Abstract: An exemplary embodiment of the present disclosure provides a polyurethane core for use in a floor or wall panel, the core comprising a polyol made, at least in part, from one or more recycled materials, and an isocyanate.

Abstract: Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance (“MR”) imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional (“4D”) convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.

Abstract: In accordance with one aspect of the disclosure, an image analysis system is provided. The image analysis system includes at least one processor configured to access image tiles associated with a patient, each tile comprising a portion of a whole slide image, individually provide a first group of image tiles to a first trained model, receive a first set of feature objects from the first trained model, cluster feature objects from the first set of feature objects to form a number of clusters, calculate a number of attention scores based on the first set of feature objects, select a second group of tiles, individually provide the second group of image tiles to a second trained model, receive a second set of feature objects from the second trained model, generate a cancer grade indicator, and cause the cancer grade indicator to be output.

Abstract: A method for tissue classification includes receiving at least two images associated with a patient, the at least two images being of an anatomical region or tissue. The method also includes identifying a region of interest in the at least two images, analyzing the region of interest to identify changes in the tissue and generating a probability map of the region of interest based on the changes in the tissue. The probability map indicates a likelihood of formation of cancer in the tissue within a predetermined time period. The method also includes displaying the probability map on a display.

Abstract: Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance (“MR”) imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional (“4D”) convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.

Abstract: Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance (“MR”) imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional (“4D”) convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.

Abstract: A system and method for estimating perfusion parameters using medical imaging is provided. In one aspect, the method includes receiving a perfusion imaging dataset acquired from a subject using an imaging system, and assembling for a selected voxel in the perfusion imaging dataset a perfusion patch that extends in at least two spatial dimensions around the selected voxel and time. The method also includes correlating the perfusion patch with an arterial input function (AIF) patch corresponding to the selected voxel, and estimating at least one perfusion parameter for the selected voxel by propagating the perfusion patch and AIF patch through a trained convolutional neural network (CNN) that is configured to receive a pair of inputs. The method further includes generating a report indicative of the at least one perfusion parameter estimated.

Abstract: Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance (“MR”) imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional (“4D”) convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.