Model-based iterative reconstruction for radial fast spin-echo MRI

Joint T1 and T2 Mapping With Tiny Dictionaries and Subspace-Constrained Reconstruction

A novel method is developed that adaptively generates tiny dictionaries for joint T1-T2 mapping in magnetic resonance imaging. This work breaks the bond between dictionary size and representation accuracy (i) by approximating the Bloch-response manifold by piece-wise linear functions and (ii) by adaptively refining the sampling grid depending on the locally-linear approximation error. Data acquisition is accomplished with use of an 2D radially sampled Inversion-Recovery Hybrid-State Free Precession sequence. Adaptive dictionaries are generated with different error tolerances and compared to a heuristically designed dictionary. Based on simulation results, tiny dictionaries were used for T1-T2 mapping in phantom and in vivo studies. Reconstruction and parameter mapping were performed entirely in subspace. All experiments demonstrated excellent agreement between the proposed mapping technique and template matching using heuristic dictionaries. Adaptive dictionaries in combination with manifold projection allow to reduce the necessary dictionary sizes by one to two orders of magnitude.

Model-Based Chemical Exchange Saturation Transfer MRI for Robust z-Spectrum Analysis

This paper introduces a novel, model-based chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), in which asymmetric spectra of interest are directly estimated from complete or incomplete measurements by incorporating subspace-based spectral signal decomposition into the measurement model of CEST MRI for a robust z-spectrum analysis. Spectral signals are decomposed into symmetric and asymmetric components. The symmetric component, which varies smoothly, is delineated by the linear superposition of a finite set of vectors in a basis trained from the simulated (Lorentzian) signal vectors augmented with data-driven signal vectors, while the asymmetric component is to be inherently lower than or equal to zero due to saturation transfer phenomena. Spectral decomposition is performed directly on the measured spectral data by solving a constrained optimization problem that employs the linearized spectral decomposition model for the symmetric component and the weighted Frobenius norm regularization for the asymmetric component while utilizing additional spatial sparsity and low-rank priors. The simulations and in vivo experiments were performed to demonstrate the feasibility of the proposed method as a reliable molecular MRI.

Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging

Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite differences, and low rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the image formation. On the other hand, MRI signal dynamics are governed by physical laws, which can be explicitly modeled and used as priors for reconstruction. These explicit and implicit signal priors can be synergistically combined in an inverse problem framework to recover sharp, multi-contrast images from highly accelerated scans. Furthermore, the physics-based constraints provide a recipe for recovering quantitative, bio-physical parameters from the data. This article introduces physics-based modeling constraints in MRI and shows how they can be used in conjunction with compressed sensing for image reconstruction and quantitative imaging. We describe model-based quantitative MRI, as well as its linear subspace approximation. We also discuss approaches to selecting user-controllable scan parameters given knowledge of the physical model. We present several MRI applications that take advantage of this framework for the purpose of multi-contrast imaging and quantitative mapping.

Improvement of Fast Model-Based Acceleration of Parameter Look-Locker T1 Mapping

Quantitative mapping is desirable in many scientific and clinical magneric resonance imaging (MRI) applications. Recent inverse recovery-look locker sequence enables single-shot T₁ mapping with a time of a few seconds but the main computational load is directed into offline reconstruction, which can take from several minutes up to few hours. In this study we proposed improvement of model-based approach for T₁-mapping by introduction of two steps fitting procedure. We provided analysis of further reduction of k-space data, which lead us to decrease of computational time and perform simulation of multi-slice development. The region of interest (ROI) analysis of human brain measurements with two different initial models shows that the differences between mean values with respect to a reference approach are in white matter-0.3% and 1.1%, grey matter-0.4% and 1.78% and cerebrospinal fluid-2.8% and 11.1% respectively. With further improvements we were able to decrease the time of computational of single slice to 6.5 min and 23.5 min for different initial models, which has been already not achieved by any other algorithm. In result we obtained an accelerated novel method of model-based image reconstruction in which single iteration can be performed within few seconds on home computer.

Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH cardiovascular magnetic resonance

BACKGROUND

This study develops a model-based myocardial T1 mapping technique with sparsity constraints which employs a single-shot inversion-recovery (IR) radial fast low angle shot (FLASH) cardiovascular magnetic resonance (CMR) acquisition. The method should offer high resolution, accuracy, precision and reproducibility.

METHODS

The proposed reconstruction estimates myocardial parameter maps directly from undersampled k-space which is continuously measured by IR radial FLASH with a 4 s breathhold and retrospectively sorted based on a cardiac trigger signal. Joint sparsity constraints are imposed on the parameter maps to further improve T1 precision. Validations involved studies of an experimental phantom and 8 healthy adult subjects.

RESULTS

In comparison to an IR spin-echo reference method, phantom experiments with T1 values ranging from 300 to 1500 ms revealed good accuracy and precision at simulated heart rates between 40 and 100 bpm. In vivo T1 maps achieved better precision and qualitatively better preservation of image features for the proposed method than a real-time CMR approach followed by pixelwise fitting. Apart from good inter-observer reproducibility (0.6% of the mean), in vivo results confirmed good intra-subject reproducibility (1.05% of the mean for intra-scan and 1.17, 1.51% of the means for the two inter-scans, respectively) of the proposed method.

CONCLUSION

Model-based reconstructions with sparsity constraints allow for single-shot myocardial T1 maps with high spatial resolution, accuracy, precision and reproducibility within a 4 s breathhold. Clinical trials are warranted.

Model-based super-resolution reconstruction of T2 maps

PURPOSE

High-resolution isotropic T₂ mapping of the human brain with multi-echo spin-echo (MESE) acquisitions is challenging. When using a 2D sequence, the resolution is limited by the slice thickness. If used as a 3D acquisition, specific absorption rate limits are easily exceeded due to the high power deposition of nonselective refocusing pulses. A method to reconstruct 1-mm³ isotropic T₂ maps is proposed based on multiple 2D MESE acquisitions. Data were undersampled (10-fold) to compensate for the prolonged scan time stemming from the super-resolution acquisition.

THEORY AND METHODS

The proposed method integrates a classical super-resolution with an iterative model-based approach to reconstruct quantitative maps from a set of undersampled low-resolution data. The method was tested on numerical and multipurpose phantoms, and in vivo data. T₂ values were assessed with a region-of-interest analysis using a single-slice spin-echo and a fully sampled MESE acquisition in a phantom, and a MESE acquisition in healthy volunteers.

RESULTS

Numerical simulations showed that the best trade-off between acceleration and number of low-resolution datasets is 10-fold acceleration with 4 acquisitions (acquisition time = 18 min). The proposed approach showed improved resolution over low-resolution images for both phantom and brain. Region-of-interest analysis of the phantom compartments revealed that at shorter T₂ , the proposed method was comparable with the fully sampled MESE. For the volunteer data, the T₂ values found in the brain structures were consistent across subjects (8.5-13.1 ms standard deviation).

CONCLUSION

The proposed method addresses the inherent limitations associated with high-resolution T₂ mapping and enables the reconstruction of 1 mm³ isotropic relaxation maps with a 10 times faster acquisition.

Quantitative T2 mapping accelerated by GRAPPATINI for evaluation of muscles in patients with myositis

OBJECTIVE

Dermatomyositis (DM) and polymyositis (PM) make up the largest group of potentially treatable myopathies and require early diagnosis. This study investigates whether the edema of thigh muscles in DM/PM can be quantitatively assessed by a novel accelerated T2 mapping technique-GRAPPATINI.

METHODS

Three conventional MR sequences and GRAPPATINI accelerated T2 mapping of bilateral thighs from 20 patients (7 DM and 13 PM) and 10 healthy volunteers were prospectively carried out on a 3 T MR scanner. Afterwards, T2 values of 477 thigh muscles from the patients and the healthy controls were manually measured. In addition, the correlations between T2 values and serum muscle enzymes in patients were also analyzed.

RESULTS

The new GRAPPATINI technique made quantitative T2 mapping of bilateral thighs feasible with a scanning time of only 2 min 18 s. Moreover, GRAPPATINI-generated T2 values of muscles from patients were markedly higher than those from healthy subjects (p < 0.001). GRAPPATINI accelerated T2 mapping appeared a more sensitive technique in that some DM/PM muscles appearing normal per conventional MRI had increased T2 relaxation time. Furthermore, GRAPPATINI-generated T2 values of DM/PM thigh muscles positively correlated with serum enzyme levels (p < 0.001), which reflected the severity of myopathy.

CONCLUSION

GRAPPATINI can significantly shorten acquisition time of T2 mapping and may potentially be applied clinically in DM and PM.

ADVANCES IN KNOWLEDGE

GRAPPATINI acceleration makes T2 mapping feasible in clinical practice in providing quantitative information regarding thigh muscle inflammation in DM and PM.

Probability-based 3D k-space sorting for motion robust 4D-MRI

BACKGROUND

Current 4D-MRI techniques are prone to breathing-variation-induced motion artifacts. This study developed a novel method for motion-robust multi-cycle 4D-MRI using probability-based multi-cycle sorting to overcome this deficiency.

METHODS

The main cycles were first extracted from the breathing signal. 3D k-space data were then sorted using a result-driven method for each main cycle. The new method was tested on a 4D-extended cardiac-torso (XCAT) phantom with a patient and an artificially generated breathing curve. For comparison, the k-space data were sorted using conventional phase sorting to generate single-cycle 4D-MRI images. Signal-to-noise ratio (SNR) of tumor and liver, tumor volume consistency, and average intensity projection (AIP) accuracy were compared between the two methods. The original phantom images were used as references for the evaluation.

RESULTS

The new method showed improved tumor-to-liver SNR and tumor volume consistency as compared to 3D k-space phase sorting in both the simulated artificial and real patient breathing signals. For the artificial breathing cycles, the average tumor-to-liver SNR and standard deviation (SD) of tumor volume were 2.53 and 3.80% for cycle 1, 2.24 and 6.16% for cycle 2 of probability-based sorting as compared to 1.47 and 21.83% obtained using the phase sorting method; for the patient breathing curve, values of 1.99 and 2.71%, 1.97 and 3.29%, 1.88 and 4.16% were observed for cycle 1, cycle 2 and cycle 3 of probability-based sorting, versus 1.44 and 7.20% for phase sorting method. Furthermore, the AIP accuracy was improved in the probability-based sorting approach when compared to phase sorting, with the average intensity difference per voxel reduced from 0.39 to 0.15 for the artificial curve, and from 0.46 to 0.21 for the patient curve.

CONCLUSIONS

We demonstrated the feasibility of probability-based 3D k-space sorting for motion-robust multi-cycle 4D-MRI reconstruction with breathing variation induced motion artifact reduction compared with conventional 2D image sorting and 3D phase sorting methods. This new technique can potentially improve the accuracy of radiation treatment guidance for mobile targets.

SUPER: A blockwise curve-fitting method for accelerating MR parametric mapping with fast reconstruction

PURPOSE

To investigate Shift Undersampling improves Parametric mapping Efficiency and Resolution (SUPER), a novel blockwise curve-fitting method for accelerating parametric mapping with very fast reconstruction.

METHODS

SUPER uses interleaved k-space undersampling, which enables a blockwise decomposition of the otherwise large-scale cost function to improve the reconstruction efficiency. SUPER can be readily combined with SENSE to achieve at least 4-fold acceleration. D-factor, a parametric-mapping counterpart of g-factor, was proposed and formulated to compare spatially heterogeneous noise amplification because of different acceleration methods. As a proof-of-concept, SUPER/SUPER-SENSE was validated using T1 mapping, by comparing them to alternative model-based methods, including MARTINI and GRAPPATINI, via simulations, phantom imaging, and in vivo brain imaging (N = 5), over criteria of normalized root-mean-squares error (NRMSE), average d-factor, and computational time per voxel (TPV). A novel SUPER-SENSE MOLLI cardiac T1 -mapping sequence with improved resolution (1.4 mm × 1.4 mm) was compared to standard MOLLI (1.9 mm × 2.5 mm) in 8 healthy subjects.

RESULTS

In brain imaging, 2-fold SUPER achieved lower NRMSE (0.04 ± 0.02 vs. 0.11 ± 0.02, P < 0.01), lower average d-factor (1.01 ± 0.002 vs. 1.12 ± 0.004, P < 0.001), and lower TPV (4.6 ms ± 0.2 ms vs. 79 ms ± 3 ms, P < 0.001) than 2-fold MARTINI. Similarly, 4-fold SUPER-SENSE achieved lower NRMSE (0.07 ± 0.01 vs. 0.13 ± 0.03, P = 0.02), lower average d-factor (1.15 ± 0.01 vs. 1.20 ± 0.01, P < 0.001), and lower TPV (4.0 ms ± 0.1 ms vs. 72 ms ± 3 ms, P < 0.001) than 4-fold GRAPPATINI. In cardiac T1 mapping, SUPER-SENSE MOLLI yielded similar myocardial T1 (1151 ms ± 63 ms vs. 1159 ms ± 32 ms, P = 0.6), slightly lower blood T1 (1643 ms ± 86 ms vs. 1680 ms ± 79 ms, P = 0.004), but improved spatial resolution compared with standard MOLLI in the same imaging time.

CONCLUSION

SUPER and SUPER-SENSE provide fast model-based reconstruction methods for accelerating parametric mapping and improving its clinical appeal.

Matrix-induced autologous chondrocyte implantation grafting in osteochondral lesions of the talus: Evaluation of cartilage repair using T2 mapping

Osteochondral lesions of the talus may be treated with different autologous biological approaches. These include platelet-rich plasma, stem cells or MACI and ACI. MACI implants are used to cover cartilage lining defects in the ankle. A total of 18 patients were treated with MACI implants. NMR images were taken before and after the procedure. T2 mapping was used to quantify the changes in cartilage collagen after a 6 12-month postoperative period. Increase in collagen was recorded on all patients. Both open and arthroscopic procedures were performed depending on the technical difficulties encountered during the repair.

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.

MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR parameter mapping

PURPOSE

To develop and evaluate a novel deep learning-based image reconstruction approach called MANTIS (Model-Augmented Neural neTwork with Incoherent k-space Sampling) for efficient MR parameter mapping.

METHODS

MANTIS combines end-to-end convolutional neural network (CNN) mapping, incoherent k-space undersampling, and a physical model as a synergistic framework. The CNN mapping directly converts a series of undersampled images straight into MR parameter maps using supervised training. Signal model fidelity is enforced by adding a pathway between the undersampled k-space and estimated parameter maps to ensure that the parameter maps produced synthesized k-space consistent with the acquired undersampling measurements. The MANTIS framework was evaluated on the T₂ mapping of the knee at different acceleration rates and was compared with 2 other CNN mapping methods and conventional sparsity-based iterative reconstruction approaches. Global quantitative assessment and regional T₂ analysis for the cartilage and meniscus were performed to demonstrate the reconstruction performance of MANTIS.

RESULTS

MANTIS achieved high-quality T₂ mapping at both moderate (R = 5) and high (R = 8) acceleration rates. Compared to conventional reconstruction approaches that exploited image sparsity, MANTIS yielded lower errors (normalized root mean square error of 6.1% for R = 5 and 7.1% for R = 8) and higher similarity (structural similarity index of 86.2% at R = 5 and 82.1% at R = 8) to the reference in the T₂ estimation. MANTIS also achieved superior performance compared to direct CNN mapping and a 2-step CNN method.

CONCLUSION

The MANTIS framework, with a combination of end-to-end CNN mapping, signal model-augmented data consistency, and incoherent k-space sampling, is a promising approach for efficient and robust estimation of quantitative MR parameters.

Accelerated imaging of metallic implants using model-based nonlinear reconstruction

PURPOSE

To accelerate imaging near metallic implants with multi-spectral imaging (MSI) techniques by exploiting a signal model in the spectral dimension.

METHODS

MSI techniques resolve metal-induced field perturbations by acquiring separate 3D spatial encodings at multiple excitation frequencies, which are referred to as spectral bins. The proposed model-based reconstruction exploits the correlation between spectral bins in image reconstruction by enforcing a signal model to describe the signal profile across bins. This work evaluates the accuracy of the MSI signal model in simulations and in vivo experiments. The proposed model-based reconstruction was evaluated in 6 subjects at an overall undersampling factor of 17.4 and compared with model-free parallel imaging and compressed sensing (PI & CS). The quality of reconstructed images was evaluated using normalized RMS error (nRMSE) and structural similarity index (SSIM) comparisons, with paired Wilcoxon tests in 6 subjects used to determine whether there was a significant difference in the metrics.

RESULTS

Both simulations and in vivo experiments show that the proposed signal model can represent the MSI signal profiles in the spectral dimension compactly and accurately. In the in vivo experiments, the model-based reconstruction significantly improved image quality over model-free PI & CS, with P < 0.05 for both nRMSE and SSIM at 17.4× acceleration.

CONCLUSION

This work presents the feasibility of using a model-based reconstruction to accelerate MSI techniques for faster MR imaging near metal.

Rapid T1 quantification from high resolution 3D data with model-based reconstruction

Purpose

Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model‐based optimization framework in conjunction with undersampling 3D radial stack‐of‐stars data.

Theory and Methods

High resolution 3D T₁ maps are generated from subsampled data by employing model‐based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non‐linear, non‐differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss‐Newton method. The importance of 3D‐spectral regularization is demonstrated by a comparison to 2D‐spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look‐Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data.

Results

Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T₁ maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions.

Conclusions

The proposed algorithm is able to recover T₁ maps with an isotropic resolution of 1 mm³ from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.

SCOPE: signal compensation for low-rank plus sparse matrix decomposition for fast parameter mapping

Magnetic resonance (MR) parameter mapping is useful for many clinical applications. However, its practical utility is limited by the long scan time. To address this problem, this paper developed a novel image reconstruction method for fast MR parameter mapping. The proposed method (SCOPE) used a low-rank plus sparse model to reconstruct the parameter-weighted images from highly undersampled acquisitions. A signal compensation strategy was introduced to promote low rankness along the parametric direction and thus improve the reconstruction accuracy. Specifically, compensation was performed by multiplying the original signal by the inversion of the mono-exponential decay at each voxel. The performance of SCOPE was evaluated via quantitative T _1ρ mapping. The results of the simulation and in vivo experiments with acceleration factors from 3 to 5 are shown. The performance of SCOPE was verified via comparisons with several low-rank and sparsity-based methods. The experimental results showed that the T _1ρ maps obtained using SCOPE were more accurate than those obtained using competing methods and were comparable to the reference, even when the acceleration factor reached 5. SCOPE can greatly reduce the scan time of parameter mapping while still achieving high accuracy. This technique might therefore help facilitate fast MR parameter mapping in clinical use.

Fast Interleaved Multislice T1 Mapping: Model-Based Reconstruction of Single-Shot Inversion-Recovery Radial FLASH

Purpose

To develop a high-speed multislice T1 mapping method based on a single-shot inversion-recovery (IR) radial FLASH acquisition and a regularized model-based reconstruction.

Methods

Multislice radial k-space data are continuously acquired after a single nonselective inversion pulse using a golden-angle sampling scheme in a spoke-interleaved manner with optimized flip angles. Parameter maps and coil sensitivities of each slice are estimated directly from highly undersampled radial k-space data using a model-based nonlinear inverse reconstruction in conjunction with joint sparsity constraints. The performance of the method has been validated using a numerical and experimental T1 phantom as well as demonstrated for studies of the human brain and liver at 3T.

Results

The proposed method allows for 7 simultaneous T1 maps of the brain at 0.5 × 0.5 × 4 mm³ resolution within a single IR experiment of 4 s duration. Phantom studies confirm similar accuracy and precision as obtained for a single-slice acquisition. For abdominal applications, the proposed method yields three simultaneous T1 maps at 1.25 × 1.25 × 6 mm³ resolution within a 4 s breath hold.

Conclusion

Rapid, robust, accurate, and precise multislice T1 mapping may be achieved by combining the advantages of a model-based nonlinear inverse reconstruction, radial sampling, parallel imaging, and compressed sensing.

Hybrid T2 - and T1 -weighted radial acquisition for free-breathing abdominal examination

PURPOSE

Most clinical MR examinations require acquisition of different image contrasts. For abdominal exams, the scans are conventionally performed as separate acquisitions using respiratory gating or repeated breath holding, which can be time-inefficient and challenging for patients. Here, a hybrid imaging approach is described that creates T₂ - and T₁ -weighted images from a single scan and allows for free-breathing acquisition.

THEORY AND METHODS

T₂ -weighted data is collected using 3D fast spin-echo (FSE) acquisition with motion-robust radial stack-of-stars sampling. The wait time between the FSE trains is used to acquire T₁ -weighted gradient-echo (GRE) data. Improved robustness is achieved by extracting a respiratory signal from the GRE data and using it for motion-weighted reconstruction.

RESULTS

As validated in simulations and phantom scans, GRE acquisition in the wait time has minor effect on the signal strength and contrast. Volunteer scans at 1.5T showed that T₂ - and T₁ -weighted hybrid imaging is feasible during free-breathing. Furthermore, it has been demonstrated in a patient that hybrid imaging with T₁ -weighted Dixon acquisition is possible.

CONCLUSION

The described hybrid sequence enables comprehensive T₂ - and T₁ -weighted imaging in a single scan. In addition to free-breathing abdominal examination, it promises value for clinical applications that are frequently affected by motion artifacts.

Accelerated T2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI

BACKGROUND

Quantitative T₂ measurements are sensitive to intra- and extracellular water accumulation and myelin loss. Therefore, quantitative T₂ promises to be a good biomarker of disease. However, T₂ measurements require long acquisition times.

PURPOSE

To accelerate T₂ quantification and subsequent generation of synthetic T₂ -weighted (T₂ -w) image contrast for clinical research and routine. To that end, a recently developed model-based approach for rapid T₂ and M₀ quantification (MARTINI) based on undersampling k-space, was extended by parallel imaging (GRAPPA) to enable high-resolution T₂ mapping with access to T₂ -w images in less than 2 minutes acquisition time for the entire brain.

STUDY TYPE

Prospective cross-sectional study.

SUBJECTS/PHANTOM

Fourteen healthy subjects and a multipurpose phantom.

FIELD STRENGTH/SEQUENCE

Carr-Purcell-Meiboom-Gill sequence at a 3T scanner.

ASSESSMENT

The accuracy and reproducibility of the accelerated T₂ quantification was assessed. Validations comprised MRI studies on a phantom as well as the brain, knee, prostate, and liver from healthy volunteers. Synthetic T₂ -w images were generated from computed T₂ and M₀ maps and compared to conventional fast spin-echo (SE) images.

STATISTICAL TESTS

Root mean square distance (RMSD) to the reference method and region of interest analysis.

RESULTS

The combination of MARTINI and GRAPPA (GRAPPATINI) lead to a 10-fold accelerated T₂ mapping protocol with 1:44 minutes acquisition time and full brain coverage. The RMSD of GRAPPATINI increases less (4.3%) than a 10-fold MARTINI reconstruction (37.6%) in comparison to the reference. Reproducibility tests showed low standard deviation (SD) of T₂ values in regions of interest between scan and rescan (<0.4 msec) and across subjects (<4 msec).

DATA CONCLUSION

GRAPPATINI provides highly reproducible and fast whole-brain T₂ maps and arbitrary synthetic T₂ -w images in clinically compatible acquisition times of less than 2 minutes. These abilities are expected to support more widespread clinical applications of quantitative T₂ mapping.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:359-368.

Accelerated Parameter Mapping of Multiple-Echo Gradient-Echo Data Using Model-Based Iterative Reconstruction

A new reconstruction method, coined MIRAGE, is presented for accurate, fast, and robust parameter mapping of multiple-echo gradient-echo (MEGE) imaging, the basis sequence of novel quantitative magnetic resonance imaging techniques such as water content and susceptibility mapping. Assuming that the temporal signal can be modeled as a sum of damped complex exponentials, MIRAGE performs model-based reconstruction of undersampled data by minimizing the rank of local Hankel matrices. It further incorporates multi-channel information and spatial prior knowledge. Finally, the parameter maps are estimated using nonlinear regression. Simulations and retrospective undersampling of phantom and in vivo data affirm robustness, e.g., to strong inhomogeneity of the static magnetic field and partial volume effects. MIRAGE is compared with a state-of-the-art compressed sensing method, -ESPIRiT. Parameter maps estimated from reconstructed data using MIRAGE are shown to be accurate, with the mean absolute error reduced by up to 50% for in vivo results. The proposed method has the potential to improve the diagnostic utility of quantitative imaging techniques that rely on MEGE data.

Iterative Reconstruction Designed for Brain CT: A Correlative Study With Filtered Back Projection for the Diagnosis of Acute Ischemic Stroke

OBJECTIVES

The objective of this study is to evaluate the usefulness of iterative model reconstruction designed for brain computed tomography (CT) (IMR-Neuro) for the diagnosis of acute ischemic stroke.

METHODS

This retrospective study included 20 patients with acute middle cerebral artery infarction who have undergone brain CT and 20 nonstroke patients (control). We reconstructed axial images with filtered back projection (FBP) and IMR-Neuro (slice thickness, 1 and 5 mm). We compared the CT number of the infarcted area, the image noise, contrast, and the contrast to noise ratio of the infarcted and the noninfarcted areas between the different reconstruction methods. We compared the performance of 10 radiologists in the detection of parenchymal hypoattenuation between 2 techniques using the receiver operating characteristic (ROC) techniques with the jackknife method.

RESULTS

The image noise was significantly lower with IMR-Neuro [5 mm: 2.5 Hounsfield units (HU) ± 0.5, 1 mm: 3.9 HU ± 0.5] than with FBP (5 mm: 4.9 HU ± 0.5, 1 mm: 10.1 HU ± 1.4) (P < 0.01). The contrast to noise ratio was significantly greater with IMR-Neuro (5 mm: 2.6 ± 2.1, 1 mm: 1.6 ± 1.3) than with FBP (5 mm: 1.2 ± 1.0; 1 mm: 0.6 ± 0.5) (P < 0.01). The value of the average area under the receiver operating curve was significantly higher with IMR-Neuro than FBP (5 mm: 0.79 vs 0.74, P = 0.04; 1 mm: 0.76 vs 0.69, P = 0.04).

CONCLUSIONS

Compared with FBP, IMR-Neuro improves the image quality and the performance for the detection of parenchymal hypoattenuation with acute ischemic stroke.

An eigenvalue approach for the automatic scaling of unknowns in model-based reconstructions: Application to real-time phase-contrast flow MRI

The purpose of this work is to develop an automatic method for the scaling of unknowns in model-based nonlinear inverse reconstructions and to evaluate its application to real-time phase-contrast (RT-PC) flow magnetic resonance imaging (MRI). Model-based MRI reconstructions of parametric maps which describe a physical or physiological function require the solution of a nonlinear inverse problem, because the list of unknowns in the extended MRI signal equation comprises multiple functional parameters and all coil sensitivity profiles. Iterative solutions therefore rely on an appropriate scaling of unknowns to numerically balance partial derivatives and regularization terms. The scaling of unknowns emerges as a self-adjoint and positive-definite matrix which is expressible by its maximal eigenvalue and solved by power iterations. The proposed method is applied to RT-PC flow MRI based on highly undersampled acquisitions. Experimental validations include numerical phantoms providing ground truth and a wide range of human studies in the ascending aorta, carotid arteries, deep veins during muscular exercise and cerebrospinal fluid during deep respiration. For RT-PC flow MRI, model-based reconstructions with automatic scaling not only offer velocity maps with high spatiotemporal acuity and much reduced phase noise, but also ensure fast convergence as well as accurate and precise velocities for all conditions tested, i.e. for different velocity ranges, vessel sizes and the simultaneous presence of signals with velocity aliasing. In summary, the proposed automatic scaling of unknowns in model-based MRI reconstructions yields quantitatively reliable velocities for RT-PC flow MRI in various experimental scenarios.

T1-refBlochi: high resolution 3D post-contrast T1 myocardial mapping based on a single 3D late gadolinium enhancement volume, Bloch equations, and a reference T1

BACKGROUND

High resolution 3D T1 mapping is important for assessment of diffuse myocardial fibrosis in left atrium or other thin-walled structures. In this work, we investigated a fast single-TI 3D high resolution T1 mapping method that directly transforms a 3D late gadolinium enhancement (LGE) volume to a 3D T1 map.

METHODS

The proposed method, T1-refBlochi, is based on Bloch equation modeling of the LGE signal, a single-point calibration, and assumptions that proton density and T2* are relatively uniform in the heart. Several sources of error of this method were analyzed mathematically and with simulations. Imaging was performed in phantoms, eight swine and five patients, comparing T1-refBlochi to a standard spin-echo T1 mapping, 3D multi-TI T1 mapping, and 2D ShMOLLI, respectively.

RESULTS

The method has a good accuracy and adequate precision, even considering various sources of error. In phantoms, over a range of protocols, heart-rates and T1 s, the bias ±1SD was -3 ms ± 9 ms. The porcine studies showed excellent agreement between T1-refBlochi and the multi-TI method (bias ±1SD = -6 ± 22 ms). The proton density and T2* weightings yielded ratios for scar/blood of 0.94 ± 0.01 and for myocardium/blood of 1.03 ± 0.02 in the eight swine, confirming that sufficient uniformity of proton density and T2* weightings exists among heterogeneous tissues of the heart. In the patients, the mean T1 bias ±1SD in myocardium and blood between T1-refBlochi and ShMOLLI was -9 ms ± 21 ms.

CONCLUSION

T1-refBlochi provides a fast single-TI high resolution 3D T1 map of the heart with good accuracy and adequate precision.

Model-based T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH

PURPOSE

To develop a model-based reconstruction technique for single-shot T₁ mapping with high spatial resolution, accuracy, and precision using an inversion-recovery (IR) fast low-angle shot (FLASH) acquisition with radial encoding.

METHODS

The proposed model-based reconstruction jointly estimates all model parameters, that is, the equilibrium magnetization, steady-state magnetization, 1/ T1*, and all coil sensitivities from the data of a single-shot IR FLASH acquisition with a small golden-angle radial trajectory. Joint sparsity constraints on the parameter maps are exploited to improve the performance of the iteratively regularized Gauss-Newton method chosen for solving the nonlinear inverse problem. Validations include both a numerical and experimental T₁ phantom, as well as in vivo studies of the human brain and liver at 3 T.

RESULTS

In comparison to previous reconstruction methods for single-shot T₁ mapping, which are based on real-time MRI with pixel-wise fitting and a model-based approach with a predetermination of coil sensitivities, the proposed method presents with improved robustness against phase errors and numerical precision in both phantom and in vivo studies.

CONCLUSION

The comprehensive model-based reconstruction with L1 regularization offers rapid and robust T₁ mapping with high accuracy and precision. The method warrants accelerated computing and online implementation for extended clinical trials. Magn Reson Med 79:730-740, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Ultrafast compartmentalized relaxation time mapping with linear algebraic modeling

PURPOSE

To dramatically accelerate compartmental-average longitudinal (T₁ ) and transverse (T₂ ) relaxation measurements using the minimal-acquisition linear algebraic modeling (SLAM) method, and to validate it in phantoms and humans.

METHODS

Relaxation times were imaged at 3 Tesla in phantoms, in the abdomens of six volunteers, and in six brain tumor patients using standard inversion recovery and multi-spin-echo sequences. k-space was fully sampled to provide reference T₁ and T₂ measurements, and SLAM was performed using a limited set of phase encodes from central k-space. Anatomical compartments were segmented on scout images post-acquisition, and SLAM reconstruction was implemented using two algorithms. Compartment-average T₁ and T₂ measurements were determined retroactively from fully sampled data sets, and proactively from SLAM data sets at acceleration factors of up to 16. Values were compared with reference measurements. The compartment's localization properties were analyzed using the discrete spatial response function.

RESULTS

At 16-fold acceleration, compartment-average SLAM T₁ measurements agreed with the full k-space compartment-average results to within 0.0% ± 0.7%, 1.4% ± 3.4%, and 0.5% ± 2.9% for phantom, abdominal, and brain T₁ measurements, respectively. The corresponding T₂ measurements agreed within 0.2% ± 1.9%, 0.9% ± 7.9%, and 0.4% ± 5.8%, respectively.

CONCLUSION

SLAM can dramatically accelerate relaxation time measurements when compartmental or lesion-average values can suffice, or when standard relaxometry is precluded by scan-time limitations. Magn Reson Med 79:286-297, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

T2 shuffling: Sharp, multicontrast, volumetric fast spin-echo imaging

PURPOSE

A new acquisition and reconstruction method called T2 Shuffling is presented for volumetric fast spin-echo (three-dimensional [3D] FSE) imaging. T2 Shuffling reduces blurring and recovers many images at multiple T2 contrasts from a single acquisition at clinically feasible scan times (6-7 min).

THEORY AND METHODS

The parallel imaging forward model is modified to account for temporal signal relaxation during the echo train. Scan efficiency is improved by acquiring data during the transient signal decay and by increasing echo train lengths without loss in signal-to-noise ratio (SNR). By (1) randomly shuffling the phase encode view ordering, (2) constraining the temporal signal evolution to a low-dimensional subspace, and (3) promoting spatio-temporal correlations through locally low rank regularization, a time series of virtual echo time images is recovered from a single scan. A convex formulation is presented that is robust to partial voluming and radiofrequency field inhomogeneity.

RESULTS

Retrospective undersampling and in vivo scans confirm the increase in sharpness afforded by T2 Shuffling. Multiple image contrasts are recovered and used to highlight pathology in pediatric patients. A proof-of-principle method is integrated into a clinical musculoskeletal imaging workflow.

CONCLUSION

The proposed T2 Shuffling method improves the diagnostic utility of 3D FSE by reducing blurring and producing multiple image contrasts from a single scan. Magn Reson Med 77:180-195, 2017. © 2016 Wiley Periodicals, Inc.

Compressed sensing for body MRI

The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2017;45:966-987.

A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging

Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct highquality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality.

Submillisievert Radiation Dose Coronary CT Angiography: Clinical Impact of the Knowledge-Based Iterative Model Reconstruction

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the noise and image quality of images reconstructed with a knowledge-based iterative model reconstruction (knowledge-based IMR) in ultra-low dose cardiac computed tomography (CT).

MATERIALS AND METHODS

We performed submillisievert radiation dose coronary CT angiography on 43 patients. We also performed a phantom study to evaluate the influence of object size with the automatic exposure control phantom. We reconstructed clinical and phantom studies with filtered back projection (FBP), hybrid iterative reconstruction (hybrid IR), and knowledge-based IMR. We measured effective dose of patients and compared CT number, image noise, and contrast noise ratio in ascending aorta of each reconstruction technique. We compared the relationship between image noise and body mass index for the clinical study, and object size for phantom study.

RESULTS

The mean effective dose was 0.98 ± 0.25 mSv. The image noise of knowledge-based IMR images was significantly lower than those of FBP and hybrid IR images (knowledge-based IMR: 19.4 ± 2.8; FBP: 126.7 ± 35.0; hybrid IR: 48.8 ± 12.8, respectively) (P < .01). The contrast noise ratio of knowledge-based IMR images was significantly higher than those of FBP and hybrid IR images (knowledge-based IMR: 29.1 ± 5.4; FBP: 4.6 ± 1.3; hybrid IR: 13.1 ± 3.5, respectively) (P < .01). There were moderate correlations between image noise and body mass index in FBP (r = 0.57, P < .01) and hybrid IR techniques (r = 0.42, P < .01); however, these correlations were weak in knowledge-based IMR (r = 0.27, P < .01).

CONCLUSION

Compared to FBP and hybrid IR, the knowledge-based IMR offers significant noise reduction and improvement in image quality in submillisievert radiation dose cardiac CT.

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.

Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors

High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the data space and for image reconstruction from highly undersampled data. More specifically, the proposed method acquires two datasets with complementary sampling patterns, one for subspace estimation and the other for image reconstruction; image reconstruction from highly undersampled data is accomplished by fitting the measured data with a sparsity constraint on the core tensor and a group sparsity constraint on the spatial coefficients jointly using the alternating direction method of multipliers. The usefulness of the proposed method is demonstrated in MRI applications; it may also have applications beyond MRI.

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.

Multiscale reconstruction for MR fingerprinting

PURPOSE

To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting.

METHODS

An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method.

RESULTS

The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time.

CONCLUSION

The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc.

Direct and accelerated parameter mapping using the unscented Kalman filter

PURPOSE

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem.

METHODS

In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling.

RESULTS

Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies.

CONCLUSION

This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping.

Experimental O-space turbo spin echo imaging

PURPOSE

Turbo spin echo (TSE) imaging reduces imaging time by acquiring multiple echoes per repetition (TR), requiring fewer TRs. O-space can also require fewer TRs by using a combination of nonlinear magnetic gradient fields and surface coil arrays. Although to date, O-space has only been demonstrated for gradient echo imaging, it is valuable to combine these two techniques. However, collecting multiple O-space echoes per TR is difficult because of the different local k-space trajectories and variable T2-weighting.

THEORY AND METHODS

A practical scheme is demonstrated to combine the benefits of TSE and O-space for highly accelerated T2-weighted images. The scheme uses a modified acquisition order and filtered projection reconstruction to reduce artifacts caused by T2 decay, while retaining T2 contrast that corresponds to a specific echo time.

RESULTS

The experiments revealed that the proposed method can produce highly accelerated T2-weighted images. Moreover, the method can generate multiple images with different T2 contrasts from a single dataset.

CONCLUSIONS

The proposed O-space TSE imaging method requires fewer echoes than conventional TSE and fewer repetitions than conventional O-space imaging. It retains resilience to undersampling, clearly outperforming Cartesian SENSE at high levels of undersampling, and can generate undistorted images with a range of T2 contrast from a single acquired dataset.

Model-based reconstruction for real-time phase-contrast flow MRI: Improved spatiotemporal accuracy

PURPOSE

To develop a model-based reconstruction technique for real-time phase-contrast flow MRI with improved spatiotemporal accuracy in comparison to methods using phase differences of two separately reconstructed images with differential flow encodings.

METHODS

The proposed method jointly computes a common image, a phase-contrast map, and a set of coil sensitivities from every pair of flow-compensated and flow-encoded datasets obtained by highly undersampled radial FLASH. Real-time acquisitions with five and seven radial spokes per image resulted in 25.6 and 35.7 ms measuring time per phase-contrast map, respectively. The signal model for phase-contrast flow MRI requires the solution of a nonlinear inverse problem, which is accomplished by an iteratively regularized Gauss-Newton method. Aspects of regularization and scaling are discussed. The model-based reconstruction was validated for a numerical and experimental flow phantom and applied to real-time phase-contrast MRI of the human aorta for 10 healthy subjects and 2 patients.

RESULTS

Under all conditions, and compared with a previously developed real-time flow MRI method, the proposed method yields quantitatively accurate phase-contrast maps (i.e., flow velocities) with improved spatial acuity, reduced phase noise and reduced streaking artifacts.

CONCLUSION

This novel model-based reconstruction technique may become a new tool for clinical flow MRI in real time. Magn Reson Med 77:1082-1093, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Accelerated exponential parameterization of T2 relaxation with model-driven low rank and sparsity priors (MORASA)

PURPOSE

This work is to develop a novel image reconstruction method from highly undersampled multichannel acquisition to reduce the scan time of exponential parameterization of T2 relaxation.

THEORY AND METHODS

On top of the low-rank and joint-sparsity constraints, we propose to exploit the linear predictability of the T2 exponential decay to further improve the reconstruction of the T2-weighted images from undersampled acquisitions. Specifically, the exact rank prior (i.e., number of non-zero singular values) is adopted to enforce the spatiotemporal low rankness, while the mixed L2-L1 norm of the wavelet coefficients is used to promote joint sparsity, and the Hankel low-rank approximation is used to impose linear predictability, which integrates the exponential behavior of the temporal signal into the reconstruction process. An efficient algorithm is adopted to solve the reconstruction problem, where corresponding nonlinear filtering operations are performed to enforce corresponding priors in an iterative manner.

RESULTS

Both simulated and in vivo datasets with multichannel acquisition were used to demonstrate the feasibility of the proposed method. Experimental results have shown that the newly introduced linear predictability prior improves the reconstruction quality of the T2-weighted images and benefits the subsequent T2 mapping by achieving high-speed, high-quality T2 mapping compared with the existing fast T2 mapping methods.

CONCLUSION

This work proposes a novel fast T2 mapping method integrating the linear predictable property of the exponential decay into the reconstruction process. The proposed technique can effectively improve the reconstruction quality of the state-of-the-art fast imaging method exploiting image sparsity and spatiotemporal low rankness. Magn Reson Med 76:1865-1878, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Trust Region Methods for the Estimation of a Complex Exponential Decay Model in MRI With a Single-Shot or Multi-Shot Trajectory

Joint estimation of spin density R2* decay and OFF-resonance frequency maps is very useful in many magnetic resonance imaging applications. The standard multi-echo approach can achieve high accuracy but requires a long acquisition time for sampling multiple k-space frames. There are many approaches to accelerate the acquisition. Among them, single-shot or multi-shot trajectory-based sampling has recently drawn attention due to its fast data acquisition. However, this sampling strategy destroys the Fourier relationship between k-space and images, leading to a great challenge for the reconstruction. In this paper, we present two trust region methods based on two different linearization strategies for the nonlinear signal model. A trust region is defined as a local area in the variable space where a local linear approximation is trustable. In each iteration, the method minimizes a local approximation within a trust region so that the step size can be kept in a suitable scale. A continuation scheme is applied to reduce the regularization gradually over the parameter maps and facilitates convergence from poor initializations. The two trust region methods are compared with the two other previously proposed methods--the nonlinear conjugate gradients and the gradual refinement algorithm. Experiments based on various synthetic data and real phantom data show that the two trust region methods have a clear advantage in both speed and stability.

Accelerating T1ρ cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE

PURPOSE

To accelerate T1ρ quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE.

METHODS

To reconstruct T1ρ images from accelerated acquisition at different time of spin-lock (TSLs), we propose an approach to combine an advanced compressed sensing (CS) based reconstruction technique, LAISD (locally adaptive iterative support detection), and an advanced parallel imaging technique, JSENSE. Specifically, the reconstruction process alternates iteratively among local support detection in the domain of principal component analysis, compressed sensing reconstruction of the image sequence, and sensitivity estimation with JSENSE. T1ρ quantification results from accelerated scans using the proposed method are evaluated using in vivo knee cartilage data from bilateral scans of three healthy volunteers.

RESULTS

T1ρ maps obtained from accelerated scans (acceleration factors of 3 and 3.5) using the proposed method showed results comparable to conventional full scans. The T1ρ errors in all compartments are below 1%, which is well below the in vivo reproducibility of cartilage T1ρ reported from previous studies.

CONCLUSION

The proposed method can significantly accelerate the acquisition process of T1ρ quantification on human cartilage imaging without sacrificing accuracy, which will greatly facilitate the clinical translation of quantitative cartilage MRI.

Accelerated and motion-robust in vivo T2 mapping from radially undersampled data using bloch-simulation-based iterative reconstruction

PURPOSE

Development of a quantitative transverse relaxation time (T2)-mapping platform that operates at clinically feasible timescales by employing advanced image reconstruction of radially undersampled multi spin-echo (MSE) datasets.

METHODS

Data was acquired on phantom and in vivo at 3 Tesla using MSE protocols employing radial k-space sampling trajectories. In order to overcome the nontrivial spin evolution associated with MSE protocols, a numerical signal model was precalculated based on Bloch simulations of the actual pulse-sequence scheme used in the acquisition process. This signal model was subsequently incorporated into an iterative model-based image reconstruction process, producing T2 and proton-density maps.

RESULTS

T2 maps of phantom and in vivo brain were successfully constructed, closely matching values produced by a single spin-echo reference scan. High-resolution mapping was also performed for the spinal cord in vivo, differentiating the underlying gray/white matter morphology.

CONCLUSION

The presented MSE data-processing framework offers reliable mapping of T2 relaxation values in a ∼ 5-minute timescale, free of user- and scanner-dependent variations. The use of radial k-space sampling provides further advantages in the form of high immunity to irregular physiological motion, as well as enhanced spatial resolutions, owing to its inherent ability to perform alias-free limited field-of-view imaging.

Compressive sensing in medical imaging

The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

A model-based reconstruction for undersampled radial spin-echo DTI with variational penalties on the diffusion tensor

Radial spin-echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging, due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled diffusion-tensor imaging (DTI). A model-based reconstruction implicitly exploits redundancies in the diffusion-weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a total variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (three and two volunteers, respectively). Evaluation of the new approach was conducted by comparing the results with reconstructions performed with gridding, combined parallel imaging and compressed sensing and a recently proposed model-based approach. The experiments demonstrated improvements in terms of reduction of noise and streaking artifacts in the quantitative parameter maps, as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin-echo diffusion-tensor imaging without degrading parameter quantification and/or SNR.

Reducing contrast contamination in radial turbo-spin-echo acquisitions by combining a narrow-band KWIC filter with parallel imaging

PURPOSE

Cartesian turbo spin-echo (TSE) and radial TSE images are usually reconstructed by assembling data containing different contrast information into a single k-space. This approach results in mixed contrast contributions in the images, which may reduce their diagnostic value. The goal of this work is to improve the image contrast from radial TSE acquisitions by reducing the contribution of signals with undesired contrast information.

METHODS

Radial TSE acquisitions allow the reconstruction of multiple images with different T2 contrasts using the k-space weighted image contrast (KWIC) filter. In this work, the image contrast is improved by reducing the band-width of the KWIC filter. Data for the reconstruction of a single image are selected from within a small temporal range around the desired echo time. The resulting dataset is undersampled and, therefore, an iterative parallel imaging algorithm is applied to remove aliasing artifacts.

RESULTS

Radial TSE images of the human brain reconstructed with the proposed method show an improved contrast when compared with Cartesian TSE images or radial TSE images with conventional KWIC reconstructions.

CONCLUSION

The proposed method provides multi-contrast images from radial TSE data with contrasts similar to multi spin-echo images. Contaminations from unwanted contrast weightings are strongly reduced.

Fast T2 mapping with improved accuracy using undersampled spin-echo MRI and model-based reconstructions with a generating function

A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo magnetic resonance imaging (MRI) data. The technique employs an advanced signal model for T2 relaxation that accounts for contributions from indirect echoes in a train of multiple spin echoes. An iterative solution of the nonlinear inverse reconstruction problem directly estimates spin-density and T2 maps from undersampled raw data. The algorithm is validated for simulated data as well as phantom and human brain MRI at 3T. The performance of the advanced model is compared to conventional pixel-based fitting of echo-time images from fully sampled data. The proposed method yields more accurate T2 values than the mono-exponential model and allows for retrospective undersampling factors of at least 6.Although limitations are observed for very long T2 relaxation times, respective reconstruction problems may be overcome by a gradient dampening approach. The analytical gradient of the utilized cost function is included as appendix. The source code is made available to the community.

Model-based MR parameter mapping with sparsity constraints: parameter estimation and performance bounds

Magnetic resonance parameter mapping (e.g., T1 mapping, T2 mapping, T*2 mapping) is a valuable tool for tissue characterization. However, its practical utility has been limited due to long data acquisition time. This paper addresses this problem with a new model-based parameter mapping method. The proposed method utilizes a formulation that integrates the explicit signal model with sparsity constraints on the model parameters, enabling direct estimation of the parameters of interest from highly undersampled, noisy k-space data. An efficient greedy-pursuit algorithm is described to solve the resulting constrained parameter estimation problem. Estimation-theoretic bounds are also derived to analyze the benefits of incorporating sparsity constraints and benchmark the performance of the proposed method. The theoretical properties and empirical performance of the proposed method are illustrated in a T2 mapping application example using computer simulations.