Compressed Sensing; Free-Running Imaging; 5D imaging; Ventricular Function; Cardiac Magnetic Resonance Imaging; Coronary Artery Anatomy
Ma Liliana, Yerly Jérôme, Di Sopra Lorenzo, Piccini Davide, Lee Jeesoo, DiCarlo Amanda, Passman Rod, Greenland Philip, Kim Daniel, Stuber Matthias, Markl Michael (2021), Using 5D flow MRI to decode the effects of rhythm on left atrial 3D flow dynamics in patients with atrial fibrillation, in Magnetic Resonance in Medicine
Ma Liliana E., Yerly Jérôme, Piccini Davide, Di Sopra Lorenzo, Roy Christopher W., Carr James C., Rigsby Cynthia K., Kim Daniel, Stuber Matthias, Markl Michael (2020), 5D Flow MRI: A Fully Self-gated, Free-running Framework for Cardiac and Respiratory Motion–resolved 3D Hemodynamics, in Radiology: Cardiothoracic Imaging
, 2(6), e200219-e200219.
Franceschiello Benedetta, Di Sopra Lorenzo, Minier Astrid, Ionta Silvio, Zeugin David, Notter Michael P., Bastiaansen Jessica A.M., Jorge João, Yerly Jérôme, Stuber Matthias, Murray Micah M. (2020), 3-Dimensional magnetic resonance imaging of the freely moving human eye, in Progress in Neurobiology
, 194, 101885-101885.
Piccini Davide, Demesmaeker Robin, Heerfordt John, Yerly Jérôme, Di Sopra Lorenzo, Masci Pier Giorgio, Schwitter Juerg, Van De Ville Dimitri, Richiardi Jonas, Kober Tobias, Stuber Matthias (2020), Deep Learning to Automate Reference-Free Image Quality Assessment of Whole-Heart MR Images, in Radiology: Artificial Intelligence
, 2(3), e190123-e190123.
Masala Nemanja, Bastiaansen Jessica A. M., Di Sopra Lorenzo, Roy Christopher W., Piccini Davide, Yerly Jérôme, Colotti Roberto, Stuber Matthias (2020), Free‐running 5D coronary MR angiography at 1.5T using LIBRE water excitation pulses, in Magnetic Resonance in Medicine
Heerfordt John, Stuber Matthias, Maillot Aurélien, Bianchi Veronica, Piccini Davide (2019), A quantitative comparison between a navigated Cartesian and a self‐navigated radial protocol from clinical studies for free‐breathing 3D whole‐heart bSSFP coronary MRA, in Magnetic Resonance in Medicine
Bastiaansen Jessica A.M., Piccini Davide, Di Sopra Lorenzo, Roy Christopher W., Heerfordt John, Edelman Robert R., Koktzoglou Ioannis, Yerly Jérôme, Stuber Matthias (2019), Natively fat‐suppressed 5D whole‐heart MRI with a radial free‐running fast‐interrupted steady‐state (FISS) sequence at 1.5T and 3T, in Magnetic Resonance in Medicine
, 83(1), 45-55.
Di Sopra Lorenzo, Piccini Davide, Coppo Simone, Stuber Matthias, Yerly Jérôme (2019), An automated approach to fully self‐gated free‐running cardiac and respiratory motion‐resolved 5D whole‐heart MRI, in Magnetic Resonance in Medicine
, 82(6), 2118-2132.
Significant aspects of cardiac MRI data acquisition strategies have largely remained unchanged over the past two decades, highly specialized personnel is needed to ensure quality, and data acquisition remains time consuming. In a deliberate attempt to remove these hurdles, we propose to break away from the decades-old paradigm where data are collected in a prospectively triggered and gated fashion and propose instead to sample image data uninterrupted, continuously, without triggering or gating, and irrespective of the heart’s position and contractile state. Based on our strong preliminary developments both on the data acquisition and reconstruction side, we posit that the combination of a free-running imaging sequence and compressed sensing reconstruction leads to 5D imaging, a new paradigm where the image that best answers to a clinical question is extracted retrospectively after the scan. This will translate to an improved ease-of-use of cardiac MRI and the opportunity to collect not only more but also more precise information about the heart per unit time. As this relates to uncharted territory to a large degree, a focused research effort from which we will learn how to best apply these new concepts, from which we will learn about the degree of success, and from which we will also learn at which point success meets failure, is now critically important. In the pursuit of that ambitious goal, a series of elaborate components need to be integrated and tested. At the CHUV, our team consisting of scientists and engineers has developed a number of such components that can be leveraged: 1.) a free-running 4D imaging sequence, 2.) a flexible compressed sensing reconstruction framework that enables respiratory motion correction without the need for navigators, breath-holding, or self-navigation, 3.) a short spectrally selective water excitation radiofrequency pulse that supports highly effective fat signal suppression without interrupting steady-state, and 4.), a framework that extracts both cardiac and respiratory motion signals directly from the continuously acquired image data. A tried and tested team of interdisciplinary researchers that includes basic scientists and medical professionals proposes here to logically and mechanistically integrate these components, to leverage new developments and concepts, to rigorously research strengths and weaknesses, to learn about optimal parameter ranges and algorithms, to translate this potentially transformative technology to the in vivo setting, and to finally study the performance in clinical patients using gold standard comparisons.