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.

An imaging device (1) includes a positron emission tomography (PET) scanner (10) including radiation detectors (12) and coincidence circuitry for detecting electron-positron annihilation events as 511 keV gamma ray pairs defining lines of response (LORs) with each event having a detection time difference At between the 511 keV gamma rays of the pair. At least one processor (30) is programmed to reconstruct a dataset comprising detected electron-positron annihilation events acquired for a region of interest by the PET scanner to form a reconstructed PET image wherein the reconstruction includes TOF localization of the events along respective LORs using a TOF kernel having a location parameter dependent on At and a TOF kernel width or shape that varies over the region of interest. A display device (34) is configured to display the reconstructed PET image.

The present disclosure directs to a system and method for CT imaging. The method may include acquiring computed tomography (CT) data, wherein the CT data is generated by scanning a subject using a CT scanner, the CT scanner including a focal spot and a detector, and the detector including a plurality of detector units. The method may also include obtaining a forward projection model and a back projection model, wherein the forward projection model and the back projection model are associated with sizes of the detector units and a size of the focal spot of the CT scanner. The method may further include reconstructing a CT image of the subject iteratively based on the CT data, the forward projection model, and the back projection model.

A method and system to correct for alignment errors between assumed and actual geometric parameters of an acquisition geometry during image reconstruction in a chest tomosynthesis application includes receiving at least 2 raw projection images acquired on at least 2 different positions in a known acquisition geometry, determining an actual geometric parameter value by determining the minimum of a redundant planes cost function which is calculated for a varying range of the geometric parameter values, and which is determined by: a) at least one plane which intersects an X-ray source trajectory with at least two points, b) an intersection between the planes and a detector surface for which points the source positions are determined, and c) for which the parameters determining the intersection (, , l) are used for the construction of the cost function, applying the calculated actual geometric parameter value of the acquisition geometry for the image reconstruction of the plurality of images, characterized in that, a weighting function is applied to the plurality of acquired images prior to calculating the cost function.

When performing nuclear medicine image reconstruction, lesion proxies (208) are introduced by a clinician and merged with real acquired scan data outside or inside the patient in the patient image. By monitoring the image attributes of the lesion proxies during reconstruction and processing, reconstruction and processing parameters can be dynamically adapted or adjusted in order to optimize image quality and quantitation to improve delivery of precise, personalized medical treatment.

A method for the reconstruction of a test part in an X-ray CT method in an X-ray CT system, which has an X-ray with a focus, an X-ray detector, and a manipulator which moves the test part within the X-ray CT system. To generate recordings of the test part in various positions, the manipulator travels a predefinable parameterizable path-curve and makes recordings at triggered positions. For each recording, the position of the manipulator is determined and the respective associated projective geometry is calculated. Thereafter, a further path curve is followed having different parameters from the preceding path curve. The path curve is determined iteratively by means of an optimization algorithm, at the value of which the quality function is minimal. For each test part, a CT reconstruction is carried out by means of a suitable algorithm with reference to the allocation of the individual recordings to the respective projective geometry.

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.

An improved system and method for the reduction of beam hardening artifacts in CT image reconstruction based on polyenergetic x-ray projection data and polyenergetic x-ray calibration data from a known calibration phantom.

The present disclosure relates to systems and methods for reconstructing an image in an imaging system. The methods may include obtaining scan data representing an intensity distribution of energy detected at a plurality of detector elements and determining an image estimate. The methods may further include determining an objective function based on the scan data and the image estimate. The objective function may include a regularization parameter. The methods may further include iteratively updating the image estimate until the objective function satisfies a termination criterion, and for each update, updating the regularization parameter based on a gradient of an updated image estimate. The methods may further include outputting a final image based on the updated image estimate when the objective function satisfies the termination criterion.

When performing nuclear medicine image reconstruction, lesion proxies (208) are introduced by a clinician and merged with real acquired scan data outside or inside the patient in the patient image. By monitoring the image attributes of the lesion proxies during reconstruction and processing, reconstruction and processing parameters can be dynamically adapted or adjusted in order to optimize image quality and quantitation to improve delivery of precise, personalized medical treatment.