InfiMed

Nov 17, 2003

InfiMed will show its new PlatinumOne Cardiac and PlatinumOne Combo upgrades for digital catheterization.

The vendor-neutral digital systems gives cath labs seamless digital acquisition and DICOM-sharing at a fraction of the cost of a new lab, according to the Liverpool, NY-based vendor. The PlatinumOne Combo offers built-in DSA and RF capabilities for flexibility in angiographic and cardiology applications.

Both systems include the latest PC features for expanded image storage, faster image processing, automated DICOM connectivity for remote viewing and archiving, and InfiMed’s latest high-speed CCD camera design, InfiMed said. The system software allows manual calibration of the camera and image processor. Both PlatinumOne Cardiac systems will begin shipping in the first quarter of 2004.

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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