Methods for quantitatively separating x-ray images of a subject having three or more component materials into component images using spectral imaging or multiple-energy imaging with 2D radiographic hardware implemented with scatter removal methods. The multiple-energy system may be extended by implementing DRC multiple energy decomposition and K-edge subtraction imaging methods.

An image acquisition unit acquires two radiographic images based on radiations which are transmitted through a subject containing a plurality of compositions and have energy distributions different from each other. A body thickness derivation unit derives, as a first body thickness and a second body thickness, body thicknesses of the subject for pixels of the two radiographic images. A composition ratio derivation unit derives composition ratios of the subject for the pixels of the radiographic images based on the first body thickness and the second body thickness.

A PET image reconstruction method, including: 1) injecting a PET radioactive tracer into a biological tissue, scanning by a PET device, and detecting and counting coincidence photons to obtain an original protection data matrix; 2) establishing a measurement equation model; 3) splitting the reconstruction problem into a first sub-problem and a second sub-problem; 4) solving the first sub-problem by a filtered back-projection layer, solving the second sub-problem by an improved denoising convolutional neural network, where the filtered back-projection layer and the improved denoising convolutional neural network are connected in series to form a filtered back-projection network (FBP-Net); 5) inputting original projection data into the FBP-Net, and using an image as a tag to adjust parameters of the FBP-Net to reduce an error between an output of the FBP-Net and the tag; and 6) inputting projection data to be reconstructed into the trained FBP-Net to obtain a desired reconstructed image.

A method for geometric calibration of a volume imaging apparatus disposes calibration phantom in a radiation path that includes a subject positioned between an x-ray source and a detector. The phantom has a number of radio-opaque markers formed of a marker material. In a repeated sequence, at each of a number of positional relationships of the x-ray source to the detector: 2D projection image data is acquired for the subject and the phantom, wherein the 2D projection image data distinguishes at least first and second x-ray energy distributions; source-to-detector geometry of the imaging apparatus is calculated, corresponding to the acquired 2D projection image data for the first and second x-ray energy distributions. The method reconstructs and displays a 3D volume image of the subject according to acquired anatomy image data from the subject and source-to-detector geometry within the 2D projection images.

A radiographic imaging apparatus and a radiation detector are provided, which are capable of sufficiently reducing the sensitivity difference between pixels even if the incident photon rate is high. A radiographic imaging apparatus includes: a radiation source for irradiating an object with radiation; a plurality of detection element modules each having a semiconductor layer that generates electrical charges depending on photon energy of the radiation, and a photon counting circuit for counting the electrical charges for each pixel; and a collimator that is disposed between the radiation source and the semiconductor layer, and has a plurality of walls forming a plurality of passage holes through which the radiation passes. A plurality of subpixels is formed on the semiconductor layer, and when one or more subpixels defined by the walls of the collimator are grouped as a macro pixel, a plurality of macro pixels arranged from each end of each of the detection element modules is smaller in size than a macro pixel other than the plurality of macro pixels arranged from the end of the detection element module.

A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator (126) configured to determine a fractional flow reserve value. The system further includes a processor (120) configured to execute the biophysical simulator (126), which employs machine learning to determine the fractional flow reserve value with spectral volumetric image data. The system further includes a display configured to display the determine fractional flow reserve value.

A radiation imaging apparatus comprises an image generating unit configured to generate a material characteristic image by using a plurality of radiation images of different radiation energy levels; an evaluation information calculation unit configured to calculate evaluation information which indicates a correlation between a plurality of material characteristic images; and a scattered ray amount estimation unit configured to estimate, based on the evaluation information, an amount of scattered rays included in the plurality of radiation images.

According to some aspects, a carrier configured for use with a broadband x-ray source comprising an electron source and a primary target arranged to receive electrons from the electron source to produce broadband x-ray radiation in response to electrons impinging on the primary target is provided. The carrier comprising a housing configured to be removeably coupled to the broadband x-ray source and configured to accommodate a secondary target capable of producing monochromatic x-ray radiation in response to incident broadband x-ray radiation, the housing comprising a transmissive portion configured to allow broadband x-ray radiation to be transmitted to the secondary target when present, and a blocking portion configured to absorb broadband x-ray radiation.

A method for detecting at least one object of interest in at least one raw data x-ray image includes the steps of emitting an incident x-ray radiation beam through a scanning volume having an object therein, detecting x-ray signals transmitted through at least one of the scanning volume and the object, deriving the at least one raw data x-ray image from the detected x-ray signals, inputting the raw data x-ray image, expressed according to an attenuation scale, into a neural network, for each pixel in the raw data x-ray image, outputting from the neural network a probability value assigned to that pixel, and, classifying each pixel in the raw data x-ray image into a first classification if the probability value associated with the pixel exceeds a predetermined threshold probability value and in a second classification if the probability value associated with the pixel is below the predetermined threshold probability value.

The present disclosure is related to an imaging system. The imaging system may include at least one array radiation source and a detector. Each of the at least one array radiation source may include a plurality of point radiation sources. The at least one array radiation source may be configured to emit at least one radiation beam. The detector may be configured to detect at least part of the at least one radiation beam.