Thoracic imaging outcomes in COVID-19 survivors

The coronavirus disease 2019(COVID-19)pandemic presents a significant global public health challenge.One in five individuals with COVID-19 presents with symptoms that last for weeks after hospital discharge,a condition termed“long COVID”.Thus,efficient follow-up of patients is needed to assess the resolution of lung pathologies and systemic involvement.Thoracic imaging is multimodal and involves using different forms of waves to produce images of the organs within the thorax.In general,it includes chest X-ray,computed tomography,lung ultrasound and magnetic resonance imaging techniques.Such modalities have been useful in the diagnosis and prognosis of COVID-19.These tools have also allowed for the follow-up and assessment of long COVID.This review provides insights on the effectiveness of thoracic imaging techniques in the follow-up of COVID-19 survivors who had long COVID.

Fast and Accurate Thoracic SPECT Image Reconstruction

In Single-Photon Emission Computed Tomography(SPECT),the reconstructed image has insufficient contrast,poor resolution and inaccurate volume of the tumor size due to physical degradation factors.Generally,nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images.This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy.The suggested algorithm is composed of three steps.The first one consists of denoising the acquired projections using the benefits of the complementary properties of both the Curvelet transformand theWavelet transforms to provide the best noise reduction.The second step is a simultaneous reconstruction of the axial slices using the 3D Ordered Subset Expectation Maximization(OSEM)algorithm.The last step is post-processing of the reconstructed axial slices using one of the newest anisotropic diffusion models named Partial Differential Equation(PDE).The method is tested on two digital phantoms and clinical bone SPECT images.A comparative study with four algorithms reviewed on state of the art proves the significance of the proposed method.In simulated data,experimental results show that the plot profile of the proposed model keeps close to the original one compared to the other algorithms.Furthermore,it presents a notable gain in terms of contrast to noise ratio(CNR)and execution time.The proposed model shows better results in the computation of contrast metric with a value of 0.68±7.2 and the highest signal to noise ratio(SNR)with a value of 78.56±6.4 in real data.The experimental results prove that the proposed algorithm is more accurate and robust in reconstructing SPECT images than the other algorithms.It could be considered a valuable candidate to correct the resolution of bone in the SPECT images.