(120 days)
Not Found
Yes
The device description explicitly states that the software implements an image enhancement algorithm using a "convolutional network based filtering" and that a "single neural network is trained" for adaptive noise reduction and sharpness increase. This directly indicates the use of deep learning, a subset of machine learning and AI.
No
This device is an image processing software that enhances MRI images for diagnostic purposes, not a therapeutic device that treats or prevents disease.
No
SubtleHD is described as image processing software that enhances MRI images for noise reduction and increased sharpness. It does not provide a medical diagnosis on its own, but rather improves the quality of images that radiologists and technologists use for diagnostic purposes.
Yes
The device description explicitly states "SubtleHD is Software as a Medical Device (SaMD) consisting of a software algorithm". It processes existing DICOM images and outputs enhanced images, without requiring or including any specific hardware components beyond the computing environment where the software runs.
Based on the provided information, SubtleHD is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information about a person's health. This testing is performed outside of the body (in vitro).
- SubtleHD's Function: SubtleHD processes medical images (MRI scans) taken from the human body. It enhances the quality of these images for interpretation by radiologists and technologists. It does not analyze biological specimens.
SubtleHD falls under the category of medical imaging software or Software as a Medical Device (SaMD) that aids in the interpretation of medical images. Its purpose is to improve the visual quality of the images, not to perform diagnostic tests on biological samples.
Yes
The letter explicitly states, "FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP)." This language directly indicates that the PCCP has been cleared for this specific device.
Intended Use / Indications for Use
SubtleHD is an image processing software that can be used for image enhancement of all body parts MRI images. It can be used for noise reduction and increasing image sharpness.
Product codes (comma separated list FDA assigned to the subject device)
OIH
Device Description
SubtleHD is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. As it only processes images for the device has no user interface. It is intended to be used by radiologists and technologists in an imaging center, clinic, or hospital. The SubtleHD software can be used with MR images acquired as part of standard of care and accelerated MRI exams as the input. The outputs are the corresponding images with enhanced image quality. Original DICOM images are passed onto the SubtleHD software as an input argument and the enhanced images are saved in the designated location prescribed when running the SubtleHD software. The functionality of SubtleHD (noise reduction and sharpness enhancement) is identified from the DICOM series description and/or through configuration is specified as configuration files and OS environment variables.
SubtleHD software implements an image enhancement algorithm using a convolutional network based filtering. Original images are enhanced by running through a cascade of filter banks, where thresholding and scaling operations are applied. A single neural network is trained for adaptive noise reduction and sharpness increase. The parameters within the neural network were obtained through an image-guided optimization process. Additional nonlocal mean based denoising and unsharp masking based sharpening filters are applied to the deep learning processed image.
The software operates on DICOM files, enhances the images, and sends the enhanced images to any desired destination with an AE Title (e.g., PACS, MR device, workstation, and more). Enhanced images coexist with the original images.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
MRI
Anatomical Site
all body parts
Indicated Patient Age Range
Pediatric and Adult subject ages (ranging from 3 to 94 years old)
Intended User / Care Setting
radiologists and technologists in an imaging center, clinic, or hospital.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Standalone Image Quality Metric Testing:
Paired Test Set (Unaligned SOC as the Reference) and Paired Test Set (Unaligned SubtleHD-enhanced SOC as the Reference): Each had 97 samples for statistical comparison.
Aligned Test Set: 471 samples.
Selection criteria for paired test data:
- Not used for algorithm training.
- Split between all anatomies (abdomen, ankle, brain, breast, C-spine, foot, hand, hip, knee, L-spine, neck, pelvis, prostate, shoulder, t-spine, and wrist).
- Have pediatric data: 20 children (ages 2 to 11), 22 adolescents (ages 12 to 21).
- Have 0%, 20%, 30%, 40%, 50%, 60%, 70%, and 80% time reduced images.
- Have axial, coronal, and sagittal orientations.
- Have 2D and 3D acquisitions.
- Have the following protocols: DWI, FLAIR, GRE, MRA, PD, RADIAL, STIR, SWI, T1, T2, and MIP.
- Contrasted and non-contrasted images.
- Have various Acceleration Method Distribution.
- Have 3.0T, 1.5T, and low field strengths.
- Have a variety of imaging scanner vendors and models.
- Have some data from sites that did not also supply training data.
- More than 50% of data is from sites in the United States.
- Have various clinical conditions and normal.
- Reconstruction Matrix (Image Sizes) up to 512 x 448.
Selection criteria for aligned test data:
- Not used for algorithm training.
- Inputs (low-quality images) and targets (high-quality images) are spatially aligned to support the pixelwise based metrics (e.g. L1 loss, SSIM, PSNR) analysis.
- Split between all anatomies (abdomen, ankle, arm, brain, extremity, knee, I-spine, pelvis, shoulder, spine).
- For known ages, have a variety of age ranges (28 years old - 80 years old).
- Have axial, coronal, and sagittal orientations.
- Have 2D acquisitions.
- Have the following protocols: DWI, PD, STIR, T1, T1 FLAIR, T2, T2 FLAIR, T2 STIR.
- The quality of inputs are aligned with the quality of accelerated clinical protocols.
- Have 3.0T, 1.5T, and low field strengths.
- Have various acceleration factors: 1.0 - 6.0.
- Have GE and Siemens scanners with various models.
- Have US and OUS data.
Performance Validation & Reader Study Dataset Characteristics:
The SubtleHD performance validation test dataset consists of:
- Body (breast, abdomen, prostate, and pelvis), Cardiac, Neuro (head, neck, and cervical, lumbar, and thoracic spine), and Musculoskeletal (shoulder, wrist, hand, hip, knee, foot, and ankle) anatomical regions.
- DIXON, DWI, FLAIR, GRE, MRA, PD, RADIAL, STIR, SWI, T1, T2, T2*, and MIP input protocols.
- Contrasted and non-contrasted images.
- Axial, coronal, and sagittal orientations.
- 2D and 3D acquisition types.
- 0.25T, 0.30T, 0.50T, 0.55T, 0.60T, 1.16T, 1.50T, and 3.0T field strengths.
- ASG Superconductors, ESAOTE, FONAR, Toshiba/Canon, Philips Medical Systems, Hitachi/Fujifilm, GE MEDICAL SYSTEMS, and SIEMENS imaging scanner vendors.
- Reconstruction Matrix (Image Sizes) up to 1344x1344.
- Standard-of-care data and time reduced data (up to 80% time reduction).
- Even distribution of subject sex (53% female, 47% male).
- Pediatric and Adult subject ages (ranging from 3 to 94 years old).
- Data was selected from sources not included in the training dataset (36.50% of the dataset is from non-training sources).
- The majority of performance data comes from sources in the United States (65%).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Standalone Image Quality Metric Testing:
- Study Type: Standalone image quality metric testing.
- Sample Size: Paired Test Set (Unaligned SOC as the Reference) had 97 samples; Paired Test Set (Unaligned SubtleHD-enhanced SOC as the Reference) had 97 samples; Aligned Test Set had 471 samples.
- Key results:
- Paired Test Set (Unaligned SOC as the Reference): L1 Loss 9.993% ± 92.487% (p=0.189), SSIM 0.0115 ± 0.0403 (p=0.001), PSNR -0.307 dB ± 2.863 dB (p=0.150).
- Paired Test Set (Unaligned SubtleHD-enhanced SOC as the Reference): L1 Loss -6.908% ± 22.422% (p=0.022), SSIM 0.0210 ± 0.0448 (p
§ 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).
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, and then the word "ADMINISTRATION" in a smaller font in blue.
February 12, 2025
Subtle Medical, Inc. % Jared Seehafer Regulatory Consultant Enzyme Corporation 611 Gateway Blvd Ste 120 South San Francisco. California 94080
Re: K243250
Trade/Device Name: SubtleHD (1.x) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OIH Dated: January 13, 2025 Received: January 13, 2025
Dear Jared Seehafer:
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.
1
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device. or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
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 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-reportingcombination-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
2
requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-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-device-safety/medical-device-reportingmdr-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/medicaldevices/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-device-advice-comprehensive-regulatoryassistance/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, Ph.D. Assistant Director Magnetic Resonance and Nuclear Medicine Team 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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Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 07/31/2026 See PRA Statement below.
Submission Number (if known)
Device Name
SubtleHD (1.x)
Indications for Use (Describe)
SubtleHD is an image processing software that can be used for image enhancement of all body parts MRI images. It can be used for noise reduction and increasing image sharpness.
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)
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SubtleHD 510(k) Summary
| Date Summary Prepared: | 2025-02-10 |
|---|---|
| Contact Details: | |
| Applicant Name: | Subtle Medical, Inc. |
| Applicant Address: | 883 Santa Cruz Ave, Suite 205 |
| Menlo Park, CA 94025 United States | |
| Applicant Contact: | Ms. Ronny Elor |
| Applicant Contact Telephone: | (925) 324-8467 |
| Applicant Contact Email: | ronny@subtlemedical.com |
| Correspondent Name: | Enzyme Corporation |
| Correspondent Address: | 611 Gateway Blvd, Ste 120 |
| South San Francisco, CA 94080 United States | |
| Correspondent Contact: | Mr. Jared Seehafer |
| Correspondent Contact Telephone: | (415) 638-9554 |
| Correspondent Contact Email: | jared@enzyme.com |
| Device Name: | |
| Device Trade Name: | SubtleHD (1.x) |
| Common Name: | Medical image management and processing system |
| Classification Name: | System, Image Processing, Radiological |
| Regulation Number: | 892.2050 |
| Product Code: | QIH |
| Device Class: | Class II |
| Legally Marketed Predicate Devices: | Primary Predicate #: K230854 |
| Predicate Trade Name: SwiftMR | |
| Predicate Manufacturer: AIRS Medical Inc. | |
| Secondary Predicate #: K223623 | |
| Predicate Trade Name: SubtleMR | |
| Predicate Manufacturer: Subtle Medical, Inc. |
Table 1. Contact Details and Device Name
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Device Description Summary
SubtleHD is Software as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. As it only processes images for the device has no user interface. It is intended to be used by radiologists and technologists in an imaging center, clinic, or hospital. The SubtleHD software can be used with MR images acquired as part of standard of care and accelerated MRI exams as the input. The outputs are the corresponding images with enhanced image quality. Original DICOM images are passed onto the SubtleHD software as an input argument and the enhanced images are saved in the designated location prescribed when running the SubtleHD software. The functionality of SubtleHD (noise reduction and sharpness enhancement) is identified from the DICOM series description and/or through configuration is specified as configuration files and OS environment variables.
SubtleHD software implements an image enhancement algorithm using a convolutional network based filtering. Original images are enhanced by running through a cascade of filter banks, where thresholding and scaling operations are applied. A single neural network is trained for adaptive noise reduction and sharpness increase. The parameters within the neural network were obtained through an image-guided optimization process. Additional nonlocal mean based denoising and unsharp masking based sharpening filters are applied to the deep learning processed image.
The software operates on DICOM files, enhances the images, and sends the enhanced images to any desired destination with an AE Title (e.g., PACS, MR device, workstation, and more). Enhanced images coexist with the original images.
Intended Use / Indications for Use
SubtleHD is an image processing software that can be used for image enhancement of all body parts MRI images. It can be used for noise reduction and increasing image sharpness.
Intended Use / Indications for Use Comparison
SubtleHD and its predicates are all intended to denoise and sharpen MRI images of various input protocols, imaging scanners, and anatomical regions.
Technological Comparison
SubtleHD and its predicates are all used for image enhancement. They operate on DICOM files, enhance the images, and send the enhanced images to any desired destination. The receipt of original DICOM image files and delivery of enhanced images as DICOM files depends on other software systems. Both subject and predicate devices use convolutional network based filtering. Original images are enhanced by running through a cascade of filter banks, where thresholding and scaling operations are applied. The software performs noise reduction and sharpness increase. The parameters within the software are obtained through an image-quided optimization process. Additional pre- and post-processing is applied to configure desired perceived image quality.
The following table provides a detailed description of the technological characteristics of subject and predicate devices.
6
| Comparison | SubtleHD
(Subject Device) | SwiftMR
(Primary
Predicate
Device)
(K230854) | SubtleMR
(Secondary
Predicate
Device)
(K223623) | Noted
Differences |
|--------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Workflow | The software operates
on DICOM files,
enhances the images,
and sends the
enhanced images to
any desired destination
with an AE Title (e.g.,
PACS, MR device,
workstation, and
more). Enhanced
images coexist with
the original images. | The software operates
on DICOM files,
enhances the images,
and stores the
enhanced images on
PACS or on a MR
device. Enhanced
images coexist with
the original images. | The software operates
on DICOM files on the
file system, enhances
the images, and stores
the enhanced images
on the file system. The
receipt of original
DICOM image files
and delivery of
enhanced images as
DICOM files depends
on other software
systems. | SubtleHD can send
enhanced images to
any destination as long
as it is associated with
an IP and AE Title.
SubtleMR by default is
intended to send
enhanced images to
PACS, but can be
configured to other
destinations. SwiftMR
can send enhanced
images to PACS or an
MR Device.
Substantially
Equivalent. |
| Product Code | QIH | LLZ | LLZ | Substantially
Equivalent. |
| Physical
Characteristics | Software package
Operates on a virtual
machine | Same | Same | Same |
| Intended User | Radiologists | Same | Same | Same |
| Intended Location | Medical facility
(hospitals, clinics,
imaging center, etc.) | Same | Same | Same |
| Modalities | MRI | Same | Same | Same |
| Operating System /
Computer | Linux Compatible; PC
or Mac | PC Compatible | Same | SubtleHD and
SubtleMR are PC or
Mac, while SwiftMR is
only PC. Substantially
Equivalent. |
| Rx or OTC | Rx | Same | Same | Same |
| User Interface | None | Same | Same | Same |
| DICOM
Standard Compliance | The software
processes
DICOM-compliant
image data. | Same | Same | Same |
| Comparison | SubtleHD
(Subject Device) | SwiftMR
(Primary
Predicate
Device)
(K230854) | SubtleMR
(Secondary
Predicate
Device)
(K223623) | Noted
Differences |
| Image Enhancement
Algorithm Description | SubtleHD software
implements an image
enhancement
algorithm using a
convolutional neural
network based filtering.
Original images are
enhanced by running
through a cascade of
filter banks, where
thresholding and
scaling operations are
applied. A single
neural network is
trained for adaptive
noise reduction and
sharpness increase.
The parameters within
the neural network
were obtained through
an image-guided
optimization process.
Additional nonlocal
mean based denoising
and unsharp masking
based sharpening
filters are applied to
the deep learning
processed image. | SwiftMR implements
an image
enhancement
algorithm using
convolutional neural
network-based
filtering. Original
images are enhanced
by running through a
cascade of filter banks,
where thresholding
and scaling operations
are applied. Neural
network-based filters
that perform noise
reduction and/or
sharpening are
obtained. The
parameters of the
filters were obtained
through an
image-guided
optimization process.
Sharpening filter is
additionally applied to
the deep learning
processed image. | SubtleMR software
implements an image
enhancement
algorithm using
convolutional neural
network based filtering.
Original images are
enhanced by running
through a cascade of
filter banks, where
thresholding and
scaling operations are
applied. Separate
neural network based
filters are obtained for
noise reduction and
sharpness increase.
The parameters of the
filters were obtained
through an
image-guided
optimization process. | Substantially
Equivalent. |
| Model Architecture | Single SRE/DNE
model with filters/
pre/post-processing. | DNE is a model; SRE
is achieved via multiple
filters | SRE is a model, DNE
is a model, with filters/
pre/post-processing | Deep-learning
algorithm and
processing steps for
noise reduction and
sharpness
enhancement.
Substantially
Equivalent. |
| Function | Combined SRE and
DNE for all anatomies. | Combined SRE and
DNE, can turn either
off as an option. | Separate, DNE head,
spine, neck, abdomen,
pelvis, prostate,
breast, and
musculoskeletal, SRE
head only. | Substantially
Equivalent. |
| Enhancement levels | Optional high
denoising level and
optional high
sharpening level. | Denoising level from
level 0 to level 8,
Sharpness level from
level 0 to level 5. | No levels (single level). | SubtleHD and SwiftMR
are substantially
equivalent. SubtleMR
has a single SRE and
DNE option. |
| Performance Validation | Endpoints and
acceptance criteria
using retrospective
clinical images for both
noise reduction and
sharpness increase
functions. | Same | Same | Same |
Table 2. Comparison of Technological Characteristics
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Subtle Medical Inc. - 510(k) – SubtleHD – 510(k) Summary
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Predetermined Change Control Plan (PCCP)
SubtleHD has a Predetermined Change Control Plan (PCCP), which details planned device modifications, the associated methodology to develop, validate, and implement those modifications, and an assessment of the impact of those modifications. In general, Subtle Medical utilizes PCCPs for planned modifications that aim to improve customer satisfaction with respect to perceived image quality, generalizability, and flexibility of the product.
The SubtleHD PCCP contains one planned modification: to add more denoising and sharpening levels as a configuration option. This modification is intended to provide additional flexibility for the user. Prior to release, the software verification and performance validation described in this summary shall be re-executed with the same endpoints and validation dataset as those used to support this submission, with the acceptance criteria for the new configurable levels of denoising and sharpness being adjusted to have increasing stringency. Re-executing the software verification and validation and performance validation will ensure substantial equivalence is maintained following the modification. The algorithm will be locked with fixed model parameters prior to release.
This modification shall be performed as a part of Subtle Medical's Design Change Control process in a Quality Management System that is ISO 13485:2016 and MDSAP certified. It shall be accompanied by a software version-specific Customer Release Notes describing the change along with software version-specific User Manual. The updated labeling shall be delivered to users by Subtle Medical customer success personnel.
Non-Clinical and/or Clinical Tests Summary & Conclusions
Subtle Medical conducted the following performance testing:
- Software Verification and Validation testing (unit, integration, and system testing) to demonstrate ● that software requirements are implemented. These tests passed.
- . Standalone image quality metric testing to report the Least Absolute Deviations (L1 loss), Structural Similarity (SSIM), and peak signal-to-noise ratio (PSNR) values. This testing demonstrated significant reduction in L1 loss and significant increase in SSIM and PSNR.
- . Performance Validation testing utilizing retrospective clinical data to demonstrate the software enhanced image quality in MR images via a reduction of noise or sharpness enhancement. These tests passed.
- . A Reader Study utilizing retrospective clinical data to demonstrate the software enhanced image quality in MR images via a reduction of noise or sharpness enhancement. These tests passed.
Standalone Image Quality Metric Testing:
A subset of our performance validation set, which is detailed below, where standard-of-care (SOC) images were acquired, was used to create a paired test set for evaluating L1 loss, SSIM, and PSNR. To enhance the spatial alignment between the two separately acquired images, affine registration was applied to the accelerated images (i.e., inputs) to align them with the SOC images. However, perfect alignment of image pairs was not achievable due to inter-scan motions (e.g., non-rigid respiratory/pulsation). Additionally, slight image contrast differences in focal regions between the two scans (e.g., non-repeatable blood/CSF suppression, protocol adjustment-induced minor contrast changes) were possible. Furthermore, the acquired SOC images may not have all been of high quality, with residual noise and image blurring potentially persisting on the SOC images. All of these factors could induce additional errors (beyond the performance of the SubtleHD algorithm) when measuring pixel-wise
9
metrics using the SOC images as the reference. To eliminate residual noise and reduce blurring on SOC images, the metrics were also calculated using the SubtleHD-enhanced SOC as the reference, and then metric analysis was performed between the input and SubtleHD enhanced input.
The following selection criteria was used for the paired test data:
- Not used for algorithm training. ●
- Split between all anatomies (abdomen, ankle, brain, breast, C-spine, foot, hand, hip, knee, . L-spine, neck, pelvis, prostate, shoulder, t-spine, and wrist).
- . Have pediatric data:
- o 20 children (ages 2 to 11)
- O 22 adolescents (ages 12 to 21)
- Have 0%, 20%, 30%, 40%, 50%, 60%, 70%, and 80% time reduced images. ●
- Have axial, coronal, and sagittal orientations.
- Have 2D and 3D acquisitions.
- Have the following protocols: DWI, FLAIR, GRE, MRA, PD, RADIAL, STIR, SWI, T1, T2, and MIP
- Contrasted and non-contrasted images
- Have various Acceleration Method Distribution.
- Have 3.0T, 1.5T, and low field strengths.
- Have a variety of imaging scanner vendors and models.
- Have some data from sites that did not also supply training data.
- More than 50% of data is from sites in the United States.
- . Have various clinical conditions and normal.
- Reconstruction Matrix (Image Sizes) up to 512 x 448
This selection criteria represents a well characterized clinically-relevant reference dataset.
In addition to a paired test set, an aligned test set was also evaluated. The purpose of the aligned test set was to better facilitate pixel-wise metrics analysis, which necessitates spatially aligned ground truth reference data. Any misalignment could contribute to metrics that do not reflect the model's performance. Furthermore, the quality of input images should align with the accelerated clinical protocols to ensure that the test results closely approximate practical scenarios. For this test set, therefore, the low-quality input images and high-quality ground truth images were paired from the same undersampled k-space data. Undersampling was performed using commercially available acceleration methods on each vendor's MRI scanner. This ensures that low-quality input images are representative of the types of images that would be processed by these devices in real-world clinical scenarios, where acceleration techniques are frequently used to improve workflow efficiency. The high-quality ground truth images were reconstructed using the vendor's commercially available deep learning pipeline and represent standard-of-care image quality. Importantly, these pairs have perfect spatial alignment, which better facilitates pixel-wise metric analysis, such as L1 loss, SSM, and PSNR. This alignment is critical for accurately assessing the performance of the image enhancement algorithm.
The following selection criteria was used for the aligned test data:
- Not used for algorithm training.
- Inputs (low-quality images) and targets (high-quality images) are spatially aligned to support the pixelwise based metrics (e.g. L1 loss, SSIM, PSNR) analysis
- . Split between all anatomies (abdomen, ankle, arm, brain, extremity, knee, I-spine, pelvis, shoulder, spine)
- For known ages, have a variety of age ranges (28 years old - 80 years old)
- Have axial, coronal, and sagittal orientations.
10
- Have 2D acquisitions. ●
- Have the following protocols: DWI, PD, STIR, T1, T1 FLAIR, T2, T2 FLAIR, T2 STIR. ●
- The quality of inputs are aligned with the quality of accelerated clinical protocols
- Have 3.0T, 1.5T, and low field strengths.
- Have various acceleration factors: 1.0 - 6.0
- Have GE and Siemens scanners with various models. ●
- . Have US and OUS data.
Each of the paired test sets (SOC as reference, SubtleHD-enhanced SOC as reference) had 97 samples for statistical comparison. The aligned test set had 471 samples. For each test pair, each metric was evaluated across all 2D slices and then averaged across slices to obtain the metric value for that test pair. The mean and standard deviation were calculated for L1 loss, SSIM, and PSNR.
The following table summarizes the results of this testing.
| Test Set | Study Test | Result (mean ± std dev) | T-Test P-Value |
|---|---|---|---|
| Paired Test Set | |||
| (Unaligned SOC as | |||
| the Reference) | L1 Loss | 9.993% ± 92.487% | 0.189 |
| SSIM | 0.0115 ± 0.0403 | 0.001 | |
| PSNR | -0.307 dB ± 2.863 dB | 0.150 | |
| Paired Test Set | |||
| (Unaligned | |||
| SubtleHD-enhanced | |||
| SOC as the | |||
| Reference) | L1 Loss | -6.908% ± 22.422% | 0.022 |
| SSIM | 0.0210 ± 0.0448 |