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The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has affected almost every country in the world resulting in severe morbidity, mortality and economic hardship, altering the landscape of healthcare forever. Its devastating and most frequent thoracic and cardiac manifestations have been well reported since the start of the pandemic. Its extra-thoracic manifestations are myriad and understanding them is critical in diagnosis and disease management. The role of radiology is growing in the second wave and second year of the pandemic as the multiorgan manifestations of COVID-19 continue to unfold. Musculoskeletal, neurologic and vascular disease processes account for a significant number of COVID-19 complications and understanding their frequency, clinical sequelae and imaging manifestations is vital in guiding management and improving overall survival. The authors aim to provide a comprehensive overview of the pathophysiology of the virus along with a detailed and systematic imaging review of the extra-thoracic manifestation of COVID-19. In Part I, abdominal manifestations of COVID-19 in adults and multisystem inflammatory syndrome in children will be reviewed. In Part II, manifestations of COVID-19 in the musculoskeletal, central nervous and vascular systems will be reviewed. • Hematomas due to anticoagulation and decubiti due to prolonged hospitalization are the more commonly seen musculoskeletal manifestations of COVID-19. • The majority of COVID-19 patients who undergo neuroimaging for altered mental status or encephalopathy have no detectable abnormality. • Up 1.1% of hospitalized COVID-19 patients will have positive neuroimaging findings, with infarct most and hemorrhage second most common. • Large vessel occlusion cerebral infarcts can be the initial presenting symptom of COVID-19 in young patients. • Arterial thromboembolic disease is much less common than venous thromboembolic disease in COVID-19 with limb ischemia the most common site. • Aortic floating thrombi are observed in COVID-19 and may be silent or cause abdominal pain, but they can cause visceral and peripheral embolism. [ABSTRACT FROM AUTHOR]

Purpose: Calcification detection and segmentation in CT angiography (CTA) is the basis of preoperative calcification assessment and treatment determination in endovascular interventional surgery for lower-extremity atherosclerotic occlusion disease. However, the complex calcification-lumen contrast and difficult-to-locate occluded superficial femoral artery (SFA) make it challenging. This paper proposes a fast and accurate method without artery extraction to segment and detect SFA calcification in CTA using a convolutional neural network., Method: The thigh region containing the target SFA is first automatically extracted based on the human anatomical position. Then, 3D Unet with a large receptive field is used to segment calcifications in image patches with a large field of view. The lumen label is introduced and a calcification-lumen contrast data augmentation method is developed to improve the segmentation performance on images with varying calcification-lumen contrast. Finally, false-positive errors far from the SFA are eliminated based on the SFA centerline estimated from the segmentation results., Results: Five-fold cross validation experiments were conducted on a local dataset of CTA images containing 128 SFAs. The average Dice scores of calcification segmentation on the entire, occluded and non-occluded arteries achieved 89.12%, 92.98% and 88.96%, respectively. The average recall and precision of calcification detection on each slice were 93.50% and 91.51%, respectively. The total processing time was about 2 min., Conclusions: This paper proposes a CNN-based method to segment and detect SFA calcification in CTA without artery extraction for varying calcification-lumen intensity contrast and arterial occlusion situations. The work can be used to improve clinical calcification analysis., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Rationale and Objectives: The purpose of this work was to measure the accuracy of dual-energy computed tomography for identifying iodine and calcium and to determine the effects of calcium suppression in phantoms and lower-extremity computed tomographic (CT) angiographic data sets. Materials and Methods: Using a three-material basis decomposition method for 80- and 140-kVp data, the accuracy of correctly identified contrast medium and calcium voxels and the mean attenuation before and after calcium suppression were computed. Experiments were first performed on a phantom of homogenous contrast medium and hydroxyapatite samples with mean attenuation of 57.2, 126, and 274 Hounsfield units (HU) and 50.0, 122, and 265 HU, respectively. Experiments were repeated in corresponding attenuation groups of voxels from manually segmented bones and contrast medium–enhanced arteries in a lower-extremity CT angiographic data set with mean attenuation of 293 and 434 HU, respectively. Calcium suppression in atherosclerotic plaques of a cadaveric specimen was also studied, using micro–computed tomography as a reference, and in a lower-extremity CT angiographic data set with substantial below-knee calcified plaques. Results: Higher concentrations showed increased accuracy of iodine and hydroxyapatite identification of 87.4%, 99.7%, and 99.9% and 88.0%, 95.0%, and 99.9%, respectively. Calcium suppression was also more accurate with higher concentrations of iodine and hydroxyapatite, with mean attenuation after suppression of 47.1, 122, and 263 HU and 7.14, 11.6, and 12.6 HU, respectively. Similar patterns were seen in the corresponding attenuation groups of the contrast medium–enhanced arteries and bone in the clinical data set, which had overall accuracy of 81.3% and 78.9%, respectively, and mean attenuation after calcium suppression of 254 and 73.7 HU, respectively. The suppression of calcified atherosclerotic plaque was accurate compared with the micro-CT reference; however, the suppression in the clinical data set showed probable inappropriate suppression of the small vessels. Conclusion: Dual-energy computed tomography can detect and differentiate between contrast medium and calcified tissues, but its accuracy is dependent on the CT density of tissues and limited when CT attenuation is low. [Copyright &y& Elsevier]