Thomas Sartoretti1,2,3, Antonio G Gennari1,2, Elisabeth Sartoretti1,2, Stephan Skawran1,2, Alexander Maurer1,2, Ronny R Buechel1,2, Michael Messerli4,5,6. 1. Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. 2. University of Zurich, Zurich, Switzerland. 3. Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands. 4. Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. michael.messerli@usz.ch. 5. University of Zurich, Zurich, Switzerland. michael.messerli@usz.ch. 6. Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands. michael.messerli@usz.ch.
Abstract
BACKGROUND: To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS AND RESULTS: Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores. CONCLUSION: A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI.
BACKGROUND: To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI). METHODS AND RESULTS: Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores. CONCLUSION: A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI.
Authors: Antonio G Gennari; Hannes Grünig; Dominik C Benz; Stephan Skawran; Alexander Maurer; Ahmad M A Abukwaik; Alexia Rossi; Catherine Gebhard; Ronny R Buechel; Michael Messerli Journal: J Nucl Cardiol Date: 2022-02-17 Impact factor: 5.952
Authors: David J Winkel; V Reddappagari Suryanarayana; A Mohamed Ali; Johannes Görich; Sebastian Johannes Buß; Axel Mendoza; Chris Schwemmer; Puneet Sharma; U Joseph Schoepf; Saikiran Rapaka Journal: Eur Heart J Cardiovasc Imaging Date: 2022-06-01 Impact factor: 9.130
Authors: Claudia Morf; Thomas Sartoretti; Antonio G Gennari; Alexander Maurer; Stephan Skawran; Andreas A Giannopoulos; Elisabeth Sartoretti; Moritz Schwyzer; Alessandra Curioni-Fontecedro; Catherine Gebhard; Ronny R Buechel; Philipp A Kaufmann; Martin W Huellner; Michael Messerli Journal: Diagnostics (Basel) Date: 2022-08-03
Authors: Joo Hyeok Choi; Min Jae Cha; Iksung Cho; William D Kim; Yera Ha; Hyewon Choi; Sun Hwa Lee; Seng Chan You; Jee Suk Chang Journal: Front Oncol Date: 2022-09-20 Impact factor: 5.738