Magnetic resonance fingerprinting (MRF) is a magnetic resonance imaging (MRI)-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid ...acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using the Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from the MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF (cMRF).
Women's engagement in medicine, and more specifically cardiovascular imaging and cardiovascular MRI (CMR), has undergone a slow evolution over the past several decades. As a result, an increasing ...number of women have joined the cardiovascular imaging community to contribute their expertise. This collaborative work summarizes the barriers that women in cardiovascular imaging have overcome over the past several years, the positive interventions that have been implemented to better support women in the field of CMR, and the challenges that still remain, with a special emphasis on women physicians.
Purpose
This work aims to develop an approach for simultaneous water–fat separation and myocardial T1 and T2 quantification based on the cardiac MR fingerprinting (cMRF) framework with rosette ...trajectories at 3T and 1.5T.
Methods
Two 15‐heartbeat cMRF sequences with different rosette trajectories designed for water–fat separation at 3T and 1.5T were implemented. Water T1 and T2 maps, water image, and fat image were generated with B0 inhomogeneity correction using a B0 map derived from the cMRF data themselves. The proposed water–fat separation rosette cMRF approach was validated in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and water/oil phantoms. It was also applied for myocardial tissue mapping of healthy subjects at both 3T and 1.5T.
Results
Water T1 and T2 values measured using rosette cMRF in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom agreed well with the reference values. In the water/oil phantom, oil was well suppressed in the water images and vice versa. Rosette cMRF yielded comparable T1 but 2~3 ms higher T2 values in the myocardium of healthy subjects than the original spiral cMRF method. Epicardial fat deposition was also clearly shown in the fat images.
Conclusion
Rosette cMRF provides fat images along with myocardial T1 and T2 maps with significant fat suppression. This technique may improve visualization of the anatomical structure of the heart by separating water and fat and could provide value in diagnosing cardiac diseases associated with fibrofatty infiltration or epicardial fat accumulation. It also paves the way toward comprehensive myocardial tissue characterization in a single scan.
This work aims to develop an approach for simultaneous water-fat separation and myocardial T
and T
quantification based on the cardiac MR fingerprinting (cMRF) framework with rosette trajectories at ...3T and 1.5T.
Two 15-heartbeat cMRF sequences with different rosette trajectories designed for water-fat separation at 3T and 1.5T were implemented. Water T
and T
maps, water image, and fat image were generated with B
inhomogeneity correction using a B
map derived from the cMRF data themselves. The proposed water-fat separation rosette cMRF approach was validated in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and water/oil phantoms. It was also applied for myocardial tissue mapping of healthy subjects at both 3T and 1.5T.
Water T
and T
values measured using rosette cMRF in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom agreed well with the reference values. In the water/oil phantom, oil was well suppressed in the water images and vice versa. Rosette cMRF yielded comparable T
but 2~3 ms higher T
values in the myocardium of healthy subjects than the original spiral cMRF method. Epicardial fat deposition was also clearly shown in the fat images.
Rosette cMRF provides fat images along with myocardial T
and T
maps with significant fat suppression. This technique may improve visualization of the anatomical structure of the heart by separating water and fat and could provide value in diagnosing cardiac diseases associated with fibrofatty infiltration or epicardial fat accumulation. It also paves the way toward comprehensive myocardial tissue characterization in a single scan.
Cardiovascular magnetic resonance is a versatile tool that enables noninvasive characterization of cardiac tissue structure and function. Parametric mapping techniques have allowed unparalleled ...differentiation of pathophysiological differences in the myocardium such as the delineation of myocardial fibrosis, hemorrhage, and edema. These methods are increasingly used as part of a tool kit to characterize disease states such as cardiomyopathies and coronary artery disease more accurately. Currently conventional mapping techniques require separate acquisitions for T
and T
mapping, the values of which may depend on specifics of the magnetic resonance imaging system hardware, pulse sequence implementation, and physiological variables including blood pressure and heart rate. The cardiac magnetic resonance fingerprinting (cMRF) technique has recently been introduced for simultaneous and reproducible measurement of T
and T
maps in a single scan. The potential for this technique to provide consistent tissue property values independent of variables including scanner, pulse sequence, and physiology could allow an unbiased framework for the assessment of intrinsic properties of cardiac tissue including structure, perfusion, and parameters such as extracellular volume without the administration of exogenous contrast agents. This review seeks to introduce the basics of the cMRF technique, including pulse sequence design, dictionary generation, and pattern matching. The potential applications of cMRF in assessing diseases such as nonischemic cardiomyopathy are also briefly discussed, and ongoing areas of research are described.
Purpose
This work proposes principal component analysis (PCA) coil compression and weight sharing to reduce acquisition and reconstruction time of through‐time radial GRAPPA.
Methods
Through‐time ...radial GRAPPA enables ungated free‐breathing motion‐resolved cardiac imaging but requires a long calibration acquisition and GRAPPA weight calculation time. PCA coil compression reduces calibration data requirements and associated acquisition time, and weight sharing reduces the number of unique GRAPPA weight sets and associated weight computation time. In vivo cardiac data reconstructed with coil compression and weight sharing are compared to a gold standard to demonstrate improvement in calibration acquisition and reconstruction performance with minimal loss of image quality.
Results
Coil compression from 30 physical to 12 virtual coils (90% of signal variance) decreases requisite calibration data by 60%, reducing calibration acquisition time to 6.7 s/slice from 31.5 s/slice reported in original through‐time radial GRAPPA work. Resulting images have small increase in RMS error (RMSE). Reconstruction with a weight sharing factor of 8 results in eight‐fold reduction in GRAPPA weight calculation time with a comparable RMSE to reconstructions with no weight sharing. Optimized parameters for coil compression and weight sharing applied to reconstructions enables images to be collected with a temporal resolution of 66 ms/frame and spatial resolution of 2.34 × 2.34 mm while reducing calibration acquisition time from 34 to 6.7 s, weight calculation time from 200 to 3 s, and weight application time 18 to 5 s.
Conclusion
Coil compression and weight sharing applied to through‐time radial GRAPPA enables fast free‐breathing ungated cardiac cine without compromising image quality.
To develop a deep learning method for rapidly reconstructing T
and T
maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images.
A neural network ...was developed that outputs T
and T
values when given a measured cMRF signal time course and cardiac RR interval times recorded by an ECG. Over 8 million cMRF signals, corresponding to 4000 random cardiac rhythms, were simulated for training. The training signals were corrupted by simulated k-space undersampling artifacts and random phase shifts to promote robust learning. The deep learning reconstruction was evaluated in Monte Carlo simulations for a variety of cardiac rhythms and compared with dictionary-based pattern matching in 58 healthy subjects at 1.5T.
In simulations, the normalized root-mean-square error (nRMSE) for T
was below 1% in myocardium, blood, and liver for all tested heart rates. For T
, the nRMSE was below 4% for myocardium and liver and below 6% for blood for all heart rates. The difference in the mean myocardial T
or T
observed in vivo between dictionary matching and deep learning was 3.6 ms for T
and -0.2 ms for T
. Whereas dictionary generation and pattern matching required more than 4 min per slice, the deep learning reconstruction only required 336 ms.
A neural network is introduced for reconstructing cMRF T
and T
maps directly from undersampled spiral images in under 400 ms and is robust to arbitrary cardiac rhythms, which paves the way for rapid online display of cMRF maps.
PDF
