Quantum nabs Mo. contract

AuntMinnie.com staff writers

May 15, 2012

Radiography equipment vendor Quantum Medical Imaging has won an installation of its Q-Rad-Digital DRX Series radiographic system at a hospital in Missouri.

St. Francis Hospital in Cape Girardeau is a 280-bed facility serving more than 650,000 people throughout Missouri, Illinois, Kentucky, Tennessee, and Arkansas. The installation included Quantum's ceiling-mounted imaging system with DRX-1 wireless detectors and EVP+ image processing from Quantum parent Carestream Health. The system also includes a Quiet-Lift elevating table and a Verti-Q wall stand.

Quantum dealer Interstate Imaging facilitated the sale, the company said.

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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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Stroke Skull Xray 400

AI proposed to help radiologists detect child abuse injuries

A team at Michigan State University has developed an AI model designed to detect rib fractures in children under 3 years old.

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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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Transgender Heart

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April 1, 2024

(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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An image illustrating the use of novel x-ray technology in crash tests.

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The tumor localization/visualization results obtained by the proposed AI algorithm. Original chest x-ray images with a lesion (red circle = radiologists’ annotation of the lesion location) and AI's lesion localization result. These examples demonstrate that the proposed technique can successfully detect humeral tumors. Image and caption courtesy of Radiology: Artificial Intelligence.

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Image of patient (upper-left) in which, according to the radiologist's report, there is only consolidation, but the algorithm detects an abnormal rib (upper-right), consolidation (lower-left), and two nodules (lower-right). It is worth noting the confusion of a consolidation with mammary tissue and of two nodules with the two mammary areolae. Image courtesy of Scientific Reports.

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