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.

One or more example embodiments relates to a computer-implemented method for providing key elements of the examination region in an X-ray image.

An image processing device acquires an image obtained by irradiating an area of a living body with light having a wavelength of 955 [nm] to 2025 [nm]. The image processing device inputs the acquired image to a learned model or a statistical model generated in advance for detecting, from the image, a tumor present in the area, and determines whether or not a tumor is present at each point in the image.

A medical information processing system according to an embodiment includes processing circuitry. The processing circuitry is configured to acquire information on an examination target, identify a reference image corresponding to the examination target on the basis of the information on the examination target, generate an edited image generated by editing the reference image, and transmit order information to which the edited image has been attached.

An image processing apparatus according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to obtain volume data of a subject. The processing circuitry is configured to obtain base tubular object data by segmenting the volume data. The processing circuitry is configured to obtain small tubular object data from the volume data. The processing circuitry is configured to generate updated base tubular object data, on the basis of the small tubular object data and the base tubular object data. The processing circuitry is configured to output the updated base tubular object data.

A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

The present invention relates to a system for real-time determination of a local strain of a lung during artificial ventilation. The system comprises a device for electrical impedance tomography (EIT), which device is configured to capture an electrical impedance distribution along at least one two-dimensional section through a human thorax, and further comprises a device for assigning the captured electrical impedance distribution, which device is configured to divide the captured electrical impedance distribution at different times during the artificial ventilation into a multiplicity of EIT pixels and to assign a specific value of the electrical impedance at a specific time to a specific EIT pixel.

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.

The present disclosure provides a tissue density analysis system. The system includes an acquisition module configured to obtain image data and tissue density distribution data; a display module configured to display the obtained tissue density distribution data in one or more charts; a processing module configured to adjust the tissue density distribution data displayed in the one or more charts; and a storage module configured to store the image data, the tissue density distribution data and an instruction.

Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.