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US 12,608,806 B2Granted
Automated medical image quality control system
AssigneeSubtle Medical, Inc.
InventorsBen Andrew Duffy, Tao Zhang
Life of this patent
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Abstract
An automated image quality control system is provided. The system comprises: a first component including a first deep learning network model for estimating a quality of medical images acquired by an imaging device; and a second component for determining whether the medical images are out of a distribution of a training dataset used for training the first deep learning network model.
Claims19
An automated image quality control system comprising:
- (a) a first component including a first deep learning network model trained to output a quality score indicative of a quality level of medical images acquired by an imaging device, wherein the medical images are at least one of the following imaging modalities:
- Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), ultrasound, X-ray imaging, Computed Tomography (CT) and wherein the first deep learning network model comprises a plurality of intermediate layers;
- (b) a second component implemented by one or more of the plurality of intermediate layers, wherein the second component is trained to detect one or more of the medical images are out of a distribution of a training dataset used for training the first deep learning network model (out-of-distribution) thereby distinguishing an anomalous caused by out of the distribution from a low quality of the input medical images;
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- (c) a controller in communication with the imaging device, and upon receiving the quality score from the first component or a detection of the out-of-distribution from the second component, automatically adjust one or more acquisition parameters for acquiring medical images using the imaging device.
2 more elements
A computer-implemented method for automating image quality control comprising:
- (a) providing a first component including a first deep learning network model trained to output a quality score indicative of a quality level of medical images acquired by an imaging device, wherein the medical images are at least one of the following imaging modalities:
- Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), ultrasound, X-ray imaging, Computed Tomography (CT) and wherein the first deep learning network model comprises a plurality of intermediate layers;
- (b) providing a second component implemented by one or more of the plurality of intermediate layers, wherein the second component is trained to detect one or more of the medical images are out of a distribution of a training dataset used for training the first deep learning network model (out-of-distribution) thereby distinguishing an anomalous caused by out of the distribution from a low quality of the input medical images;
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- (c) upon receiving, by a controller in communication with the imaging device, the quality score from the first component or a detection of the out-of-distribution from the second component, automatically adjusting one or more acquisition parameters for acquiring medical images using the imaging device.
2 more elements
Dependencies are inferred from the claim text.