Breast Tomosynthesis Reconstruction Using Artificial Neural Networks with Deep Learning
ResumoThe Filtered Backprojection (FBP) algorithm for Computed Tomography (CT) reconstruction can be mapped entire in an Artificial Neural Network (ANN), with the backprojection (BP) operation simulated analytically in a layer and the Ram-Lak filter simulated as a convolutional layer. Thus, this work adapt the BP layer for DBT reconstruction, making possible the use of FBP simulated as a ANN to reconstruct DBT images. For evaluation, Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics were calculated to measure the improvement of the images made by the ANN, regarding a dataset containing 100 virtual breast phantoms to perform the experiments. We shown that making the Ram-Lak layer trainable, the reconstructed image can be improved in terms of noise reduction. And, considering an additional post-filtering step performed by Denoising Convolutional Neural Network (DnCNN), it shown comparable and superior results than a stateof-the-art DBT reconstruction method, averaging 37.644 dB and 0.869 values of PSNR and SSIM, respectively. Finally, this study enables additional proposals of ANN with Deep Learning models for DBT reconstruction and denoising.
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