A medical image processing apparatus according to one embodiment includes processing circuitry. The processing circuitry acquires a plurality of pieces of energy bin data that are generated based on execution of photon counting CT scan. The processing circuitry reconstructs a plurality of energy band images based on the plurality of pieces of energy bin data. The processing circuitry generates, based on the plurality of energy band images, a three-dimensional histogram based on a plurality of pixel values and a plurality of energy bands included in the plurality of energy band images.

Metal artifact correction including projecting x-rays to scan a volumetric region of an object, the projecting generates corresponding cone beam computed tomography (CBCT) image data, reconstructing an enlarged CBCT volume from the CBCT image data, the enlarged CBCT volume representative of the volumetric region and a volume outside the volumetric region, generating from the enlarged CBCT volume and a projection geometry, maximum intensity projections on a virtual plane, detecting attenuated image areas in the maximum intensity projections corresponding to metal, corresponding the detected attenuated image areas corresponding to the metal to areas of the CBCT image data, and reconstructing a final CBCT volume using the CBCT image data by suppression of the areas of the CBCT image data corresponding to the detected attenuated image areas of the maximum intensity projections. Systems for metal artifact correction are also disclosed.

Systems and methods for training a machine-learning model for artifact reduction are provided. Such methods include retrieving a three-dimensional digital phantom reconstructed from CT imaging data. The method then selects a first Z position along the central axis and simulates a first set of forward projections from the digital phantom taken along an axial trajectory at the first Z position along the central axis. The first set of forward projections has a first simulated collimation in the axial direction. The method then reconstructs a first simulated image from the first set of forward projections and identifies a plurality of secondary Z positions along the central axis other than the first Z position. For each of the secondary Z positions and the first Z position itself, the method then simulates a set of secondary forward projections from the digital phantom taken along corresponding axial trajectories at the corresponding secondary Z position.

Provided are an image analysis device and a method for controlling an image analysis device that can easily correct contours of a target structure given to two types of ultrasound images showing different cross sections.

An image analysis device includes: a contour giving unit that gives a first contour and a second contour of a target structure to a first ultrasound image and a second ultrasound image, respectively; a manual correction receiving unit that receives a correction applied to the second contour by a user; a first feature extraction unit that extracts a first feature from the first ultrasound image; a second feature extraction unit that extracts a second feature from information of the correction applied to the second contour by the user; and a contour resetting unit that automatically resets the first contour, based on the first feature and the second feature.

An image processing apparatus generates a first image by extracting a first region which is a first artifact generation source from a CT image, generates a second image by subtracting a value determined according to a specific part from the first image, generates a third image by performing forward projection and back projection on the second image, generates a fourth image which is a difference image between the second image and the third image, and generates a fifth image by correcting an artifact in the CT image using the fourth image.

The invention relates to a control system for controlling optical coherence tomography imaging means for imaging a subject, the control system being configured to perform the following steps of an imaging process: receiving (212) a scan data set from the subject being acquired by means of optical coherence tomography, the scan data set including one or several spectra (270), performing (214) data processing on the spectrum or on each of the several spectra of the scan data set (122), including per spectrum: determining (216) a scaling factor (274) for the spectrum (270, 370, 372, 374), scaling (218) a baseline spectrum (272) with a scaling factor (274), and removing (220) the scaled baseline spectrum (276) from the spectrum (270); and providing (224) a baseline corrected image data set of the subject for an image of the subject to be displayed, to an optical coherence tomography imaging system and to a corresponding method.

A dynamic pulmonary magnetic resonance imaging method, including: with an ultrashort echo time sequence, acquiring pulmonary magnetic resonance signals in a free-breathing condition; during data acquisition, monitoring a respiratory condition, obtaining a respiration curve; with the respiration curve, performing motion-resolved reconstruction on the acquired pulmonary magnetic resonance data, obtaining motion-resolved lung images; performing motion field estimation on the motion-resolved lung images; performing motion-state weighted motion-compensated reconstruction on results of the motion field estimation, obtaining dynamic pulmonary magnetic resonance images; performing ventilation map estimation on the dynamic pulmonary magnetic resonance images, obtaining a pulmonary ventilation.

Methods and systems for PET image reconstruction are provided. A method may include obtaining an image sequence associated with a subject. The image sequence may include one or more images generated via scanning the subject at one or more consecutive time periods. The method may also include obtaining a target machine learning model. The method may further include generating at least one target image using the target machine learning model based on the image sequence. The at least one target image may present a dynamic parameter associated with the subject. The target machine learning model may provide a mapping between the image sequence and the at least one target image.

A medical image processing apparatus includes processing circuitry to receive radiation image data for each of a plurality of different channels, wherein the radiation image data for all of the plurality of channels represents a same anatomical region of a same subject; and, for each of a plurality of positions represented in the radiation image data: estimate, based on data values for the position in each of the plurality of channels, material probabilities which indicate a respective probability of each of a plurality of materials existing at the position; and specify a value for at least one rendering parameter at the position based on the material probabilities.

Systems and methods for training a machine-learning model to generate denoised and dealiased image data are provided. The present disclosure provides techniques for training a machine-learning (ML) model to generate denoised and dealiased imaging data. A method includes (1) training a first ML model using a first training dataset comprising first image data to obtain a second ML model; and (2) training (a) the second ML model or (b) a third ML model using a second training dataset to obtain a fourth ML model. The second training dataset includes (i) the first image data and (ii) training image data obtained by applying at least one of the second ML model or the third ML model to second image data. The denoising and dealiasing ML model may be either the fourth ML model or derived from the fourth ML model.