Marly van Assen1, Simon S Martin2, Akos Varga-Szemes3, Saikiran Rapaka4, Serkan Cimen4, Puneet Sharma4, Pooyan Sahbaee5, Carlo N De Cecco6, Rozemarjin Vliegenthart7, Tyler J Leonard8, Jeremy R Burt8, U Joseph Schoepf9. 1. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, USA; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University | Emory Healthcare, Inc., Atlanta, GA, USA. 2. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, USA; Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany. 3. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, USA. 4. Medical Imaging Technologies, Siemens Healthineers, Princeton, NJ, USA. 5. Siemens Medical Solutions USA, Malvern, PA, USA. 6. Computed Tomography Research & Development, Siemens Healthineers, Forchheim, Germany; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University | Emory Healthcare, Inc., Atlanta, GA, USA. 7. Computed Tomography Research & Development, Siemens Healthineers, Forchheim, Germany; University of Groningen, University Medical Center Groningen, Departments of Radiology, Groningen, the Netherlands. 8. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, USA; Computed Tomography Research & Development, Siemens Healthineers, Forchheim, Germany. 9. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, USA; Computed Tomography Research & Development, Siemens Healthineers, Forchheim, Germany. Electronic address: schoepf@musc.edu.
Abstract
PURPOSE: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. METHODS: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. RESULTS: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. CONCLUSION: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions.
PURPOSE: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. METHODS: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. RESULTS: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. CONCLUSION: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions.
Authors: Giuseppe Muscogiuri; Valentina Volpato; Riccardo Cau; Mattia Chiesa; Luca Saba; Marco Guglielmo; Alberto Senatieri; Gregorio Chierchia; Gianluca Pontone; Serena Dell'Aversana; U Joseph Schoepf; Mason G Andrews; Paolo Basile; Andrea Igoren Guaricci; Paolo Marra; Denisa Muraru; Luigi P Badano; Sandro Sironi Journal: Heliyon Date: 2022-10-05