Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia.

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

Imaging Dynamic Expiration: Feasibility of MRI Spirometry Using Hyperpolarized Xenon Gas.

PURPOSE: To examine the feasibility of imaging-based spirometry using high-temporal-resolution projection MRI and hyperpolarized xenon 129 (129Xe) gas. MATERIALS AND METHODS: In this prospective exploratory study, five healthy participants (age range, 25-45 years; three men) underwent an MRI spirometry technique using inhaled hyperpolarized 129Xe and rapid two-dimensional projection MRI. Participants inhaled 129Xe, then performed a forced expiratory maneuver while in an MR imager. Images of the lungs during expiration were captured in time intervals as short as 250 msec. Volume-corrected images of the lungs at expiration commencement (0 second), 1 second after expiration, and 6 seconds after expiration were extracted to generate forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio pulmonary maps. For comparison, participants performed conventional spirometry in the sitting position using room air, in the supine position using room air, and in the supine position using a room air and 129Xe mixture. Paired t tests with Bonferroni corrections for multiple comparisons were used for statistical analyses. RESULTS: The mean MRI-derived FEV1/FVC value was lower in comparison with conventional spirometry (0.52 ± 0.03 vs 0.70 ± 0.05, P < .01), which may reflect selective 129Xe retention. A secondary finding of this study was that 1 L of inhaled 129Xe negatively impacted pulmonary function as measured by conventional spirometry (in supine position), which reduced measured FEV1 (2.70 ± 0.90 vs 3.04 ± 0.85, P < .01) and FEV1/FVC (0.70 ± 0.05 vs 0.79 ± 0.04, P < .01). CONCLUSION: A forced expiratory maneuver was successfully imaged with hyperpolarized 129Xe and high-temporal-resolution MRI. Derivation of regional lung spirometric maps was feasible.Keywords: MR-Imaging, MR-Dynamic Contrast Enhanced, MR-Functional Imaging, Pulmonary, Thorax, Diaphragm, Lung, Pleura, Physics Supplemental material is available for this article. © RSNA, 2021.

A hidden lung cancer in a patient with granulomatosis with polyangiitis.

Granulomatosis with polyangiitis is a systemic necrotizing vasculitis that affects the small- and medium-sized blood vessels. The diagnosis can be challenging since the clinical and imaging findings have similarities with infection, and malignancy. Serologic and histopathological investigations often help confirm the diagnosis. However, this can be falsely reassuring. We present a unique case of the coexistence of vasculitis and squamous cell carcinoma in the same cavitating lung mass. The case highlights the importance of recognizing changes in disease behaviour early to allow for timely management.

Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls.

A deep learning artificial neural network was adapted to the task of sex determination of skeletal remains. The neural network was trained on images of 900 skulls virtually reconstructed from hospital CT scans. When tested on previously unseen images of skulls, the artificial neural network showed 95% accuracy at sex determination. Artificial intelligence methods require no significant expertise to implement once trained, are rapid to use, and have the potential to eliminate human bias from sex estimation of skeletal remains.