Abstract: Methods and systems are provided for reducing anomalies in ultrasound images. One example method includes combining a plurality of sub-band components to attenuate anomalies in a first ultrasound image formed by the combination of the plurality of sub-band components, wherein the plurality of sub-band components are combined based on a plurality of adaptive weights output from a machine learning model and wherein the plurality of adaptive weights are associated with the plurality of sub-band components. The method further includes outputting for a display device, the first ultrasound image.

Abstract: Disclosed are systems and methods for mapping a patient's lymphatic system. An exemplary method includes generating a three-dimensional (3D) model of a bronchial network in the patient's chest, generating a lymphatic tree map by fitting a model lymph node map to the 3D model, receiving locations of a plurality of identified lymph nodes, updating the positions of lymph nodes on the lymphatic tree map, and displaying the updated lymphatic tree map.

Abstract: Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.

Abstract: The present disclosure is directed to image reconstruction techniques used in magnetic resonance imaging. The techniques disclosed include calculating, for each of image reconstruction tasks to be performed, a calculation capability requirement value of the task by a magnetic resonance system, and determining whether the calculation capability requirement value of the task is greater than a predetermined threshold. If so, the task is sent to a shared image reconstruction apparatus, so that the shared image reconstruction apparatus performs the task. Otherwise, the task is sent to a local image reconstruction apparatus, so that the local image reconstruction apparatus performs the task. The techniques described herein facilitate a reduction in hardware cost required for image reconstruction in MRI.

Abstract: A visualization system including multiple light sources, an image sensor configured to detect imaging data from the multiple light sources, and a control circuit is disclosed. At least one of the light sources is configured to emit a pattern of structured light. The control circuit is configured to receive the imaging data from the image sensor, generate a three-dimensional digital representation of the anatomical structure from the pattern of structured light detected by the imaging data, obtain metadata from the imaging data, overlay the metadata on the three-dimensional digital representation, receive updated imaging data from the image sensor, and generate an updated three-dimensional digital representation of the anatomical structure based on the updated imaging data. The visualization system can be communicatively coupled to a situational awareness module configured to determine a surgical scenario based on input signals from multiple surgical devices.

Abstract: Presented herein are efficient and reliable systems and methods for calculating and extracting three-dimensional central axes of bones of animal subjects—for example, animal subjects scanned by in vivo or ex vivo microCT platforms—to capture both the general and localized tangential directions of the bone, along with its shape, form, curvature, and orientation. With bone detection and segmentation algorithms, the skeletal bones of animal subjects scanned by CT or microCT scanners can be detected, segmented, and visualized. Three dimensional central axes determined using these methods provide important information about the skeletal bones.

Abstract: The present invention relates to processing X-ray images of an object. In order to improve the accuracy for interactive geometrical measurements, a device (10) for processing of an X-ray image of an object (30) is provided. The device comprises an input unit (12) and a processing unit (14). The input unit is configured to provide a shape related information (16) from an object (30) to be irradiated. The input unit is also configured to provide a generic object model (20), and to provide an actual X-ray image (18) of the object. The processing unit is configured to adapt the generic object model based on the shape related information in order to generate an individual object model (22). The processing unit is also configured to determine, based on the individual object model, an individual image processing modificator (24) for processing at least one part of the X-ray image, and to apply the individual image processing modificator for further processing of the X-ray image.

Abstract: The invention relates to a method for providing at least one evaluation method for samples (2) in at least one optical application system (5) of a microscope-based application technology, where the following steps are performed: developing the evaluation method at least by an automated training (130) of an evaluation means (60) for an evaluation (120) of a specific type of sample on the basis of the application technology by an optical training system (4), the training (130) determining a training information (200) which at least partially defines the evaluation method, at least the training information (200) for distributing (140) the evaluation method to the at least one application system (5), wherein the provision takes place as a function of the type of sample and of the application technology.

Abstract: This disclosure relates to systems and methods for solving generic inverse problems by providing a coupled representation architecture using transform learning. Convention solutions are complex, require long training and testing times, reconstruction quality also may not be suitable for all applications. Furthermore, they preclude application to real-time scenarios due to the mentioned inherent lacunae. The methods provided herein require involve very low computational complexity with a need for only three matrix-vector products, and requires very short training and testing times, which makes it applicable for real-time applications. Unlike the conventional learning architectures using inductive approaches, the CASC of the present disclosure can learn directly from the source domain and the number of features in a source domain may not be necessarily equal to the number of features in a target domain.

Abstract: An X-ray CT system according to an embodiment includes processing circuitry configured to: execute first scanning of acquiring a first subject data set corresponding to first X-ray energy by irradiating a first region of a subject with X-rays; execute, after the first scanning, second scanning of acquiring a second subject data set corresponding to second X-ray energy and a third subject data set corresponding to third X-ray energy different from the second X-ray energy by irradiating a second region included in the first region with X-rays; and perform material decomposition among a plurality of reference materials based on: a fourth subject data set obtained based on the first subject data set and one of the second subject data set and the third subject data set; and the other of the second subject data set and the third subject data set.

Abstract: Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition.

Abstract: Method and system for reducing a number of eigenvectors. For example, a computer-implemented method for reducing a number of eigenvectors, the method comprising: obtaining a plurality of to-be-processed matrices; mapping the plurality of to-be-processed matrices to a space of symmetric positive definite matrices to form a Riemannian manifold corresponding to a Riemannian kernel function; obtaining a kernel-function matrix by using at least a principal component analysis to calculate one or more inner products of the mapped plurality of matrices based on at least the Riemannian kernel function; calculating a first group of eigenvectors of the kernel-function matrix, the first group of eigenvectors including a first number of eigenvectors; and selecting one or more eigenvectors from the first group of eigenvectors to obtain a second group of eigenvectors, the second group of eigenvectors including a second number of eigenvectors; wherein the second number is less than the first number.

Abstract: The present specification discloses a method and an apparatus for training a transaction feature generation model, and a method and an apparatus for generating a transaction feature. The method for generating a transaction feature can include the following: obtaining a target dataset, where the target dataset includes some pieces of transaction data; obtaining some original features of the transaction data and determining one or more combination methods for the original features; determining a feature vector of a new feature that is obtained by combining the original features based on each combination method; inputting the feature vector into a trained transaction feature generation model, and outputting a prediction result of the new feature; and selecting some new features whose prediction results meet a specified condition as transaction features generated for the target dataset.

Abstract: The Parkinson's disease information providing apparatus using a neuromelanin image according to an aspect of the present disclosure includes an image receiving unit which acquires an MRI image obtained by capturing a brain of a patient; an image preprocessing unit which preprocesses the acquired MRI image to observe the neuromelanin region used as an image bio marker of the Parkinson's disease; an image processing unit which analyzes the preprocessed MRI image to classify a first image including the neuromelanin region and detects the neuromelanin region from the classified first image; and an image analyzing unit which diagnoses whether the patient has the Parkinson's disease by analyzing whether the detected the neuromelanin region is normal.

Abstract: A unified coil assembly for magnetic resonance imaging is disclosed. The coil assembly includes an RF coil element and a shim coil array with a shim coil element. The shim coil element is physically separated or partially separated from the RF coil element. The shim coil element includes a DC current loop having a DC power supply connection to allow DC current to generate a local BO magnetic field. The unified coil array assembly is configured to simultaneously provide an RF mode for at least one of transmit or receive and a direct current mode to generate a local B0 magnetic field for B0 shimming Larger number of shim coils relative to the RF coil element provides superior shimming performance. The mutual inductance between the shim coil element and the RF coil element is minimized by proposed geometrical decoupling methods in order to minimize the RF interaction between the two.

Abstract: Techniques are provided for multi-modal sensitive recognition. A digital data set for an object is obtained according to a modality, where the digital data set includes digital representations of the object at different values of a dimension of relevance of the modality. A reference location associated with the object is identified. A modal descriptor is derived for the modality according to an implementation of a multi-modal recognition algorithm by deriving a set of feature descriptors for the reference location and at the different values of the corresponding dimension of relevance, calculating a set of differences between the feature descriptors in the set of feature descriptors, and aggregating the set of differences into the modal descriptor. A device is then configured to initiate an action as a function of the modal descriptor.

Abstract: Treatment trajectory guidance systems and methods are provided. In one embodiment, the method for treatment trajectory guidance in a patient's brain includes obtaining a three- dimensional (3D) brain model that includes a model of an anatomy, the model of the anatomy including a plurality of feature points; modifying the 3D brain model based on magnetic resonance (MR) data of the patient's brain from a magnetic resonance imaging (MRI) device to obtain a plurality of modified feature points on a modified model of the patient's anatomy in the patient's brain; displaying on a display a first planned trajectory for treating the patient's anatomy based on the plurality of modified feature points; and displaying, on the display, a first estimated treatment result for the first planned trajectory.

Abstract: A method of image completion comprises: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types; inputting the training image corresponding to the plurality of objective types such that the plurality of conditional generative adversarial networks respectively conduct a corruption feature training; inputting the image in need of repair and respectively conducting an image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images; and judging a reasonable probability of the plurality of repaired images through a probability analyzer, choosing an accomplished image and outputting the accomplished image through an output interface.

Abstract: Apparatus and methods are described for use with an endoluminal device that includes one or more radiopaque portions and that moves through a lumen of a subject. A sequence of radiographic images of a portion of the subject's body, in which the lumen is disposed, is acquired, during movement of the endoluminal device through the lumen. Locations at which the one or more radiopaque portions of the endoluminal device were imaged during the movement of the endoluminal device through the lumen are identified, by analyzing the sequence of radiographic images. A set of locations at which the one or more radiopaque portions were disposed during the movement of the endoluminal device through the lumen is defined, and an endoluminal path of the device through the lumen is estimated based upon the set of locations. Other applications are also described.

Abstract: A method and system for fusing image data. The method may include obtaining a first volume image and a second volume image. The method may further include casting a plurality of rays through at least one of the first volume image or the second volume image. Each of the plurality of rays may correspond to a pixel of an image to be displayed. For each of at least a portion of the plurality of rays, the at least one processor may further be directed to cause the system to set a series of sampling positions along the ray. The method may further include selecting a reference position from the series of sampling positions. The method may further include determining fusion data of the ray. The method may further include determining a pixel value of a pixel of the image to be displayed that corresponds to the ray.

Abstract: A method of providing a quantitative volumetric assessment of organ health or a quantitative map of an organ. The method comprises obtaining a volumetric map of organ health comprising information defining a state of tissue health across at least part of an organ, receiving an input defining at least one organ section, determining an assessment organ volume based at least partly on the at least one defined organ section, calculating an organ-viability measure for the assessment organ volume based at least partly on information within the volumetric map defining the state of tissue health across the organ volume, and outputting an indication of the organ-viability measure.

Abstract: In some examples, a method includes receiving a test image from a user or client, the test image depicting an anatomical region or structure of a human body, obtaining a reference image corresponding to the anatomical region or structure depicted in the test image, and analyzing the test image and the reference image to obtain a set of differences between the two images. In some examples, the method further includes, based at least in part on the set of differences, detecting a possible abnormality in the test image and outputting a result to the user or client. In some examples, a fast R-CNN is used to detecting the possible abnormality in the test image.

Abstract: An image processing apparatus extracts a first region and a second region from a medical image, identifies a third region that is included in the second region and that is at a distance greater than or equal to a threshold from the first region, and acquires a feature value that is a value indicating a feature of the second region on the basis of the third region.

Abstract: An image processing apparatus supports detection of pathological change over time in portion of a captured region commonly included in a first image and a second image, the first image and the second image acquired by capturing a subject at different respective times. The image processing apparatus includes an acquisition unit configured to acquire a subtraction image of the first image and the second image representing the pathological change over time as a difference and a recording unit configured to record information indicating said portion using a name different from a name of the commonly included region in a storage unit in association with the subtraction image.

Abstract: Embodiments of the present systems and methods may provide techniques that are applicable to a variety of imaging modalities and that utilize prior knowledge of the dynamics of a physiological system to analytically generate augmented samples for machine learning. For example, in an embodiment, a method implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise generating a transform for an image of tissue based on deformation of the tissue under compression, obtaining an image of tissue using an imaging modality, and generating an output image by transforming the image of the tissue using the transform.

Abstract: In one embodiment, a medical image processing apparatus includes a memory storing a predetermined program and processing circuitry. The processing circuitry is configured, by executing the predetermined program, to acquire a plurality of images that are obtained by imaging a same object and are different in imaging time, classify tissue property of the object into a plurality of tissue-property classes by analyzing the tissue property of the object based on pixel values of respective regions of the plurality of images, assign the classified tissue-property classes to the respective regions of the plurality of images, and estimate change in disease state of the object from change in the classified tissue-property classes in respectively corresponding regions of the plurality of images.

Abstract: The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks to detect abnormalities in images of a human organ. In one implementation, a method is provided for training a neural network system, the method may include applying a perception branch of an object detection network to frames of a first subset of a plurality of videos to produce a first plurality of detections of abnormalities. Further, the method may include using the first plurality of detections and frames from a second subset of the plurality of videos to train a generator network to generate a plurality of artificial representations of polyps, and training an adversarial branch of the discriminator network to differentiate between artificial representations of the abnormalities and true representations of abnormalities.

Abstract: Abnormalities of blood vessels associated with brains of individuals can be detected by analyzing images that include the brains of the individuals. Blood vessels included in the images can be identified and densities of blood vessels across hemispheres can be determined. Differences between densities of blood vessels in different hemispheres of the brains of the individuals can be used to determine a probability of an abnormality with respect to one or more blood vessels associated with the brains of the individuals. User interfaces can be generated indicating possible abnormalities associated with the brains of the individuals.

Abstract: Technologies are described for the automated determination of materials. For example, material can be automatically identified (e.g., unique material numbers can be determined) based on sensor data and using machine learning models. In some implementations, as part of a first phase, a first set of sensor information describing the material is obtained. Using the first set of sensor information, a material class of the material is determined. As part of a second phase, a second set of sensor information describing the material is obtained. Using the second set of sensor information, the specific material is identified (e.g., a unique material identifier is determined for the material).

Abstract: Provided is an ultrasound diagnosis apparatus configured to automatically search a cross-section including a desired feature of an object. The ultrasound diagnosis apparatus includes: a display; a memory storing one or more instructions; and at least one processor configured to execute the stored one or more instructions to: acquire three-dimensional (3D) ultrasound volume data regarding a region of interest of an object; identify a plurality of two-dimensional (2D) cross-sections, each including at least one feature to be observed, based on the 3D ultrasound volume data; determine priority levels of the plurality of 2D cross-sections based on the at least one feature included in each of the plurality of 2D cross-sections; and control the display to display a plurality of 2D ultrasound images respectively corresponding to at least some of the plurality of 2D cross-sections based on the priority levels.

Abstract: The present disclosure provides methods and devices for determining liver segments in a medical image. The methods may be implemented on the devices. The method may include: obtaining a scan image; obtaining a segmentation protocol; obtaining segmentation information associated with the scan image; determining one or more marked points based on the segmentation information and the segmentation protocol; determining one or more segmentation surfaces based on the one or more marked points; and determining a segmentation result of at least part of a liver in the scan image based on the one or more segmentation surfaces.

Abstract: A computer-implemented method for reconstructing a MRI image, including: receiving a plurality of MRI measurement data sets ƒ1 to ƒN, wherein each data set is acquired from an examination object based on a different MRI protocol of an MRI system; receiving MRI images u10 to uN0 corresponding to the MRI measurement data sets ƒ1 to ƒN; applying, in at least a first step GD1, trained functions to the MRI images u10 to uN0, using a neural network and a forward-sampling operator, wherein at least one output MRI image uT is generated; and providing the at least one output MRI image uT, wherein the forward-sampling operator determines an agreement between at least one MRI image u10 to uN0 and the corresponding MRI measurement data set ƒ1 to ƒN.

Abstract: An x-ray imaging apparatus and associated methods are provided to process projection data from an offset detector during a helical scan, including view completion. The detector may be offset in the channel and/or axial direction. Projection data measured from a current view is combined with projection data measured from at least one conjugate view to reconstruct a target image. A two-dimensional aperture weighting scheme is used to address data redundancy.

Abstract: Reconstruction of projection images of a CBCT scan is performed by generating simulated projection data, comparing the simulated projection data to the projection images of the CBCT scan, determining a residual volume based on the comparison, and using the residual volume to determine an accurate reconstructed volume. The reconstructed volume can be used to segment a tumor (and potentially one or more organs) and align the tumor to a planning volume (e.g., from a CT scan) to identify changes, such as shape of the tumor and proximity of the tumor to an organ. These changes can be used to update a radiation therapy procedure, such as by altering a radiation treatment plan and fine-tuning a patient position.

Abstract: A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

Abstract: A method for imaging may include directing a PET scanner to perform scans of a subject injected with a tracer during a first time period. The method may also include generating PET images by reconstructing the PET data that are generated by the PET scanner based on the scans of the subject. The method may also include determining a first portion of a blood input function of the tracer in the subject based on the PET images. The method may also include determining an integral of a second portion of the blood input function based on a kinetic model, the PET data, and the first portion of the blood input function. The method may also include determining kinetic parameters of the kinetic model based on the PET data, the first portion of the blood input function, and the integral of the second portion of the blood input function.

Abstract: Disclosed herein is a system and method, which utilize primary beam modulation to enable single-scan dual-energy CT (DECT) on a conventional CT scanner. An attenuation sheet with a spatially-varying pattern is placed between the x-ray source and the imaged object. During the CT scan, the modulator selectively hardens the x-ray beam at specific detector locations. Thus, this method simultaneously acquires high and low energy data at each projection angle. High and low energy CT images can then reconstructed from the projections via an iterative CT reconstruction algorithm, which accounts for the spatial modulation of the projected x-rays.

Abstract: There is provided a computer implemented method of automatic segmentation of three dimensional (3D) anatomical region of interest(s) (ROI) that includes predefined anatomical structure(s) of a target individual, comprising: receiving 3D images of a target individual, each including the predefined anatomical structure(s), each 3D image is based on a different respective imaging modality. In one implementation, each respective 3D image is inputted into a respective processing component of a multi-modal neural network, wherein each processing component independently computes a respective intermediate, and the intermediate outputs are inputted into a common last convolutional layer(s) for computing the indication of segmented 3D ROI(s). In another implementation, each respective 3D image is inputted into a respective encoding-contracting component a multi-modal neural network, wherein each encoding-contracting component independently computes a respective intermediate output.

Abstract: An apparatus acquires a first converted image by converting resolution of a first image of a subject into first resolution, acquires a second converted image by converting resolution of a second image into the first resolution, acquires first deformation information for executing image registration between the first converted image and the second converted image with the first resolution, and generates a subtraction image with the first resolution based on the first deformation information, the subtraction image showing a difference between the first converted image and the second converted image, or generates a second subtraction image based on second deformation information obtained in such a manner that resolution of the first deformation information is converted into the resolution of the first image, the second subtraction image showing a difference between the first image and the second image.

Abstract: The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.

Abstract: A method for generating attenuation map is disclosed. The method includes acquiring an anatomic image and PET data indicative of a subject, wherein the anatomic image comprises a plurality of voxels. The method also includes fetching a reference image to register the anatomic image, the reference image includes voxel segmentation information. The method further includes segmenting the anatomic image into a plurality of regions based on the voxel segmentation information. The method further includes generating a first attenuation map corresponding to the anatomic image by assigning attenuation coefficients to the plurality of regions. The method further includes calculating a registration accuracy between the anatomic image and the reference image. The method further includes determining a probability distribution of attenuation coefficient.

Abstract: Systems and methods for assisting a physician in a medical intervention comprises performing a 2D-3D deformable registration, and more particularly, performing a 2D-3D registration based on multiple live 2D fluoroscopic views, and implemented on a multi-core processing framework such as a Graphics Processing Unit.

Abstract: A method for noise reduction in a three-dimensional computed tomography dataset, which is reconstructed from two-dimensional projection images recorded with an x-ray device using different recording geometries, is provided. In a post-processing section following the reconstruction of the computed tomography dataset, to obtain a first intermediate dataset, a first, edge-preserving filter is applied to the reconstructed computed tomography dataset. To obtain a second intermediate dataset, a second, morphological filter is applied to the reconstructed computed tomography dataset. A first weighting dataset weighting edges more strongly is established from a subtraction dataset of the first intermediate dataset and the second intermediate dataset. A noise-reduced result dataset is established as a weighted sum of the first intermediate dataset and the second intermediate dataset.

Abstract: Embodiments and aspects described herein provide a method of determining pressure difference across a tube arising from fluid flow within the tube, comprising: obtaining three-dimensional time dependent fluid velocity data at a plurality of points along the tube; processing the three-dimensional time dependent fluid velocity data to determine: i) a flow rate (Q) of the fluid through the tube; ii) the kinetic energy (K) of the fluid flow through the tube; iii) an advective energy rate (A) of the fluid flow through the tube; and iv) a viscous dissipation rate (V) pertaining to the fluid flow; and calculating the pressure difference in dependence on all of the flow rate (Q), kinetic energy (K), advective energy rate (A), and viscous dissipation rate (V). Further embodiments and aspects are also described.

Abstract: Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Abstract: Apparatus and methods are described including an endoluminal device configured to move along a portion of a lumen of a subject's body, an extraluminal imaging device, and at least one computer processor. While the endoluminal device moves along the portion of the lumen, a display displays an extraluminal image of the lumen in which a first indication of a location of the lumen is shown. The extraluminal imaging device acquires a sequence of extraluminal images of the endoluminal device moving along the portion of the lumen. The indication of the location of the lumen that is displayed is updated based upon the acquired sequence of extraluminal images, and the acquired sequence of images is displayed with the updated indication of the location of the lumen overlaid upon the images. Other applications are also described.

Abstract: A computer-implemented method, an apparatus, and a system for estimating synthetic values of quantitative metrics are provided. They involve calculating new, more accurate boundaries using a classifier based on local intensity and spatial estimators, for the segmentation mask provided by a non-local means patch-based segmentation in a test image, and estimating for the pixels of interest at least one synthetic value of a quantitative metric using a given value of the quantitative metric assigned to the reference images and the boundaries. The method, apparatus, and system provide the advantage of generating synthetic values directly comparable against known values for given subjects or against predetermined scales for diagnostic or prognostic purposes. In the specific case of Alzheimer's disease, the invention stretches the predictive range up to two full decades, which constitutes a significant advance in the field of medical diagnostics.

Abstract: A method and system for spectral adjustment for a digital X-ray imaging system. The method includes obtaining a set of initial digital X-ray images and then weight factoring the images to generate a set of weight factored digital X-ray images. The weight-factored digital X-ray images are then combined to generate a composite image that is spectrally distinct from the set of initial digital X-ray images.

Abstract: Systems and methods are provided for preparation of orthodontics and prosthodontics. A method may include scanning a patient's teeth to form first 3D data of the patient's teeth including a removable element that obscures part of the dental surfaces of the patient's teeth and non-obscured tooth surfaces, removing the removable element form the patient's teeth so that the removable element no longer obscures the part of the dental surfaces of the patient's teeth, and scanning the previously obscured part of the dental surfaces of the patent's teeth and the non-obscured tooth surfaces.

Abstract: Embodiments for implementing intelligent classification of region of interest in an organism in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. The one or more regions of interest may be classified into one of a plurality of classes by applying one or more perfusion models, representing spatio-temporal behavior of the contrast agent reflected by the time series data, and by using a machine learning operation.