Disclosed is a method for reconstruction of a Chemical Exchange Saturation Transfer (CEST) contrast image. The method includes: generating training samples for a deep neural network; training the deep neural network with the training samples to obtain a trained deep neural network; and reconstructing a CEST contrast image by using the trained deep neural network and PROPELLER undersampled CEST images. The method for reconstruction of a CEST contrast image can effectively shorten the experimental time of a CEST contrast imaging and can obtain a smoother and more accurate CEST contrast image. Further disclosed is a system for reconstruction of a CEST contrast image to implement the method for reconstruction.

The present invention relates to a method for the evaluation of tissue gadolinium deposition that offers advantages compared with known methods. Comparison of different gadolinium-based contrast agents (GBCAs) based on retention, organ distribution, washout and safety is facilitated using the methods of the present invention.

A medical image processing device according to an embodiment includes a processing circuit. The processing circuit is configured: to obtain an electron density function of an examined subject and information about a nuclide administered for the examined subject; to calculate a positron range kernel related to the examined subject, on the basis of the electron density function and the nuclide; and to reconstruct a Positron Emission Tomography (PET) image of the examined subject, on the basis of the positron range kernel.

Methods and systems are provided for identifying motion in medical images. In one example, a method includes obtaining projection data of an imaging subject, reconstructing a first image of a location of the imaging subject from the projection data using a first reconstruction technique and reconstructing a second image corresponding to the same location of the imaging subject from the of projection data using a second reconstruction technique, different than the first reconstruction technique in terms of temporal sensitivity, calculating an inconsistency metric quantifying temporal inconsistencies between the first image and the second image, and taking an action based on the inconsistency metric.

An imaging method comprises the steps of: putting an object in a detection region, and biasing a detector (1-8) relative to the object; moving an imaging system along a longitudinal Z axis, enabling a ray source (1-7) and the detector (1-8) to synchronously perform circular movement around the object, performing scanning and data collection, and supplementing the data; and reconstructing the collected data to obtain a complete object image. The imaging method combines detector biasing and spiral scanning, solves the problem that an image splicing method used in conventional CT imaging generates artifacts, reduces the usage area of the detector, and reduces system cost.

Disclosed is a method for reconstructing a matrix image representative of a static scene under predetermined lighting conditions, including: —acquiring images, captured by a sensor using a lighting which is separate from one image to another; and —reconstructing the matrix image, in a reconstruction space separate from a native spectral space of the sensor, by determining, for each pixel, the spectral components by weighted combination of the spectral components of the native spectral space of the image sensor, the spectral components being photometrically adjusted and associated with the same pixel of each image of the captured images. the weighting is obtained by solving a linear equation system having at least the following parameters: a predetermined value matrix associated with the predetermined lighting conditions, a matrix representative of both the spectral response of the sensor and the spectral distribution of each lighting applied to each captured image.

Embodiments provide the capability to determine a digital 3D model of a patient's teeth obtains projection images acquired by scanning a negative impression of the patient's teeth using a computed tomographic imaging apparatus, where the impression includes a radio-opaque transfer element for a dental implant or crown post. One exemplary method reconstructs, from the projection images, an air volume model within the reconstructed volume and bounded by a transition surface defined according to the negative impression. A transfer element volume model of the transfer element is defined, segmented from the air volume model and from the transition surface. A combined digital 3D model of the patient's teeth is formed according to the air volume model and transfer element volume model. At least a portion of the combined digital 3D model of the teeth is displayed.

Systems and methods are provided for reprojection and back projection of objects of interest via homographic transforms, and particularly one-dimensional homographic transforms. In one example, a method may include acquiring imaging data corresponding to a plurality of divergent X-rays, assigning a single functional form to the plurality of divergent X-rays, determining, via a homographic transform, weights of interaction between a plurality of distribution samples and a plurality of X-ray detector bins based on the single functional form, and reconstructing an image based on the weights of interaction.

Systems and methods are provided for reprojection and back projection of objects of interest via homographic transforms, and particularly one-dimensional homographic transforms. In one example, a method may include acquiring imaging data corresponding to a plurality of divergent X-rays, assigning a single functional form to the plurality of divergent X-rays, determining, via a homographic transform, weights of interaction between a plurality of distribution samples and a plurality of X-ray detector bins based on the single functional form, and reconstructing an image based on the weights of interaction.

The disclosure relates to a system and method for determining and pre-fetching projection data in image reconstruction. The method may include: determining a sequence of a plurality of pixels including a first pixel and a second pixel relating to the first pixel; determining a first geometry calculation used for at least one processor to access a first set of projection data relating to the first pixel from a first storage; determining a second geometry calculation based on the first geometry calculation; determining a first data template relating to the first pixel and a second data template relating to the second pixel based on the second geometry calculation; and pre-fetching a second set of projection data based on the first data template and the second data template, from a storage.