R2*-corrected water-fat imaging using compressed sensing and parallel imaging

Physics-based reconstruction methods for magnetic resonance imaging

Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction-addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop

More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water-fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based "big data" studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work.

Acceleration of chemical shift encoding-based water fat MRI for liver proton density fat fraction and T2* mapping using compressed sensing

Objectives

To evaluate proton density fat fraction (PDFF) and T2* measurements of the liver with combined parallel imaging (sensitivity encoding, SENSE) and compressed sensing (CS) accelerated chemical shift encoding-based water-fat separation.

Methods

Six-echo Dixon imaging was performed in the liver of 89 subjects. The first acquisition variant used acceleration based on SENSE with a total acceleration factor equal to 2.64 (acquisition labeled as SENSE). The second acquisition variant used acceleration based on a combination of CS with SENSE with a total acceleration factor equal to 4 (acquisition labeled as CS+SENSE). Acquisition times were compared between acquisitions and proton density fat fraction (PDFF) and T2*-values were measured and compared separately for each liver segment.

Results

Total scan duration was 14.5 sec for the SENSE accelerated image acquisition and 9.3 sec for the CS+SENSE accelerated image acquisition. PDFF and T2* values did not differ significantly between the two acquisitions (paired Mann-Whitney and paired t-test P>0.05 in all cases). CS+SENSE accelerated acquisition showed reduced motion artifacts (1.1%) compared to SENSE acquisition (12.3%).

Conclusion

CS+SENSE accelerates liver PDFF and T2*mapping while retaining the same quantitative values as an acquisition using only SENSE and reduces motion artifacts.

Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo [Formula: see text]-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.

Dynamic water/fat separation and B 0 inhomogeneity mapping-joint estimation using undersampled triple-echo multi-spoke radial FLASH

PURPOSE

To achieve dynamic water/fat separation and B 0 field inhomogeneity mapping via model-based reconstructions of undersampled triple-echo multi-spoke radial FLASH acquisitions.

METHODS

This work introduces an undersampled triple-echo multi-spoke radial FLASH sequence, which uses (i) complementary radial spokes per echo train for faster spatial encoding, (ii) asymmetric echoes for flexible and nonuniform echo spacing, and (iii) a golden angle increment across frames for optimal k-space coverage. Joint estimation of water, fat, B 0 inhomogeneity, and coil sensitivity maps from undersampled triple-echo data poses a nonlinear and non-convex inverse problem which is solved by a model-based reconstruction with suitable regularization. The developed methods are validated using phantom experiments with different degrees of undersampling. Real-time MRI studies of the knee, liver, and heart are conducted without prospective gating or retrospective data sorting at temporal resolutions of 70, 158, and 40 ms, respectively.

RESULTS

Up to 18-fold undersampling is achieved in this work. Even in the presence of rapid physiological motion, large B 0 field inhomogeneities, and phase wrapping, the model-based reconstruction yields reliably separated water/fat maps in conjunction with spatially smooth inhomogeneity maps.

CONCLUSIONS

The combination of a triple-echo acquisition and joint reconstruction technique provides a practical solution to time-resolved and motion robust water/fat separation at high spatial and temporal resolution.

Accelerating multi-echo water-fat MRI with a joint locally low-rank and spatial sparsity-promoting reconstruction

OBJECTIVES

Our aim was to demonstrate the benefits of using locally low-rank (LLR) regularization for the compressed sensing reconstruction of highly-accelerated quantitative water-fat MRI, and to validate fat fraction (FF) and [Formula: see text] relaxation against reference parallel imaging in the abdomen.

MATERIALS AND METHODS

Reconstructions using spatial sparsity regularization (SSR) were compared to reconstructions with LLR and the combination of both (LLR+SSR) for up to seven fold accelerated 3-D bipolar multi-echo GRE imaging. For ten volunteers, the agreement with the reference was assessed in FF and [Formula: see text] maps.

RESULTS

LLR regularization showed superior noise and artifact suppression compared to reconstructions using SSR. Remaining residual artifacts were further reduced in combination with SSR. Correlation with the reference was excellent for FF with [Formula: see text] = 0.99 (all methods) and good for [Formula: see text] with [Formula: see text] = [0.93, 0.96, 0.95] for SSR, LLR and LLR+SSR. The linear regression gave slope and bias (%) of (0.99, 0.50), (1.01, 0.19) and (1.01, 0.10), and the hepatic FF/[Formula: see text] standard deviation was 3.5%/12.1 s[Formula: see text], 1.9%/6.4 s[Formula: see text] and 1.8%/6.3 s[Formula: see text] for SSR, LLR and LLR+SSR, indicating the least bias and highest SNR for LLR+SSR.

CONCLUSION

A novel reconstruction using both spatial and spectral regularization allows obtaining accurate FF and [Formula: see text] maps for prospectively highly accelerated acquisitions.

Free-breathing volumetric fat/water separation by combining radial sampling, compressed sensing, and parallel imaging

PURPOSE

Conventional fat/water separation techniques require that patients hold breath during abdominal acquisitions, which often fails and limits the achievable spatial resolution and anatomic coverage. This work presents a novel approach for free-breathing volumetric fat/water separation.

METHODS

Multiecho data are acquired using a motion-robust radial stack-of-stars three-dimensional GRE sequence with bipolar readout. To obtain fat/water maps, a model-based reconstruction is used that accounts for the off-resonant blurring of fat and integrates both compressed sensing and parallel imaging. The approach additionally enables generation of respiration-resolved fat/water maps by detecting motion from k-space data and reconstructing different respiration states. Furthermore, an extension is described for dynamic contrast-enhanced fat-water-separated measurements.

RESULTS

Uniform and robust fat/water separation is demonstrated in several clinical applications, including free-breathing noncontrast abdominal examination of adults and a pediatric subject with both motion-averaged and motion-resolved reconstructions, as well as in a noncontrast breast exam. Furthermore, dynamic contrast-enhanced fat/water imaging with high temporal resolution is demonstrated in the abdomen and breast.

CONCLUSION

The described framework provides a viable approach for motion-robust fat/water separation and promises particular value for clinical applications that are currently limited by the breath-holding capacity or cooperation of patients. Magn Reson Med 78:565-576, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption

The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.

Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction

MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI.

Accelerating MR Imaging Liver Steatosis Measurement Using Combined Compressed Sensing and Parallel Imaging: A Quantitative Evaluation

PURPOSE

To determine the limits of agreement of hepatic fat fraction and R2* relaxation rate quantified with accelerated magnetic resonance (MR) imaging reconstructed with combined compressed sensing and parallel imaging compared with conventional fully sampled acquisitions.

MATERIALS AND METHODS

Eleven subjects with type 2 diabetes and a healthy control subject were recruited with the approval of the Newcastle and North Tyneside 2 ethics committee and written consent. Undersampled data at ratios of 2.6×, 2.9×, 3.8×, and 4.8× were prospectively acquired in addition to fully sampled data by using five gradient echoes per repetition time at 3.0 T. Fat fraction maps were calculated by using combined compressed sensing and parallel imaging (CS-PI) reconstruction and Bland-Altman analysis performed to assess bias and 95% limits of agreement. Inter- and intrarater analysis was performed for quantitative fat fraction and R2* relaxation rate, and image quality was assessed with a four-point scale by two independent observers.

RESULTS

The fat fractions from the accelerated acquisitions had 95% limits of agreement of 1.2%, 1.2%, 1.1%, and 1.5%, respectively, with no bias. When compared with the intra- and interrater 95% limits of agreement (0.7% and 0.8%), acceleration of up to 3.8× did not greatly degrade the fat fraction measurements. No or minimal artifact was detected at 2.6× and 2.9× accelerations, moderate artifact was detected at 3.8× acceleration, and substantial artifact was detected at 4.8× acceleration.

CONCLUSION

Prospective undersampling and CS-PI reconstruction of liver fat fractions can be used to accelerate liver fat fraction measurements. The fat fractions and image quality produced were acceptable up to a factor of 3.8×, thereby shortening the required breath-hold duration from 17.7 seconds to 4.7 seconds.

Improving highly accelerated fat fraction measurements for clinical trials in muscular dystrophy: origin and quantitative effect of R2* changes

Purpose To investigate the effect of R2* modeling in conventional and accelerated measurements of skeletal muscle fat fraction in control subjects and patients with muscular dystrophy. Materials and Methods Eight patients with Becker muscular dystrophy and eight matched control subjects were recruited with approval from the Newcastle and North Tyneside 2 Research Ethics Committee and with written consent. Chemical-shift images with six widely spaced echo times (in 3.5-msec increments) were acquired to correlate R2* and muscle fat fraction. The effect of incorporating or neglecting R2* modeling on fat fraction magnitude and variance was evaluated in a typical three-echo protocol (with 0.78-msec increments). Accelerated acquisitions with this protocol with 3.65×, 4.94×, and 6.42× undersampling were reconstructed by using combined compressed sensing and parallel imaging and fat fraction maps produced with R2* modeling. Results Muscle R2* at 3.0 T (33-125 sec(-1)) depended on the morphology of fat replacement, the highest values occurring with the greatest interdigitation of fat. The inclusion of R2* modeling removed bias, which was greatest at low fat fraction, but did not increase variance. The 95% limits of agreement of the accelerated acquisitions were tight and not degraded by R2* modeling (1.65%, 1.95%, and 2.22% for 3.65×, 4.94×, and 6.42× acceleration, respectively). Conclusion Incorporating R2* modeling prevents systematic errors in muscle fat fraction by up to 3.5% without loss of precision and should be incorporated into all muscular dystrophy studies. Fat fraction measurements can be accelerated fivefold by using combined compressed sensing and parallel imaging, modeling for R2* without loss of fidelity.

Chemical shift encoded imaging of hyperpolarized (13) C pyruvate

PURPOSE

To demonstrate a reconstruction technique for separating signal from different hyperpolarized carbon-13 metabolites.

METHODS

A reconstruction method is described for chemical shift encoded separation of the signal from pyruvate and its downstream metabolites. This method uses consistency of the data with the signal model rather than an additional free-induction decay (FID) acquisition to estimate the B0 offset. Compressed sensing was also integrated into the reconstruction allowing reconstruction of metabolite images from undersampled datasets. The performance of the reconstruction was assessed using thermal phantoms, digital phantoms, and in vivo hyperpolarized [1-(13) C] pyruvate experiments.

RESULTS

Thermal and digital phantoms indicate that metabolite separation is feasible given Signal-to-noise ratio > 5 and an initial B0 offset estimate within -105 Hz to 90 Hz of the actual B0 offset. In vivo comparisons to an existing FID calibrated reconstruction show improved fidelity in regions with significant field map inhomogeneity provided that these field map variations are accounted for using an additional proton acquisition. Prospectively and retrospectively undersampled studies show acceleration factors of 2 are feasible using compressed sensing.

CONCLUSION

A reconstruction framework for the separation of signal from pyruvate and its downstream metabolites is shown. This reconstruction eliminates the need to acquire additional calibration FID acquisition and allows acceleration through compressed sensing.

Lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA) MRI

PURPOSE

The Dixon techniques provide uniform water-fat separation but require multiple image sets, which extend the overall acquisition time. Here, an alternative rapid single acquisition method, lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA), was introduced.

METHODS

The LEENA method utilized a fast imaging with steady-state free precession sequence to obtain a single k-space dataset in which successive k-space lines are acquired to allow the fat magnetization to precess 180°. The LEENA data were then unghosted using either image-domain (LEENA-S) or k-space domain (LEENA-G) parallel imaging techniques to reconstruct water-only and fat-only images. An off-resonance correction technique was incorporated to improve the uniformity of the water-fat separation.

RESULTS

Uniform water-fat separation was achieved for both the LEENA-S and LEENA-G methods for phantom and human body and leg imaging applications at 1.5T and 3T. The resultant water and fat images were qualitatively similar to conventional 2-point Dixon and fat-suppressed images.

CONCLUSION

The LEENA-S and LEENA-G methods provide uniform water and fat images from a single MRI acquisition. These straightforward methods can be adapted to 1.5T and 3T clinical MRI scanners and provide comparable fat/water separation with conventional 2-point Dixon and fat-suppression techniques.

Pushing CT and MR imaging to the molecular level for studying the "omics": current challenges and advancements

During the past decade, medical imaging has made the transition from anatomical imaging to functional and even molecular imaging. Such transition provides a great opportunity to begin the integration of imaging data and various levels of biological data. In particular, the integration of imaging data and multiomics data such as genomics, metabolomics, proteomics, and pharmacogenomics may open new avenues for predictive, preventive, and personalized medicine. However, to promote imaging-omics integration, the practical challenge of imaging techniques should be addressed. In this paper, we describe key challenges in two imaging techniques: computed tomography (CT) and magnetic resonance imaging (MRI) and then review existing technological advancements. Despite the fact that CT and MRI have different principles of image formation, both imaging techniques can provide high-resolution anatomical images while playing a more and more important role in providing molecular information. Such imaging techniques that enable single modality to image both the detailed anatomy and function of tissues and organs of the body will be beneficial in the imaging-omics field.

Investigating the quantitative fidelity of prospectively undersampled chemical shift imaging in muscular dystrophy with compressed sensing and parallel imaging reconstruction

PURPOSE

Fat fraction measurement in muscular dystrophy has an important role to play in future therapy trials. Undersampled data acquisition reconstructed by combined compressed sensing and parallel imaging (CS-PI) can potentially reduce trial cost and improve compliance. These benefits are only gained from prospectively undersampled acquisitions.

METHODS

Eight patients with Becker muscular dystrophy were recruited and prospectively undersampled data at ratios of 3.65×, 4.94×, and 6.42× were acquired in addition to fully sampled data: equivalent coherent undersamplings were acquired for reconstruction with parallel imaging alone (PI). Fat fraction maps and maps of total signal were created using a combined compressed sensing/parallel imaging (CS-PI) reconstruction.

RESULTS

The CS-PI reconstructions are of sufficient quality to allow muscle delineation at 3.65× and 4.94× undersampling but some muscles were obscured at 6.42×. When plotted against the fat fractions derived from fully sampled data, non-significant bias and 95% limits of agreement of 1.58%, 2.17% and 2.41% were found for the three CS-PI reconstructions, while a 3.36× PI reconstruction yields 2.78%, 1.8 times worse than the equivalent CS-PI reconstruction.

CONCLUSION

Prospective undersampling and CS-PI reconstruction of muscle fat fraction mapping can be used to accelerate muscle fat fraction measurement in muscular dystrophy.

Whole-heart chemical shift encoded water-fat MRI

PURPOSE

To develop and evaluate a free-breathing chemical-shift-encoded (CSE) spoiled gradient-recalled echo (SPGR) technique for whole-heart water-fat imaging at 3 Tesla (T).

METHODS

We developed a three-dimensional (3D) multi-echo SPGR pulse sequence with electrocardiographic gating and navigator echoes and evaluated its performance at 3T in healthy volunteers (N = 6) and patients (N = 20). CSE-SPGR, 3D SPGR, and 3D balanced-SSFP with chemical fat saturation were compared in six healthy subjects with images evaluated for overall image quality, level of residual artifacts, and quality of fat suppression. A similar scoring system was used for the patient datasets.

RESULTS

Images of diagnostic quality were acquired in all but one subject. CSE-SPGR performed similarly to SPGR with fat saturation, although it provided a more uniform fat suppression over the whole field of view. Balanced-SSFP performed worse than SPGR-based methods. In patients, CSE-SPGR produced excellent fat suppression near metal. Overall image quality was either good (7/20) or excellent (12/20) in all but one patient. There were significant artifacts in 5/20 clinical cases.

CONCLUSION

CSE-SPGR is a promising technique for whole-heart water-fat imaging during free-breathing. The robust fat suppression in the water-only image could improve assessment of complex morphology at 3T and in the presence of off-resonance, with additional information contained in the fat-only image.