Carestream launches new DRX-Evolution system

AuntMinnie.com staff writers

Feb 25, 2015

Carestream Health has launched a new version of its DRX-Evolution digital radiography (DR) system, DRX-Evolution Plus.

The new device offers a streamlined design with LED lighting, greater flexibility in high-ceiling rooms via an extended tube column, a new high-performance generator, and an optional table that accommodates patients up to 705 lb, according to the firm.

It also features a wall stand bucky-angulation capability that helps with cross-table and other complex x-ray exams; a tube touchscreen that allows technologists to change techniques and view images from the tube; pediatric tools, including automatic technique and image processing for seven pediatric body sizes; bone suppression software; a transbay option that allows for fast tube movement across multiple trauma bays; and automatic acquisition and stitching for long-length and supine exams.

The device supports a choice of three DRX detectors, including two cesium iodide detectors (35 x 43 cm and 25 x 30 cm), and can be configured with a 43 x 43-cm fixed detector in the wall stand, as well as one or two additional wireless detectors that can be used for table bucky and tabletop exams, Carestream said.

The company plans to begin shipping DRX-Evolution Plus later this year.

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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