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New Trends in Quantitative Nuclear Cardiology Methods

  • Cardiac Nuclear Imaging (A Cuocolo and M Petretta, Section Editors)
  • Published:
Current Cardiovascular Imaging Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

The use of quantitative analysis in single photon emission computed tomography (SPECT) and positron emission tomography (PET) has become an integral part of current clinical practice and plays a crucial role in the detection and risk stratification of coronary artery disease. Emerging technologies, new protocols, and new quantification methods have had a significant impact on the diagnostic performance and prognostic value of nuclear cardiology imaging while reducing the need for clinician oversight. In this review, we aim to describe recent advances in automation and quantitative analysis in nuclear cardiology.

Recent Findings

Recent publications have shown that fully automatic processing is feasible, limiting human input to specific cases where aberrancies are detected by the quality control software. Furthermore, there is evidence indicating that fully quantitative analysis of myocardial perfusion imaging is feasible and can achieve at least similar diagnostic accuracy as visual interpretation by an expert clinician. In addition, the use of fully automated quantification in combination with machine learning algorithms can provide incremental diagnostic and prognostic value over the traditional method of expert visual interpretation.

Summary

Emerging technologies in nuclear cardiology focus on automation and the use of artificial intelligence as part of the interpretation process. This review highlights the benefits and limitations of these applications and outlines future directions in the field.

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Abbreviations

CAD:

Coronary artery disease

LV:

Left ventricular

MPI:

Myocardial perfusion imaging

PET:

Positron-emission tomography

SPECT:

Single photon emission computed tomography

TPD:

Total perfusion deficit

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

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Funding

This work was supported in part by grant R01HL089765 from the National Heart, Lung, and Blood Institute/National Institutes of Health (NHLBI/NIH) (PI: Piotr Slomka). The views expressed in this manuscript are those of the authors and do not necessarily reflect the official views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the Department of Health and Human Services.

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Authors and Affiliations

  1. Division of Cardiology, Cook County Health and Hospitals System, 1901 W. Harrison St., Suite # 3620, Chicago, IL, 60612, USA

    Javier Gomez & Rami Doukky

  2. Division of Cardiology, Rush University Medical Center, Chicago, IL, USA

    Rami Doukky

  3. Departments of Imaging, Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Guido Germano & Piotr Slomka

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  1. Javier Gomez
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  2. Rami Doukky
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Correspondence to Rami Doukky.

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Conflicts of Interest

Rami Doukky receives research funding grants and serves on an advisory board for Astellas Pharma Astellas Pharma Global Development (Northbrook, IL). Guido Germano and Piotr Slomka receive royalties from Cedars-Sinai for licensing of quantitative algorithms for nuclear cardiology. The other authors have nothing to disclose.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Cardiac Nuclear Imaging

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Gomez, J., Doukky, R., Germano, G. et al. New Trends in Quantitative Nuclear Cardiology Methods. Curr Cardiovasc Imaging Rep 11, 1 (2018). https://doi.org/10.1007/s12410-018-9443-7

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