Jung et al., 2020 - Google Patents
Compton background elimination for in vivo X-ray fluorescence imaging of gold nanoparticles using convolutional neural networkJung et al., 2020
- Document ID
- 5402572418320975148
- Author
- Jung S
- Lee J
- Cho H
- Kim T
- Ye S
- Publication year
- Publication venue
- IEEE Transactions on Nuclear Science
External Links
Snippet
This article reports the first application of a convolutional neural network (CNN) to in vivo X- ray fluorescence (XRF) images of gold nanoparticles (GNPs) obtained by a benchtop X-ray system to eliminate Compton-scattered photons. The XRF imaging system comprises a 2-D …
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold data:image/svg+xml;base64,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 data:image/svg+xml;base64,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 [Au] 0 title abstract description 52
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/48—Diagnostic techniques
- A61B6/482—Diagnostic techniques involving multiple energy imaging
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
- A61B6/032—Transmission computed tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
- G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
- G01T1/29—Measurement performed on radiation beams, e.g. position or section of the beam; Measurement of spatial distribution of radiation
- G01T1/2914—Measurement of spatial distribution of radiation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/50—Clinical applications
- A61B6/507—Clinical applications involving determination of haemodynamic parameters, e.g. perfusion CT
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/58—Testing, adjusting or calibrating devices for radiation diagnosis
- A61B6/582—Calibration
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP3735177B1 (en) | Full dose pet image estimation from low-dose pet imaging using deep learning | |
US11302003B2 (en) | Deep learning based data-driven approach for attenuation correction of pet data | |
JP4414410B2 (en) | Image reconstruction method | |
US11328391B2 (en) | System and method for controlling noise in multi-energy computed tomography images based on spatio-spectral information | |
US12073492B2 (en) | Method and system for generating attenuation map from SPECT emission data | |
WO2002086821A1 (en) | Image processing method and image processing device | |
Jung et al. | Compton background elimination for in vivo X-ray fluorescence imaging of gold nanoparticles using convolutional neural network | |
US20230059132A1 (en) | System and method for deep learning for inverse problems without training data | |
JP7517420B2 (en) | Method for generating an absorption coefficient image, nuclear medicine diagnostic device, and method for creating a trained model | |
Johnston et al. | Temporal and spectral imaging with micro‐CT | |
Zimmerman et al. | Experimental investigation of neural network estimator and transfer learning techniques for K‐edge spectral CT imaging | |
Yabe et al. | Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level | |
Izadi et al. | Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks | |
CN116385582A (en) | X-ray fluorescence CT self-absorption correction method based on deep learning | |
Shi et al. | Reconstruction of x-ray fluorescence computed tomography from sparse-view projections via L1-norm regularized EM algorithm | |
CN115553794B (en) | Three-dimensional Compton scattering imaging method and system based on parallel hole collimator | |
JP7681419B2 (en) | Image processing device, image processing method, and tomographic image acquisition system | |
JP4864909B2 (en) | Image processing device | |
Ahlers et al. | High-energy X-ray phase-contrast CT of an adult human chest phantom | |
Shah et al. | Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits | |
Chen et al. | X-ray fluorescence CT reconstruction based on residual encoder-decoder networks | |
Fan et al. | Beam Hardening Correction for Image-Domain Material Decomposition in Photon-Counting CT | |
Yang et al. | DDHANet: Dual-Domain Hybrid Attention-Guided Network For CT Scatter Correction | |
Us | Reduction of Limited Angle Artifacts in Medical Tomography via Image Reconstruction | |
Haase et al. | Estimation of statistical weights for model-based iterative CT reconstruction |