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Sartoretti T, Gennari AG, Sartoretti E, Skawran S, Maurer A, Buechel RR, Messerli M. Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging. J Nucl Cardiol 2023; 30:313-320. [PMID: 35301677 PMCID: PMC9984313 DOI: 10.1007/s12350-022-02940-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/12/2022] [Indexed: 10/18/2022]
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.
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Affiliation(s)
- Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
- Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands
| | - Antonio G Gennari
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Elisabeth Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Ronny R Buechel
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
- University of Zurich, Zurich, Switzerland.
- Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands.
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Gokhale R. Divergent pattern: Pattern recognition or marker for adverse events. J Nucl Cardiol 2022; 29:3123-3125. [PMID: 35149977 DOI: 10.1007/s12350-022-02913-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 01/02/2022] [Indexed: 01/14/2023]
Affiliation(s)
- Rohit Gokhale
- Division of Cardiology, NYU Langone Medical Center, New York, NY, USA.
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