Longitudinal Self-Supervision for COVID-19 Pathology Quantification (preprint)

Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been proposed to help radiologists automatically quantify COVID-19 pathologies on longitudinal CT scans. However, the learning process of deep learning methods demands extensive training data to learn the complex characteristics of infected regions over longitudinal scans. It is challenging to collect a large-scale dataset, especially for longitudinal training. In this study, we want to address this problem by proposing a new self-supervised learning method to effectively train longitudinal networks for the quantification of COVID-19 infections. For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans. Experimental results show that the proposed method is effective, helping the model better exploit the semantics of longitudinal data and improve two COVID-19 quantification tasks.

Dark-field Chest X-ray Imaging for the Assessment of COVID‑19‑Pneumonia (preprint)

Medical imaging is performed in daily clinical routine for the assessment of pulmonary involvement in patients infected with COVID-19. As conventional (attenuation-based) chest radiography provides only a low sensitivity for COVID-19-pneumonia, CT is the gold standard for lung imaging in COVID-19 patients. However, CT imaging exposes the patient to a considerable amount of radiation, and is not as widely available as plain chest X-rays. Therefore, alternative low-dose imaging X-ray techniques are highly desirable. Here we present the first clinical results employing a novel dark-field chest X-ray imaging method for the assessment of COVID-19-pneumonia. The work is based on recent technological advancements in a human-scale X-ray dark-field chest imaging prototype that enable the acquisition of quantitative dark-field radiographs with diagnostic image quality at a radiation dose comparable to conventional X-rays. In a reader study, we found that dark-field imaging has a higher sensitivity for COVID-19-pneumonia than attenuation-based imaging, and that the combination of both is superior to one imaging modality alone. Furthermore, a quantitative image analysis showed a significant reduction of signal in X-ray dark-field chest radiographs of COVID-19 patients. While our results demonstrate that dark-field chest radiography presents an ultra-low-dose alternative to CT imaging for the assessment of COVID-19-pneumonia, we anticipate that the presented technique will also be useful for therapy follow-up of patients with long-COVID-syndrome and, more generally, for the imaging of other pulmonary pathologies.

Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans (preprint)

Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic and interactive segmentation models for medical images only use data from a single time point (static). However, valuable segmentation information from previous time points is often not used to aid the segmentation of a patient's follow-up scans. Also, fully automatic segmentation techniques frequently produce results that would need further editing for clinical use. In this work, we propose a new single network model for interactive segmentation that fully utilizes all available past information to refine the segmentation of follow-up scans. In the first segmentation round, our model takes 3D volumes of medical images from two-time points (target and reference) as concatenated slices with the additional reference time point segmentation as a guide to segment the target scan. In subsequent segmentation refinement rounds, user feedback in the form of scribbles that correct the segmentation and the target's previous segmentation results are additionally fed into the model. This ensures that the segmentation information from previous refinement rounds is retained. Experimental results on our in-house multiclass longitudinal COVID-19 dataset show that the proposed model outperforms its static version and can assist in localizing COVID-19 infections in patient's follow-up scans.

SARS-CoV-2 serology increases diagnostic accuracy in CT-suspected, PCR-negative COVID-19 patients during pandemic (preprint)

Background:In the absence of PCR detection of SARS-CoV-2 RNA, accurate diagnosis of COVID-19 is challenging. Low-dose computed tomography (CT) detects pulmonary infiltrates with high sensitivity, but findings may be non-specific. This study assesses the diagnostic value of SARS-CoV-2 serology for patients with distinct CT features but negative PCR.Methods:IgM/IgG chemiluminescent immunoassay was performed for 107 patients with confirmed (group A: PCR+; CT±) and 46 patients with suspected (group B: repetitive PCR-; CT+) COVID-19, admitted to a German university hospital during the pandemic’s first wave. A standardized, in-house CT classification of radiological signs of a viral pneumonia was used to assess the probability of COVID-19.Results:Seroconversion rates (SR) determined on day 5, 10, 15, 20 and 25 after symptom onset (SO) were 8%, 25%, 65%, 76% and 91% for group A, and 0%, 10%, 19%, 37% and 46% for group B, respectively; (p<0.01). Compared to hospitalized patients with a non-complicated course, seroconversion tended to occur at lower frequency and delayed in patients on intensive care units. SR of patients with CT findings classified as high certainty for COVID-19 were 8%, 22%, 68%, 79% and 93% in group A, compared with 0%, 15%, 28%, 50% and 50% in group B (p<0.01). SARS-CoV-2 serology established a definite diagnosis in 12/46 group B patients. In 88% (8/9) of patients with negative serology >14 days after symptom onset (group B), clinico-radiological consensus reassessment revealed probable diagnoses other than COVID-19. Sensitivity of SARS-CoV-2 serology was superior to PCR >17d after symptom onset.Conclusions:Approximately one-third of patients with distinct COVID-19 CT findings are tested negative for SARS-CoV-2 RNA by PCR rendering correct diagnosis difficult. Implementation of SARS-CoV-2 serology testing alongside current CT/PCR-based diagnostic algorithms improves discrimination between COVID-19-related and non-related pulmonary infiltrates in PCR negative patients. However, sensitivity of SARS-CoV-2 serology strongly depends on the time of testing and becomes superior to PCR after the 2nd week following symptom onset.

Obesity and COVID-19: The role of visceral adipose tissue (preprint)

INTRODUCTION: During the unprecedented health crisis of the COVID-19 pandemic it was suggested that obesity might aggravate severe acute respiratory syndrome coronavirus-2 (SARS CoV-2). Therefore, this study aims to investigate the association between Compute Tomography (CT)-based measurements of visceral and subcutaneous fat as measures of obesity and COVID-19 severity. METHODS: 30 patients with laboratory-confirmed COVID-19 and a mean age of 65.59 plus/minus 13.06 years from a level one medical center in Berlin, Germany, were retrospectively analyzed and included in the present analysis. SARS-CoV-2 was confirmed by polymerase chain reaction from throat swaps or deep nasal swabs on the day of admission. Severe clinical courses of COVID-19 were defined by hospitalization in intensive care unit (ICU) and invasive mechanical ventilation. All patients received low-dose chest CT-based fat measurements at the level of the first lumbar vertebra. RESULTS: An increase in visceral fat area (VFA) by one square decimeter was associated with a 22.53-fold increased risk for ICU treatment and a 16.11-fold increased risk for mechanical ventilation (adjusted for age and sex). For upper abdominal circumference, each additional centimeter of circumference showed a 1.13-fold increased risk for ICU treatment and a 1.25-fold increased risk for mechanical ventilation. There was no significant correlation of subcutaneous fat area (SFA) or body mass index (BMI) with severe clinical courses of COVID-19. CONCLUSIONS: Our results suggest that visceral adipose tissue and upper abdominal circumference specifically increasing the risk of COVID-19 severity. CT-based quantification of visceral adipose tissue and upper abdominal circumference in routinely acquired chest CTs may therefore be a simple tool for risk assessment in SARS-CoV-2-patients.