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CN-114901148-B - Apparatus for generating photon counting spectral image data

CN114901148BCN 114901148 BCN114901148 BCN 114901148BCN-114901148-B

Abstract

The invention relates to a device (10) for generating photon-counting spectral image data, comprising an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to receive non-photon counting X-ray spectral energy data. The processing unit is configured to implement a deep learning regression algorithm to generate photon counting X-ray spectral data, and the generating includes utilizing non-photon counting X-ray spectral energy data. The output unit is configured to output photon counting X-ray spectral data.

Inventors

  • M.P. FREEMAN
  • L. Goshen

Assignees

  • 皇家飞利浦有限公司

Dates

Publication Date
20260505
Application Date
20201216
Priority Date
20191216

Claims (15)

  1. 1. An apparatus (10) for generating photon-counting spectral image data, comprising: An input unit (20); A processing unit (30), and An output unit (40); Wherein the input unit is configured to receive non-photon counting X-ray spectral energy data comprising non-photon counting image data comprising a first spectral image at a first X-ray energy and a second spectral image at a second X-ray energy; wherein the processing unit is configured to implement a deep learning regression algorithm to generate photon-counting X-ray spectral data, and the generating includes utilizing the non-photon-counting X-ray spectral energy data, and Wherein the output unit is configured to output the photon counting X-ray spectral data.
  2. 2. The apparatus of claim 1, wherein the photon-counting X-ray spectral data comprises at least one photon-counting spectral image.
  3. 3. The apparatus of claim 1, wherein the non-photon-counting image data comprises a compton scattering image and a photoelectric image, and the photon-counting X-ray spectral data comprises at least one photon-counting spectral image.
  4. 4. The apparatus of any of claims 1 to 3, wherein the processing unit is configured to implement a reconstructor to process the non-photon-counting X-ray spectral data to generate the non-photon-counting image data.
  5. 5. The apparatus of any of claims 2 to 3, wherein the at least one photon-counting spectral image comprises one or more photon-counting spectral images from the group consisting of a photon-counting image at the first X-ray energy, a photon-counting image at the second X-ray energy, a photon-counting compton image, a photon-counting optoelectronic image, a photon-counting virtual monochromatic image, a photon-counting contrast agent quantitative image, a photon-counting non-contrast image, a photon-counting cancellation image, a photon-counting iodine image, a photon-counting K-edge image.
  6. 6. The apparatus of any of claims 1 to 3, wherein the input unit is configured to receive reconstruction parameters employed by a reconstructor to generate the non-photon-counting image data, and the generation of the photon-counting X-ray spectral data includes utilizing the reconstruction parameters.
  7. 7. A device according to any one of claims 1 to 3, wherein the input unit is configured to receive acquisition parameters employed by the image acquisition unit to acquire the non-photon-counting X-ray spectral energy data, and the generation of the photon-counting X-ray spectral data comprises utilizing the acquisition parameters.
  8. 8. The device of any of claims 1 to 3, wherein the input unit is configured to receive patient parameters of a patient from which the non-photon-counting X-ray spectral energy data was acquired by an image acquisition unit, and the generation of the photon-counting X-ray spectral data comprises utilizing the patient parameters.
  9. 9. The device of any of claims 1 to 3, wherein the input unit is configured to receive reference non-photon-counting X-ray spectral data and reference photon-counting X-ray spectral data, and the processing unit is configured to train the deep learning regression algorithm that includes utilizing the reference non-photon-counting X-ray spectral data and the reference photon-counting X-ray spectral data.
  10. 10. The apparatus of claim 9, wherein the reference non-photon counting X-ray spectral data comprises reference non-photon counting image data, and the input unit is configured to receive reconstruction parameters used to generate the reference non-photon counting image data, and training of the deep learning regression algorithm comprises utilizing the reconstruction parameters.
  11. 11. The apparatus of claim 9, wherein the reference photon counting X-ray spectral data comprises image data.
  12. 12. The apparatus of claim 9, wherein the input unit is configured to receive acquisition parameters employed by one or more image acquisition units to acquire the reference non-photon-counting X-ray spectral energy data, and the training of the deep learning regression algorithm includes utilizing the acquisition parameters.
  13. 13. The apparatus of claim 9, wherein the input unit is configured to receive patient parameters of at least one patient from which the reference non-photon counting X-ray spectral energy data was acquired by one or more image acquisition units, and training of the deep learning regression algorithm includes utilizing the patient parameters.
  14. 14. An imaging system (100), comprising: Image acquisition unit (104) The device (10) according to any one of claims 1 to 13; wherein the image acquisition unit is configured to acquire non-photon counting X-ray spectral data and to provide the non-photon counting X-ray spectral data to an input unit of the device.
  15. 15. The imaging system of claim 14, wherein the processing unit of the device is configured to implement a reconstructor to process the non-photon counting X-ray spectral data to generate non-photon counting image data.

Description

Apparatus for generating photon counting spectral image data Technical Field The present invention relates to an apparatus for generating photon-counting spectral image data, and an imaging system. Background Conventional Computed Tomography (CT) scanners typically include an x-ray tube mounted on a rotatable gantry opposite a detector array that includes one or more rows of detector pixels. The x-ray tube rotates about an examination region between the x-ray tube and the detector array and emits polychromatic radiation that traverses the examination region and an object or subject disposed in the examination region. The detector array detects radiation that passes through the examination region and generates projection data indicative of the examination region and an object or subject disposed in the examination region. The reconstructor processes the projection data and generates volumetric image data indicative of the examination region and the object or subject disposed in the examination region. The volumetric image data can be processed to generate one or more images including scanned portions of the object or subject. The resulting image includes pixels, which are typically represented in gray scale values corresponding to relative radiodensity. Such information reflects the attenuation characteristics of the subject and/or object being scanned and typically displays structures such as anatomical structures within the patient, physical structures of inanimate objects, and the like. The detected radiation also includes spectral information, as the absorption of the radiation by the subject and/or object depends on the energy of the x-rays. Such spectral information may provide additional information, such as information indicative of elemental or material composition (e.g., atomic number) of the subject and/or tissue and/or material of the object. With such scanners, however, the projection data does not reflect the spectral characteristics, as the signal output by the detector array is proportional to the energy flux integrated over the energy spectrum. Thus, the generated data is monochromatic in nature. Thus, one development of this conventional CT method is to acquire spectral data. Thus, a computed tomography scanner (spectral scanner) configured for spectral imaging utilizes such spectral information to provide further information indicative of the elemental or material composition. One method includes using two X-ray tubes, each emitting an X-ray beam having a different energy spectrum. Another method includes fast kVp switching, where the voltage on the tube is switched between two different voltages, so that measurements are made at two energies. Another method includes a multi-layer indirect conversion detector having an uppermost layer that detects low energy X-rays and a lowermost layer that detects high energy X-rays. Such spectral data is referred to herein as non-photon counting X-ray spectral data. The output of such a spectrum scanner (which may be a dual energy scanner that acquires data at two X-ray energies) may be two images, one at a high X-ray energy and one at a low X-ray energy. However, the mass attenuation coefficient of a substance used to reconstruct a computed tomography image actually integrates several physical phenomena that affect the attenuation of x-ray photons by the substance. These phenomena include photoelectric effect, compton scattering, K-edge effect. See, for example, J.Hsieh Computed Tomography Principles, design, ARTIFACTS, AND RECENT ADVANCES, SPIE,2015. Thus, non-photon counting spectral data can also be processed with two attenuation values acquired simultaneously at two photon energies to account for the fundamental components of the photoelectric effect and Compton scattering. Since any two of the two basis functions are linearly independent and span the entire attenuation coefficient space, any substance can be represented by a linear combination of the two basis substances. The base material composition may be used to produce Compton scatter images and photoelectric images. Thus, a dual energy CT system uses two attenuation values acquired at two different photon energies to account for the photoelectric effect and compton scattering, while ignoring contributions from other components of the mass attenuation coefficient of the material (e.g., one or more K-edges). See, for example, R.E. Alvarez and A.MacOvski, "Energy-SELECTIVE RECONSTRUCTIONS IN X-ray computerized tomography", phys.Med.biol.,1976. The dual energy method allows reconstructing a high x-ray energy image and a low x-ray energy image, or a compton scatter image and a photoelectric image. However, the basis functions may also be used to determine virtual monochromatic images, iodine concentration maps, virtual non-contrast images, etc., as well as conventional CT images. This allows reconstructing virtual monochromatic images, iodine concentration maps, virtual non-contrast images, etc.,