Artificial intelligence in nuclear cardiology

Introduction

The utilization of computer algorithms to mimic the cognitive functions typically performed by humans is referred to as artificial intelligence (AI). AI-based techniques have emerged as highly efficient tools to help improve the accuracy and efficiency of diagnostic imaging interpretation and, as such, may play a particularly important role in nuclear cardiology. These tools can be integrated to improve image quality, image segmentation, and image quantification. AI has recently revolutionized image analysis. In particular, deep learning (DL), one type of AI, has gained considerable attention recently as a method to accurately classify images. ^, Although nuclear cardiology analysis has been robustly automated using traditional image processing approaches, AI can further improve automation and accurately extract information directly from cardiovascular images. There is a wealth of imaging and clinical information associated with a typical nuclear cardiology study, which can be challenging to integrate even for experienced clinicians. Machine learning (ML), a subfield of AI, refers to algorithms that assess and learn from observations based on expert-engineered features that are predictive of outcomes to more accurately predict those outcomes in future observations. Novel AI methods, such as DL approaches, extract these features themselves directly from the data. These algorithms are ideally suited to integrate the vast amount of clinical, stress testing, and imaging information from nuclear cardiology studies to improve disease diagnosis or risk prediction. Lastly, explainable AI techniques are being developed to help overcome the traditional perception of AI as a so-called black box by presenting the rationale for the computed decision or recommendation. Novel convolutional neural networks (CNNs), a DL method, can also highlight specific image regions that trigger the AI predictions by creating an attention map. ^,

The majority of nuclear cardiology studies are single photon emission computed tomography (SPECT) or positron emission tomography (PET) myocardial perfusion imaging (MPI) for the diagnosis and management of coronary artery disease (CAD). Therefore, these studies will be the primary focus of this chapter. Many SPECT and PET scanners are offered in hybrid configuration with a computed tomography (CT) scanner for attenuation correction; therefore, we will also discuss AI applications in CT. Finally, we will review some practical considerations regarding AI implementation into clinical nuclear cardiology practice.

Using artificial intelligence to improve image processing

Accurate image analysis (e.g., localization, segmentation, quantification) is critical to ensuring the clinical utility of SPECT or PET MPI. Although this process has been automated, AI-based algorithms can improve the precision of this process and reduce the need for time-consuming manual adjustments. Although traditional segmentation algorithms are relatively robust, accurate definition of the mitral valve plane still presents a challenge and frequently requires manual corrections. Betancur et al. proposed a support vector machine (SVM) algorithm for mitral valve plane localization in SPECT MPI, which was validated against the anatomic position of the valve from coronary CT angiography. The fully automated AI-based method had similar Bland-Altman confidence intervals compared with manual adjustment by experienced readers. Wang et al. described preliminary results with an end-to-end fully CNN to segment left ventricular (LV) myocardium by delineating its endocardial and epicardial surfaces. The CNN was trained with LV contours delineated by healthcare providers and demonstrated excellent precision (with an LV myocardium volume error of - 1.1 ± 3.7%).

AI-based techniques have also been developed for image registration of external CT-based attenuation correction maps with SPECT myocardial perfusion data. Ko et al. developed a CNN-based algorithm to achieve automatic coregistration of imaging data. The algorithm was trained to predict the true offset between images in three dimensions (3D) compared with manually coregistered and verified solid-state SPECT MPI and noncontrast CT images. The algorithm was trained in 402 cases and tested in 100 cases (with mean residual misalignment between image pairs 1.71 ± 1.32 mm for the training data set and 2.38 ± 2.00 mm for the testing data set).

Using artificial intelligence to improve image quality

AI could be used to improve the quality of the image reconstruction of nuclear cardiology studies, potentially reducing noise, thereby allowing for shorter imaging times and/or reduction in injected dose to help reduce radiation exposure. DL networks have been demonstrated to automatically detect motion artifacts on coronary calcium scoring in a chest CT. Generative adversarial networks have also been applied to reduce noise in low-dose CT scans, with preliminary work also reported for cardiac PET studies. For example, Ladefoged et al. evaluated the potential of denoising cardiac ¹⁸ F-fluorodeoxyglucose (FDG)-gated PET images with a DL approach. The study simulated low-dose imaging by subsampling the full dose imaging data set to only 1% of the injected dose. A total of 146 patients were used to train their DL model, with model testing in a group of 20 patients. Quantitatively, the uncorrected 1% low-dose images showed an average underestimation in end-diastolic and end-systolic volumes of 25%. The bias was nearly removed using the proposed DL denoising method, which led to an average underestimation of only 2%. In simulations, DL could also be used to reduce ¹⁸ F–sodium fluoride (NaF) scan times by 10-fold. Ramon et al. demonstrated the feasibility of denoising low-dose SPECT MPI using a 3D convolutional neural network, based on stacked convolutional autoencoders. Images denoised with the CNN, using data simulating one-sixteenth of the clinical dose, achieved similar image quality to simulations using one-eighth of the clinical dose with standard denoising methods. Recently, Song et al. demonstrated a 3D residual CNN trained and tuned in 95 patients, then tested in 24 patients simulating image reconstruction with a fourfold reduction in radiation exposure. Compared with standard dose images, the CNN improved spatial resolution compared with conventional image processing with Gaussian filters or spatiotemporal nonlocal means filtering.