MRI Reconstructor
Project details
Description
This was my master's thesis project
The medical image modality of magnetic resonance imaging (MRI) plays an increasingly important role in modern medicine, despite the high costs of these exams when compared to the decreasing costs of other imaging exams. Despite advances and investments in technologies, the attempt to take advantage of these studies in cost reduction is still modest in recent years.
This research proposal aims to study and evaluate the reconstruction of MRI images with subsampled data in the frequency space (K space) through artificial intelligence (AI), with reduction of the examination time and consequently the exam costs. This study compares the feasibility and efficiency in different percentages of the sample.
The raw data in K space was sampled in a controlled way to reconstruct the images, resulting in an image with sparse data reducing the number of image information. Then, the sparse data undergoes a reconstruction process through AI techniques such as Deep Learning (DL).
The proccess resultss was compared to the original image, analyzing the image efficiency through reconstructed quantitative measures. In this work, the division of the image into separate rows and columns was used and worked as single sequences.
These sequences were used as input to a neural network and reconstructed to form the original sequence, before subsampling. Finally, an algorithm was used to join these sequences and shapes to the new rows and columns of the reconstructed image.
The results of this process were compared with the original image, analyzing the image efficiency obtained through quantitative measures of the quality of the reconstructed images, in this case, the structural similarity index (SSIM) and the mean square error (mean square error) - MSE), for the different subsampling weights in the K space.
Therefore, with this study, it was possible to obtain a more effective method than the conventional method, reducing the time required for this process.
Result
Recommendations
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