There are advantages to conducting cardiovascular magnetic resonance (CMR) studies at a field strength of 3.0 Telsa, including the increase in bulk magnetization, the increase in frequency separation of off-resonance spins, and the increase in T1 of many tissues. However, there are significant challenges to routinely performing CMR at 3.0T, including the reduction in main magnetic field homogeneity, the increase in RF power deposition, and the increase in susceptibility-based artifacts.

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.

Devices incorporating artificial intelligence and machine learning (AI/ML) can be found in many areas of medicine, and the U.S. Food and Drug Administration (FDA) and Center for Devices and Radiological Health (CDRH) have a history regulating medical AI/ML technologies.¹ As an example, a semi-automated cervical cytology slide reader incorporating neural network processors was first approved by the FDA in 1995.² FDA receives a high volume of premarket submission inquiries for products leveraging AI/ML technologies, and we expect this trend to continue going forward. Q-submissions are a mechanism for device developers to ask questions and obtain official FDA feedback prior to a formal premarket submission.³ Q-submissions are highly recommended to help address a question prior to a full device submission.