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[The Application Value of Artificial Intelligence-based Filtering and Interpolated Image Reconstruction Algorithm in Abdominal Magnetic Resonance Image Denoising].
Xu, Xu; Peng, Wan-Lin; Zhang, Jin-Ge; Liu, Ke-Ling; Hu, Si-Xian; Zeng, Ling-Ming; Xia, Chun-Chao; Li, Zhen-Lin.
Afiliação
  • Xu X; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Peng WL; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Zhang JG; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Liu KL; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Hu SX; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Zeng LM; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Xia CC; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Li ZL; Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Sichuan Da Xue Xue Bao Yi Xue Ban ; 52(2): 293-299, 2021 Mar.
Article em Zh | MEDLINE | ID: mdl-33829705
OBJECTIVE: To compare the noise reduction performance of conventional filtering and artificial intelligence-based filtering and interpolation (AIFI) and to explore for optimal parameters of applying AIFI in the noise reduction of abdominal magnetic resonance imaging (MRI). METHODS: Sixty patients who underwent upper abdominal MRI examination in our hospital were retrospectively included. The raw data of T1-weighted image (T1WI), T2-weighted image (T2WI), and dualecho sequences were reconstructed with two image denoising techniques, conventional filtering and AIFI of different levels of intensity. The difference in objective image quality indicators, peak signal-to-noise ratio (pSNR) and image sharpness, of the different denoising techniques was compared. Two radiologists evaluated the image noise, contrast, sharpness, and overall image quality. Their scores were compared and the interobserver agreement was calculated. RESULTS: Compared with the original images, improvement of varying degrees were shown in the pSNR and the sharpness of the images of the three sequences, T1W1, T2W2, and dual echo sequence, after denoising filtering and AIFI were used (all P<0.05). In addition, compared with conventional filtering, the objective quality scores of the reconstructed images were improved when conventional filtering was combined with AIFI reconstruction methods in T1WI sequence, AIFI level≥3 was used in T2WI and echo1 sequence, and AIFI level≥4 was used in echo2 sequence (all P<0.05). The subjective scores given by the two radiologists for the image noise, contrast, sharpness, and overall image quality in each sequence of conventional filtering reconstruction, AIFI reconstruction (except for AIFI level=1), and two-method combination reconstruction were higher than those of the original images (all P<0.05). However, the image contrast scores were reduced for AIFI level=5. There was good interobserver agreement between the two radiologists (all r>0.75, P<0.05). After multidimensional comparison, the optimal parameters of using AIFI technique for noise reduction in abdominal MRI were conventional filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in the T2WI and dualecho sequences. CONCLUSION: AIFI is superior to filtering in imaging denoising at medium and high levels. It is a promising noise reduction technique. The optimal parameters of using AIFI for abdominal MRI are Filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in T2WI and dualecho sequences.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Imageamento por Ressonância Magnética Tipo de estudo: Observational_studies / Prognostic_studies Limite: Humans Idioma: Zh Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Imageamento por Ressonância Magnética Tipo de estudo: Observational_studies / Prognostic_studies Limite: Humans Idioma: Zh Ano de publicação: 2021 Tipo de documento: Article