Open AccessMRI Image Reconstruction Based on Artificial Intelligence
Author(s) -
NaNa Liang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1852/2/022077
Subject(s) - artificial intelligence , computer science , convolutional neural network , deep learning , magnetic resonance imaging , artificial neural network , construct (python library) , similarity (geometry) , pattern recognition (psychology) , medical imaging , image (mathematics) , radiology , medicine , programming language
Artificial intelligence is a new high and new technology, which has powerful functional advantages in processing big data, complex and non-deterministic data, and in-depth mining of hidden information behind the data. Magnetic resonance is a common diagnosis and treatment technology in medical clinics. The use of magnetic resonance imaging technology to construct medical images has a great auxiliary effect for doctors to make accurate medical diagnosis. This article proposes to combine artificial intelligence and magnetic resonance technology to construct magnetic resonance images based on artificial intelligence, in order to reconstruct high-quality magnetic resonance images, thereby improving the efficiency of doctors’ diagnosis and treatment and reducing misdiagnosis. This paper uses the deep learning algorithm of artificial intelligence to compare the analytic dictionary method, the synthetic dictionary method and the deep dictionary method based on convolutional neural network, and obtain the peak-to-noise ratio of the image reconstructed by the three algorithms under the same sampling rate. They are 42.56, 44.17 and 45.55 respectively; the structural similarity is 0.9387, 0.9536 and 0.9867, respectively. The study concluded that the deep dictionary method based on convolutional neural network has more advantages in reconstructing MRI images, and the effect is more obvious.