DeepFoqus (DeepFoqus-Accelerate)
Overview
DeepFoqus-Accelerate, developed by Foqus Technologies, is an AI-powered MRI reconstruction software designed to significantly reduce MRI scan times while maintaining or enhancing image quality. It leverages proprietary AI technology and quantum algorithms to process accelerated MRI scans, enabling healthcare providers to increase scanner throughput, reduce patient wait times, and improve access to high-quality diagnostic care. The software integrates with existing MRI systems and workflows, specifically supporting non-contrast enhanced brain MRI images (T1, T2, and FLAIR sequences) acquired with 1.5T or 3T Siemens and GE scanners. It is a stand-alone software solution intended for use on adult scans only. DeepFoqus-Accelerate aims to revolutionize the MRI experience by making scans faster, more efficient, and more accessible without requiring costly hardware upgrades.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered MRI reconstruction
- Quantum algorithms for signal enhancement
- Reduces MRI scan times by up to 75% (or 4x acceleration)
- Maintains or enhances image quality
- Compatible with existing 1.5T and 3T Siemens and GE MRI scanners
- Supports T1, T2, and FLAIR sequences for brain MRI
- Accepts HDF5 and DICOM input files
- Produces clinical-quality scans from undersampled data
- DICOM output for enhanced images
- Stand-alone software solution
Use Cases
- Accelerating MRI scan procedures
- Increasing MRI scanner throughput
- Reducing patient wait times for MRI appointments
- Improving patient access to MRI diagnostics
- Enhancing diagnostic efficiency in radiology departments
- Facilitating early detection of diseases through higher-quality imaging
What Physicians Need to Know
DeepFoqus-Accelerate can significantly reduce MRI scan times for brain imaging, potentially improving patient comfort and throughput. The software is intended for adult, non-contrast enhanced brain MRI acquired on compatible 1.5T or 3T Siemens and GE scanners using Sagittal, Axial, or Coronal T1, T2, or FLAIR sequences. Quantitative and qualitative evaluations suggest that the diagnostic image quality is preserved despite the acceleration. Consider its use for optimizing MRI scheduling and potentially reducing motion artifacts due to shorter scan durations.
DeepFoqus-Accelerate is a standalone software solution that integrates with existing MRI systems and workflows. It accepts HDF5 and DICOM files and converts final volumes to DICOM format.
Details
| Category | Radiology & Imaging AI |
| Pricing | Contact vendor for details |
| Deployment | Stand-alone software solution |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status | Cleared AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Neurology, Neurosurgery, Radiology |
What the Web Says
DeepFoqus-Accelerate is an AI-enabled software designed to significantly speed up MRI scans, potentially reducing scan times by up to 75% while maintaining diagnostic image quality. It has received FDA 510(k) clearance and Health Canada approval, and studies involving neuroradiologists and MRI technologists indicate that its AI-reconstructed images are clinically equivalent to standard-of-care images.
Overall: PositiveStrengths
- Enables up to fourfold accelerated brain MRI acquisition, reducing scan time by 75%.
- Preserves diagnostic image quality, with AI-reconstructed images being clinically equivalent to fully sampled standards.
- Proprietary network design and k-space–based reconstruction maintain image details and anatomical fidelity.
- Supports reliable diagnostic interpretation across various clinical scenarios.
- Can be deployed on existing MRI scanners, offering a less expensive solution than new MRI machines.
- Potential to improve patient throughput, comfort, and MRI suite efficiency.
Limitations
- Infrequent, minor intensity nonuniformity or occasional artifacts have been noted, though they did not impede diagnostic interpretation.
- Inter-rater agreement among reviewers was slight to moderate in one study.
- A small number of outliers showed reduced image similarity scores, aligning with reader-noted artifacts.
Based on reviews from: arxiv.org, financialpost.com, semanticscholar.org, headtopics.com
Last updated: 2026-10-30
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