaging. Hippocampal striation blurring was suspected on routine 3mm MRI, but became more obvious on 1.5mm MRI + SwiftMR.

Suh et al.. Korean Journal of Radiology. 2024;25(4):374-383.

Shorter scan time means less patient movement, minimizing chances of motion artifacts, directly improving image quality as a secondary benefit.

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

Prospective comparison showing 32.3% average acquisition time saving versus standard protocol, with no degradation in image quality or diagnostic performance.

Yoo et al.. Eur Radiol. 2023;33(12):8656-8668.

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

Prospective, multi-reader, multi-vendor study demonstrating average scan time reduction of 41% using SwiftMR, without degrading image quality or diagnostic performance across readers or scanner platforms.

Lee et al.. Sci Rep. 2023;13:17264. PMID: 37828048.

Artifact Reduction

Improve truncation and susceptibility artifacts, producing cleaner images from the same acquisition without reacquisition or protocol changes.

Susceptibility artifacts reduction

Geometric distortion improvement

Brain DWI b1000 · 2D EPI Diffusion · Same acquisition3.0T Siemens MAGNETOM Skyra

1:30 — Without SwiftMR · 1.5×1.9×3.0 mm

1:30 — With SwiftMR · 1.5×1.9×3.0 mm

Truncation artifact improvement

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Our clinical team can provide in-depth clinical and technical presentations on SwiftMR.

Select publications

This white paper details the reference population and statistical methodology behind SwiftSight Brain volumetric analysis, including a novel shifted-softplus normative model for age- and sex-adjusted assessment. The database draws on 3D T1-weighted MRI across major scanner vendors and field strengths to deliver stable, clinically interpretable percentile and z-score comparisons.

In this white paper, we compared SwiftMR-processed MR exams to the standard of care. We analyzed 184 MR exam pairs from 12 anatomical locations, considering various pathologies.

This study explored the use of SwiftMR for detecting brain metastases. The findings showed significant improvements in both quantitative and qualitative metrics.

SwiftMR, an AI-powered MRI reconstruction software, offers clear economic benefits to imaging centers. In an economic model, we estimated that a typical imaging center using SwiftMR can see meaningful gains.

This study investigated the use of SwiftMR in 4D time-resolved contrast-enhanced MRA, showing measurable gains for ischemic stroke imaging.

“All-in-One Deep Learning Framework for MR Image Reconstruction” highlights a groundbreaking innovation in MRI technology that underpins SwiftMR.

Whitepaper by Geunu Jeong, MD, Head of SwiftMR Research at AIRS Medical. Introduces SwiftMR, a deep learning-based technology designed to enhance MRI quality across diverse applications.

Peer-reviewed review paper exploring existing literature on the diverse applications of deep learning reconstruction (DLR) techniques for fast MR neuroimaging.

This study explores the integration of SwiftMR in enhancing the diagnostic performance of MRI for temporal lobe epilepsy.

This study investigated the application of SwiftMR in enhancing the efficiency and quality of lumbar spine MRI scans.

AI applications can enhance imaging processes, reduce scan times, and improve image quality, increasing efficiency. Review of SwiftMR’s impact on revenue and operations.

This study evaluated the performance of SwiftMR in accelerating knee MRI without degrading image quality or diagnostic performance.

For a full list of publications, contact bd@airsmed.com.