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Deep Transfer Learn...
Deep Transfer Learning-Based Multi-Modal Digital Twins for Enhancement and Diagnostic Analysis of Brain MRI Image
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- Wang, Jinxia (författare)
- Shaanxi Fash Engn Univ, Sch Art & Design, Xian 712046, Shaanxi, Peoples R China.
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- Qiao, Liang (författare)
- Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Shandong, Peoples R China.
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- Lv, Haibin (författare)
- Minist Nat Resources, North Sea Bur, North China Sea Offshore Engn Survey Inst, Qingdao 266061, Shandong, Peoples R China.
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- Lv, Zhihan, Dr. 1984- (författare)
- Uppsala universitet,Institutionen för speldesign
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Shaanxi Fash Engn Univ, Sch Art & Design, Xian 712046, Shaanxi, Peoples R China Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Shandong, Peoples R China. (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-5963 .- 1557-9964. ; 20:4, s. 2407-2419
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Objective: it aims to adopt deep transfer learning combined with Digital Twins (DTs) in Magnetic Resonance Imaging (MRI) medical image enhancement.Methods: MRI image enhancement method based on metamaterial composite technology is proposed by analyzing the application status of DTs in medical direction and the principle of MRI imaging. On the basis of deep transfer learning, MRI super-resolution deep neural network structure is established. To address the problem that different medical imaging methods have advantages and disadvantages, a multi-mode medical image fusion algorithm based on adaptive decomposition is proposed and verified by experiments.Results: the optimal Peak Signal to Noise Ratio (PSNR) of 34.11dB can be obtained by introducing modified linear element and loss function of deep transfer learning neural network structure. The Structural Similarity Coefficient (SSIM) is 85.24%. It indicates that the MRI truthfulness and sharpness obtained by adding composite metasurface are improved greatly. The proposed medical image fusion algorithm has the highest overall score in the subjective evaluation of the six groups of fusion image results. Group III had the highest score in Magnetic Resonance Imaging- Positron Emission Computed Tomography (MRI-PET) image fusion, with a score of 4.67, close to the full score of 5. As for the objective evaluation in group I of Magnetic Resonance Imaging- Single Photon Emission Computed Tomography (MRI-SPECT) images, the Root Mean Square Error (RMSE), Relative Average Spectral Error (RASE) and Spectral Angle Mapper (SAM) are the highest, which are 39.2075, 116.688, and 0.594, respectively. Mutual Information (MI) is 5.8822.Conclusion: the proposed algorithm has better performance than other algorithms in preserving spatial details of MRI images and color information direction of SPECT images, and the other five groups have achieved similar results.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Medical diagnostic imaging
- Magnetic resonance imaging
- Diseases
- Superresolution
- Predictive models
- Convolutional neural networks
- Mathematical models
- Digital twins
- deep transfer learning
- multimodal image fusion
- MRI image enhancement
- adaptive medical image fusion
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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