Kubtec to launch neonatal DR unit at RSNA 2014

AuntMinnie.com staff writers

Nov 20, 2014

Digital x-ray firm Kubtec plans to launch Kub 250, a portable low-dose digital radiography (DR) system for dedicated neonatal imaging, at this month's RSNA 2014 meeting after receiving U.S. Food and Drug Administration (FDA) clearance for the product.

Kubtec focused on developing a system with low radiation dose and high pixel resolution; Kub 250 sports 96-micron resolution, compared with 150- to 170-micron resolution for conventional DR systems, according to the company. The higher resolution makes it less likely that images will have to be reacquired, and the company's tests have indicated that it can reduce radiation exposure up to 40%.

Kub 250 is also compact and lightweight, with a 24 x 24-inch footprint and weight of 200 lb, easily fitting next to incubators. The system also sports an articulating arm for decubitus views and a lightweight DR detector that slides directly into the incubator slot, eliminating the need to move the neonate and reducing stress.

Kubtec believes that installing a dedicated system like Kub 250 in the neonatal intensive care unit (NICU) decreases the risk of cross-contamination from other areas of the hospital. The firm will highlight the system at the RSNA 2014 meeting later this month.

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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