(120 days)
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.
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.
The provided text describes the acceptance criteria and study proving the device, SubtleHD (1.x), meets these criteria.
Here's the breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
The provided text includes two distinct sets of acceptance criteria and performance results: one for "Performance Validation" and one for a "Reader Study."
Performance Validation Summary:
| Endpoint | Acceptance Criteria | SubtleHD Mode | Result | Conclusion |
|---|---|---|---|---|
| Denoising (SNR) - Primary | SNR shall improve by at least 40% in homogeneous ROI regions for at least 90% of the dataset. | Default | PASS | SubtleHD performs denoising, in terms of improved SNR, MRI images. |
| SNR shall improve by at least 40% in homogeneous ROI regions for at least 95% of the dataset. | High Denoising | PASS | ||
| Sharpness (Image Intensity Change) - Primary | Slope in a line ROI is increased for at least 90% of the dataset. | Default | PASS | SubtleHD sharpens, in terms of improvement in visibility of the edge at a tissue interface by image intensity slope measure, MRI images. |
| Slope in a line ROI is increased for at least 95% of the dataset. | High Sharpening | PASS | ||
| Sharpness (Image Intensity Change for Brains) - Secondary | Thickness, in terms of FWHM in a line ROI, is reduced for at least 90% of the dataset. | Default | PASS | SubtleHD sharpens, in terms of improvement in visibility of an anatomical structure by image intensity FWHM measure, MRI images. |
| Thickness, in terms of FWHM in a line ROI, is reduced for at least 95% of the dataset. | High Sharpening | PASS | ||
| Sharpness and Over Smoothing (Gradient Entropy) - Primary | At least 90% of cases demonstrate a lower gradient entropy value after SubtleHD processing. | Default | PASS | SubtleHD does not result in over-smoothed images, in terms of improvement in gradient entropy. |
| At least 95% of cases demonstrate a lower gradient entropy value after SubtleHD processing. | High Sharpening | PASS | ||
| There is a statistically significant improvement in gradient entropy when comparing the original and SubtleHD enhanced images across the performance dataset per a two-sided paired t-test. | Default and High Sharpening | PASS |
Reader Study Summary:
| Endpoint | Endpoint Description | Acceptance Criteria | Result | Conclusion |
|---|---|---|---|---|
| Denoising (Primary) | Signal-to-Noise Ratio | Statistically significantly better with p-value |
§ 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).