Swoop Portable MR Imaging System
Hyperfine, Inc. • Radiology • 07/28/2022
Device Purpose
The Swoop® Portable MR Imaging System™ provides bedside magnetic resonance imaging of the head for diagnostic support in situations where conventional MRI is impractical, serving patients from 1.6kg upwards. It operates using a 63.3 mT magnet and leverages deep learning algorithms to enhance T1W, T2W, and FLAIR image quality by reducing noise and blurring, with recent updates to hardware components.
Summary
The Swoop® Portable MR Imaging System™ is a point-of-care magnetic resonance imaging device designed to produce images of the internal structure of the head at the patient's bedside. Its primary purpose is to provide diagnostic information when a full, conventional diagnostic examination is not clinically practical, catering to both adult and pediatric patients with a minimum weight of 1.6kg. The system is operated via a commercial off-the-shelf tablet, which facilitates patient data access, examination setup and execution, image quality control viewing, and cloud storage interactions, enabling the generation of MRI datasets with a broad range of contrasts. The system's scientific methodology incorporates a low magnetic field strength of 63.3 mT (permanent magnet) and utilizes a deep learning algorithm for image reconstruction. This approach is specifically designed to improve the image quality of T1-weighted (T1W), T2-weighted (T2W), and Fluid-Attenuated Inversion Recovery (FLAIR) sequences by significantly reducing image noise and blurring. The device has undergone modifications to its hardware components and has an updated minimum patient weight limit. Its capabilities are supported by performance evaluations demonstrating adherence to image quality criteria (e.g., NEMA, ACR guidelines) and safety standards (electrical, EMC, SAR), with image post-processing including advanced denoising, orientation transforms, geometric distortion correction, and receive coil intensity correction.
AI Regulatory Concerns
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Device Metadata
Relevant Clinical Questions
- Q1. How does the performance of the deep learning algorithm in the Swoop® system for noise and blur reduction in T1W, T2W, and FLAIR sequences specifically compare, quantitatively (e.g., SNR, CNR), to traditional reconstruction methods typically used in low-field MRI?; What specific hardware modifications were implemented in this version of the Swoop® system, and what was their direct impact on imaging performance parameters or patient workflow, aside from accommodating a lower minimum patient weight?; What are the practical implications and diagnostic benefits of extending the system's use to patients weighing as little as 1.6kg, particularly regarding image quality and resolution for neonatal cranial structures?; Considering the 63.3 mT field strength, what are the achievable acquisition times for the core T1W, T2W, and FLAIR sequences, and how does this impact its utility in time-sensitive clinical scenarios like emergency rooms?; Beyond advanced denoising, what specific techniques are employed for geometric distortion correction and receive coil intensity correction, and how effective are they in ensuring the anatomical accuracy of images from this portable, low-field system?
Risk Classification
ESL AnalysisFDA Class II via 510(k) pathway; IEC 62304 Class B (software safety classification); Authorized before §524B enforcement
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Powered by SBOMator™Based on AI-generated device summaries, not the full FDA submission. Items not detected here may still be present in the complete submission package.
Supply Chain BOM Evidence
Based on the CycloneDX / OWASP ECMA-424 xBOM ecosystem. Detection is from the AI-generated summary.
Analysis:
• Analysis is based on the AI-generated device summary, not the full FDA submission package. Click "Analyze Full FDA PDF" below to scan the actual submission document.
• Evidence of ML-BOM, SaaSBOM, HBOM detected in AI-generated device summary.
• This device was authorized before §524B took effect. Cybersecurity evidence requirements were less prescriptive at the time.
• Postmarket cybersecurity monitoring plan not clearly documented in AI-generated device summary.
• Not detected in AI-generated device summary: SBOM, CBOM, VDR/VEX, OBOM. These may be present in the full FDA submission.
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