Search Results

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.

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.

AI/ML Overview

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

Ask a Question

Ask a specific question about this device

Page 1 of 1