An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction. (May 2023) - Sarvari, A.V.P.; Sridevi, K.
Highlights: To propose an enhanced optimized self-attention network model for reconstructing CT images from the under sampled image by varying the sampling rate. The extended cascaded filter (ECF) is emphasized to suppress the presence of artifacts in the under sampled image and maintain the reconstruction accuracy, the extended cascaded filter (ECF) is emphasized. To reconstruct CT images from the under sampled image, a novel enhanced battle royale self-attention based bi-directional long short term memory model (EBRSA-bi LSTM) is proposed. Enhanced battle royale optimization (EBRO) is proposed to update weights in each neural network layer and reduce loss function. Evaluating the performance of the proposed method in terms of reconstructed image quality such as SSIM, PSNR, RMSE and reconstruction accuracy. Abstract: COVID-19 has spread all over the world, causing serious panic around the globe. Chest computed tomography (CT) images are integral in confirming COVID positive patients. Several investigations were conducted to improve or maintain the image reconstruction quality for the sample image reconstruction. Deep learning (DL) methods have recently been proposed to achieve fast reconstruction, but many have focused on a single domain, such as the image domain of k-space. In this research, the highly under-sampled enhanced battle royale self-attention based bi-directional long short-term (EBRSA-bi LSTM) CT image reconstruction model is proposed to reconstruct the image from the under-sampled data. The research is adapted with two phases, namely, pre-processing and reconstruction. The extended cascaded filter (ECF) is proposed for image pre-processing and tends to suppress the noise and enhance the reconstruction accuracy. In the reconstruction model, the battle royale optimization (BrO) is intended to diminish the loss function of the reconstruction network model and weight updation. The proposed model is tested with two datasets, COVID-CT- and SARS-CoV-2 CT. The reconstruction accuracy of the proposed model with two datasets is 93.5 % and 97.7 %, respectively. Also, the image quality assessment parameters such as Peak-Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Structural Similarity Index metric (SSIM) are evaluated, and it yields an outcome of (45 and 46 dB), (0.0026 and 0.0022) and (0.992, 0.996) with two datasets.