Systems and methods for reconstructing medical images are disclosed. Measurement data from positron emission tomography (PET) data, and measurement data from an anatomy modality, such as magnetic resonance (MR) data or computed tomography (CT) data, is received from an image scanning system. A PET image is generated based on the PET measurement data, and an anatomy image is generated based on the anatomy measurement data. A trained neural network is applied to the PET image and the anatomy image to generate an attenuation map. The neural network may be trained based on anatomy and PET images. In some examples, the trained neural network generates an initial attenuation map based on the anatomy image, registers the initial attenuation map to the PET image, and generates an enhanced attenuation map based on the registration. Further, a corrected image is reconstructed based on the generated attenuation map and the PET image.

There is provided a method for registration of intravital anatomical imaging modality image data and nuclear medicine image data of a patient's heart comprising: obtaining anatomical image data including a heart of a patient outputted by an anatomical intravital imaging modality; obtaining at least one nuclear medicine image data outputted by a nuclear medicine imaging modality, the nuclear medicine image data including the heart of the patient; identifying a segmentation of a network of vessels of the heart in the anatomical image data; identifying a contour of at least part of the heart in the nuclear medicine image data, the contour including at least one muscle wall border of the heart; correlating between the segmentation and the contour; registering the correlated segmentation and the correlated contour to form a registered image of the anatomical image data and the nuclear medicine image data; and providing the registered image for display.

An X-ray imaging system (XI) configured for phase contrast and/or dark-field imaging The system comprises an X-ray source (XS) operable to cause X-radiation to emanate from a focal spot (SF) of the source (XS) and an X-ray sensitive detector (D) operable to SMF detect the X-radiation after interaction of said X-radiation with an object to be imaged, if present, between the X-ray source and the detector (D). A control logic (CL) is operable to cause the X-ray imaging apparatus to operate in any one of two modes, an object image acquisition mode and a scattering measurement mode. When in scattering measurement mode, the X-radiation receivable at the detector comprises a higher proportion of scattering radiation than in X-radiation receivable when the system is in object image acquisition mode.

A method for identifying a misidentified study can utilize a set of photographs captured at substantially the same time as a corresponding set of medical images. The method can include determining similarities between the photographs through machine learning models and determining that a misidentified study exists when the similarity between the photographs fails to satisfy a threshold similarity.

The present disclosure provides a method and system for a dynamic fluoroscopy of a C-shaped arm device. The method comprises: photographing a subject during a photography cycle, obtaining, during the photography cycle, first fluoroscopic data of a radiation source irradiating the subject at a first energy, and obtaining second fluoroscopic data of the radiation source irradiating the subject at a second energy different from the first energy (210); photographing the subject in multiple successive photography cycles (220); and displaying a dynamic image of the subject based on the first fluoroscopic data and the second fluoroscopic data obtained in each of the multiple successive photography cycles (230).

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

A method includes receiving a selection of an examination template including a hierarchical examination workflow and baseline configuration parameters corresponding to the examination template, wherein the examination template is associated with an anatomical area; automatically configuring an imaging system with the baseline configuration parameters from the examination template; capturing image data of a patient using the imaging system according to the hierarchical examination workflow; storing the image data on a storage system, such that the image data is associated with the anatomical area; displaying an anatomical model of a plurality of anatomical areas of the patient; and displaying thumbnails of the image data on respective areas of the anatomical model corresponding to anatomical areas of the patient where the image data was captured by the imaging system.

A method performed by a computing system comprises receiving a fluoroscopic image of a patient anatomy while a portion of a medical instrument is positioned within the patient anatomy. The fluoroscopic image has a fluoroscopic frame of reference. The portion has a sensed position in an anatomic model frame of reference. The method further comprises identifying the portion in the fluoroscopic image and identifying an extracted position of the portion in the fluoroscopic frame of reference using the identified portion in the fluoroscopic image. The method further comprises registering the fluoroscopic frame of reference to the anatomic model frame of reference based on the sensed position of the portion and the extracted position of the portion.

A biometric information display apparatus (30) for displaying a measurement result obtained by measuring a biometric signal, includes a maximum value calculation unit (63) configured to calculate a maximum value of the measurement result in a certain period of time for at least one of blocks into which a measurement area, in which the biometric signal is measured, is divided, a determination unit (64) configured to determine whether a measurement value in the at least one of blocks is greater than or equal to a threshold value obtained by multiplying the maximum value by a fractional value, the fractional value being determined in advance, and a display control unit configured to display, in response to an occurrence of an event in which the measurement value is determined to be greater than or equal to the threshold value, the measurement result in such a manner as to indicate the occurrence of the event.