Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. Furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. In this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. The Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. This is followed by a second non-linear correction step using the Dixon water images to remove residual motion. The proposed Dixon motion correction technique was compared to the state-of-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images.

[1]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[2]  Daniel Rueckert,et al.  Evaluation of Rigid and Non-rigid Motion Compensation of Cardiac Perfusion MRI , 2008, MICCAI.

[3]  M. Veta,et al.  Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI , 2019, Journal of magnetic resonance imaging : JMRI.

[4]  H. Eggers,et al.  Dual‐echo Dixon imaging with flexible choice of echo times , 2011, Magnetic resonance in medicine.

[5]  Patrick Clarysse,et al.  A review of cardiac image registration methods , 2002, IEEE Transactions on Medical Imaging.

[6]  María J. Ledesma-Carbayo,et al.  Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis , 2012, Medical Image Anal..

[7]  Peter Kellman,et al.  T  2* measurement during first‐pass contrast‐enhanced cardiac perfusion imaging , 2006, Magnetic resonance in medicine.

[8]  M. Jerosch-Herold Quantification of myocardial perfusion by cardiovascular magnetic resonance , 2010, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[9]  Hui Xue,et al.  Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients , 2019, Magnetic resonance in medicine.

[10]  Steven P Sourbron,et al.  Classic models for dynamic contrast‐enhanced MRI , 2013, NMR in biomedicine.

[11]  Farida Cheriet,et al.  Robust universal nonrigid motion correction framework for first‐pass cardiac MR perfusion imaging , 2017, Journal of magnetic resonance imaging : JMRI.

[12]  Alistair A. Young,et al.  An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI , 2017, IEEE Journal of Biomedical and Health Informatics.

[13]  Luuk J. Spreeuwers,et al.  Automatic quantitative analysis of cardiac MR perfusion images , 2001, SPIE Medical Imaging.

[14]  Sonia Nielles-Vallespin,et al.  Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification , 2017, Journal of Cardiovascular Magnetic Resonance.

[15]  T. Ismail,et al.  Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance , 2018, Journal of Cardiovascular Magnetic Resonance.

[16]  Andrés Santos,et al.  Nonrigid Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data , 2011, Bildverarbeitung für die Medizin.

[17]  J P W Pluim,et al.  Evaluation of motion correction for clinical dynamic contrast enhanced MRI of the liver. , 2017, Physics in medicine and biology.

[18]  Hildur Ólafsdóttir,et al.  Unsupervised motion-compensation of multi-slice cardiac perfusion MRI , 2005, Medical Image Anal..

[19]  Mathews Jacob,et al.  Deformation Corrected Compressed Sensing (DC-CS): A Novel Framework for Accelerated Dynamic MRI , 2014, IEEE Transactions on Medical Imaging.

[20]  Aleksandra Radjenovic,et al.  Myocardial blood flow at rest and stress measured with dynamic contrast‐enhanced MRI: Comparison of a distributed parameter model with a fermi function model , 2013, Magnetic resonance in medicine.

[21]  R. B. Kingsley,et al.  WET, a T1- and B1-insensitive water-suppression method for in vivo localized 1H NMR spectroscopy. , 1994, Journal of magnetic resonance. Series B.

[22]  Boudewijn P. F. Lelieveldt,et al.  Fully Automated Motion Correction in First-Pass Myocardial Perfusion MR Image Sequences , 2008, IEEE Transactions on Medical Imaging.

[23]  Andrés Santos,et al.  Exploiting Quasiperiodicity in Motion Correction of Free-Breathing Myocardial Perfusion MRI , 2010, IEEE Transactions on Medical Imaging.

[24]  Emile A. Hendriks,et al.  Cardiac MR perfusion image processing techniques: A survey , 2012, Medical Image Anal..

[25]  Jack Lee,et al.  Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data , 2019, IEEE Transactions on Medical Imaging.

[26]  Josien P. W. Pluim,et al.  Motion correction of dynamic contrast enhanced MRI of the liver , 2017, Medical Imaging.

[27]  Marcel Breeuwer,et al.  Hierarchical Bayesian myocardial perfusion quantification , 2019, Medical Image Anal..

[28]  E. Vink,et al.  Modified dixon‐based renal dynamic contrast‐enhanced MRI facilitates automated registration and perfusion analysis , 2017, Magnetic resonance in medicine.

[29]  Andriy Myronenko,et al.  Intensity-Based Image Registration by Minimizing Residual Complexity , 2010, IEEE Transactions on Medical Imaging.

[30]  V. Fuster,et al.  Optimization of dual-saturation single bolus acquisition for quantitative cardiac perfusion and myocardial blood flow maps , 2015, Journal of Cardiovascular Magnetic Resonance.

[31]  D Atkinson,et al.  Registration of dynamic contrast-enhanced MRI using a progressive principal component registration (PPCR) , 2007, Physics in medicine and biology.

[32]  Michael Jerosch-Herold,et al.  12-lead ECG in a 1.5 Tesla MRI: Separation of real ECG and MHD voltages with adaptive filtering for gating and non-invasive cardiac output , 2010 .

[33]  Alistair A. Young,et al.  Motion Correction for Dynamic Contrast-Enhanced CMR Perfusion Images Using a Consecutive Finite Element Model Warping , 2014, STACOM.

[34]  Christine H. Lorenz,et al.  Unsupervised Inline Analysis of Cardiac Perfusion MRI , 2009, MICCAI.

[35]  E. Nagel,et al.  Development of a universal dual-bolus injection scheme for the quantitative assessment of myocardial perfusion cardiovascular magnetic resonance , 2011, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[36]  Ganesh Adluru,et al.  Model‐based registration for dynamic cardiac perfusion MRI , 2006, Journal of magnetic resonance imaging : JMRI.

[37]  L. Bidaut,et al.  Automated registration of dynamic MR images for the quantification of myocardial perfusion , 2001, Journal of magnetic resonance imaging : JMRI.