Abstract: The present invention relates to a method and system for the transformation of raw mammograms to normalised presentation and where the pixel values are independent of imaging conditions. The performed method includes: contrast enhancement, for improved visibility of the breast tissue composition, whereby a region of the breast is segmented and a contrast-stretching algorithm applied to the segmented region to preferably create an enhanced raw image or mammogram; local ‘maximum’ transform, whereby a 2-dimensional first filter is designed to extract the maximum pixel value from a region of interest (ROI) to preferably create a local maximum image or map; ratio map derivation, whereby the pixel value of the ratio map measures a relative response of the said pixel to its local maximum thus capturing the difference between breast composition regardless of mammogram variations.

Abstract: The present inventive concept provides a voltage generating apparatus for X-rays including a console that receives an X-ray irradiation signal to generate a control signal and detects the X-ray irradiation signal to generate a first detection signal, a pulse control unit that receives the control signal and the first detection signal from the console to generate a second detection signal and generates a pulse signal according to the control signal and the second detection signal, and a high voltage generating unit that generates a high voltage according to the pulse signal from the pulse control unit, and an X-ray generating apparatus having the same.

Abstract: An exemplary embodiment of the present disclosure provides an MRI-compatible robot comprising one or more fiducial markers, a first planar stage comprising a first joint configured to receive a surgical tool and a first mechanism configured to move the surgical tool, a second planar stage comprising a second joint configured to receive the surgical tool and a second mechanism configured to move the surgical tool, and wherein the second planar stage is generally parallel with the first planar stage.

Abstract: A method includes obtaining, by a downhole tool, a first shape of sensor data representing one or more characteristics of a wellbore, comparing, by the downhole tool, the first shape with a set of pre-determined reference shapes stored in a downhole memory to select a pre-determined reference shape that can be imposed on the first shape, calculating one or more delta values between the first shape and the selected pre-determined reference shape, and transmitting, by the downhole tool, the pre-determined reference shape and the delta values to a processor at a surface of the wellbore.

Abstract: A system is described for generating a revascularization score for a blockage of a coronary artery in a heart. The system accesses indications of viability of myocardial tissue in the heart, a blockage state of the blockage that includes a blockage location and a blockage amount, and the perfusion territory of the myocardial tissue. Based on the myocardial tissue state, blockage state, and perfusion territory, the system generates a revascularization score for the blockage. The system generates a graphic of the heart that illustrates coronary arteries, myocardial tissue state, blockage state, and the revascularization score. The system displays the graphic to provide a visual representation of the revascularization score for the blockage of the coronary artery.

Abstract: A method for generating F-mode display of an ultrasound image includes: collecting ultrasound image data that include a plurality of physical properties; mapping the physical properties to a plurality of color components of a color space; and combining the color components to generate an F-mode display of the ultrasound image. Each of the physical properties corresponds a corresponding color component.

Abstract: A medical image processing apparatus includes processing circuitry configured to acquire medical image data representing a region including a target site, divide the medical image data into a plurality of pieces of divisional data according to an anatomical structure, extract a target region corresponding to the target site from each of the pieces of divisional data according to a particular condition, and cause an output circuit to output information on the extracted target region.

Abstract: A method for training a machine learning image segmentation algorithm to segment structural features of a blood vessel in a computed tomography (CT) image is described herein. The method comprises receiving a labelled training set for the machine learning image segmentation algorithm. The labelled training set comprising a plurality of CT images, each CT image of the plurality of CT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding CT image of the plurality of CT images.

Abstract: One or more devices, systems, methods, and storage mediums using artificial intelligence application(s) using an apparatus or system that uses and/or controls one or more imaging modalities, such as, but not limited to, angiography, Optical Coherence Tomography (OCT), Multi-modality OCT, near-infrared fluorescence (NIRAF), OCT-NIRAF, etc. are provided herein.

Abstract: The disclosure relates generally to the field of vascular system and peripheral vascular system data collection, imaging, image processing and feature detection relating thereto. In part, the disclosure more specifically relates to methods for detecting position and size of contrast cloud in an x-ray image including with respect to a sequence of x-ray images during intravascular imaging. Methods of detecting and extracting metallic wires from x-ray images are also described herein such as guidewires used in coronary procedures. Further, methods for of registering vascular trees for one or more images, such as in sequences of x-ray images, are disclosed. In part, the disclosure relates to processing, tracking and registering angiography images and elements in such images. The registration can be performed relative to images from an intravascular imaging modality such as, for example, optical coherence tomography (OCT) or intravascular ultrasound (IVUS).

Abstract: Intravascular imaging systems and methods for making and using intravascular imaging devices are disclosed. An example intravascular imaging device may comprise a catheter including an imaging device. A processor may be coupled to the catheter. The processor may be configured to process image data received from the imaging device. The processor may be configured to generate a calcium map. The calcium map may include an indicator of calcium depth to a vessel lumen surface, calcium distance to a center of the catheter, or both. A display unit may be coupled to the processor. The display unit may be configured to show a display including the calcium map.

Abstract: An average offset image is acquired without irradiation of a radiation. A first image is acquired when a first time elapses from continuous irradiation with the radiation for imaging a subject on a pixel region. A second image is acquired when a second time longer than the first time elapses from an end of the continuous irradiation. The irradiation with the radiation for imaging the subject is performed on the pixel region after an elapse of the second time from the end of the continuous irradiation and a pixel signal from the pixel region is read out to acquire a radiographic image. An offset image representing an offset component and an afterimage representing an afterimage component according to a time of the continuous irradiation, the first time, the second time, and a defined time are generated based on the first image, the second image, and the average offset image.

Abstract: Systems and methods for processing electronic images from a medical device comprise receiving a first image frame and a second image frame from a medical device, and determining a region of interest by subtracting the first image frame from the second image frame, the region of interest corresponding to a visual obstruction in the first image frame and/or second image frame. Image processing may be applied to the first image frame and/or second image frame based on a comparison between a first area of the first image frame corresponding to the region of interest and a second area of the second image frame corresponding to the region of interest, and the first image frame and/or second image frame may be provided for display to a user.

Abstract: A medical data processing apparatus includes a memory and processing circuitry. The memory stores a learned model including an input layer to which first MR data and second MR data having the same imaging target as the first MR data and an imaging parameter different from the first MR data are inputted, an output layer from which third MR data is output with a missing portion of the first MR data restored, and at least one intermediate layer arranged between the input layer and the output layer. The processing circuitry generates third MR data relating to the subject, from the first MR data serving as a process target and relating to the subject and the second MR data relating to the subject and acquired by an imaging parameter different from the first MR data serving as the process target, in accordance with the learned model.

Abstract: Surgical systems, computer-implemented methods, and software programs for producing a patient-specific virtual boundary configured to constrain movement and/or operation of a surgical tool in response to the surgical tool interacting with the patient-specific virtual boundary. The implementations include obtaining a generic virtual boundary including a generic surface with a generic edge, and positioning the generic virtual boundary relative to a virtual anatomical model such that the generic surface intersects the virtual anatomical model. The implementations include computing an intersection of the generic surface and the virtual anatomical model to define a cross-sectional contour of the virtual anatomical model, and morphing the generic edge to the cross-sectional contour to produce a customized surface with a patient-specific edge.

Abstract: There is described a system for determining a coronal artery tissue type. The system generally has an optical coherence tomography (OCT) imaging system being configured for acquiring an OCT image of coronal artery tissue; and a controller configured to perform the steps of: using trained feature extraction engines, extracting corresponding feature vectors comprising a plurality of features in at least a region of interest of the OCT image; using trained classification engines, determining corresponding preliminary coronal artery tissue types associated to the region of interest of the OCT image based on corresponding ones of the plurality of feature vectors; and using a majority voting engine, majority voting an output coronal artery tissue type associated to the region of interest of the OCT image based on the previously determined preliminary coronal artery tissue types.

Abstract: A method for providing an optimum subtraction data set includes receiving first image data sets that are recorded by a medical imaging device and map an examination object within a first temporal phase. At least one second image data set that maps the examination object within a second temporal phase and is recorded by the same or another medical imaging device is received. Mask data sets are determined. The mask data sets include at least one of the first image data sets and/or an averaging of at least one combination of the first image data sets. Subtraction data sets are generated by subtracting one of the mask data sets from the at least one second image data set, and an image quality parameter is determined for each of the subtraction data sets. An optimum subtraction data set is provided by a comparison of the image quality parameters.

Abstract: A computer-implemented method for determining the presence of a coronary lesion for a patient, including a first step of receiving at least one curvilinear or stretched multiplanar medical CT image of a coronary artery of the patient. The method further includes a step of determining a CAD-RADS (Coronary Artery Disease—Reporting and Data System value) classification value of a coronary lesion on the image or on a part of the image by using a first trained deep neural network applied directly to the detected images or parts of images.

Abstract: A voxel-based technique is provided for performing quantitative imaging and analysis of tissue image data. Serial image data is collected for tissue of interest at different states of the issue. The collected image data may be deformably registered, after which the registered image data is analyzed on a voxel-by-voxel basis, thereby retaining spatial information for the analysis. Various thresholds are applied to the registered tissue data to identify a tissue condition or state, such as classifying chronic obstructive pulmonary disease by disease phenotype in lung tissue, for example.

Abstract: An image acquisition apparatus includes: a positioner controller communicatively coupled to a positioner, wherein the positioner controller is configured to generate a control signal to cause the positioner to rectilinearly translate a patient support relative to an imager, and/or to rectilinearly translate the imager relative to the patient support; an imaging controller configured to operate the imager to generate a first plurality of two-dimensional images for a patient while the patient is supported by the patient support, and while the positioner rectilinearly translates the patient support and/or the imager; and an image processing unit configured to obtain the first plurality of two-dimensional images and arrange the two-dimensional images relative to each other to obtain a first composite image.

Abstract: Provided is a method for performing image guidance, including: acquiring a 3D magnetic resonance (MR) image of a target individual, wherein at least one region range of an object of interest is marked in the 3D MR image; acquiring a reference 3D image of the target individual, wherein the reference 3D image is a 3D-reconstructed computed tomography (CT) image; performing a 3D-3D registration on the 3D MR image and the reference 3D image, so as to mark each region range of the object of interest in the reference 3D image; and performing image guidance on the basis that the reference 3D image is adopted to characterize an initial position state.

Abstract: An image processing unit includes a grayscale image creating unit, a shininess region removal unit, a region of interest tracing unit, and a time intensity curve measuring unit. The time intensity curve measuring unit includes a pixel value measuring unit which measures the pixel value of a specific point in the fluorescence image photographed by the fluorescence image pickup element, and a measurement position movement unit which moves the measurement position of the pixel value in the pixel value measuring unit in accordance with the region of interest traced by the region of interest tracing unit.

Abstract: A system and method for generating and displaying contours. In some embodiments, the method includes: calculating a first threshold, based on a first target point in a first slice; and generating a first contour, the first contour being a boundary of a first region, the first region being a region within which: the elements of the first slice are greater than the first threshold, or the elements of the first slice are less than the first threshold.

Abstract: This application relates to a method for transmitting a scene image of a virtual scene. The method includes: rendering a first image to obtain a first rendered image, wherein the first image is obtained through acquisition of display data of a virtual scene when the display data changes; acquiring a first time point at which the rendering of the first image is completed; encoding the first rendered image to obtain an encoded image when an interval between the first time point and a second time point is not less than a first interval threshold, wherein the second time point is a time point at which image encoding is performed last time; and transmitting the encoded image to a terminal for decoding and displaying.

Abstract: A computer-implemented method for providing a vascular image data record includes receiving a plurality of projection-X-ray images recorded in temporal succession. The plurality of projection-X-ray images at least partially map a common examination region of an examination object. The plurality of projection-X-ray images map a temporal change in the examination region of the examination object. A change image data record is determined in each case based on at least one region of interest of the plurality of projection-X-ray images. The at least one region of interest includes a plurality of image points. The change image data record in each case includes a time-intensity curve for each of the image points. The vascular image data record is generated based on the change image data record, and the vascular image data record is provided.

Abstract: A medical image processing apparatus comprises processing circuitry configured to select a reference image from blood vessel images based on contrast images acquired in time series and conduct a transformation process on other blood vessel images such that blood vessel shapes in the other blood vessel images match a blood vessel shape in the reference image, generate a color image where a color that corresponds to a temporal change in a pixel value is assigned to each pixel based on the blood vessel images after the transformation process, and display the color image on a display.

Abstract: Embodiments related to methods of use of an image analysis system for identifying residual cancer cells after surgery are disclosed. In some embodiments, a patient-specific threshold used to detect abnormal cells in a surgical site can be determined. A medical imaging device can be configured to produce a set of images of an anatomy of a patient. An image analysis system, comprising one or more processors, can be configured to receive the set of images, and analyze the set of images to determine a patient-specific threshold to use to detect abnormal tissue of the patient.

Abstract: A squeeze-enhanced axial transformer (SeaFormer) for mobile semantic segmentation is disclosed, including a shared stem, a context branch, a spatial branch, a fusion module and a light segmentation head, wherein the shared stem produces a feature map; the context branch obtains context-rich information; the spatial branch obtains spatial information; the fusion module incorporates the features in the context branch into the spatial branch; and the light segmentation head receives the feature from the fusion module and output the results. This application is also related to the layer of the SeaFormer, as well as methods thereof.

Abstract: A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

Abstract: Machine learning technologies are used to identify and separating abnormal and normal subjects and identifying possible disease types with images (e.g., optical coherence tomography (OCT) images of the eye), where the machine learning technologies are trained with only normative data. In one example, a feature or a physiological structure of an image is extracted, and the image is classified based on the extracted feature. In another example, a region of the image is masked and then reconstructed, and a similarity is determined between the reconstructed region and the original region of the image. A label (indicating an abnormality) and a score (indicating a severity) can be determined based on the classification and/or the similarity.

Abstract: Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

Abstract: An example method for updating convolutional neural network includes: obtaining a sample with a classification label; performing a first operation on the sample based on parameters of each layer of front-end network, to obtain a first operation result; performing a second operation on the sample based on the first operation result and the parameters of each layer of back-end network that the first GPU has, to obtain a second operation result; separately sending the first operation result to the other GPUs; receiving a third operation result obtained after each other GPU performs a third operation on the sample based on their parameters of each layer of back-end network and the first operation result; combining the second and third operation results to obtain a classification result; determining a prediction error based on the classification result and the classification label; and updating the convolutional neural network based on the prediction error.

Abstract: The disclosure relates to a method for creating a three-dimensional digital subtraction angiography image of a vascular system of a patient. The method includes: providing a first reconstructed three-dimensional filling image which was acquired during at least partial contrast agent filling of the vascular system with a first contrast agent; providing a second reconstructed three-dimensional filling image which was acquired during at least partial contrast agent filling of the vascular system with a second contrast agent; and subtracting the first three-dimensional filling image from the second three-dimensional filling image so that a three-dimensional subtraction angiography image is produced, wherein the first contrast agent and the second contrast agent differ in that one of the two causes increased X-ray absorption and the other causes reduced X-ray absorption relative to a vascular system without contrast agent.

Abstract: The present disclosure relates to a method for configuring a medical device. The method comprises: providing a set of one or more parameters for configuring the medical device. Each parameter of the set has predefined values. A set of values of the set of parameters may be selected from the predefined values. Using the selected values the set of parameters may be set, which results in an operational configuration of the medical device. The medical device may be controlled to operate in accordance with the operational configuration, thereby an operating status of the medical device may be determined. Based on at least the operating status the operational configuration may be maintained or the selecting, setting and controlling may be repeatedly performed until a desired operating status of the medical device can be determined based on the operating statuses resulting from the controlling.

Abstract: A method includes obtaining multiple spatially-displaced input images of a scene based on image data captured using multiple imaging sensors. The method also includes dividing each of the input images into multiple overlapping sub-images. The method further includes generating multiple overlapping sub-image gain maps based on the sub-images. In addition, the method includes combining the sub-image gain maps to produce a final gain map identifying relative gains of the imaging sensors. An adjacent and overlapping pair of sub-image gain maps are combined by renormalizing gain values in at least one of the pair of sub-image gain maps so that average gain values in overlapping regions of the pair of sub-image gain maps are equal or substantially equal.

Abstract: An object detection method for a 3D mammogram is disclosed. The object detection method comprises steps of: controlling N filters to execute a filtering computation in the 3D mammogram respectively to generate N 3D filtering images; computing a difference variation among the plurality of voxels to obtain a blurriness value of the plurality of voxels; using the blurriness value of the plurality of voxels in a decision module to execute a plurality of first decision operators to generate a plurality of first decision results, and using one of the plurality of first decision results to execute the plurality of second decision operators to generate a plurality of second decision results; and executing a final decision operator by using the plurality of first decision results and the plurality of second decision results to generate a detection object of the 3D mammogram.

Abstract: A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, and matching the initial 3D model with the subsequent 3D model based on a registration.

Abstract: A system including a camera configured to capture a first image of an offshore structure and a second image of the offshore structure, and a processor coupled to the camera, the processor configured to receive the first image and the second image from the camera, recognize a first visual pattern in the first image and the second image, extract pixel coordinates associated with a first position of the first visual pattern in the first image and a second position of the first visual pattern in the second image, and determine a first distance between the first position and the second position, or the first position of the first visual pattern and a first position of a second visual pattern fixably attached to the offshore structure in the first image.

Abstract: A method for providing an optimum subtraction data set includes: receiving first image data sets acquired by a medical imaging device and which map an object under examination within a first time phase; receiving at least one second image data set acquired by the same or another medical imaging device and which maps a change in the object under examination within a second time phase; dividing the at least one second image data set into a plurality of image regions; generating subtraction image regions for the plurality of image regions; determining an image quality parameter for each subtraction image region; determining an optimum subtraction image region for each image region of the plurality of image regions of the at least one second image data set by comparing the image quality parameters; generating the optimum subtraction data set from the optimum subtraction image regions; and providing the optimum subtraction data set.

Abstract: The present application provides a medical imaging method and system and a non-transitory computer-readable storage medium. The medical imaging method comprises obtaining an original image acquired by an X-ray imaging system, and post-processing the original image based on a trained network to obtain an optimized image after processing.

Abstract: Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

Abstract: A system uses range and Doppler velocity measurements from a lidar system and images from a video system to estimate a six degree-of-freedom trajectory of a target. The system estimates this trajectory in two stages: a first stage in which the range and Doppler measurements from the lidar system along with various feature measurements obtained from the images from the video system are used to estimate first stage motion aspects of the target (i.e., the trajectory of the target); and a second stage in which the images from the video system and the first stage motion aspects of the target are used to estimate second stage motion aspects of the target. Once the second stage motion aspects of the target are estimated, a three-dimensional image of the target may be generated.

Abstract: Systems and methods for processing electronic images from a medical device comprise receiving a first image frame and a second image frame from a medical device, and determining a region of interest by subtracting the first image frame from the second image frame, the region of interest corresponding to a visual obstruction in the first image frame and/or second image frame. Image processing may be applied to the first image frame and/or second image frame based on a comparison between a first area of the first image frame corresponding to the region of interest and a second area of the second image frame corresponding to the region of interest, and the first image frame and/or second image frame may be provided for display to a user.

Abstract: A method of performing a procedure on a breast of a patient includes compressing the breast of the patient with a paddle of an imaging system and performing an initial imaging procedure on the breast of the patient. The initial imaging procedure includes imaging the breast with an x-ray source at a first energy to obtain a first image and a second energy to obtain a second image. A composite image is generated from the first image and the second image and displayed. A region of interest on the composite image is targeted and a biopsy is performed.

Abstract: A method and a device are for detecting an anatomical feature of a section of a blood vessel. In an embodiment, the device includes a first interface embodied to receive a medical image data record, including a region of an anatomy of a patient to be examined, which has the section of a blood vessel; a first computing unit, embodied to apply a first algorithm pretrained on an anatomical feature for detecting the anatomical feature based upon the medical image data record; and a second interface embodied to provide an item of information based upon the detection of the anatomical feature. The anatomical feature includes a change in caliber of a blood vessel. Further, the presence of the anatomical feature is indicative of vascular pulmonary disease, in particular of chronic thromboembolic pulmonary hypertension.

Abstract: According to one embodiment, a medical image processing apparatus includes processing circuitry. The processing circuitry specifies, before position alignment between a first X-ray image and a second X-ray image which is acquired with a device inserted, a device area candidate in the second X-ray image as a candidate of an area where the device appears. The processing circuitry performs the position alignment using first processing of removing the specified device area candidate or second processing of reducing a contribution of the specified device area candidate.

Abstract: An image processing system and method obtains one or more source images in which a damaged vehicle is represented, and performs one or more image processing techniques on the obtained images to determine, predict, estimate, and/or detect damage that has occurred at various locations on the vehicle. Specifically, the system/method generates damage parameter values based on the image-processed images, where each value corresponds to damage at a particular location on the vehicle. The image processing techniques may include generating a composite image of the damaged vehicle, aligning and/or isolating the image, applying convolutional neural network techniques to the image to generate the damage parameter values, etc. Based on the generated values, the image processing system/method determines, predicts, estimates, and/or selects a set of parts that are needed to repair the vehicle and, in some embodiments, orders the parts needed to repair the vehicle.

Abstract: A medical data processing apparatus includes a memory and processing circuitry. The memory stores a learned model including an input layer to which first MR data and second MR data having the same imaging target as the first MR data and an imaging parameter different from the first MR data are inputted, an output layer from which third MR data is output with a missing portion of the first MR data restored, and at least one intermediate layer arranged between the input layer and the output layer. The processing circuitry generates third MR data relating to the subject, from the first MR data serving as a process target and relating to the subject and the second MR data relating to the subject and acquired by an imaging parameter different from the first MR data serving as the process target, in accordance with the learned model.