A method for visualizing a tubular object from a set of volumetric data may include the steps of: determining a viewing direction for the tubular object; selecting a constraint subset of the tubular object within the volumetric data; defining a cut-surface through the volumetric data and including the constraint subset of the tubular object within the volumetric data; and rendering an image based upon the determined viewing direction and the volumetric data of the tubular object along the intersection of the volumetric data and the defined cut-surface. Additionally or alternatively, the method may identify a plurality of bifurcations in the tubular object; assign a weighting factor to each identified bifurcation; determine a bifurcation normal vector associated with each bifurcation; determine a weighted average of the bifurcation normal vectors; and render an image of the volumetric data from a perspective parallel to the weighted average of the bifurcation normal vectors.

A method is provided. The method includes acquiring simultaneously multiple magnetic resonance (MR) images and multiple ultrasound images of an anatomical region of a subject over a scanned duration. The method also includes training an unsupervised deep learning-based deformable registration network. This training includes training a MR registration subnetwork based on the multiple MR images to generate MR deformation and transformation vectors, training an ultrasound registration subnetwork based on the multiple ultrasound images to generate ultrasound deformation and transformation vectors, and training a MR-to-ultrasound subnetwork based the multiple MR images and the multiple ultrasound images to generate MR-to-ultrasound deformation and transformation vectors between corresponding pairs of MR images and ultrasound images at each time point.

A radiographic imaging apparatus includes an image processor configured to perform image processing on a radiation image generated by an image generator, and a display configured to display a processed image that has been subjected to the image processing in the image processor. The image processor is configured to perform control to create the processed image showing an expanded portion in which a balloon introduced into a subject is expanded.

The present technology relates to a display control apparatus and a method, an image processing apparatus and a method, and a program that enable easy and noninvasive observation of an object to be observed. A first display control unit configured to perform display control of a cell image with one or a plurality of images including cells, and a second display control unit configured to perform display control such that a part or all of a motion amount generated for each of subregions with the one or the plurality of images is associated with each of the subregions, and is superimposed on the cell image and is displayed, are provided. The present disclosure can be applied to a display control apparatus or an image processing apparatus.

A method includes obtaining reference imaging data. The reference imaging data is acquired at a first time and includes tissue of interest. The method further includes obtaining update imaging data. The update imaging data is acquired at a second time. The second time is subsequent to the first time. The method further includes identifying a difference between the reference imaging data and the update imaging data. The method further includes changing the reference imaging data based on the identified difference. The method further includes displaying the changed reference imaging data.

The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from PET data where two groups differ and measuring PET signal within determined critical areas in a new subject in order to assign risk or diagnosis.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

A method is for classifying an examination object by way of recordings. In an embodiment, the method includes capturing at least one optical recording of the examination object; determining and quantifying a number of defined characteristics of the examination object based upon an analysis of the optical recordings with the aid of a machine learning method; and affecting the classification of the examination object in respect of a classification criterion, based upon the quantified characteristics with the aid of a machine learning method. Also described are a classification entity and a medical imaging modality.

Adaptive image processing, image analysis, pattern recognition, and time-to-event prediction in various imaging modalities associated with assisted reproductive technology. The reference image may be processed according to one or more adaptive processing frameworks for de-speckling or noise processing of ultrasound images. The subject image is processed according to various computer vision techniques for object detection, recognition, annotation, segmentation, and classification of reproductive anatomy, such as follicles, ovaries and the uterus. An image processing framework may also analyze secondary data along with subject image data to analyze time-to-event progression of the subject image.

Methods and systems for determining whether brain tissue is indicative of a disorder, such as a neurodegenerative disorder, are provided. The methods and systems generally utilize data processing techniques to assess a level of congruence between measured parameters obtained from magnetic resonance imaging (MRI) data and simulated parameters obtained from computational modeling of brain tissues.