A system and method for constructing fluoroscopic-based three dimensional volumetric data from two dimensional fluoroscopic images including a computing device configured to facilitate navigation of a medical device to a target area within a patient and a fluoroscopic imaging device configured to acquire a fluoroscopic video of the target area about a plurality of angles relative to the target area. The computing device is configured to determine a pose of the fluoroscopic imaging device for each frame of the fluoroscopic video and to construct fluoroscopic-based three dimensional volumetric data of the target area in which soft tissue objects are visible using a fast iterative three dimensional construction algorithm.

A contrast-enhanced digital tomosynthesis system with a source configured to emit penetrating particles toward an object, a detector configured to acquire a series of projection images of the object in response to the penetrating particles from the source, a positioning apparatus configured to position the source relative to the object and the detector, and an imaging system coupled to the source, the detector, and the positioning apparatus. The imaging system is configured to control the positioning apparatus to position the source and detector relative to the object, control the source and the detector to acquire the series of projection images, and construct a tomographic volume capable of exhibiting super-resolution morphology and contrast-enhancement arising from injection of an exogenous contrast agent from data representing the acquired series of projection images or a subset thereof.

A system for assisting guiding an endovascular instrument in vascular structures of an anatomical region of interest of a patient. This system includes an imaging device for capturing three-dimensional images of parts of the body of a patient, a programmable device and a viewing unit. The imaging device captures partially superposed fluoroscopic images of the region, and the programmable device forms a first augmented image, representative of a complete panorama of bones of the region, and cooperates with the imaging device to obtain a second augmented image including a representation of the vascular structures of the region. The imaging device captures a current fluoroscopic image of a part of the region, and the programmable device registers the current fluoroscopic image with respect to the first augmented image and locates and displays, on the viewing unit, an image region corresponding to the current fluoroscopic image in the second augmented image.

Various methods and systems are provided for tracking a biopsy target across one or more images. In one example, a method includes determining a position of a biopsy target in a selected image of a patient based on an image registration process with a reference image of the patient, and displaying a graphical representation of the position of the biopsy target on the selected image.

The disclosure relates to PET imaging systems and methods. The systems may obtain a plurality of PET images of a subject and a CT image acquired by performing a spiral CT scan on the subject. Each gated PET image may include a plurality of sub-gated PET images. The CT image may include a plurality of sub-CT images each of which corresponds to one of the plurality of sub-gated PET images. The systems may determine a target motion vector field between a target physiological phase and a physiological phase of the CT image based on the plurality of sub-gated PET images and the plurality of sub-CT images. The systems may reconstruct an attenuation corrected PET image corresponding to the target physiological phase based on the target motion vector field, the CT image, and PET data used for the plurality of gated PET images reconstruction.

Methods and systems are disclosed for radiating a moving object. The method may comprise acquiring a plurality of indicators of the phase of a physiological cycle of a patient and a plurality of images of the patient that include a target. Each image may be taken at a different phase of the physiological cycle and may be registered to the phase at which the image was taken. The method may also include identifying the target in each of the plurality of images, calculating a dose of radiation required to treat the target, calculating the number, orientation, and dwell time of one or more radiation beams required to deliver the calculated required dose of radiation to the target, and calculating a position of each of the one or more radiation beams required to achieve the calculated orientation. Each position may be a function of the phase of the physiological cycle to which each of the plurality of images is registered.

An image alignment apparatus includes at least one processor, and the processor derives, for each of first and second three-dimensional images each including a plurality of tomographic images and a common structure, first and second three-dimensional coordinate information that define an end part of the structure in a direction intersecting the tomographic image. The processor aligns the first three-dimensional image and the second three-dimensional image by using the first and second three-dimensional coordinate information to align the common structure included in each of the first three-dimensional image and the second three-dimensional image at least in the direction intersecting the tomographic image.

A method for automated detection of landmarks from 3D medical image data using deep learning according to the present inventive concept, the method includes receiving a 3D volume medical image, generating a 2D intensity value projection image based on the 3D volume medical image, automatically detecting an initial anatomical landmark using a first convolutional neural network based on the 2D intensity value projection image, generating a 3D volume area of interest based on the initial anatomical landmark and automatically detecting a detailed anatomical landmark using a second convolutional neural network different from the first convolutional neural network based on the 3D volume area of interest.

Methods are provided for dynamically visualizing information in image data of an object of interest of a patient, which include an offline phase and an online phase. In the offline phase, first image data of the object of interest acquired with a contrast agent is obtained with an interventional device is present in the first image data. The first image data is used to generate a plurality of roadmaps of the object of interest. A plurality of reference locations of the device in the first image data is determined, wherein the plurality of reference locations correspond to the plurality of roadmaps. In the online phase, live image data of the object of interest acquired without a contrast agent is obtained with the device present in the live image data, and a roadmap is selected from the plurality of roadmaps. A location of the device in the live image data is determined. The reference location of the device corresponding to the selected roadmap and the location of the device in the live image data is used to transform the selected roadmap to generate a dynamic roadmap of the object of interest. A visual representation of the dynamic roadmap is overlaid on the live image data for display. In embodiments, the first image data of the offline phase covers different of phases of the cardiac cycle of the patient, and the plurality of roadmaps generated in the offline phase covers the different phases of the patient's cardiac cycle. Related systems and program storage devices are also described and claimed.

The present invention relates to a medical data processing method of determining the representation of an anatomical body part (2) of a patient (1) in a sequence of medical images, the anatomical body part (2) being subject to a vital movement of the patient (1), the method being constituted to be executed by a computer and comprising the following steps: a) acquiring advance medical image data comprising a time-related advance medical image comprising a representation of the anatomical body part (2) in a specific movement phase; b) acquiring current medical image data describing a sequence of current medical images, wherein the sequence comprises a specific current medical image comprising a representation of the anatomical body part (2) in the specific movement phase, and a tracking current medical image which is different from the specific current medical image and comprises a representation of the anatomical body part (2) in a tracking movement phase which is different from the specific movement phase; c) determining, based on the advance medical image data and the current medical image data, specific image subset data describing a specific image subset of the specific current medical image, the specific image subset comprising the representation of the anatomical body part (2); d) determining, based on the current medical image data and the image subset data, subset tracking data describing a tracked image subset in the tracking current medical image, the tracked image subset comprising the representation of the anatomical body part (2).