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Keywords: Cardiovascular: Cardiac

Cardiac MRI can be challenging in patients for a variety of reasons.  Heart rates may be undesirable to obtain information need for the images.  Twenty second breath holds may be difficult for a patient to maintain.  Body habitus can place the heart in an orientation that will need a larger field of view.  Patients with implanted devices may need to be scanned on 1.5T or lower field strength scanners to reduce artifacts.  Ideally, image reconstruction should keep pace with the images acquisition.

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Keywords: Cardiovascular: Cardiac, Cardiovascular: Cardiovascular

Technologists face many challenges throughout a Cardiovascular MR (CMR) exam.  This talk aims to provide methods to help reduce claustrophobia, reduce scan times while maintaining spatial and temporal resolution without the assistance of AI or denoising algorithms.  We will also discuss being creative with workflow strategies to decrease overall exam time.

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Keywords: Cardiovascular: Cardiac function, Physics & Engineering: Physics, Image acquisition: Fast imaging

In this educational talk, we will discuss real-time cardiac MRI, a non-invasive imaging technique that allows for the visualization of the heart's structure and function in real-time. We will cover the various clinical applications of this imaging modality, such as the assessment of cardiac function, myocardial perfusion, and tissue characterization. Additionally, we will discuss the advantages of real-time cardiac MRI over other imaging techniques and the limitations of the method. The talk aims to provide a comprehensive overview of real-time cardiac MRI, highlighting its potential role in the diagnosis and management of various cardiac diseases.

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Thomas Küstner

Keywords: Image acquisition: Reconstruction, Image acquisition: Machine learning, Cardiovascular: Cardiovascular

Cardiovascular MR (CMR) is a versatile non-invasive imaging that provides a comprehensive assessment of cardiac function and anatomy in a single examination. CMR plays a major role in the diagnosis and management of cardiovascular disease. When setting up and optimizing a clinical CMR protocol, the inherent trade-off between spatial and temporal resolution, scan time and signal-to-noise ratio (SNR) must be taken into consideration. Several approaches have been proposed to speed up CMR, including parallel imaging, k-t accelerated imaging, or pseudo-random sub-Nyquist sampling. An overview of compressed sensing, low-rank and deep learning reconstructions is presented for these accelerated scans.

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Keywords: Cardiovascular: Cardiovascular, Image acquisition: Multiparametric

Free-running, multidimensional CMR has gained increasing traction over the last decade. In multi-dimensional CMR, advances in hardware, optimized sampling patterns and reconstruction techniques have come together synergistically into a user-friendly scanning protocol. This talk aims to cover the basics of free-running techniques as well as different approaches to dimensionality.  State-of-the-art techniques including 5D MRI, 5D flow MRI, MR fingerprinting, MR multitasking and their cardiac applications will also be briefly covered.

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Keywords: Cardiovascular: Cardiac, Image acquisition: Machine learning, Image acquisition: Image processing

Cardiac motion artefacts affect further downstream analysis, or even render images unusable for clinical diagnosis. This affects clinical workflow in hospitals and may require patient recall, delaying timely diagnosis and treatment starts. Identifying motion corruption, preferably at time of scanning, or applying retrospective motion correction, would help to alleviate these problems. Cardiac motion artefacts can in principle be detected and corrected for at the time of scanning. This paves the way for online quality control as well as active scanning.

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Keywords: Cardiovascular: Cardiac, Cardiovascular: Cardiac function, Image acquisition: Machine learning

Cardiac MR (CMR) is a key investigation in cardiovascular medicine. CMR is the best modality for measuring cardiac structure and function, but it is plagued by the need for a clinician to analyse the images. Clinicians are slow, which introduces a bottleneck, and they are often imprecise, which can impact downstream patient care. AI promises to solve these issues, but clinical translation is challenging, as is appropriate evaluation. I will discuss some clinically meaningful measures of AI performance and describe an inline implementation that allows images to be analysed as they are acquired, delivering AI directly to clinical care.