A method and system of diagnosing a medical condition of a target area of a patient using a mobile device are provided. One or more magnetic field images of a target area of a patient are received. One or more hyperspectral images of the target area of the patient are received. For each of the one or more magnetic field images and the one or more hyperspectral images, a three-dimensional (3D) position of the mobile device is tracked with respect to the target are of the patient. A 3D image of the target area is generated based on the received one or more magnetic field images, one or more hyperspectral images, and the corresponding tracked 3D position of the phone with respect to each image. A medical condition of the target area is diagnosed or monitored based on the generated 3D image.

An image processing apparatus for processing a radiation image, comprises a calculation unit configured to calculate, in a calculation region, a physical amount representing a characteristic of a material, the calculation region being obtained using (a) a specific region regarding a specific material in an image representing the characteristic of the material and (b) a relative positional relationship of a radiation tube, a radiation detector, and an object, wherein the image representing the characteristic of the material is obtained using information about a plurality of radiation energies.

Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.

A computer-implemented method to improve the point collection process during registration of a bone for a computer-assisted surgical procedure is provided. Based on bone digitization data, a simulation is performed to confirm the accuracy of the registration for different digitization regions. Results are tested to identify which digitization regions meet a predefined accuracy requirement. The resulting information is used to perform a computer-assisted surgical procedure. A computerized simulation method for registration of a bone for a computer-assisted surgical procedure is also provided based on processor executing random stroking an expected exposed surface of a bone model with multiple of stroke curves to cover most of the bone model surface with uniform noise and a random sample consensus is applied to remove outlying point to yield the best registration results, to find the top subset as to overlap. A method to perform computer-assisted surgery is also provided.

In a creation method of a trained model, a reconstructed image (60) obtained by reconstructing three-dimensional X-ray image data (80) is generated. A projection image (61) is generated from a three-dimensional model of an image element (50) by a simulation. The projection image is superimposed on the reconstructed image to generate a superimposed image (67). A trained model (40) is created by performing machine learning using the superimposed image, and the reconstructed image or the projection image.

An image registration method, a computer device, and a non-transitory storage medium. The method includes: acquiring a target moving image and a target reference image to be registered, the target moving image and the target reference image being scanned medical images with the same dimension; and performing a registration process on the target moving image and the target reference image by using a pre-trained image registration model to obtain a target registration parameter. The image registration model is a deep learning model for performing registration process on a moving image and a reference image between which a scan field of view difference is greater than a predetermined difference value. The method can improve registration efficiency.

Systems and methods for ascertaining a position of an orthopedic element in space comprising: capturing a first and second images of an orthopedic element in different reference frames using a radiographic imaging technique, detecting spatial data defining anatomical landmarks on or in the orthopedic element using a deep learning network, applying a mask to the orthopedic element defined by an anatomical landmark, projecting the spatial data from the first image and the second image to define volume data, applying the deep learning network to the volume data to generate a reconstructed three-dimensional model of the orthopedic element; and mapping the three-dimensional model of the orthopedic element to the spatial data to determine the position of the three-dimensional model of the orthopedic element in three-dimensional space.

A processor derives a bone part image of a subject including a bone part from a first radiation image and a second radiation image acquired by imaging the subject with radiation having different energy distributions, derives a bone density image representing bone density in a bone region of the subject from the bone part image, derives a trabecula image representing a trabecula structure from the first radiation image, the second radiation image, or the bone part image, and superimposes the trabecula image on the bone density image to display the trabecula image and the bone density image.

A processor derives a first composition image representing a first composition included in a subject including three or more compositions from at least one radiation image acquired by imaging the subject, derives at least one removal radiation image obtained by removing the first composition from the at least one radiation image by using the first composition image, derives a plurality of other composition images representing a plurality of other compositions different from the first composition included in the subject by using the at least one removal radiation image, and derives a composite image obtained by synthesizing the first composition image and the plurality of other composition images at a predetermined ratio.

A computer visual guidance system for guiding a surgeon through an arthroscopic debridement of a bony pathology, wherein the computer visual guidance system is configured to: (i) receive a 2D image of the bony pathology from a source; (ii) automatically analyze the 2D image so as to determine at least one measurement with respect to the bony pathology; (iii) automatically annotate the 2D image with at least one annotation relating to the at least one measurement determined with respect to the bony pathology so as to create an annotated 2D image; and (iv) display the annotated 2D image to the surgeon so as to guide the surgeon through the arthroscopic debridement of the bony pathology.