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Review
. 2021 Mar 25;5(1):12.
doi: 10.1186/s41747-021-00207-3.

Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence

Martin J Willemink  1 , Akos Varga-Szemes  2 , U Joseph Schoepf  2 , Marina Codari  1 , Koen Nieman  3   4 , Dominik Fleischmann  1   4 , Domenico Mastrodicasa  5   6
Affiliations
Review

Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence

Martin J Willemink et al. Eur Radiol Exp. .

Abstract

After an ischemic event, disruptive changes in the healthy myocardium may gradually develop and may ultimately turn into fibrotic scar. While these structural changes have been described by conventional imaging modalities mostly on a macroscopic scale-i.e., late gadolinium enhancement at magnetic resonance imaging (MRI)-in recent years, novel imaging methods have shown the potential to unveil an even more detailed picture of the postischemic myocardial phenomena. These new methods may bring advances in the understanding of ischemic heart disease with potential major changes in the current clinical practice. In this review article, we provide an overview of the emerging methods for the non-invasive characterization of ischemic heart disease, including coronary ultrafast Doppler angiography, photon-counting computed tomography (CT), micro-CT (for preclinical studies), low-field and ultrahigh-field MRI, and 11C-methionine positron emission tomography. In addition, we discuss new opportunities brought by artificial intelligence, while addressing promising future scenarios and the challenges for the application of artificial intelligence in the field of cardiac imaging.

Keywords: Artificial intelligence; Coronary artery disease; Myocardial infarction; Myocardial ischemia; Radiology.

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Conflict of interest statement

MJW: Activities related to the present article: none. Activities not related to the present article: research grants from American Heart Association (18POST34030192), Philips Healthcare, and Stanford University, consulting for Arterys, Inc, and co-founder/shareholder of Segmed, Inc. Other relationships: disclosed no relevant relationships.

AVS receives institutional research support and/or personal fees from Elucid Bioimaging and Siemens. AVS is one of the Guest Editors of this thematic series. The paper was therefore reviewed and handled by the European Radiology Experimental Editor-in-Chief Prof. Francesco Sardanelli and the Guest Editor Prof. Pal Suranyi.

UJS receives institutional research support and/or personal fees from Astellas, Bayer, Bracco, Elucid Bioimaging, Guerbet, HeartFlow, and Siemens.

KN received institutional research support from Siemens Healthineers, Bayer healthcare, GE Healthcare, and Heartflow Inc.

DF: Activities related to the present article: none. Activities not related to the present article: received research support from Siemens Healthineers and GE Healthcare; is on the Speakers’ Bureau at Siemens Healthineers; has ownership interest in iSchemaView. Other relationships: disclosed no relevant relationships. The other authors have no conflict of interest to disclose.

Figures

Fig. 1
Fig. 1
Overview of the emerging methods and tools for the noninvasive characterization of the ischemic myocardial tissue
Fig. 2
Fig. 2
Left: conventional ultrasound image showing the region of interest selected for coronary ultrafast Doppler angiography (CUDA) processing. This real-time imaging mode of the ultrasound scanner was used for positioning (the scale bar is in centimeters). Middle: ultrafast B-mode images of the region of interest depicted in yellow on the images in the left column. Right: CUDA images of coronary veins in systole and coronary arteries in diastole. (Reprinted, with permission, from [10])
Fig. 3
Fig. 3
Axial and coronal views of systole and end-diastole in a mouse model with intravascular iodine contrast. Projection data were sampled using a prospective gating scheme. These images represent two of the ten cardiac acquired phases, i.e., diastole and systole. Reconstruction was performed using a four-dimensional iterative technique based on the split Bregman method [16]. Figure courtesy of Dr. Cristian T. Badea, PhD, Duke University
Fig. 4
Fig. 4
Systolic and diastolic cine images acquired at 7 T (A, B) and 1.5 T (C, D) in the same subject. Despite the challenges with electrocardiography interference and triggering, diagnostic quality cine images can be acquired with high temporal and spatial resolution. Figure courtesy of Dr. Harold I. Litt, University of Pennsylvania
See this image and copyright information in PMC

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