A three-dimensional region of interest (ROI) is established with a high degree of accuracy, by a simple method without increasing a burden on the operator, in generating a three-dimensional projected image from medical volume data according to rendering, achieving more efficient interpretation of three-dimensional image and streamlining of diagnostic flow, with the use of the diagnostic image generation apparatus. An energy map is generated on a predetermined tomographic plane, assuming a preset start point as a reference and searching for a path that minimizes the energy, and then the path is set as a boundary of the three-dimensional ROI. The start point may be decided on the basis of the boundary inputted by a user, or the user may set the start point. The user may be allowed to adjust the boundary having been set. The boundary may also be determined on another plane orthogonal to the predetermined tomographic plane.

A deep learning model may be trained to provide an estimated image of the interior of a patient, based on a number of image sets, each image set comprising an interior image of the interior of a person and a contour image of the person's outer contour at a specific point in time. The model is trained to establish an optimized parametrized conversion function G specifying the correlation between the interior of the person and the persons outer contour based on the image sets. The conversion function G can then be used to provide estimated images of patient's interior based on their contours.

An information processing apparatus comprising: an extraction unit configured to extract a predetermined region of an anatomical part from an examination image of a subject; a first feature amount acquisition unit configured to acquire a first feature amount of the predetermined region related to a movement of the anatomical part; a second feature amount acquisition unit configured to acquire a second feature amount of the predetermined region related to the movement; a division unit configured to divide the predetermined region; and an integration unit configured to integrate the first feature amount and the second feature amount based on a result of division by the division unit.

A method for determining respiratory induced blood mass change from a four-dimensional computed tomography (4D CT) includes receiving a 4D CT image set which contains a first three-dimensional computed tomographic image (3D CT) and a second 3D CT image. The method includes executing a deformable image registration (DIR) function on the received 4D CT image set, and determining a displacement vector field indicative of the lung motion induced by patient respiration. The method further includes segmenting the received 3D CT images into a first segmented image and a second segmented. The method includes determining the change in blood mass between the first 3D CT image and the second 3D CT image from the DIR solution, the segmented images, and measured CT densities.

A compartmentalized dynamic anatomic atlas is disclosed, comprising static atlas data comprising spatial element data and element representation data, wherein the spatial element data describes spatial properties of a spatial atlas element and wherein the element representation data describes representational properties assignable to the spatial atlas element, the atlas further comprising dynamic atlas data comprising information on a dynamic property which information is respectively linked to the spatial atlas element.

Imaging methods, imaging apparatus and computer program products are disclosed. An imaging method comprises: receiving image data of a 3-dimensional object; and allocating a confidence level to at least a portion of an image frame of the image data using a machine-learning algorithm, the confidence level indicating a likelihood of that image frame having a specified element imaged on a specified plane through the 3-dimensional object. In this way, particular elements when imaged in a desired way can be identified from image data of the 3-dimensional object.

A method for performing magnetic resonance fingerprinting includes acquiring a plurality of MR image datasets using at least two pulse sequence types, the plurality of MR image datasets representing signal evolutions for image elements in a region of interest, comparing the plurality of MR image datasets to a dictionary of signal evolutions to identify at least one parameter of the MR image datasets and generating a report indicating the at least one parameter of the MR image datasets.

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 invention relates to the field of medical imaging in the absence of contrast agents. In one form, the invention relates to the field of imaging vessels, particularly blood vessels such as the pulmonary vasculature and is suitable for use as a technique for detecting pulmonary embolism (PE), such as acute PE. Embodiments of the present invention provide improved image processing techniques having the capability to extract and use image data to overcome the need for contrast agents to distinguish between different types of tissue. Furthermore, it has also been realised that the image data accessed by the improved image processing can be used to identify irregularities in vessels.

Embodiments of the disclosure provide methods and systems for disease condition prediction from images of a patient. The system may include a communication interface configured to receive a sequence of images acquired of the patient by an image acquisition device. The sequence of images are acquired at a sequence of prior time points during progression of a disease. The system may include a processor, configured to determine regions of interest based on the sequence of images. The processor applies a progressive condition prediction network to the regions of interest to predict a level of disease progression at a future time point during the progression of the disease. The progressive condition prediction network predicts the level of disease progression based on the regions of interest and disease conditions at the sequence of prior time points. The processor further provides a diagnostic output based on the predicted level of disease progression.