Image reconstruction and coding method in spectral tomography
A technology of tomography and image reconstruction, which is applied in the field of image reconstruction and coding in spectral tomography, and can solve the problems of matrix multiplication and large computing workload
Inactive Publication Date: 2021-01-26
NANJING XIAOZHUANG UNIV
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These matrix multiplication operations are computationally intensive and require large amounts of computer memory to store the matrix data
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[0036] The present invention will be further described below through specific embodiments.
[0037] "First", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Likewise, "comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, and do not exclude other elements or items. "Connected" or "connected" and similar terms are not limited to physical or mechanical connection, but may include electrical connection, whether direct or indirect.
[0038] Flow charts are used in the present disclosure to illustrate the steps of the method according to the embodiments of the present disclosure. It should be understood that the preceding or subsequent steps do not necessarily have to be performed in a precise order. Instead, various steps may be processed in ...
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The invention provides an image reconstruction and coding method in spectral tomography, which comprises the following steps: acquiring a spectral tomography image signal X as training data, performing gray scale preprocessing on the data, and completing signal corrosion to obtain X1; building a coding sub-network of the self-coding denoising sparse network, wherein the coding sub-network is a three-layer full-connection neural network, and the spectral tomography image signal X obtains a measurement value y through the coding sub-network; a decoding sub-network of the self-encoding denoisingsparse network is built, the decoding sub-network is a three-layer full-connection neural network symmetrical to the encoding sub-network in structure, and a reconstructed picture X2 is obtained fromthe measured value y through the decoding sub-network; sparsity limitation is introduced to generate a loss function; and joint training is performed on the coding and decoding sub-networks, a loss function is optimized through a back propagation algorithm, parameters are updated and an optimal sparse denoising self-coding network is obtained. According to the method provided by the invention, thereconstruction time is greatly reduced while the quality of the reconstructed picture is improved, and the calculation workload and the use amount of a computer memory are greatly reduced.
Description
technical field [0001] The invention relates to the field of image processing, in particular to an image reconstruction and encoding method in spectral tomography. Background technique [0002] Spectral tomography (CT) is one of the most important innovations in medical imaging in recent decades, which can provide fast and efficient analysis and lesion identification through 3D visual inspection of patients' tissues and organs. With the development of CT technology, cone beam CT (CBCT) has attracted increasing attention in radiology imaging and therapy due to its high spatial resolution, easy operation and relatively low acquisition cost. Despite the high quality of on-board volumetric imaging, there are still concerns about excessive X-ray radiation into the patient, which is much higher than conventional diagnostic X-rays. CT scanning and imaging procedures are expected to minimize X-ray radiation while maintaining CT image quality. To reduce the number of X-ray projecti...
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IPC IPC(8): G06T11/00G06N3/08
CPCG06T11/005G06N3/084
Inventor 仇飞颜森林
Owner NANJING XIAOZHUANG UNIV