DeepFoqus-Accelerate is a stand-alone software solution intended to be used for acceptance, enhancement, and transfer of brain MRI images in DICOM format. It can be used for reconstruction of non-contrast enhanced MRI images acquired with 1.5T or 3T Siemens and GE scanners using Sagittal, Axial, or Coronal T1, T2, or FLAIR sequences.

DeepFoqus-Accelerate is intended to be used on adult scans only and not intended for use on mobile devices.

Device Description

DeepFoqus-Accelerate is an AI-powered MRI software that accepts up to 4x accelerated scans and reconstructs them to yield acceptable scans when compared to standard unaccelerated scans.

DeepFoqus allows input of HDF5 and DICOM files, acquired with common MRI machines, containing accelerated T1, T2, and FLAIR image sets. Once inputted, DeepFoqus performs a series of reconstruction steps to produce clinical-quality scans from the undersampled data in the phase encoding direction.

The full pipeline involves the following steps:

Ingestion of MRI scans as k-space and DICOM files.
Preprocessing steps to ensure that the input is in a standard format and meets acquisition requirements.
Processing through a pipeline that combines a collection of machine learning and signal processing modules that ensures that an appropriate range of fine-tuned models is applied to reconstruct a final volume.
Post-processing steps which include adjustments for windowing and image size.
Conversion of the final volume to DICOM format.

The software provides a workflow for an MRI/Radiology Technologist to:

Input undersampled data into the software through a user interface
Start a processing pipeline and observe progress
Export reconstructed data for use in downstream applications

AI/ML Overview

The provided text describes the FDA 510(k) clearance for DeepFoqus (DeepFoqus-Accelerate), an AI-powered MRI software. While the document outlines various tests and validations performed, it does not explicitly list quantitative acceptance criteria with corresponding reported device performance values for a clinical performance study in a table format, nor does it provide detailed information on sample sizes, expert qualifications, or adjudication methods for the clinical validation beyond general statements.

However, based on the information provided, here's an attempt to answer the questions, highlighting where specific details are missing and assumptions are made based on typical regulatory submissions:

Acceptance Criteria and Device Performance

The document broadly states that "Software Validation" included "Analytical performance validation" and "Clinical performance validation." For clinical performance, it mentions: "Radiologists reviewed a sample of diverse accelerated scans (including pathological and non-pathological brains) to determine whether the accelerated scans were equivalent to the ground truth."

Without specific numerical acceptance criteria and corresponding performance metrics from the provided document, a table cannot be fully constructed. The document only lists the metrics used for analytical performance.

Placeholder Table (Based on typical expectations for such devices, as specific quantitative criteria are NOT provided in the document):

Performance Metric	Acceptance Criteria (Assumed/Typical)	Reported Device Performance (Not specified in document)
Analytical Performance
SSIM (Structural Similarity Index Measure)	Maintain similarity > X (e.g., 0.95) compared to unaccelerated scans	"demonstrate acceptable performance and equivalence with the predicate device" (No specific value given)
PSNR (Peak Signal to Noise Ratio)	Maintain PSNR > Y (e.g., 30 dB) compared to unaccelerated scans	"demonstrate acceptable performance and equivalence with the predicate device" (No specific value given)
HaarPSI (Haar wavelet-based perceptual similarity index)	Demonstrate perceptual similarity (e.g., 0.9) compared to unaccelerated scans	"demonstrate acceptable performance and equivalence with the predicate device" (No specific value given)
Clinical Performance (Qualitative Radiologist Review)
Equivalence to Ground Truth (Radiologist Assessment)	Accelerated scans are determined to be "equivalent" to ground truth in a majority of cases by expert radiologists.	"Radiologists reviewed...to determine whether the accelerated scans were equivalent to the ground truth." (No specific percentage or rating given)
Non-inferiority to Predicate	Performance outcomes (e.g., image quality, diagnostic utility) are non-inferior to the predicate device.	"performs comparably to the predicate device"

Study Details

2. Sample Size and Data Provenance

Test Set (Clinical Performance Validation): The document states "Radiologists reviewed a sample of diverse accelerated scans (including pathological and non-pathological brains)".
- Sample Size: Not specified.
- Data Provenance: Not explicitly stated (e.g., country of origin). It mentions "diverse" datasets, which could imply multi-center or varied origins, but this is not confirmed.
- Retrospective/Prospective: Not specified. Typically, for such validation, retrospective datasets are used.

3. Number of Experts and Qualifications for Ground Truth

Number of Experts: Not specified. It only states "Radiologists reviewed a sample".
Qualifications of Experts: Not specified beyond "Radiologists". Typical expectations are board-certified radiologists with experience in neuro-imaging.

4. Adjudication Method for the Test Set

Adjudication Method: Not specified. It only states "Radiologists reviewed". Common methods include consensus reading, majority vote (e.g., 2+1, 3+1), or independent readings.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Was an MRMC study done? The document describes a "Clinical performance validation" where "Radiologists reviewed" scans to determine their equivalence to ground truth. It also states that the device "performs comparably to the predicate device."
- It doesn't explicitly state if this was formalized as a multi-reader, multi-case comparative effectiveness study with and without AI assistance to measure human reader improvement. It focuses on the device's output "equivalence" to ground truth and its comparability to the predicate.
- Therefore, the direct effect size of how much human readers improve with AI vs. without AI assistance is not provided or explicitly described as a primary endpoint. The device acts as a reconstruction tool for accelerated scans, aiming to produce clinical-quality images from undersampled data, rather than directly assisting in diagnosis of an already acquired scan.

6. Standalone (Algorithm Only) Performance

Was a standalone study done? Yes, implicitly. The "Analytical performance validation" using metrics like SSIM, PSNR, and HaarPSI directly assesses the algorithm's output against a reference (standard unaccelerated scans). This is algorithm-only performance, as it doesn't involve human interpretation to generate these metrics.
The clinical performance validation also seems to assess the output of the algorithm for "equivalence to ground truth" via radiologist review, which can be considered a standalone assessment of the reconstructed images.

7. Type of Ground Truth Used

Ground Truth Type:
- For analytical performance, the ground truth was "standard unaccelerated scans". This can be considered a reference standard image.
- For clinical performance, "ground truth" was established by comparing the reconstructed accelerated scans to "standard unaccelerated scans" as reviewed by radiologists. This implies the non-accelerated, conventionally acquired MRI served as the clinical reference for quality and diagnostic interpretability. The document mentions "pathological and non-pathological brains", suggesting the ground truth also encompassed known clinical findings.

8. Sample Size for the Training Set

Training Set Sample Size: Not specified.
However, the document states: "Training has been conducted on a range of datasets which include diverse magnet strengths, sequences, manufacturers, and image orientations." This indicates a broad and varied training corpus.

9. How Ground Truth for Training Set was Established

Ground Truth for Training Set: The document states the "overall pipeline has been trained to minimize the distance between reconstructed final images and standard accelerated scans." This implies that pairs of accelerated (undersampled) and "standard unaccelerated" MRI scans were used for training.
- The "standard unaccelerated scans" served as the reference or ground truth for the Deep Learning models to learn to reconstruct the accelerated data. This is a common supervised learning approach in image reconstruction, where the model learns to map an undersampled input to a fully sampled, high-quality output.

Summary

FDA 510(k) Clearance Letter - DeepFoqus (DeepFoqus-Accelerate)

Page 1

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

Doc ID # 04017.07.05

April 4, 2025

Foqus Technologies Inc.
Sadegh Raeisi
Official Correspondent
The Tannery, 151 Charles St W Suite# 199
Kitchener, ON N2G1H6
Canada

Re: K241982
Trade/Device Name: DeepFoqus (DeepFoqus-Accelerate)
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: February 28, 2025
Received: March 3, 2025

Dear Sadegh Raeisi:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

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K241982 - Sadegh Raeisi Page 2

Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-

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K241982 - Sadegh Raeisi Page 3

assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Daniel M. Krainak, PhD
Assistant Director
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

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FORM FDA 3881 (8/23) Page 1 of 1

DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.

510(k) Number (if known): K241982

Device Name: DeepFoqus (DeepFoqus-Accelerate)

Indications for Use (Describe)

DeepFoqus-Accelerate is intended to be used on adult scans only and not intended for use on mobile devices.

Type of Use (Select one or both, as applicable)

☒ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.

DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

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510(k) #: K241982
Prepared on: 2025-04-04

1.0 Contact Details 21 CFR 807.92(a)(1)

Field	Information
Applicant Name	Foqus Technologies Inc.
Applicant Address	The Tannery, 151 Charles St W Suite# 199 Kitchener ON N2G1H6 Canada
Applicant Contact Telephone	647-4254787
Applicant Contact	Mr. Sadegh Raeisi
Applicant Contact Email	sadegh.raeisi@foqus.ca

2.0 Device Name 21 CFR 807.92(a)(2)

Field	Information
Device Trade Name	DeepFoqus (DeepFoqus-Accelerate)
Common Name	Medical image management and processing system
Classification Name	Automated Radiological Image Processing Software
Regulation Number	892.2050
Product Code(s)	QIH

3.0 Legally Marketed Predicate Devices 21 CFR 807.92(a)(3)

Predicate #	Predicate Trade Name (Primary Predicate is listed first)	Product Code
K210999	SwiftMR	LLZ

4.0 Device Description Summary 21 CFR 807.92(a)(4)

DeepFoqus-Accelerate is an AI-powered MRI software that accepts up to 4x accelerated scans and reconstructs them to yield acceptable scans when compared to standard unaccelerated scans.

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DeepFoqus-Accelerate uses AI and machine learning models to reconstruct MRI images from up to 4x accelerated MRI scans. The models combine various signal processing and machine learning techniques including several convolutional neural network (CNN) and U-net architecture models.

The full pipeline involves the following steps:

Ingestion of MRI scans as k-space and DICOM files.
Preprocessing steps to ensure that the input is in a standard format and meets acquisition requirements.
Processing through a pipeline that combines a collection of machine learning and signal processing modules that ensures that an appropriate range of fine-tuned models is applied to reconstruct a final volume.
Post-processing steps which include adjustments for windowing and image size.
Conversion of the final volume to DICOM format.

The overall pipeline has been trained to minimize the distance between reconstructed final images and standard accelerated scans. Training has been conducted on a range of datasets which include diverse magnet strengths, sequences, manufacturers, and image orientations.

The software provides a workflow for an MRI/Radiology Technologist to:

Input undersampled data into the software through a user interface
Start a processing pipeline and observe progress
Export reconstructed data for use in downstream applications

5.0 Intended Use/Indications for Use 21 CFR 807.92(a)(5)

DeepFoqus-Accelerate is intended to be used on adult scans only and not intended for use on mobile devices.

6.0 Indications for Use Comparison 21 CFR 807.92(a)(5)

The indications for use for the predicate device and the subject device are the same.

7.0 Technological Comparison 21 CFR 807.92(a)(6)

DeepFoqus-Accelerate is substantially equivalent to AIRS Medical SwiftMR (K210999).

Both the Subject and Predicate devices are similar in terms of technology and intended use. Both software systems contain deep learning algorithms which produce automatic images quality enhancement. Both devices are only intended to be used for professional purposes and are not intended for use on mobile devices. The workflow between the two systems is the same:

MR images are uploaded into the software

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Page 7

The software uses a deep learning model to enhance the images
The images can be downloaded in DICOM format from the software

Neither software product directly interfaces with the MRI or other data collection equipment.

The primary difference between the Predicate and Subject device is the reconstruction/enhancement algorithm. The Predicate implements an image enhancement algorithm using convolutional neural network-based filtering. The Subject implements an image reconstruction algorithm using ensembles of deep learning models. Ensembling aggregates several model architectures in order to produce a single reconstruction of accelerated image data.

This difference was verified and validated using the following methods:

Performance and comparison evaluations (PSNR, HaarPSI, SSIM)
Phantom Bench testing (geometric accuracy, intensity uniformity, percentage ghosting, signal-to-noise ratio, resolution, and low-contrast detectability)
Artifact assessment (quantitative (SSIM) and qualitative assessment of images which are expected to have artifacts)
Clinician evaluation (reconstruction quality score)

The essential underlying device outputs and use are the same. The intended uses are equivalent. Direct comparison of major functionality was possible and showed largely identical results.

The above comparison to the Predicate device in conjunction with the design verification and validation activities described in this 510(k) submission support substantial equivalence of SwiftMR.

8.0 Non-Clinical and/or Clinical Tests Summary & Conclusions 21 CFR 807.92(b)

Software verification, validation, and standards compliance were all relied on in the determination of substantial equivalence.

Standards Compliance

The subject device is in conformity with the requirements of the following:

ISO 14971:2019
IEC 62366-1:2015+AMD1:2020
IEC 62304:2006/A1:2016
NEMA PS 3.1-3.20 2022d
ISO 15223-1 (2021)
AAMI TIR 57:2016
AAMI TIR 97:2019
AAMI SW96:2023

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Software Verification

The following types of verification were performed, all testing met acceptance criteria.

Software code review
Static code analysis
Unit verification
SOUP verification
Architecture and software requirements reviews
Software functional verification
Penetration testing, secure code review, and dependency vulnerability scanning

Software Validation

The following types of validation were performed, all testing met acceptance criteria.

Analytical performance validation - Three specific metrics were used to demonstrate acceptable performance and equivalence with the predicate device - Structural Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR), Haar wavelet-based perceptual similarity index and (HaarPSI)
Phantom bench testing - Done to ensure that the reconstruction algorithm meets the necessary quality standards according to "Large and Medium Phantom Test Guidance for the ACR MRI Accreditation Program".
Artifact assessment - Conducted to demonstrate the robustness of the AI-based DeepFoqus-Accelerate reconstruction against common MRI artifacts, ensuring that no additional reconstruction errors are introduced.
Clinical performance validation - Radiologists reviewed a sample of diverse accelerated scans (including pathological and non-pathological brains) to determine whether the accelerated scans were equivalent to the ground truth.
User acceptance testing and summative evaluation - Radiology technologists, IT administrators, and radiologists reviewed critical tasks inherent in the software and user requirements for acceptability.

The design verifications conducted support the conclusion that DeepFoqus-Accelerate performs as intended. The design validation, along with comparison and performance evaluations, support the conclusion that DeepFoqus-Accelerate performs comparably to the predicate device that is currently marketed for the same intended use.

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Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).