A pulmonary function identifying method includes: obtaining a first image, having first image elements, and a second image, having second image elements, respectively corresponding to a first state and a second state of a lung; extracting first feature points of the first image and second feature points of the second image; registering the first image with the second image using a boundary point set registeration method and an inner tissue registeration method according to the first feature points and the second feature points, so that the first image elements correspond to the second image elements and tissue units of the lung; and determining functional index values representative of the tissue units of the lung using a ventilation function quantification method according to the first image elements and the second image elements corresponding to the first image elements.

A machine learning device includes: a generation unit generating a first shape model representing a shape of an object before deformation and a second shape model representing a shape of the object after the deformation based on measurement data before and after the deformation; and a learning unit learning a feature amount including a difference value between each micro region and another micro region that constitute the first shape model, and a relation providing a displacement from the each micro region of the first shape model to each corresponding micro region of the second shape model.

A computer implemented method for determining a two dimensional DRR referred to as dynamic DRR based on a 4D-CT, the 4D-CT describing a sequence of three dimensional medical computer tomographic images of an anatomical body part of a patient, the images being referred to as sequence CTs, the 4D-CT representing the anatomical body part at different points in time, the anatomical body part comprising at least one primary anatomical element and secondary anatomical elements, the computer implemented method comprising the following steps: acquiring the 4D-CT; acquiring a planning CT, the planning CT being a three dimensional image used for planning of a treatment of the patient, the planning CT being acquired based on at least one of the sequence CTs or independently from the 4D-CT, acquiring a three dimensional image, referred to as undynamic CT, from the 4D-CT, the undynamic CT comprising at least one first image element representing the at least one primary anatomical element and second image elements representing the secondary anatomical elements; acquiring at least one trajectory, referred to as primary trajectory, based on the 4D-CT, the at least one primary trajectory describing a path of the at least one first image element as a function of time; acquiring trajectories of the second image elements, referred to as secondary trajectories, based on the 4D-CT; for the image elements of the undynamic CT, determining trajectory similarity values based on the at least one primary trajectory and the secondary trajectories, the trajectory similarity values respectively describing a measure of similarity between a respective one of the secondary trajectories and the at least one primary trajectory; determining the dynamic DRR by using the determined trajectory similarity values, and, in case the planning CT is acquired independently from the 4D-CT, further using a transformation referred to as planning transformation from the undynamic CT to the planning CT, at least a part of image values of image elements of the dynamic DRR being determined by using the trajectory similarity values.

The present disclosure is related to systems and methods for orthosis design. The method includes obtaining a three-dimensional (3D) model associated with a subject. The method includes obtaining one or more reference images associated with the subject. The method includes determining, based on the 3D model and the one or more reference images, orthosis design data for the subject. The orthosis design data may be used to determine an orthosis for the subject.

A device for providing 3D image registration includes: a collection unit acquiring 3D depth data of a part and an image of a registration body from a depth recognition camera and acquiring three-dimensional coordinates of a positioning tool on the registration body from a positioning device; a first registration unit performing surface registration of a pre-stored 3D medical image of patient and the 3D depth data; a second registration unit extracting camera reference three-dimensional coordinates of a landmark attached to the registration body from the image of the registration body and converting pre-stored relative position information of the landmark with reference to the three-dimensional coordinates of the positioning tool, to perform registration of camera reference three-dimensional coordinates of the landmark and the converted position information thereof; and a third registration unit performing final registration by using results of registration performed by the first registration unit and the second registration unit.

Among other things, one or more systems and/or techniques for visually augmenting regions within images are provided herein. An image of an object, such as a bag, is segmented to identify an item (e.g., a metal gun barrel). Features of the item are extracted from voxels representing the item within the image (e.g., voxels within a first region), such as a size, shape, density, and orientation of the item. Response to the features of the item matching predefined features of a target item to detect, one or more additional regions are identified, such as a second region proximate to the first region based upon a location of the second region corresponding to where a connected part of the item (e.g., a plastic handle of the gun) is predicted to be located. The one or more regions are visually distinguished within the image from other regions (e.g., colored, highlighted, etc.).

A method for processing digital subtraction angiography (DSA) or computed tomography (CT) images for reduced radiation exposure to a DSA or CT subject includes receiving, as input, a plurality of captured DSA or CT image frames of a contrast agent flowing through a volume of interest in a subject. The method further includes fitting a mathematical model to measured contrast agent density of individual voxels of the captured DSA or CT image frames to produce a mathematical model of contrast agent flow across the captured DSA or CT image frames. The method further includes sampling the mathematical model of contrast agent flow for the individual voxels to produce reconstructed DSA or CT image frames. The method further includes outputting at least one of the reconstructed CT or DSA image frames.

A medical image processing apparatus according to the present invention includes a moving direction identification unit configured to identify a moving direction of an observed region of a subject depicted in a plurality of volume data collected by a medical diagnostic apparatus, each volume data of the plurality of volume data being collected for each time phase; and a display direction setting unit configured to set a display direction of the plurality of volume data based on the identified moving direction.

A method and apparatus for radiation therapy using functional measurements of branching structures. The method includes determining a location of each voxel of a plurality of voxels in a reference frame of a radiation device. The method further includes obtaining measurements that indicate a tissue type at each voxel. The method further includes determining a subset of the voxels based on an anatomical parameter of a respective branching structure of a set of branching structures indicated by the measurements. The method further includes determining a subset of the voxels that enclose an organ-at-risk (OAR) volume. The method further includes determining a value of a utility measure at each voxel. The method further includes determining a series of beam shapes and intensities which minimize a value of an objective function based on a computed dose delivered to each voxel and the utility measure for that voxel summed over all voxels.

Provided are a simultaneous implementation method of 3D subtraction arteriography, 3D subtraction venography, and 4D color angiography through post-processing of image information of 4D magnetic resonance (MR) angiography, and a medical imaging system.