Ultrasound system with deep learning network for image artifact identification and removal
AssigneeKoninklijke Philips N.V.
InventorsAnup Agarwal, Keith William Johnson, Liang Zhang, Earl M. Canfield
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Abstract
An ultrasound system with a deep learning neural network feature is used to eliminate haze artifacts in B mode images of the carotid artery by analysis of orthogonal information. In a described implementation the orthogonal information comprises the structural information of a B mode image and motion information of the same field of view as that of the B mode image. In another embodiment the neural network reduces haze artifacts by reducing TGC gain at the depth of artifacts.
Claims12
An ultrasonic diagnostic imaging system for improving the image quality of ultrasound images using deep learning comprising:
- an ultrasound probe adapted to acquire ultrasound image signals;
- a B mode image processor, coupled to the probe, which is adapted to produce B mode ultrasound images;
- a neural network model, coupled to receive the B mode ultrasound images, and adapted to identify artifacts in a blood vessel in the B mode ultrasound images;
- time gain control circuitry, coupled to receive the ultrasound image signals, wherein the time gain control circuitry is adapted to be responsive to the identification of artifacts to reduce gain at an image depth of identified artifacts;
- and
- a display adapted to display the B mode images with reduced artifact content.
Dependencies are inferred from the claim text.