Abstract
The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.
Highlights
The COVID-19, a pandemic declared since March 11, 2020, emerging from China, has seriously affected 213 countries and territories
The error between the measured and postulated projections is computed using the cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE) and this forms the basis of their comparison for their efficacy in image reconstruction through simulated annealing (SA) algorithm
The objective of this work is to introduce ultralow-dose X-ray computed tomography (CT) methods along with a suitable cost function for early and reliable diagnosis of elderly and individuals subjected to the pandemic with dire consequences
Summary
The COVID-19, a pandemic declared since March 11, 2020, emerging from China, has seriously affected 213 countries and territories. COVID-19 affects the lungs, causing symptoms primarily such as pneumonia, resulting in diffused damage to both lungs of the patient necessitating the prompt action for its obliteration (Pan Y. et al, 2020). In this context, there are two alternatives for early diagnostic of COVID-19: first, real-time RT-PCR of viral nucleic acid in COVID-19 diagnosis having varying turnaround times with false-negative results, and second, the computed tomography (CT) imaging (Qureshi and Ul Rehman, 2020). For radiologists and other healthcare professionals, methods such as artificial intelligencebased volume segmentation may help by providing a faster way of objectively evaluating the radiological CT images
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