Current status and future directions in artificial intelligence for nuclear cardiology

ABSTRACT

Introduction

Myocardial perfusion imaging (MPI) is one of the most commonly ordered cardiac imaging tests. Accurate motion correction, image registration, and reconstruction is critical for high-quality imaging, but this can be technically challenging and traditionally has relied on expert manual processing. With accurate processing, there is a rich variety of clinical, stress, functional, and anatomic data that can be integrated to guide patient management.

Areas covered

Pubmed and Google Scholar were reviewed for articles related to artificial intelligence in nuclear cardiology published between 2020 and 2024. We will outline the prominent roles for artificial intelligence (AI) solutions to provide motion correction, image registration, and reconstruction. We will review the role for AI in extracting anatomic data for hybrid MPI which is otherwise neglected. Lastly, we will discuss AI methods to integrate the wealth of data to improve disease diagnosis or risk stratification.

Expert opinion

There is growing evidence that AI will transform the performance of MPI by automating and improving on aspects of image acquisition and reconstruction. Physicians and researchers will need to understand the potential strengths of AI in order to benefit from the full clinical utility of MPI.

KEYWORDS:

• Artificial intelligence
• deep learning
• hybrid imaging
• machine learning
• myocardial perfusion imaging
• nuclear cardiology

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Article highlights

• Artificial intelligence has been applied to improve motion correction for a variety of nuclear cardiology imaging studies.

• Many studies have demonstrated that artificial intelligence can be used to improve image quality, to allow nuclear cardiology studies to be acquired with lower doses or shorter acquisition times.

• Artificial intelligence can be applied to provide synthetic attenuation correction of SPECT images to improve diagnostic accuracy.

• With the emergence of hybrid SPECT/CT and PET/CT imaging, artificial intelligence has been increasingly applied to segment structures such as coronary artery calcium and epicardial adipose tissue to provide physicians with additional anatomic information.

• Deep learning can provide automated image interpretation for disease diagnosis or risk prediction, with some methods integrating methods for explaining their predictions.

Declaration of interest

P Slomka participates in software royalties for QPS software at Cedars-Sinai Medical Center and has received research grant support from Siemens Medical Systems. R Miller has received research support and consulting fees from Pfizer and research support from Alberta Innovates. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.