An imaging system may comprise a first rectangular boundary array of a first plurality of ultrasonic transducers, a second rectangular boundary array of a second plurality of ultrasonic transducers positioned within the first rectangular boundary array, and a controller coupled to the first rectangular boundary array and the second rectangular boundary array. A method for image reconstruction may comprise determining whether to deliver energy to a first rectangular boundary array of a first plurality of ultrasonic transducers, or a second rectangular boundary array of a second plurality of ultrasonic transducers, the second rectangular boundary array being positioned within the first rectangular boundary array. The method may comprise determining an amount of energy to be delivered to the first plurality of ultrasonic transducers or the second plurality of ultrasonic transducers, and delivering the amount of energy to the first plurality of ultrasonic transducers or the second plurality of ultrasonic transducers.

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

A method includes receiving an image and classifying the image using a machine learning engine. The machine learning engine is trained using a training image, where the training image is labeled with a label associated with a three-dimensional volume responsive to one or more image factors for the training image satisfying one or more respective criteria. The image factor(s) include an image factor based on (1) the area of intersection between the three-dimensional volume and an image plane defined by the training image, and (2) the area of a projection of a face of the three-dimensional volume onto the image plane.

A method includes receiving an image and classifying the image using a machine learning engine. The machine learning engine is trained using a training image, where the training image is labeled with a label associated with a three-dimensional volume responsive to one or more image factors for the training image satisfying one or more respective criteria. The image factor(s) include an image factor based on (1) the area of intersection between the three-dimensional volume and an image plane defined by the training image, and (2) the area of a projection of a face of the three-dimensional volume onto the image plane.

A device may one or more memories storing the frontal view image. A device may one or more processors coupled to the one or more memories and configured to: obtain, based on the frontal view image and via an implicit field engine in a geometry pathway, a depth map, obtain, based on the frontal view image, a masked image, generate, based on the masked image and via a semantic pathway, a reconstructed image, train, based on the depth map and the reconstructed image, a model; and finetune the model using a portion of task-specific labels to obtain a finetuned model that performs semantic view mapping on input images.

For reconstruction in sampling-based imaging, such as reconstruction in MR imaging, an iterative, multiple-mapping based hierarchal machine-learned network reconstruction may produce artifact corrected images based on under-sampled scans. Two or more mappings may be used to reduce the presence of artifacts, in some cases including localized low-noise-contribution artifacts, relative to reconstructions based on fully-sampled scans.

The present disclosure provides a system and method for magnetic resonance imaging. The method may include obtaining a first set of imaging data, the first set of imaging data being sampled in multiple shots, each shot of the multiple shots corresponding to a plurality of echo times, the first set of imaging data including partially sampled data in a first k space; obtaining a second set of imaging data, the second set of imaging data including fully sampled data in a central region of a second k space; determining fitting data in the first k space based on the first set of imaging data and the second set of imaging data; and/or generating a target image based on the fitting data in the first k space and the first set of imaging data in the first k space.