Abstract: Methods, apparatus, and storage medium for determining a condition of a biostructure by a neural network based on quantitative imaging data (QID) corresponding to an image of the biostructure. The method includes obtaining specific quantitative imaging data (QID) corresponding to an image of a biostructure; determining a context spectrum selection from context spectrum including a range of selectable values by: applying the specific QID to an input layer of a context-spectrum neural network, wherein the context-spectrum neural network is trained, according to a combination of focal loss and dice loss, based on previous QID and constructed context spectrum data associated with the previous QID; mapping the context spectrum selection to the image to generate a context spectrum mask for the image; and determining a condition of the biostructure based on the context spectrum mask.

Abstract: Systems and methods of reconstructing photoacoustic imaging data corresponding to a brain of a subject through a skull of a subject utilizing a reconstruction method that incorporates a spatial model of one or more acoustic properties of the brain and skull of the subject derived from an adjunct imaging dataset.

Abstract: Systems and methods of reconstructing photoacoustic imaging data corresponding to a brain of a subject through a skull of a subject utilizing a reconstruction method that incorporates a spatial model of one or more acoustic properties of the brain and skull of the subject derived from an adjunct imaging dataset.

Abstract: Systems and methods of reconstructing photoacoustic imaging data corresponding to a brain of a subject through a skull of a subject utilizing a reconstruction method that incorporates a spatial model of one or more acoustic properties of the brain and skull of the subject derived from an adjunct imaging dataset.

Abstract: A method for reducing PROPELLER MRI data acquisition times, by combining k-space under-sampling and iterative reconstruction using NUFFT, while maintaining similar image quality as in PROPELLER MRI with sufficient k-space sampling. Iterative image reconstruction using NUFFT minimizes image artifacts produced with conventional PROPELLER image reconstruction in under-sampled acquisitions. The data acquisition and image reconstruction parameters are selected in order to achieve image quality similar to that of sufficiently-sampled PROPELLER acquisitions for significantly shorter imaging time. An advantage of using under-sampled PROPELLER imaging is a reduction in acquisition time by as much as 50% without introducing significant artifacts, and while maintaining other benefits of PROPELLER imaging.