1,198
views
0
recommends
+1 Recommend
1 collections
    32
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Novel SPECT Technologies and Approaches in Cardiac Imaging

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Recent novel approaches in myocardial perfusion single photon emission CT (SPECT) have been facilitated by new dedicated high-efficiency hardware with solid-state detectors and optimized collimators. New protocols include very low-dose (1 mSv) stress-only, two-position imaging to mitigate attenuation artifacts, and simultaneous dual-isotope imaging. Attenuation correction can be performed by specialized low-dose systems or by previously obtained CT coronary calcium scans. Hybrid protocols using CT angiography have been proposed. Image quality improvements have been demonstrated by novel reconstructions and motion correction. Fast SPECT acquisition facilitates dynamic flow and early function measurements. Image processing algorithms have become automated with virtually unsupervised extraction of quantitative imaging variables. This automation facilitates integration with clinical variables derived by machine learning to predict patient outcome or diagnosis. In this review, we describe new imaging protocols made possible by the new hardware developments. We also discuss several novel software approaches for the quantification and interpretation of myocardial perfusion SPECT scans.

          Related collections

          Most cited references 79

          • Record: found
          • Abstract: found
          • Article: not found

          Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm.

          We aimed to improve the diagnostic accuracy of automatic myocardial perfusion SPECT (MPS) interpretation analysis for the prediction of coronary artery disease (CAD) by integrating several quantitative perfusion and functional variables for noncorrected (NC) data by Support Vector Machine (SVM) algorithm, a computer method for machine learning.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Automatic Valve Plane Localization in Myocardial Perfusion SPECT/CT by Machine Learning: Anatomic and Clinical Validation

            Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement. Methods: A total of 392 consecutive patients undergoing 99m Tc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared. Results: Bland–Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, −5 to 7 mm) and AC rest images (bias, 1; 95% CI, −7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, −8 to 8 mm) and AC rest images (bias, 0; 95% CI, −10 to 10 mm) ( P < 0.01). Bland–Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, −4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, −7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, −6 to 5 mm) and non-AC rest images (bias, −1; 95% CI, −9 to 7 mm) ( P was not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7–0.87]; AUC, 0.81 [95% CI, 0.73–0.89]) and the SVM (0.82 [0.74–0.9]) for AC data were the same ( P = NS) and were higher than those for the unadjusted VP (0.63 [0.53–0.73]) ( P < 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69–0.89]; AUC, 0.8 [95% CI, 0.72–0.88]) and the SVM (0.79 [0.71–0.87]) were the same ( P = NS) and were higher than those for the unadjusted VP (0.65 [0.56–0.75]) ( P < 0.01). Conclusion: Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              High-efficiency SPECT MPI: comparison of automated quantification, visual interpretation, and coronary angiography.

              Recently introduced high-efficiency (HE) SPECT cameras with solid-state CZT detectors have been shown to decrease imaging time and reduce radiation exposure to patients. An automated, computer-derived quantification of HE MPI has been shown to correlate well with coronary angiography on one HE SPECT camera system (D-SPECT), but has not been compared to visual interpretation on any of the HE SPECT platforms.
                Bookmark

                Author and article information

                Journal
                CVIA
                Cardiovascular Innovations and Applications
                CVIA
                Compuscript (Ireland )
                2009-8782
                2009-8618
                December 2016
                March 2017
                : 2
                : 1
                : 31-46
                Affiliations
                1Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
                2Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Changhua, Taiwan
                Author notes
                Correspondence: Piotr Slomka, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA, E-mail: piotr.slomka@ 123456cshs.org
                Article
                cvia20160052
                10.15212/CVIA.2016.0052
                Copyright © 2017 Cardiovascular Innovations and Applications

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

                Product
                Categories
                REVIEW

                Comments

                Comment on this article