The disclosure relates to a system and method for medical imaging. The method may include: move, by a motion controller, a phantom along an axis of a scanner to a plurality of phantom positions; acquire, by a scanner of the imaging device, a first set of PET data relating to the phantom at the plurality of phantom positions; and store the first set of PET data as an electrical file. The length of an axis of the phantom may be shorter than the length of an axis of the scanner, and at least one of the plurality of phantom positions may be inside a bore of the scanner.

The present disclosure relates to a method, system and non-transitory computer readable medium. In some embodiments, the method includes: acquiring image data of a target subject positioned on a scanning table of an imaging device; determining, by a processor, first position information of the target subject by inputting the image data into a first machine learning model, the first position information of the target subject including a posture of the target subject relative to the imaging device; determining, by the processor, second position information related to a scan region of the target subject by inputting the image data into a second machine learning model, the second position information including a position of the scan region relative to the scanning table and the imaging device; and causing the imaging device to scan the target subject based on the first position information and the second position information.

A method and system for facilitating identification and marking of a target in a displayed Fluoroscopic Three-Dimensional Reconstruction (F3DR) of a body region of a patient. The system includes a display and a storage device storing instructions for receiving an initial selection of the target in the F3DR, fining the F3DR based on the initial selection of the target, displaying the fined F3DR on the display, and receiving a final selection of the target in the fined F3DR via a user selection. The system further includes at least one hardware processor configured to execute said instructions. The method and instructions may also include receiving a selection of a medical device in two two-dimensional fluoroscopic images, where the medical device is located in an area of the target, and initially fining the F3DR based on the selection of the medical device.

There is provided a PCCT apparatus capable of correcting a band artifact of one material decomposition image and a band artifact of another material decomposition image. The PCCT apparatus obtains projection data divided into plural energy bins by irradiating a subject with X-rays, and includes: a first correction unit that corrects a band artifact of a first material decomposition image among plural material decomposition images created on the basis of the projection data, and calculates a first correction amount that is a correction amount for the band artifact; an energy calculation unit that calculates an average energy of X-rays that transmit the subject; and a second correction unit that corrects the band artifact of a second material decomposition image using a second correction amount that is a correction amount calculated on the basis of the first correction amount and the average energy.

Methods and systems are provided for adaptive scan control. In one embodiment, a method includes, upon an injection of a contrast agent, processing acquired projection data of a monitoring area of a subject to measure a contrast signal of the contrast agent, estimating two or more target times of the contrast agent at the monitoring area of the subject based on the contrast signal, and carrying out a contrast scan that includes a two or more acquisitions each performed at a respective target time.

Devices, systems, and methods receive scan data that were generated by scanning a region of a subject with a computed tomography apparatus; generate multiple partial angle reconstruction (PAR) images based on the scan data; obtain corresponding characteristics of the multiple PAR images; perform correspondence mapping on the multiple PAR images based on the obtained corresponding characteristics and on the multiple PAR images, wherein the correspondence mapping generates correspondence-mapping data; and generate a motion-corrected reconstruction image based on the correspondence-mapping data and on one or both of the scan data and the PAR images.

Various methods and systems are provided for x-ray imaging. In one embodiment, a method for an image pasting examination comprises acquiring, via an optical camera and/or depth camera, image data of a subject, controlling an x-ray source and an x-ray detector according to the image data to acquire a plurality of x-ray images of the subject, and stitching the plurality of x-ray images into a single x-ray image. In this way, optimal exposure techniques may be used for individual acquisitions in an image pasting examination such that the optimal dose is utilized, stitching quality is improved, and registration failures are avoided.

A method for evaluating one or more diagnostic linages of a patient obtained in different examination sessions and evaluating the diagnostic images using trained machine learning logic to generate prognosis and treatment information related to a medical condition of the patient detected during the evaluation. The prognosis-related information is recorded and displayed.

A radiomic biomarker determination method and system for assessment of the risk of metabolic diseases. The method includes: obtaining abdominal & pelvic volumetric computed tomography (CT) scan from the given subject; determining the fat area to be analyzed from the CT scan, separating visceral fat using an image segmentation method, and normalizing the visceral fat area under physical scale; extracting N imaging features of the visceral fat; selecting n optimal imaging features from the N candidate features; dividing the normalized visceral fat area into multiple visceral fat blocks with equal thickness; extracting n corresponding optimal imaging features from each visceral fat block, named as block imaging features; and determining the representative visceral fat block from the candidate blocks and taking the representative visceral fat block and the (block) imaging features extracted from the representative visceral fat block as radiomic biomarkers.

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.