Browsing by Subject "Myocardial perfusion imaging"

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    Differences in Prognostic Value of Myocardial Perfusion SPECT using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multi-Center Study
    (American Heart Association, 2022) Tamarappoo, Balaji K.; Otaki, Yuka; Sharir, Tali; Hu, Lien-Hsin; Gransar, Heidi; Einstein, Andrew J.; Fish, Mathews B.; Ruddy, Terrence D.; Kaufmann, Philipp; Sinusas, Albert J.; Miller, Edward J.; Bateman, Timothy M.; Dorbala, Sharmila; Di Carli, Marcelo; Eisenberg, Evann; Liang, Joanna X.; Dey, Damini; Berman, Daniel S.; Slomka, Piotr J.; Medicine, School of Medicine
    Background: Semiquantitative assessment of stress myocardial perfusion defect has been shown to have greater prognostic value for prediction of major adverse cardiac events (MACE) in women compared with men in single-center studies with conventional single-photon emission computed tomography (SPECT) cameras. We evaluated sex-specific difference in the prognostic value of automated quantification of ischemic total perfusion defect (ITPD) and the interaction between sex and ITPD using high-efficiency SPECT cameras with solid-state detectors in an international multicenter imaging registry (REFINE SPECT [Registry of Fast Myocardial Perfusion Imaging With Next-Generation SPECT]). Methods: Rest and exercise or pharmacological stress SPECT myocardial perfusion imaging were performed in 17 833 patients from 5 centers. MACE was defined as the first occurrence of death or myocardial infarction. Total perfusion defect (TPD) at rest, stress, and ejection fraction were quantified automatically by software. ITPD was given by stressTPD-restTPD. Cox proportional hazards model was used to evaluate the association between ITPD versus MACE-free survival and expressed as a hazard ratio. Results: In 10614 men and 7219 women, with a median follow-up of 4.75 years (interquartile range, 3.7-6.1), there were 1709 MACE. In a multivariable Cox model, after adjusting for revascularization and other confounding variables, ITPD was associated with MACE (hazard ratio, 1.08 [95% CI, 1.05-1.1]; P<0.001). There was an interaction between ITPD and sex (P<0.001); predicted survival for ITPD<5% was worse among men compared to women, whereas survival among women was worse than men for ITPD≥5%, P<0.001. Conclusions: In the international, multicenter REFINE SPECT registry, moderate and severe ischemia as quantified by ITPD from high-efficiency SPECT is associated with a worse prognosis in women compared with men.
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    Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events beyond Conventional SPECT Variables: Results from the REFINE SPECT Registry
    (American Heart Association, 2021) Kuronuma, Keiichiro; Miller, Robert J. H.; Otaki, Yuka; Van Kriekinge, Serge D.; Diniz, Marcio A.; Sharir, Tali; Hu, Lien-Hsin; Gransar, Heidi; Liang, Joanna X.; Parekh, Tejas; Kavanagh, Paul; Einstein, Andrew J.; Fish, Mathews B.; Ruddy, Terrence D.; Kaufmann, Philipp A.; Sinusas, Albert J.; Miller, Edward J.; Bateman, Timothy M.; Dorbala, Sharmila; Di Carli, Marcelo; Tamarappoo, Balaji K.; Dey, Damini; Berman, Daniel S.; Slomka, Piotr J.; Radiation Oncology, School of Medicine
    Background: Phase analysis of single-photon emission computed tomography myocardial perfusion imaging provides dyssynchrony information which correlates well with assessments by echocardiography, but the independent prognostic significance is not well defined. This study assessed the independent prognostic value of single-photon emission computed tomography-myocardial perfusion imaging phase analysis in the largest multinational registry to date across all modalities. Methods: From the REFINE SPECT (Registry of Fast Myocardial Perfusion Imaging With Next Generation SPECT), a total of 19 210 patients were included (mean age 63.8±12.0 years and 56% males). Poststress total perfusion deficit, left ventricular ejection fraction, and phase variables (phase entropy, bandwidth, and SD) were obtained automatically. Cox proportional hazards analyses were performed to assess associations with major adverse cardiac events (MACE). Results: During a follow-up of 4.5±1.7 years, 2673 (13.9%) patients experienced MACE. Annualized MACE rates increased with phase variables and were ≈4-fold higher between the second and highest decile group for entropy (1.7% versus 6.7%). Optimal phase variable cutoff values stratified MACE risk in patients with normal and abnormal total perfusion deficit and left ventricular ejection fraction. Only entropy was independently associated with MACE. The addition of phase entropy significantly improved the discriminatory power for MACE prediction when added to the model with total perfusion deficit and left ventricular ejection fraction (P<0.0001). Conclusions: In a largest to date imaging study, widely representative, international cohort, phase variables were independently associated with MACE and improved risk stratification for MACE beyond the prediction by perfusion and left ventricular ejection fraction assessment alone. Phase analysis can be obtained fully automatically, without additional radiation exposure or cost to improve MACE risk prediction and, therefore, should be routinely reported for single-photon emission computed tomography-myocardial perfusion imaging studies.
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    Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by Deep Learning
    (IEEE, 2021) Unal, Hazar Benan; Beaulieu, Taylor; Rivero, Luis Zamudio; Dharmakumar, Rohan; Sharif, Behzad; Medicine, School of Medicine
    The dark-rim artifact (DRA) remains an important challenge in the routine clinical use of first-pass perfusion (FPP) cardiac magnetic resonance imaging (cMRI). The DRA mimics the appearance of perfusion defects in the subendocardial wall and reduces the accuracy of diagnosis in patients with suspected ischemic heart disease. The main causes for DRA are known to be Gibbs ringing and bulk motion of the heart. The goal of this work is to propose a deep-learning-enabled automatic approach for the detection of motion-induced DRAs in FPP cMRI datasets. To this end, we propose a new algorithm that can detect the DRA in individual time frames by analyzing multiple reconstructions of the same time frame (k-space data) with varying temporal windows. In addition to DRA detection, our approach is also capable of suppressing the extent and severity of DRAs as a byproduct of the same reconstruction-analysis process. In this proof-of-concept study, our proposed method showed a good performance for automatic detection of subendocardial DRAs in stress perfusion cMRI studies of patients with suspected ischemic heart disease. To the best of our knowledge, this is the first approach that performs deep-learning-enabled detection and suppression of DRAs in cMRI.