Abstract: Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network (“CNN”) is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.

Abstract: Super-resolution images are generated from standard-resolution images acquired with a magnetic resonance imaging (“MRI”) system. More particularly, super-resolution (e.g., sub-millimeter isotropic resolution) images are generated from standard-resolution images (e.g., images with 1 mm or coarser isotropic resolution) using a deep learning algorithm, from which accurate cortical surface reconstructions can be generated.

Abstract: Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network (“CNN”) is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.

Abstract: Diffusion metric maps are generated from a limited input of magnetic resonance data to a suitably trained machine learning algorithm, such as a suitably trained neural network. In general, a downsampling strategy is implemented in the joint k-q space to enable the simultaneous estimation of multiple different diffusion metrics from a more limited set of input diffusion-weighted images.

Abstract: Super-resolution images are generated from standard-resolution images acquired with a magnetic resonance imaging (“MRI”) system. More particularly, super-resolution (e.g., sub-millimeter isotropic resolution) images are generated from standard-resolution images (e.g., images with 1 mm or coarser isotropic resolution) using a deep learning algorithm, from which accurate cortical surface reconstructions can be generated.

Abstract: Aspects of the present disclosure include a method for visualizing a fiber-like structure in a biological specimen, the method comprising: clearing the biological specimen comprising a fiber-like structure, wherein the fiber-like structure is detectably labeled; illuminating the cleared biological specimen with two light sheets from a first side and a second side to produce an image volume, wherein the second side is opposite to the first side and wherein the image volume comprises a representation of the fiber-like structure; defining a plurality of voxels within the representation of the fiber-like structure; processing each of the plurality of voxels to estimate a plurality of principal fiber-like structure orientations; and defining a starting point on the representation of the fiber-like structure and propagating a plurality of streamlines from the starting point, according to the plurality of principal fiber-like structure.

Abstract: Aspects of the present disclosure include a method for visualizing a fiber-like structure in a biological specimen, the method comprising: clearing the biological specimen comprising a fiber-like structure, wherein the fiber-like structure is detectably labeled; illuminating the cleared biological specimen with two light sheets from a first side and a second side to produce an image volume, wherein the second side is opposite to the first side and wherein the image volume comprises a representation of the fiber-like structure; defining a plurality of voxels within the representation of the fiber-like structure; processing each of the plurality of voxels to estimate a plurality of principal fiber-like structure orientations; and defining a starting point on the representation of the fiber-like structure and propagating a plurality of stream-lines from the starting point, according to the plurality of principal fiber-like structure