Fovia partners with iCRco

AuntMinnie.com staff writers

May 7, 2012

Fovia is collaborating with computed radiography (CR) firm iCRco to incorporate its High Definition Volume Rendering (HDVR) 3D technology into iCRco's products and technology.

The combination of Fovia's 3D capabilities and iCRco's orthopedic technology is designed to give physicians and radiologists the ability to view and manipulate data without the need for pre- and postprocessing.

Fovia's CPU-based thin-client-server technology features advanced visualization for OEMs through customizable software development toolkit integration.

Dice similarity index measurements compare the x-ray pulsatility index (XPI) to digital subtraction angiography in patients with suspected chronic thromboembolic pulmonary hypertension. XPI is green, pulmonary angiography is pink and the overlap between the two is white. Image courtesy of the ARRS.

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ARRS: Noncontrast fluoroscopy method can image chronic hypertension

By Amerigo Allegretto

A fluoroscopy method incorporating a noncontrast x-ray pulsatility index can be a screening or diagnostic test for suspected chronic hypertension.

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Dice similarity index measurements compare the x-ray pulsatility index (XPI) to digital subtraction angiography in patients with suspected chronic thromboembolic pulmonary hypertension. XPI is green, pulmonary angiography is pink and the overlap between the two is white. Image courtesy of the ARRS.

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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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(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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