Abstract: Embodiments discussed herein facilitate determination of a response to treatment and/or a prognosis for a tumor based at least in part on features of tumor-associated vasculature (TAV). One example embodiment is a method, comprising: accessing a medical imaging scan of a tumor, wherein the tumor is segmented on the medical imaging scan; segmenting tumor-associated vasculature (TAV) associated with the tumor based on the medical imaging scan; extracting one or more features from the TAV; providing the one or more features extracted from the TAV to a trained machine learning model; and receiving, from the machine learning model, one of a predicted response to a treatment for the tumor or a prognosis for the tumor.

Abstract: An ophthalmologic apparatus includes a refractive power measurement optical system, a fixation projection system, an inspection optical system, and a controller. The refractive power measurement optical system includes a first focusing element capable of changing a focal position, and is configured to project first light onto a subject's eye and to detect returning light of the first light from the subject's eye via the first focusing element. The fixation projection system is configured to project fixation light target onto the subject's eye. The inspection optical system includes a second focusing element capable of changing a focal position in conjunction with the first focusing element, and is used for a predetermined inspection in which second light is projected onto at least the subject's eye via the second focusing element.

Abstract: A medical image display apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to: identify imaged ranges respectively corresponding to a plurality of examinations, on the basis of anatomical information from a plurality of medical images taken of an examined subject in the plurality of examinations; map the imaged ranges respectively corresponding to the plurality of examinations onto a single human body model; and cause a display unit to display a list of information about the plurality of examinations and the human body model on which the imaged ranges respectively corresponding to the plurality of examinations are mapped, so as to be kept in correspondence with each other.

Abstract: An x-ray apparatus includes an x-ray source capable of producing an x-ray beam with a focal spot having a spatial shape that is selected to pre-amplify predetermined spatial frequencies exceeding half a cutoff frequency as compared to spatial frequencies below half the cutoff frequency; an x-ray detector capable of detecting the x-ray beam; and a processor adapted to reconstruct an image from the detected x-ray beam using a filter that compensates the pre-amplified predetermined spatial frequencies. The spatial shape comprises two or more disconnected regions that preferably have widths less than that of a nominal focal spot, combined widths greater than or equal to that of a nominal focal spot, and are separated by less than three times a width of a nominal focal spot.

Abstract: A method to reconstruct images from raw MRI data, and recover an image by solving an optimization problem. Hence, the method is an image reconstruction system for image recovery contrary to similar methods, the method reconstructs all images using both joint and individual objective functions. The method includes: acquiring with the MRI system, multiple images under an influence of different contrast mechanisms, wherein the different contrast mechanisms belong to a same anatomy, solving an optimization problem with an optimization algorithm using both joint and individual objective functions simultaneously for each of multiple image contrasts or a subset of the multiple image contrasts, to reconstruct the multiple images from acquired data.

Abstract: The present disclosure discloses a method and an apparatus for classifying a brain anomaly and apparatus based on a 3D MRI image, wherein the classifying method comprises: receiving a to-be-processed 3D MRI image, performing a convolution operation on an imaging sequence corresponding to the 3D MRI image based on a first neural network algorithm to obtain segment masks; and performing a classification operation on the imaging sequence corresponding to the 3D MRI image based on a second neural network algorithm and the segment masks to obtain a classification result of the brain anomaly. Supported by the technologies of artificial intelligence and big data processing, embodiments of the present disclosure enable classification of a brain anomaly shown in the received MRI image through training an MRI recognition model, thereby effectively improving classification accuracy of the brain anomaly and further enhancing diagnosis accuracy of the brain anomaly based on the MRI image.

Abstract: An image processing apparatus includes a generation unit configured to generate a first distance image having a pixel value based on a distance from an outline of a region that indicates a predetermined part of the subject and is extracted from a first image, and having a resolution lower than a resolution of the first image, and generate a second distance image having a pixel value based on a distance from an outline of a region that indicates the predetermined part and is extracted from a second image, and having a resolution lower than a resolution of the second image, a first calculation unit configured to calculate first deformation information by registering the first distance image and the second distance image, and a second calculation unit configured to calculate second deformation information by registering the first image and the second image based on the first deformation information.

Abstract: A method for calculating a coronary artery calcium score, the method comprising acquiring a target image for a coronary artery and myocardium before contrast enhancement, identifying the coronary artery included in the target image by using an artificial neural network, calculating a coronary artery calcium score based on the identified coronary artery, wherein the artificial neural network is trained based on a training database generated via alignment between a pre-acquired image of a coronary artery and myocardium before contrast enhancement and a pre-acquired image of a coronary artery and myocardium after contrast enhancement.

Abstract: A system for registering a luminal network to a 3D model of the luminal network includes a computing device configured to identify potential matches in the 3D model with location data of a location sensor, assigning one of the potential matches a registration score based on a deformation model applied to the 3D model, and displaying the potential match having the highest registration score.

Abstract: The present disclosure provides a method and an apparatus for semantic segmentation of an image, capable of solving the problem in the related art associated with low speed and inefficiency in semantic segmentation of images. The method includes: receiving the image; performing semantic segmentation on the image to obtain an initial semantic segmentation result; and inputting image information containing the initial semantic segmentation result to a pre-trained convolutional neural network for semantic segmentation post-processing, so as to obtain a final semantic segmentation result. With the solutions of the present disclosure, the initial semantic segmentation result can be post-processed using the convolutional neural network, such that the speed and efficiency of the semantic segmentation of the image can be improved.

Abstract: Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.

Abstract: Higher quality diffusion metrics and/or diffusion-weighted images are generated from lower quality input diffusion-weighted images using a suitably trained neural network (or other machine learning algorithm). High-fidelity scalar and orientational diffusion metrics can be extracted using a theoretical minimum of a single non-diffusion-weighted image and six diffusion-weighted images, achieved with data-driven supervised deep learning. As an example, a deep convolutional neural network (“CNN”) is used to map the input non-diffusion-weighted image and diffusion-weighted images sampled along six optimized diffusion-encoding directions to the residuals between the input and output high-quality non-diffusion-weighted image and diffusion-weighted images, which enables residual learning to boost the performance of CNN and full tensor fitting to generate any scalar and orientational diffusion metrics.

Abstract: Introduced here are approaches to assessing whether digital features (or simply “features”) detected in digital images by detection models are representative of artifacts that can obscure actual pathologies. A diagnostic platform may characterize each digital feature detected in a digital image based on its likelihood of being an artifact. For instance, a digital feature could be characterized as being representative of an artifact caused by improper illumination, an artifact caused by a physical element that is adhered to the lens through which light is collected by an imaging device, or a pathological feature indicative of a disease.

Abstract: A method for correcting projection images in CT image reconstruction is provided. The method may include obtaining a plurality of projection images of a subject. Each of the plurality of projection images may correspond to one of the plurality of gantry angles. The method may further include correcting a first projection image of the plurality of projection images according to a process for generating a corrected projection image. The process may include performing, based on the first projection image and a second projection image of the plurality of projection images, a first correction on the first projection image to generate a preliminary corrected first projection image. The process may also include performing, based on at least part of the preliminary corrected first projection image, a second correction on the preliminary corrected first projection image to generate a corrected first projection image corresponding to the first gantry angle.

Abstract: The present disclosure describes methods for calibrating a spectral X-ray system to perform material decomposition with a single scan of an energy discriminating detector or with a single scan at each used X-ray spectrum. The methods may include material pathlengths exceeding the size of the volume reconstructable by the system. Example embodiments include physical and matching calibration phantoms. The physical calibration phantom is used to measure the attenuation of X-rays passing therethrough with all combinations of pathlengths through the calibration's basis materials. The matching digital calibration phantom is registered with the physical calibration phantom and is used to calculate the pathlength though each material for each measured attenuation value. A created data structure includes the X-ray attenuation for each X-ray spectrum or detector energy bin for all combinations of basis material pathlengths.

Abstract: A system for tracking at least one bone in robotized computer-assisted surgery, comprises a processing unit and a non-transitory computer-readable memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: obtaining backscatter images of the at least one bone from a tracking device in a coordinate system; generating a three-dimensional geometry of a surface of the at least one bone from the backscatter images, the three-dimensional geometry of the surface being in the coordinate system; determining a position and orientation of the at least one bone in the coordinate system by matching the three-dimensional geometry of the surface of the at least one bone to a three-dimensional model of the bone; controlling an automated robotized variation of at least one of a position and orientation of the tracking device as a function of a processing of the backscatter images; and continuously outputting the position and orientation of

Abstract: A system and a method for detecting image forgery through a convolutional neural network are capable of detecting image manipulation of compressed and/or color images. The system comprises a manipulated feature pre-processing unit applying an input image to a high-pass filter to enhance features due to image forgery; a manipulated feature extraction unit extracting image manipulated feature information from the image with the enhanced features through a pre-trained convolutional neural network; a feature refining unit refining the extracted image manipulated feature information; and a manipulation classifying unit determining the image forgery based on the image manipulated feature information refined by the feature refining unit.

Abstract: An image registration system (111) for registering a live stream of ultrasound images (112) of a beamforming ultrasound probe (113) with an X-ray image (114) is described. The image registration (111) system identifies, from the X-ray image (114), the position of a medical device (116) represented in the X-ray image (114); and determines, based on ultrasound signals transmitted between the beamforming ultrasound probe (113) and an ultrasound transducer (115) disposed on the medical device (116), a location of the ultrasound transducer (115) respective the beamforming ultrasound probe (113). Each ultrasound image from the live stream (112) is registered with the X-ray (114) image based on the identified position of the medical device (116).

Abstract: A force sensed surface scanning system (20) employs a scanning robot (41) and a surface scanning controller (50). The scanning robot (41) includes a surface scanning end-effector (43) for generating force sensing data informative of a contact force applied by the surface scanning end-effector (43) to an anatomical organ. In operation, the surface scanning controller (50) controls a surface scanning of the anatomical organ by the surface scanning end-effector (43) including the surface scanning end-effector (43) generating the force sensing data, and further constructs an intraoperative volume model of the anatomical organ responsive to the force sensing data generated by the surface scanning end-effector (43) indicating a defined surface deformation offset of the anatomical organ.

Abstract: Systems and methods for generating a surgical plan for altering an abnormal bone using a generic normal bone model are discussed. For example, a system for planning a surgery on an abnormal bone can include a model receiver module configured to receive a generic normal bone model. The generic normal bone model, such as a parametric model derived from statistical shape data, can include a data set representing a normal bone having an anatomical origin comparable to the abnormal bone. An input interface can be configured to receive an abnormal bone representation including a data set representing the abnormal bone. A surgical planning module can include a registration module configured to register the generic normal bone model to the abnormal bone representation by creating a registered generic model. A surgical plan formation module can be configured to identify one or more abnormal regions of the abnormal bone using the registered generic model.

Abstract: Systems and methods for quantifying a shift of an anatomical object of a patient are provided. A 3D medical image of an anatomical object of a patient is received. An initial location of landmarks on the anatomical object in the 3D medical image is determined using a first machine learning network. A 2D slice depicting the initial location of the landmarks is extracted from the 3D medical image. The initial location of the landmarks in the 2D slice is refined using a second machine learning network. A shift of the anatomical object is quantified based on the refined location of the landmarks in the 2D slice. The quantified shift of the anatomical object is output.

Abstract: A method for training generative network, a method for generating near-infrared image and device. The method includes: obtaining a training sample set, in which the set includes near-infrared image samples and visible-light image samples; obtaining an adversarial network to be trained, in which the generative network of the adversarial network is configured to generate each near-infrared image according to an input visible-light image, the discrimination network of the adversarial network is configured to determine whether an input image is real or generated; constructing a first objective function according to a first distance between each generated near-infrared image and the corresponding near-infrared image sample in an image space and a second distance between each generated near-infrared image and the corresponding near-infrared image sample in a feature space; performing an adversarial training on the adversarial network with the set based on optimizing a value of the first objective function.

Abstract: An apparatus for evaluating validity of detection of a cancer region may be provided.

Abstract: Disclosed herein is an imaging system including a first x-ray source configured to produce first x-ray photons in a first energy range suitable for imaging, project the first x-ray photons onto an area designated for imaging, a rotatable gantry configured to rotate the first x-ray source such that the first x-ray source traverses an angular path, and a data processor having an analytical portion. The analytical portion is configured to collect first data relating to the transmission of the first x-ray photons through the area designated for imaging at a set of image-collection angles along the angular path, collect background data at a set of background-collection angles along the angular path, wherein the system acquires more than one image of the designated area for imaging between background angles. The analytical portion is also configured to remove errors in the first data using the background data, and generate a corrected image based on the removal of errors in the first data.

Abstract: In part, the disclosure relates to computer-based methods, devices, and systems suitable for detecting a delivery catheter using intravascular data. In one embodiment, the delivery catheter is used to position the intravascular data collection probe. The probe can collect data suitable for generating one or more representations of a blood vessel with respect to which the delivery catheter can be detected.

Abstract: There is provided a computed implemented method of automatically generating an adapted presentation of at least one candidate anomalous object detected from anatomical imaging data of a target individual, comprising: providing anatomical imaging data of the target individual acquired by an anatomical imaging device, analyzing the anatomical imaging data by a detection classifier for detecting at least one candidate anomalous object of the anatomical imaging data and computed associated location thereof, computing, by a presentation parameter classifier, at least one presentation parameter for adapting a presentation of a sub-set of the anatomical imaging data including the at least one candidate anomalous object according to at least the location of the candidate anomalous object, and generating according to the at least one presentation parameter, an adapted presentation of the sub-set of the anatomical imaging data including the at least one candidate anomalous object.

Abstract: Methods and systems for navigating to a target through a patient's bronchial tree are disclosed including a bronchoscope, a probe insertable into a working channel of the bronchoscope including a location sensor, and a workstation in operative communication with the probe and the bronchoscope the workstation including a user interface that guides a user through a navigation plan and is configured to present a three-dimensional (3D) view for displaying a 3D rendering of the patient's airways and a corresponding navigation plan, a local view for assisting the user in navigating the probe through peripheral airways of the patient's bronchial tree to the target, and a target alignment view for assisting the user in aligning a distal tip of the probe with the target.

Abstract: A computer-implemented method of preoperatively planning a surgical procedure on a knee of a patient including determining femoral condyle vectors and tibial plateau vectors based on image data of the knee, the femoral condyle vectors and the tibial plateau vectors corresponding to motion vectors of the femoral condyles and the tibial plateau as they move relative to each other. The method may also include modifying a bone model representative of at least one of the femur and the tibia into a modified bone model based on the femoral condyle vectors and the tibial plateau vectors. And the method may further include determining coordinate locations for a resection of the modified bone model.

Abstract: A method and system for providing a mask image including receiving medical image data including a temporal dimension and generating a frequency data set including data points with in each case one frequency value by applying a Fourier transform to the image data. The Fourier transform is applied at least along the temporal dimension. The frequency data set is segmented into two sub-areas based on at least a frequency threshold value. The mask image is generated by applying an inverse Fourier transform to the first and/or the second sub-area of the frequency data set. The generated mask image is provided.

Abstract: A method of reducing radiation dose for radiology imaging modalities and nuclear medicine by using a convolutional network to generate a standard-dose nuclear medicine image from low-dose nuclear medicine image, where the network includes N convolution neural network (CNN) stages, where each stage includes M convolution layers having K×K kernels, where the network further includes an encoder-decoder structure having symmetry concatenate connections between corresponding stages, downsampling using pooling and upsampling using bilinear interpolation between the stages, where the network extracts multi-scale and high-level features from the low-dose image to simulate a high-dose image, and adding concatenate connections to the low-dose image to preserve local information and resolution of the high-dose image, the high-dose image includes a dose reduction factor (DRF) equal to 1 of a radio tracer in a patient, the low-dose PET image includes a DRF of at least 4 of the radio tracer in the patient.

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

Abstract: An X-ray computed tomography apparatus according to an embodiment includes an X-ray detector and a processing circuitry. The X-ray detector includes a plurality of detection elements arranged in a plurality of rows at least in a channel direction. The processing circuitry acquires an estimated count number of X-rays incident on the X-ray detector for each of the plurality of detection elements and determines a bit number for transmission for detection data indicating a count number of each of the plurality of detection elements based on the estimated count number.

Abstract: Systems and methods are provided for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

Abstract: Methods and systems for training computer-aided condition detection systems. One method includes receiving a plurality of images for a plurality of patients, some of the images including an annotation associated with a condition; iteratively applying a first deep learning network to each of the images to produce a segmentation map, a feature map, and an image-level probability of the condition for each of the images; iteratively applying a second deep learning network to each feature map produced by the first network to produce a plurality of outputs; training the first network based on the segmentation map produced for each image; and training the second network based on the output produced for each of the patients. The second network includes a plurality of convolution layers and a plurality of convolutional long short-term memory (LSTM) layers. Each of the outputs includes a patient-level probability of the condition for one of the patients.

Abstract: A system for generating high-resolution video from low-resolution images is configured to access a first video stream and a second video stream capturing an environment. The first video stream is captured by a first video capture device. The second video stream is captured by a second video capture device. Image frames of the first video stream are temporally synchronized with corresponding image frames of the second video stream. The system is also configured to generate a composite video stream with a higher resolution than the first or second video streams. Each composite image frame of the composite video stream is generated using a respective image frame of the first video stream and a temporally synchronized corresponding image frame of the second video stream as input.

Abstract: Disclosed is a CT image generation method for attenuation correction of PET images. According to the method, a CT image and a PET image at T1 and a PET image at T2 are acquired and input into a trained deep learning network to obtain a CT image at T2; the CT image can be applied to the attenuation correction of the PET image, thereby obtaining more an accurate PET AC (Attenuation Correction) image. According to the CT image generation method for attenuation correction of PET images, the dosage of X-rays received by a patient in the whole image acquisition stage can be reduced, and physiological and psychological pressure of the patient is relieved. In addition, the later image acquisition only needs a PET imaging device, without the need of PET/CT device, cost of imaging resource distribution can be reduced, and the imaging expense of the whole stage is reduced.

Abstract: A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

Abstract: The present disclosure relates to systems and methods for facing a tissue block. In some embodiments, a method is provided for facing a tissue block that includes imaging a tissue block to generate imaging data of the tissue block, the tissue block comprising a tissue sample embedded in an embedding material, estimating, based on the imaging data, a depth profile of the tissue block, wherein the depth profile comprises a thickness of the embedding material to be removed to expose the tissue sample to a pre-determined criteria, and removing the thickness of the embedding material to expose the tissue to the pre-determined criteria.

Abstract: A framework for multi-scan image processing. A single real anatomic image of a region of interest is first acquired. One or more emission images of the region of interest are also acquired. One or more synthetic anatomic images may be generated based on the one or more emission images. One or more deformable registrations of the real anatomic image to the one or more synthetic anatomic images are performed to generate one or more registered anatomic images. Attenuation correction may then be performed on the one or more emission images using the one or more registered anatomic images to generate one or more attenuation corrected emission images.

Abstract: Exemplary embodiments of the present invention provide a CT imaging method of coronary artery and a computer-readable storage medium, the method comprising: generating and outputting a global optimal phase image of a coronary artery; and generating and outputting a local optimal phase image of a particular trunk of the coronary artery based on a trunk selection command.

Abstract: Systems and methods to estimate 3D TOF scatter include acquisition of 3D TOF data, determination of 2D TOF data from the first TOF data, determination of first estimated scatter based on the second TOF data, reconstruction of a first estimated image based on the first estimated scatter and the second TOF data, determination of attenuated unscattered true coincidences based on the first estimated image, determination of second estimated scatter based on the first TOF data and the attenuated unscattered true coincidences, and reconstruction of an image of the object based on the first TOF data and the second estimated scatter.

Abstract: An object of the present invention is to provide an analysis method capable of analyzing time-series images by a method simpler than ever.

Abstract: A method and system for reconstructing a magnetic particle distribution model based on time-frequency spectrum enhancement are provided. The method includes: scanning, by a magnetic particle imaging (MPI) device, a scan target to acquire a one-dimensional time-domain signal of the scan target; performing short-time Fourier transform to acquire a time-frequency spectrum; acquiring, by a deep neural network (DNN) fused with a self-attention mechanism, a denoised time-frequency spectrum; acquiring a high-quality magnetic particle time-domain signal; and reconstructing a magnetic particle distribution model. The method learns global and local information in the time-frequency spectrum through the DNN fused with the self-attention mechanism, thereby learning a relationship between different harmonics to distinguish between a particle signal and a noise signal.

Abstract: Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.

Abstract: A computer-implemented system and method for generating a medical image is provided. In some embodiments, the medical image is generated by determining a location and an alignment for a first tracking detector with respect to a particle beam system. The direction of a beam generated from the particle beam system is determined. A first position of a first particle from a detected particle hit on the first tracking detector is also determined. A determination is made as to a first residual range of the first particle from a detected particle hit on a residual range detector. The system reconstructs a path for the first particle based on the location, the alignment, the first position, and the first residual range of the first particle. The resulting medical image that is generated by the system is based on the reconstructed path for the first particle.

Abstract: A method, an apparatus, a device, and a storage medium for extracting a cardiovascular vessel from a CTA image, the method including the steps of: performing erosion operation and dilation operation on image data successively via a preset structural element to obtain a structure mask; performing a slice-by-slice transformation on the plane of section images of the structural mask to acquire the first ascending aortic structure in the structural mask, and acquiring an aortic center position and an aortic radius in the last slice of the plane of section image of the said structural mask; establishing a binarized sphere structure according to the aortic center position and the aortic radius, and synthesizing a second ascending aorta structure by combining the first ascending aorta structure with the structure mask and the binarized sphere structure.

Abstract: The disclosure describes artificial reality systems and techniques for saving and exporting artificial reality data. For example, an artificial reality system includes an application engine configured to generate artificial reality content based on a pose of a user participating in an artificial reality environment and a head-mounted display (HMD) configured to output the artificial reality content to the user. The HMD includes a buffer configured to hold data representative of the artificial reality environment during a time window on a rolling basis and a capture engine configured to, in response to user input, capture the data representative of the artificial reality environment held in the buffer at a point in time at which the user input was received.

Abstract: Devices, systems, and methods obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

Abstract: An X-ray analyzer has a configuration including an X-ray source, an X-ray detector configured to detect an X-ray irradiated from the X-ray source, a rotary stage (stage) disposed between the X-ray source and the X-ray detector, and configured to hold an imaging target, and a light irradiation mechanism configured to irradiate light coaxially with an X-ray optical axis of the X-ray irradiated from the X-ray source to project a shadow of the imaging target onto a position of the X-ray detector.

Abstract: Certain examples provide an image data processing system including an anatomy detector to detect an anatomy in an image and to remove items not included in the anatomy from the image. The example system includes a bounding box generator to generate a bounding box around a region of interest in the anatomy. The example system includes a voxel-level segmenter to classify image data within the bounding box at the voxel level to identify an object in the region of interest. The example system includes an output imager to output an indication of the object identified in the region of interest segmented in the image.