Clinical Evidence
SwiftMR™ 핵심 알고리즘에
대한 소개
에어스메디컬의 ‘정근우, SwiftMR™ 제품 연구 팀장’이 저술한 “All-in-One Deep Learning Framework for MR Image Reconstruction” 논문은 MRI 기술의 획기적인 혁신을 보여줍니다. 자사의 딥러닝 알고리즘은, 기존 모델들과는 달리, 모든 측면에서의 영상 품질 향상을 한번에 달성합니다. 즉, 다차원적인 영상 품질 향상을 통해 MR 이미지 재구성 과정을 간소화하고 전통적인 방법을 능가하는 혁신적인 성능을 제공합니다.
Clinical Implications and Use Cases of SwiftMR™
Clinical White Paper Clinical Implications and Use Cases of SwiftMR™ Our summary 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, scanner vendors, and field strengths. Eighteen board-certified radiologists from six subspecialties assessed these exams for anatomical and lesion clarity, as well as image quality factors like signal-to-noise ratio, spatial resolution, and contrast. In 93.7% of cases, the SwiftMR™-processed images demonstrated better subjective image quality, despite reduced scan times. Why it matters MRI plays a pivotal role in modern medicine, so any
Demonstrating the Economic Value of SwiftMR™
Economic White Paper Demonstrating the Economic Value of SwiftMR™ Our summary 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™ could generate an additional $271,790 in revenue over one year, achieving a 453% return on investment (ROI) with a payback period of 3 months. This financial gain is primarily due to SwiftMR™’s ability to reduce scan times, thereby increasing patient throughput. In various scenarios analyzed through sensitivity analyses, SwiftMR™ delivered an ROI of over 300%, suggesting potential economic value across different operational conditions.
SwiftMR Improves Image Quality and Diagnostic Performance of 4D Time-Resolved, Contrast-Enhanced MRA for Ischemic Stroke Imaging
Peer-Reviewed Research 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 Our summary This study investigated the improvement of image quality and diagnostic performance utilizing SwiftMR for time-resolved, contrast-enhanced magnetic resonance angiography (CE-MRA) in fast stroke imaging. Specifically, relatively low signal-to-noise ratio and spatial resolution of time-resolved angiography with interleaved stochastic trajectories (4D-TWIST-MRA) was explored. CE-MRA images from 520 patients were processed with SwiftMR, and were evaluated by two board-certified radiologists for overall image quality, aneurysm size measurement, detection accuracy, and diagnostic confidence in patients suspected
Introduction to the Core Algorithm of SwiftMR™
Technical Paper All-in-One Deep Learning Framework for MR Image Reconstruction Key Innovation The article, “All-in-One Deep Learning Framework for MR Image Reconstruction,” highlights a groundbreaking innovation in MRI technology. This deep learning framework is distinguished from previously developed models by integrating all essential functions into a single, comprehensive solution. By doing so, it streamlines the process of MR image reconstruction, offering an advanced approach that surpasses traditional methods. Why this matters The all-in-one deep learning framework is designed to enhance image quality in a multi-dimensional manner, addressing various aspects of k-space sampling. This framework ensures broad compatibility across different vendors,
Optimizing SwiftMR™ Protocols
Technical White Paper Optimizing SwiftMR Protocols for Diverse Applications About The whitepaper by Geunu Jeong, MD, Head of SwiftMR Research at AIRS Medical Inc., introduces SwiftMR™, a deep learning-based technology designed to enhance MRI quality by improving the signal-to-noise ratio (SNR) and spatial resolution without increasing scan times or introducing artifacts. This comprehensive whitepaper primarily focuses on developing customized protocols for SwiftMR to meet diverse user requirements. It provides detailed guidance on adjusting sequence parameters to tailor the technology to various clinical needs. The main objective is to demonstrate how strategic optimization of imaging protocols can maximize the benefits of
Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging
Peer-Reviewed Article Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging Our Summary This review paper explores existing literature on the diverse applications of deep learning reconstruction (DLR) techniques for fast MR neuroimaging. It provides a comprehensive overview of how DLR is being utilized to reduce scan times while maintaining or even enhancing image quality. The authors delve into various studies and technological advancements, presenting a detailed analysis of the progress and current state of DLR in the realm of neuroimaging. Why this matters Both vendor-specific and vendor-agnostic technologies are being widely used in a variety of clinical neuroimaging