Carestream nets FDA OK on 2 DR products

AuntMinnie.com staff writers

Nov 19, 2019

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Carestream Health has received 510(k) clearance from the U.S. Food and Drug Administration on its dual-energy imaging technology for digital radiography (DR) and its Focus 35C detector with Image Suite software.

The dual-energy technology is approved for Carestream's DRX-Evolution Plus DR system and features the Eclipse image processing engine and two filter materials designed to automatically switch between the high- and low-energy exposures. By doing so, it produces a soft tissue-only image with the bone structures removed and a corresponding bone-only image, which enhances both x-ray spectrums to reach dose efficiency.

Carestream's new Focus 35C detector with Image Suite software targets smaller imaging facilities and specialty practices with a retrofit option to convert analog x-ray rooms to wireless digital radiography for enhanced image processing and broader functionality. The detector and software also combine to provide a mini-PACS.

Carestream expects the Focus 35C detector to be commercially available by the end of this year, while the dual-energy technology's release is planned for early 2020.

Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

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(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

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(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

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