Methods and apparatus to adapt medical imaging interfaces based on learning are disclosed. An example apparatus includes a use monitor to monitor, in a first session, user actions and medical content data pertaining to operation of a clinical image display, a learning device including a processor to implement a learning network to develop a model for a subsequent session based on the user actions in relationship to a context of the medical content data. The model developed by defining contextual patterns of the user actions based on the context and the medical content data. The learning device is to update, prior to or during a second session subsequent the first session, a user interface based on the model.

The present invention provides a method for establishing a biomimetic nerve graft model for nerve fascicles of the extremities, which comprises the steps of: establishing a database of fascicles structures from nerve fascicles of the extremities with an imaging technique; obtaining information of a defective nerve trunk to be repaired of the extremities; and matching and fitting the information of the defective nerve trunk to be repaired of the extremities with/to the data in the database of fascicles structures, to establish a biomimetic nerve graft model that conforms to the characteristics of fascicles microstructure for nerve fascicles of the defective nerve trunk. In the present invention, the imaging technique and clinical data are fully utilized, to establish a biomimetic nerve graft model conforming to the characteristics of fascicles microstructure for varying types of detects, thus providing more accurate model information for repair of nerve trunk defects in the extremities.

A computer-implemented method for determining and evaluating an objective tumor response to an anti-cancer therapy using cross-sectional images can include receiving cross-sectional images of digital medical image data and identifying target lesions within the cross-sectional images. For each of the target lesions, a target lesion type and anatomical location is identified, a segmenting tool is activated for segmenting the target lesions into regions of interest, lesion metrics are automatically extracted from the regions of interest according to tumor response criteria, and conformity of target lesion identification is monitored using rules associated with the tumor response criteria, prompting a user to address any nonconforming target lesion. The method also includes receiving a presence/absence of metastases, determining changes in lesions metrics, and deriving an objective tumor response based on the tumor response criteria.

A pharmaceutical filling system for a high volume pharmacy is described. The system can include a spill detection subsystem and method. The system can include a cameras to take images that a processed to determine presence of a spilled pharmaceutical. A mirror portion is provided so that the camera can take images of the entire field of the filling area. The mirror portion can be a concave mirror that increases the field of view of the camera. A light panel can illuminate the field of view to allow the camera to take high speed images. A controller can receive a series of at least two images captured by the camera and determine whether an object in the captured images is a spilled pharmaceutical.

The present invention relates to a device (1) for fractional flow reserve determination. The device (1) comprises a model generator (10) configured to generate a three-dimensional model (3DM) of a portion of an imaged vascular vessel tree (VVT) surrounding a stenosed vessel segment (SVS), based on a partial segmentation of the imaged vascular vessel tree (VVT). Further, the device comprises an image processor (20) configured to calculate a blood flow (Q) through the stenosed vessel segment (SVS) based on an analysis of a time-series of X-ray images of the vascular vessel tree (VVT). Still further, the device comprises a fractional-flow-reserve determiner (30) configured to determine a fractional flow reserve (FFR) based on the three-dimensional model (3DM) and the calculated blood flow.

The present invention relates to deep learning for automated assessment of malignancy of lesions.

According to a first aspect, there is provided a computer-aided method of malignancy assessment of lesions, the method comprising the steps of: receiving input data; performing a first analysis on the input data to identify one or more lesions, generating a probability map for the one or more lesions from the input data; performing a second analysis on the input data to obtain a malignancy probability mask for the input data; and generating an overlay for the input data by combining the lesion probability map with the malignancy probability mask.

Some aspects include a method of determining change in size of an abnormality in a brain of a patient positioned within a low-field magnetic resonance imaging (MRI) device. The method comprises, while the patient remains positioned within the low-field MRI device, acquiring first and second magnetic resonance (MR) image data of the patient's brain; providing the first and second MR image data as input to a trained statistical classifier to obtain corresponding first and second output; identifying, using the first output, at least one initial value of at least one feature indicative of a size of the abnormality; identifying, using the second output, at least one updated value of the at least one feature; determining the change in the size of the abnormality using the at least one initial value of the at least one feature and the at least one updated value of the at least one feature.

A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.

Embodiments for implementing intelligent classification of region of interest in an organism in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. The one or more regions of interest may be classified into one of a plurality of classes by applying one or more perfusion models, representing spatio-temporal behavior of the contrast agent reflected by the time series data, and by using a machine learning operation.

A method for phase correction in proton resonance frequency (PRF) thermometry application includes acquiring a series of magnetic resonance (MR) images comprising a first MR image and plurality of subsequent MR images depicting an anatomical area of interest. The MR images are acquired while tissue in the anatomical area of interest is undergoing a temperature change. Each subsequent MR image is registered to the first MR image to yield a plurality of registered images. A plurality of basis images are computed from the registered images using Principal Component Analysis (PCA). The basis images are used to remove motion-related phase changes from a second series of MR images, thereby yielding a motion corrected second series of MR images. One or more temperature difference maps are generated that depict a relative temperature change for the tissue in the anatomical area of interest based on the motion corrected second series.