Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a detector section, an aggregation section, and a storage section. The detection section includes a plurality of detector elements configured to convert radiation into electric signals. The aggregation section aggregates imaging data carried by the electric signals from the detector section. The storage section is arranged in a manner corresponding to the detector elements regarding an output from the detector section and an input to the aggregation section. The storage section includes a predetermined number of non-volatile memories configured to store the imaging data from the corresponding detector elements.

Abstract: A general workflow for deep learning based image restoration in X-ray and fluoroscopy/fluorography is disclosed. Higher quality images and lower quality images are generated as training data. This training data can further be categorized by anatomical structure. This training data can be used to train a learned model, such as a neural network or deep-learning neural network. Once trained, the learned model can be used for real-time inferencing. The inferencing can be more further improved by employing a variety of techniques, including pruning the learned model, reducing the precision of the learned mode, utilizing multiple image restoration processors, or dividing a full size image into snippets.

Abstract: A method includes obtaining, at a local imaging system, projection data for an object representing an intensity of radiation detected along a plurality of rays through the object using a first set of imaging parameters; transmitting an image quality dataset related to the obtained projection data to a remote server; generating, via the remote server, localized restoration information based on the received image quality dataset; transferring the localized restoration information from the remote server to the local imaging system; and updating the local imaging system using the localized restoration information.

Abstract: A method of adjusting components in an imaging apparatus includes obtaining a first image, the first image being acquired by performing a scan using a first set of scan parameters; analyzing the first image to detect objects in the first image and corresponding features of the detected objects; based on a detection result of a first object having a corresponding first object feature in the first image, determining an action corresponding to the first object and the first object feature; determining an updated set of scan parameters based on the determined action; and controlling the imaging apparatus based on the updated set of scan parameters.

Abstract: A medical image processing method, an X-ray diagnostic apparatus, and a method of generating a learned model includes receiving first X-ray image data, inputting the first X-ray image data to a trained model, outputting, from the trained model, an X-ray image having an image quality higher than an image quality of the first X-ray image data. The learned model was trained using contrastive learning using second X-ray image data as input data, third X-ray image data and fourth X-ray image data as label data, the third X-ray image data being negative label data having worse image quality than the second X-ray image data, and the fourth image data being positive label data having better image quality than the second X-ray image data.

Abstract: A control method for controlling data processing acquired from medical imaging modalities by using multiple data processors connected to multiple medical imaging modalities via a network. The method includes obtaining image information for imaging to be performed with an imaging modality from the multiple imaging modalities. The method also includes obtaining load information of the multiple data processors before the imaging is completed. Allocating, based on graph information generated based on the obtained load information, at least a part of the multiple data processors to processing of data acquired in imaging based on the imaging information. The control method may conclude by performing processing of the acquired data with the allocated data processing resource.

Abstract: A system and method is provided for controlling an acquisition system to obtain, based on a currently acquired set of projection data, a next set of projection data that is to be acquired using a set of learned imaging conditions. The set of learned imaging conditions are, at least in part, based on training data including projection data and dose information. In one embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen at random. In another embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen based on a test scan and a trained neural network for outputting the initial dose and angle using a loss function.

Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a plurality of sets of a detector section, a storage section, and an aggregation section. The detector section includes a plurality of detector elements each being configured to convert radiation into electric signals. The aggregation section is configured to aggregate imaging data carried by the electronic signals from the detector section. The storage section is connected with an output of the detector section and an input of the aggregation section. The storage section comprises a predetermined number of non-volatile memories to store the imaging data from the corresponding detector elements.

Abstract: A projection dataset from a cone beam computed tomography (CBCT) can be input into a first set of one or more neural networks trained for at least one of saturation correction, truncation correction, and scatter correction. Reconstruction can then be performed on the output projection dataset to produce an image dataset. Thereafter, this image dataset can be input into a second set of one or more neural networks trained for at least one of noise reduction and artefact reduction, thereby generating a higher quality CBCT image.

Abstract: A system and a method by which multiple regions or objects of interest can be indicated within an X-ray image, from which a user can select a primary region or object of interest and accordingly adjust the appropriate X-ray dose for obtaining a better quality image of the selected regions or objects of interest.

Abstract: A method of imaging includes obtaining a first image including projection data representing an intensity of X-rays detected by a plurality of detectors at a first X-ray exposure setting, the X-rays being emitted from an X-ray source; based on a detection result of a first object in the first image: determining a background region of interest (ROI) around the first object, the background ROI including background ROI pixels having a first intensity value corresponding to the intensity of the X-rays; and converting, for each pixel of the background ROI pixels, the first intensity values of the background ROI pixels to a normalized X-ray attenuation factor; and determining a second X-ray exposure setting for use in obtaining a second image based on the background ROI pixels converted to the normalized X-ray attenuation factor.

Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a plurality of sets of a detector section, a storage section, and an aggregation section. The detector section includes a plurality of detector elements each being configured to convert radiation into electric signals. The aggregation section is configured to aggregate imaging data carried by the electronic signals from the detector section. The storage section is connected with an output of the detector section and an input of the aggregation section. The storage section comprises a predetermined number of non-volatile memories to store the imaging data from the corresponding detector elements.

Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a detector section, an aggregation section, and a storage section. The detection section includes a plurality of detector elements configured to convert radiation into electric signals. The aggregation section aggregates imaging data carried by the electric signals from the detector section. The storage section is arranged in a manner corresponding to the detector elements regarding an output from the detector section and an input to the aggregation section. The storage section includes a predetermined number of non-volatile memories configured to store the imaging data from the corresponding detector elements.

Abstract: A control method for controlling data processing acquired from medical imaging modalities by using multiple data processors connected to multiple medical imaging modalities via a network. The method includes obtaining image information for imaging to be performed with an imaging modality from the multiple imaging modalities. The method also includes obtaining load information of the multiple data processors before the imaging is completed. Allocating, based on graph information generated based on the obtained load information, at least a part of the multiple data processors to processing of data acquired in imaging based on the imaging information. The control method may conclude by performing processing of the acquired data with the allocated data processing resource.

Abstract: A general workflow for deep learning based image restoration in X-ray and fluoroscopy/fluorography is disclosed. Higher quality images and lower quality images are generated as training data. This training data can further be categorized by anatomical structure. This training data can be used to train a learned model, such as a neural network or deep-learning neural network. Once trained, the learned model can be used for real-time inferencing. The inferencing can be more further improved by employing a variety of techniques, including pruning the learned model, reducing the precision of the learned mode, utilizing multiple image restoration processors, or dividing a full size image into snippets.

Abstract: A method includes obtaining, at a local imaging system, projection data for an object representing an intensity of radiation detected along a plurality of rays through the object using a first set of imaging parameters; transmitting an image quality dataset related to the obtained projection data to a remote server; generating, via the remote server, localized restoration information based on the received image quality dataset; transferring the localized restoration information from the remote server to the local imaging system; and updating the local imaging system using the localized restoration information.

Abstract: A system and a method by which multiple regions or objects of interest can be indicated within an X-ray image, from which a user can select a primary region or object of interest and accordingly adjust the appropriate X-ray dose for obtaining a better quality image of the selected regions or objects of interest.

Abstract: A method of imaging includes obtaining a first image including projection data representing an intensity of X-rays detected by a plurality of detectors at a first X-ray exposure setting, the X-rays being emitted from an X-ray source; based on a detection result of a first object in the first image: determining a background region of interest (ROI) around the first object, the background ROI including background ROI pixels having a first intensity value corresponding to the intensity of the X-rays; and converting, for each pixel of the background ROI pixels, the first intensity values of the background ROI pixels to a normalized X-ray attenuation factor; and determining a second X-ray exposure setting for use in obtaining a second image based on the background ROI pixels converted to the normalized X-ray attenuation factor.

Abstract: Systems and methods are provided for image forecasting of image processing. A trained image forecaster may be used to generate a virtual image based on prior actual images. The virtual image may be preprocessed to generate an intermediate image. The intermediate image may then be used to process the next actual image to generate a final image.

Abstract: Systems and methods are provided for image forecasting of image processing. A trained image forecaster may be used to generate a virtual image based on prior actual images. The virtual image may be preprocessed to generate an intermediate image. The intermediate image may then be used to process the next actual image to generate a final image.