Effect of varying computed tomography acquisition and reconstruction parameters on semi-automated clot volume quantification

AIM To examine effects of computed tomography(CT)image acquisition/reconstruction parameters on clot volume quantification in vitro for research method validation purposes.METHODS This study was performed in conformance with HIPAA and IRB Regulations(March 2015-November 2016).A ten blood clot phantom was designed and scanned on a dual-energy CT scanner(SOMATOM Force,Siemens Healthcare Gm BH,Erlangen,Germany)with varying pitch,iterative reconstruction,energy level and slicethickness.A range of clot and tube sizes were used in an attempt to replicate in vivo emboli found within central and segmental branches of the pulmonary arteries in patients with pulmonary emboli.Clot volume was the measured parameter and was analyzed by a single image analyst using a semi-automated region growing algorithm implemented in the FDA-approved Siemens syngo.via image analysis platform.Mixed model analysis was performed on the data.RESULTS On the acquisition side,the continuous factor of energy showed no statistically significant effect on absolute clot volume quantification(P=0.9898).On the other hand,when considering the fixed factor of pitch,there were statistically significant differences in clot volume quantification(P<0.0001).On the reconstruction side,with the continuous factor of reconstruction slice thickness no statistically significant effect on absolute clot volume quantification was demonstrated(P=0.4500).Also on the reconstruction side,with the fixed factor of using iterative reconstructions there was also no statistically significant effect on absolute clot volume quantification(P=0.3011).In addition,there was excellent R^2 correlation between the scale-measured mass of the clots both with respect to the CT measured volumes and with respect to volumes measure by the water displacement method.CONCLUSION Aside from varying pitch,changing CT acquisition parameters and using iterative reconstructions had no significant impact on clot volume quantification with a semi-automated region growing algorithm.

Reproducibility of thrombus volume quantification in multicenter computed tomography pulmonary angiography studies

AIM To evaluate reproducibility of pulmonary embolism(PE) clot volume quantification using computed tomography pulmonary angiogram(CTPA) in a multicenter setting.METHODS This study was performed using anonymized data in conformance with HIPAA and IRB Regulations(March 2015-November 2016). Anonymized CTPA data was acquired from 23 scanners from 18 imaging centers using each site's standard PE protocol. Two independent analysts measured PE volumes using a semi-automated region-growing algorithm on an FDA-approved image analysis platform. Total thrombus volume(TTV) was calculated per patient as the primary endpoint. Secondary endpoints were individual thrombus volume(ITV), Qanadli score and modified Qanadli score per patient. Inter-and intra-observer reproducibility were assessed using intra-class correlation coefficient(ICC) and BlandAltman analysis. RESULTS Analyst 1 found 72 emboli in the 23 patients with a mean number of emboli of 3.13 per patient with a range of 0-11 emboli per patient. The clot volumes ranged from 0.0041-47.34 cm3(mean +/-SD, 5.93 +/-10.15 cm3). On the second read, analyst 1 found the same number and distribution of emboli with a range of volumes for read 2 from 0.0041 – 45.52 cm3(mean +/-SD, 5.42 +/-9.53 cm3). Analyst 2 found 73 emboli in the 23 patients with a mean number of emboli of 3.17 per patient with a range of 0-11 emboli per patient. The clot volumes ranged from 0.00459-46.29 cm3(mean +/-SD, 5.91 +/-10.06 cm3). Inter-and intraobserver variability measurements indicated excellent reproducibility of the semi-automated method for quantifying PE volume burden. ICC for all endpoints was greater than 0.95 for inter-and intra-observer analysis. Bland-Altman analysis indicated no significant biases.CONCLUSION Semi-automated region growing algorithm for quantifying PE is reproducible using data from multiple scanners and is a suitable method for image analysis in multicenter clinical trials.

Review of radiographic findings in COVID-19

The purpose of this study is to review the published literature for the range ofradiographic findings present in patients suffering from coronavirus disease 2019infection. This novel corona virus is currently the cause of a worldwide pandemic.Pulmonary symptoms and signs dominate the clinical picture and radiologists arecalled upon to evaluate chest radiographs (CXR) and computed tomography (CT)images to assess for infiltrates and to define their extent, distribution andprogression. Multiple studies attempt to characterize the disease course bylooking at the timing of imaging relative to the onset of symptoms. In general,plain CXR show bilateral disease with a tendency toward the lung periphery andhave an appearance most consistent with viral pneumonia. Chest CT images aremost notable for showing bilateral and peripheral ground glass and consolidatedopacities and are marked by an absence of concomitant pulmonary nodules,cavitation, adenopathy and pleural effusions. Published literature mentioningorgan systems aside from pulmonary manifestations are relatively less common,yet present and are addressed in this review. Similarly, publications focusing onimaging modalities aside from CXR and chest CT are sparse in this evolving crisisand are likewise addressed in this review. The role of imaging is examined as it iscurrently being debated in the medical community, which is not at all surprisingconsidering the highly infectious nature of Severe Acute Respiratory Syndromecoronavirus 2.

Segmentation of carotid arterial walls using neural networks

BACKGROUND Automated,accurate,objective,and quantitative medical image segmentation has remained a challenging goal in computer science since its inception.This study applies the technique of convolutional neural networks(CNNs)to the task of segmenting carotid arteries to aid in the assessment of pathology.AIM To investigate CNN’s utility as an ancillary tool for researchers who require accurate segmentation of carotid vessels.METHODS An expert reader delineated vessel wall boundaries on 4422 axial T2-weighted magnetic resonance images of bilateral carotid arteries from 189 subjects with clinically evident atherosclerotic disease.A portion of this dataset was used to train two CNNs(one to segment the vessel lumen and the other to segment the vessel wall)with the remaining portion used to test the algorithm’s efficacy by comparing CNN segmented images with those of an expert reader.Overall quantitative assessment between automated and manual segmentations was determined by computing the DICE coefficient for each pair of segmented images in the test dataset for each CNN applied.The average DICE coefficient for the test dataset(CNN segmentations compared to expert’s segmentations)was 0.96 for the lumen and 0.87 for the vessel wall.Pearson correlation values and the intra-class correlation coefficient(ICC)were computed for the lumen(Pearson=0.98,ICC=0.98)and vessel wall(Pearson=0.88,ICC=0.86)segmentations.Bland-Altman plots of area measurements for the CNN and expert readers indicate good agreement with a mean bias of 1%-8%.CONCLUSION Although the technique produces reasonable results that are on par with expert human assessments,our application requires human supervision and monitoring to ensure consistent results.We intend to deploy this algorithm as part of a software platform to lessen researchers’workload to more quickly obtain reliable results.