Systems and methods for positron emission tomography image reconstruction
AssigneeShanghai United Imaging Healthcare Co., Ltd.
InventorTao Feng
Life of this patent
Estimating the term…
Abstract
Methods and systems for PET image reconstruction are provided. A method may include obtaining an image sequence associated with a subject. The image sequence may include one or more images generated via scanning the subject at one or more consecutive time periods. The method may also include obtaining a target machine learning model. The method may further include generating at least one target image using the target machine learning model based on the image sequence. The at least one target image may present a dynamic parameter associated with the subject. The target machine learning model may provide a mapping between the image sequence and the at least one target image.
Claims20
A system, comprising:
- at least one storage device storing executable instructions, and
- at least one processor in communication with the at least one storage device, when executing the executable instructions, causing the system to perform following operations:
- obtaining multiple groups of training data associated with one or more samples, each group of the multiple groups of training data being associated with one sample of the one or more samples and including an image sequence, a reference parametric image corresponding to the image sequence, and a plasma time activity curve of the sample, wherein the reference parametric image presents a dynamic parameter associated with the one of the one or more samples;
- and
- generating a target machine learning model by training a machine learning model using the multiple groups of training data.
A method for image reconstruction implemented on a computing apparatus, the computing apparatus including at least one processor and at least one storage device, the method comprising:
- obtaining multiple groups of training data associated one or more samples, each group of the multiple groups of training data being associated with one sample of the one or more samples and including an image sequence, a reference parametric image corresponding to the image sequence, and a plasma time activity curve of the sample, wherein the reference parametric image presents a dynamic parameter associated with the one of the one or more samples;
- and
- generating a target machine learning model by training a machine learning model using the multiple groups of training data.
A non-transitory computer-readable medium storing at least one set of instructions, wherein when executed by at least one processor, the at least one set of instructions directs the at least one processor to perform acts of:
- obtaining multiple groups of training data associated with one or more samples, each group of the multiple groups of training data being associated with one sample of the one or more samples and including an image sequence, a reference parametric image corresponding to the image sequence, and a plasma time activity curve of the sample, wherein the reference parametric image presents a dynamic parameter associated with the one of the one or more samples;
- and
- generating a target machine learning model by training a machine learning model using the multiple groups of training data.
Dependencies are inferred from the claim text.