Abstract: A method for use in image reconstruction of CT projection data, which aims at reducing motion artefacts in reconstructed images caused by movement of anatomical bodies. Embodiments are based on mitigating motion artefacts based on restricting the range of data that is used for reconstructing each slice. More particularly, a sub-range of the projection data corresponding to each slice is selected, this sub-range being chosen based on determining one or more sub-windows of visibility of a target anatomical object or event within the projection data sequence. The event may be a particular phase of a movement cycle of the anatomical body. The structure could be a particular portion of the anatomical body which is of interest. Either approach leads to reduction of motion artefacts within a single slice, by restricting the data range, and focusing upon the data which is most relevant clinically.

Abstract: A method includes receiving from a client computing device (“CCD)” a request to perform a given post-processing type on compressed image data, receiving from the CCD a first amount of compressed image data, obtaining an image from the first amount of compressed image data, post-processing the image obtained from the first amount of compressed image data with a post-processing algorithm, in response to a result of the post-processing failing to satisfy a predetermined requirement, iteratively, and until the result of the post-processing satisfies the predetermined requirement: requesting a greater amount of compressed image data from the CCD, receiving the greater amount of compressed image data from the CCD, obtaining an image using the greater amount of compressed image data, and post-processing the image obtained using the greater amount of compressed image data with the post-processing algorithm, and returning to the CCD the result of the post-processing satisfying the predetermined requirement.

Abstract: Proposed concepts aim to provide schemes, solutions, concepts, designs, methods and systems pertaining to the storage, retrieval and/or communication of medical images. In particular, the medical image is de-noised to generate a (substantially) noise-free medical image. De-noising the medical image to this extent allows for a high compression ratio, and thus space-efficient storage of the medical image. However, typically the level to which medical image is de-noised has to be carefully controlled in order to minimise the chance of introducing errors to ratio the medical image. In the present invention, noise information describing residual noise data removed from the medical image is also stored. This allows for the compressed noise-free medical image to be combined with the compression noise information at a receiver, restoring any potentially lost information.

Abstract: A mechanism for generating a partially denoised image. A residual noise image, obtained by processing an image using a convolutional neural network, is weighted. The blending or combination of the weighted residual noise image and the (original) image generates the partially denoised image.

Abstract: A medical system (100) comprising a local memory (138) storing local machine executable instructions (140) and pulse sequence commands (142), a magnetic resonance imaging system (103), and a local computational system (132). The execution of the local machine executable instructions causes the local computational system to transmit (200) metadata descriptive of the magnetic resonance imaging protocol to a remote computational system (132?) via a network connection (137). The execution of the local machine executable instructions causes the computational system to repeatedly: control (202) the magnetic resonance imaging system with the pulse sequence commands to acquire one (146) of the series of discrete k-space acquisitions; construct (204) a compressed k-space acquisition (148) by compressing the one of the series of k-space acquisitions using a compression module; and transmit (206) the compressed k-space acquisition to the remote computational system via the network connection.

Abstract: Images are processed while remaining agnostic as to an image source. An input image to be processed is retrieved. A first value or set of values for an image metric associated with the input image is determined, and a first filter is generated based on a relationship between the first value or set of values and a target value or set of values. The first filter is then applied to the input image to generate a working image having a second value or set of values for the image metric substantially similar to the target value or set of values for the image metric. The working image is processed using a standardized image processing methodology. An output is generated, which may be an image, based on the processed working image and outputs the output.

Abstract: An imaging system (MIS), optionally a medical imaging system, with wireless communication capability and related method. The imaging system comprises a gantry (RG) rotatable around a rotation axis. The gantry includes a detector device (D) capable of recording, in plural spatial positions, measurement data in relation to a subject (such as a patient) (PAT) to be imaged. The system also includes a radio transmitter (TX) for generating a directed radio beam propagatable along a propagation axis to transmit the measurement data to a radio receiver (RX). The radio transmitter (TX) is arranged at the rotatable gantry and is operable so that the propagation direction intersects the rotation axis in a location that is situated away from the rotatable gantry.

Abstract: The present invention relates to an apparatus (10) for correcting computer tomography (“CT”) X-ray data acquired at high relative pitch, the apparatus comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to provide the processing unit with CT X-ray data of a body part of a person acquired at high relative pitch. The processing unit is configured to determine CT slice reconstruction data of the body part of the person with no or reduced high relative pitch operation reconstruction artefacts using a machine learning algorithm. The machine learning algorithm was trained on the basis of CT slice reconstruction data, and wherein the CT slice reconstruction data comprised first CT slice reconstruction data with high relative pitch reconstruction artefacts and comprised second CT slice reconstruction data with less, less severe, or no high relative pitch reconstruction artefacts.

Abstract: A mechanism for generating denoised basis images for a computed tomography scanner. An input dataset, comprising first and second basis image data, is processed using a machine-learning algorithm process to produce the denoised basis images. The first and second basis image data each comprise at least one basis image, wherein the type of image differs between the first and second basis image data.

Abstract: A method is provided for processing images comprising retrieving measured data for a first image. The method then generates partially filtered data by applying a first filter to the measured data. The first filter is a generic filter. The method then reconstructs the partially filtered data to generate a partially filtered image. The method then generates a partially processed image by applying a first processing routine to the partially filtered image. The method then generates a filtered image by applying a second filter to the partially processed image, where the second filter is a filter selected from a plurality of potential secondary filters. The method then outputs the filtered image. Systems are provided for implementing the claimed method and training methods for neural networks used in the method are provided as well.

Abstract: An approach for denoising a medical image. A noise map, which defines an estimate of one or more statistical parameters for each pixel of the medical image, is used to modify or normalize the medical image. The modified medical image is then processed, using a machine-learning method, to produce a denoised medical image.

Abstract: An output dataset is produced that comprises projection domain data for a desired/target imaging angle. Input datasets are processed that contain projection domain data captured/obtained at the desired/target imaging angle, as well as projection domain data captured/obtained at one or more further predetermined imaging angles. to produce the output dataset.

Abstract: A non-transitory computer readable medium stores at least one database (12) storing an index of training resources (14); and instructions readable and executable by at least one electronic processor (12) to perform a training session generation method (100) includes receiving requests for training from a plurality of medical professionals; obtaining information related to the medical professionals of the plurality of medical professionals; and determining a training session using training resources indexed in the at least one database including determining at least two of the medical professionals to invite to participate in the training session based on the received requests and the obtained information.

Abstract: At least one database stores reference content. In an annotation method, an annotation to an item of the reference content is received via a user interface, an annotation context of the annotation is determined at least based on analysis of content of the annotation, and the item of the reference content is updated in the database with the annotation tagged with the annotation context. In a reference content presentation method, an item of the reference content is presented via the user interface, a presentation context of the presenting is determined, and it is determined whether a comparison of the annotation context and the presentation context satisfies a sharing filter. In response to the sharing filter being satisfied, the annotation is presented via the user interface. The annotation is not presented if the sharing filter is not satisfied.

Abstract: A mechanism for generating a partially denoised image. A residual noise image, obtained by processing an image using a convolutional neural network, is weighted. The blending or combination of the weighted residual noise image and the (original) image generates the partially denoised image.

Abstract: A non-transitory computer readable medium stores at least one database (30) storing clinical competency framework profiles (32) for clinical competencies for a plurality of clinicians at a plurality of medical facilities; and instructions readable and executable by at least one electronic processor (16) to perform a learning activities recommendation method (100) comprising: linking educational content units (38) completed by clinicians to clinical competencies of the clinical competency framework profiles that are fulfilled by the completed learning activities; correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles; and recommending one or more of the educational content units to a clinician seeking to fulfill a clinical competency in one clinical competency framework based on the correlated clinical competency frameworks and the linked educational content units.

Abstract: A method for use in image reconstruction of CT projection data, which aims at reducing motion artefacts in reconstructed images caused by movement of anatomical bodies. Embodiments are based on mitigating motion artefacts based on restricting the range of data that is used for reconstructing each slice. More particularly, a sub-range of the projection data corresponding to each slice is selected, this sub-range being chosen based on determining one or more sub-windows of visibility of a target anatomical object or event within the projection data sequence. The event may be a particular phase of a movement cycle of the anatomical body. The structure could be a particular portion of the anatomical body which is of interest. Either approach leads to reduction of motion artefacts within a single slice, by restricting the data range, and focusing upon the data which is most relevant clinically.

Abstract: One embodiment of the present disclosure may provide a method for training and tuning a neural network model, including: adding simulated noise to an initial image of an object to generate a noisy image (601, 603), the simulated noise taking the same form as natural noise in the initial image; training a neural network model on the noisy image using the initial image as ground truth (605), wherein in the neural network model is trained on the noisy work model a tuning variable is extracted or generated, the tuning variable defining an amount of noise removed during use (607); identifying a first value for the tuning variable that minimizes a training cost function for the tuning variable is identified or for the initial image; and assigning a second value for the tuning variable (611), the second value different than the first value, wherein the neural network model identifies more noise in the noisy image when using the second value than when using the first value.

Abstract: The present invention relates to an apparatus (10) for correcting computer tomography (“CT”) X-ray data acquired at high relative pitch, the apparatus comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to provide the processing unit with CT X-ray data of a body part of a person acquired at high relative pitch. The processing unit is configured to determine CT slice reconstruction data of the body part of the person with no or reduced high relative pitch operation reconstruction artefacts using a machine learning algorithm. The machine learning algorithm was trained on the basis of CT slice reconstruction data, and wherein the CT slice reconstruction data comprised first CT slice reconstruction data with high relative pitch reconstruction artefacts and comprised second CT slice reconstruction data with less, less severe, or no high relative pitch reconstruction artefacts.

Abstract: An imaging system (MIS), optionally a medical imaging system, with wireless communication capability and related method. The imaging system comprises a gantry (RG) rotatable around a rotation axis. The gantry includes a detector device (D) capable of recording, in plural spatial positions, measurement data in relation to a subject (such as a patient) (PAT) to be imaged. The system also includes a radio transmitter (TX) for generating a directed radio beam propagatable along a propagation axis to transmit the measurement data to a radio receiver (RX). The radio transmitter (TX) is arranged at the rotatable gantry and is operable so that the propagation direction intersects the rotation axis in a location that is situated away from the rotatable gantry.