SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space

Undersampled Multi-contrast MRI Reconstruction Based on Double-domain Generative Adversarial Network

Multi-contrast magnetic resonance imaging can provide comprehensive information for clinical diagnosis. However, multi-contrast imaging suffers from long acquisition time, which makes it inhibitive for daily clinical practice. Subsampling k-space is one of the main methods to speed up scan time. Missing k-space samples will lead to inevitable serious artifacts and noise. Considering the assumption that different contrast modalities share some mutual information, it may be possible to exploit this redundancy to accelerate multi-contrast imaging acquisition. Recently, generative adversarial network shows superior performance in image reconstruction and synthesis. Some studies based on k-space reconstruction also exhibit superior performance over conventional state-of-art method. In this study, we propose a cross-domain two-stage generative adversarial network for multi-contrast images reconstruction based on prior full-sampled contrast and undersampled information. The new approach integrates reconstruction and synthesis, which estimates and completes the missing k-space and then refines in image space. It takes one fully-sampled contrast modality data and highly undersampled data from several other modalities as input, and outputs high quality images for each contrast simultaneously. The network is trained and tested on a public brain dataset from healthy subjects. Quantitative comparisons against baseline clearly indicate that the proposed method can effectively reconstruct undersampled images. Even under high acceleration, the network still can recover texture details and reduce artifacts.

Free-breathing MR elastography of the lungs: An in vivo study

PURPOSE

Lung stiffness alters with many diseases; therefore, several MR elastography (MRE) studies were performed earlier to investigate the stiffness of the right lung during breathhold at residual volume and total lung capacity. The aims of this study were 1) to estimate shear stiffness of the lungs using MRE under free breathing and demonstrate the measurements' repeatability and reproducibility, and 2) to compare lung stiffness under free breathing to breathhold and as a function of age and gender.

METHODS

Twenty-five healthy volunteers were scanned on a 1.5 Tesla MRI scanner. Spin-echo dual-density spiral and a spin-echo EPI MRE sequences were used to measure shear stiffness of the lungs during free breathing and breathhold at midpoint of tidal volume, respectively. Concordance correlation coefficient and Bland-Altman analyses were performed to determine the repeatability and reproducibility of the spin-echo dual-density spiral-derived shear stiffness. Repeated measures analyses of variances were used to investigate differences in shear stiffness between spin-echo dual-density spiral and spin-echo EPI, right and left lungs, males and females, and different age groups.

RESULTS

Free-breathing MRE sequence was highly repeatable and reproducible (concordance correlation coefficient > 0.86 for both lungs). Lung stiffness was significantly lower in breathhold than in free breathing (P < .001), which can be attributed to potential stress relaxation of lung parenchyma or breathhold inconsistencies. However, there was no significant difference between different age groups (P = .08). The left lung showed slightly higher stiffness values than the right lung (P = .14). There is no significant difference in lung stiffness between genders.

CONCLUSION

This study demonstrated the feasibility of free-breathing lung MRE with excellent repeatability and reproducibility. Stiffness changes with age and during the respiratory cycle. However, gender does not influence lungs stiffness.

Magnetic Resonance Imaging Basics

In this chapter, we will discuss the basic principles of signal generation and image formation in magnetic resonance imaging (MRI). We will start with a description of nuclear magnetic resonance (NMR) phenomenon and then gradually arrive at the mathematical expressions for MRI signal in spatial domain and k-space domain. Then we describe the image reconstruction methods typically used in MRI, the signal-to-noise ratio calculation methods in MRI, and common MR image formats. A key focus of the contents of this chapter is on the formation of phase images in MRI. We do not intend to provide a comprehensive overview of MRI. Instead, the contents are intended for readers interested in performing research in electromagnetic properties mapping using MRI. Nevertheless, considering the generality of the contents, any reader interested in developing a quick understanding of the physical and mathematical background of MRI can find this chapter helpful.

A review on deep learning MRI reconstruction without fully sampled k-space

BACKGROUND

Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable.

MAIN TEXT

In this review, we first introduce the forward model of MRI as a classic inverse problem, and briefly discuss the connection of traditional iterative methods to deep learning. Next, we will explain how to train reconstruction network without fully sampled data from the perspective of obtaining prior information.

CONCLUSION

Although the reviewed methods are used for MRI reconstruction, they can also be extended to other areas where ground-truth is not available. Furthermore, we may anticipate that the combination of traditional methods and deep learning will produce better reconstruction results.

Compressed Sensing in Sodium Magnetic Resonance Imaging: Techniques, Applications, and Future Prospects

Sodium (²³ Na) yields the second strongest nuclear magnetic resonance (NMR) signal in biological tissues and plays a vital role in cell physiology. Sodium magnetic resonance imaging (MRI) can provide insights into cell integrity and tissue viability relative to pathologies without significant anatomical alternations, and thus it is considered to be a potential surrogate biomarker that provides complementary information for standard hydrogen (¹ H) MRI in a noninvasive and quantitative manner. However, sodium MRI suffers from a relatively low signal-to-noise ratio and long acquisition times due to its relatively low NMR sensitivity. Compressed sensing-based (CS-based) methods have been shown to accelerate sodium imaging and/or improve sodium image quality significantly. In this manuscript, the basic concepts of CS and how CS might be applied to improve sodium MRI are described, and the historical milestones of CS-based sodium MRI are briefly presented. Representative advanced techniques and evaluation methods are discussed in detail, followed by an expose of clinical applications in multiple anatomical regions and diseases as well as thoughts and suggestions on potential future research prospects of CS in sodium MRI. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging

Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.

Highly accelerated magnetic resonance acoustic radiation force imaging for in vivo transcranial ultrasound focus localization: A comparison of three reconstruction methods

Magnetic resonance acoustic radiation force imaging (MR-ARFI) is a promising tool for transcranial neurosurgery planning and monitoring. However, the ultrasound dose during ARFI is quite high due to the high intensity required and the repetitive ultrasound sonication. To reduce the ultrasound deposition and prevent unwanted neurological effects, undersampling in k-space data acquisition is adopted in the current study. Three reconstruction methods, keyhole, k-space hybrid and temporal differences (TED) compressed sensing, the latter two of which were initially proposed for MR thermometry, were applied to the in vivo transcranial focus localization based on MR-ARFI data in a retrospective way. The accuracies of the three methods were compared with the results from the fully sampled data as reference. The results showed that the keyhole method tended to smooth the displacement map and underestimate the peak displacement. The K-space hybrid method was better at recovering the displacement map and was robust to the undersampling pattern, while the TED method was more time efficient under a higher image resolution. For an image of a lower resolution, the K-space hybrid and TED methods were comparable in terms of accuracy when a high undersampling rate was applied. The results reported here facilitate the choice of appropriate undersampled reconstruction methods in transcranial focal localization based on MR-ARFI.

Advances in spiral fMRI: A high-resolution study with single-shot acquisition

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination.

Dynamic contrast-enhanced magnetic resonance imaging of the lung reveals important pathobiology in idiopathic pulmonary fibrosis

Introduction

Evidence suggests that abnormalities occur in the lung microvasculature in idiopathic pulmonary fibrosis (IPF). We hypothesised that dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) could detect alterations in permeability, perfusion and extracellular extravascular volume in IPF, thus providing in vivo regional functional information not otherwise available.

Methods

Healthy controls and IPF subjects underwent DCE-MRI of the thorax using a dynamic volumetric radial sampling sequence and administration of gadoterate meglumine at a dose of 0.1 mmol·kg⁻¹ at 2 mL·s⁻¹. Model-free analysis of signal intensity versus time curves in regions of interest from a lower, middle and upper axial plane, a posterior coronal plane and the whole lung yielded parameters reflective of perfusion and permeability (peak enhancement and rate of contrast arrival (k_washin)) and the extracellular extravascular space (rate of contrast clearance (k_washout)). These imaging parameters were compared between IPF and healthy control subjects, and between fast/slow IPF progressors.

Results

IPF subjects (n=16, 56% male, age (range) 67.5 (60–79) years) had significantly reduced peak enhancement and slower k_washin in all measured lung regions compared to the healthy volunteers (n=17, 65% male, age (range) 58 (51–63) years) on unadjusted analyses consistent with microvascular alterations. k_washout, as a measure of the extravascular extracellular space, was significantly slower in the lower lung and posterior coronal regions in the IPF subjects consistent with an increased extravascular extracellular space. All estimates were attenuated after adjusting for age. Similar trends were observed, but only the associations with k_washin in certain lung regions remained statistically significant. Among IPF subjects, k_washout rates nearly perfectly discriminated between those with rapidly progressive disease versus those with stable/slowly progressive disease.

Conclusions

DCE-MRI detects changes in the microvasculature and extravascular extracellular space in IPF, thus providing in vivo regional functional information.

Dynamic contrast-enhanced MRI demonstrates important in vivo lung regional microvascular and extravascular extracellular differences between IPF patients and healthy controls. These results signify IPF pathobiology and may have prognostic significance.https://bit.ly/3l14SWM

Aliasing-free reduced field-of-view parallel imaging

PURPOSE

To reconstruct aliasing-free full field-of-view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods.

THEORY AND METHODS

The sensitivity encoding method (SENSE) was extended to the Soft-SENSE models supporting multiple-set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple-set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft-SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction.

RESULTS

Soft-SENSE using multiple-set full FOV CSM and PSF successfully reconstruct aliasing-free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high-resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem.

CONCLUSION

The proposed Soft-SENSE using full FOV CSM and PSF could reconstruct aliasing-free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction.

Accelerated image reconstruction with separable Hankel regularization in parallel MRI

Magnetic resonance imaging has been widely adopted in clinical diagnose, however, it suffers from relatively long data acquisition time. Sparse sampling with reconstruction can speed up the data acquisition duration. As the state-of-the-art magnetic resonance imaging methods, the structured low rank reconstruction approaches embrace the advantage of holding low reconstruction errors and permit flexible undersampling patterns. However, this type of method demands intensive computations and high memory consumptions, thereby resulting in a lengthy reconstruction time. In this work, we proposed a separable Hankel low rank reconstruction method to explore the low rankness of each row and each column. Furthermore, we utilized the self-consistence and conjugate symmetry property of k-space data. The experimental results demonstrated that the proposed method outperforms the state-of-the-art approaches in terms of lower reconstruction errors and better detail preservation. Besides, the proposed method requires much less computation and memory consumption.Clinical Relevance- Parallel imaging, image reconstruction, Hankel low-rank.

Improving Nonlinear Interpolation of K-Space Data Using Semi-Supervised Learning and Autoregressive Model

Parallel magnetic resonance imaging (pMRI) accelerates data acquisition by undersampling k-space through an array of receiver coils. Finding accurate relationships between acquired and missing k-space data determines the interpolation performance and reconstruction quality. Autocalibration signals (ACS) are generally used to learn the interpolation coefficients for reconstructing the missing k-space data. Based on the estimation-approximation error analysis in machine learning, increasing training data size can reduce estimation error and therefore enhance generalization ability of the interpolator, but scanning time will be longer if more ACS data are acquired. We propose to augment training data using unacquired and acquired data outside of ACS region through semi-supervised learning idea and autoregressive model. Local neighbor unacquired k-space data can be used for training tasks and reducing the generalization error. Experimental results show that the proposed method outperforms the conventional methods by suppressing noise and aliasing artifacts.

Parallel MRI Reconstruction Using Broad Learning System

As an inverse problem, parallel magnetic resonance imaging (pMRI) reconstruction accelerates imaging speed by interpolating missing k-space data from a group of phased-array coils. Deep learning methods have been used for improving pMRI reconstruction quality in recent years. However, deep learning approaches need a large amount of training data that are acquired from different hardware configurations and anatomical areas. Data distributions may be different between training data and testing data, and a long-time training is needed. In this work, we proposed a broad learning system based parallel MRI reconstruction that exploits approximation capability of one-layer neural network through broadening network structure with expanded nodes. Experimental results show that the proposed method is able to suppress noise in compared to the conventional pMRI reconstruction.

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.

Accelerated Stack-of-Spirals Free-Breathing Three-Dimensional Ultrashort Echo Time Lung Magnetic Resonance Imaging: A Feasibility Study in Patients With Breast Cancer

Purpose

To investigate the clinical feasibility of accelerated free-breathing stack-of-spirals (spiral) three-dimensional (3D) ultrashort echo time (UTE) lung magnetic resonance imaging (MRI) using iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space (SPIRiT) algorithm in patients with breast cancer.

Methods

The institutional review board approved this prospective study and patients’ informed consents were obtained. Between June and August 2018, 29 female patients with breast cancer underwent 3-T MRI including accelerated free-breathing spiral 3D UTE (0.98-mm isotropic spatial resolution; echo time, 0.05 msec) of the lungs and thin-section chest computed tomography (CT). Two radiologists evaluated the image quality and pulmonary nodules on MRI were assessed and compared, CT as a reference.

Results

The pulmonary vessels and bronchi were visible consistently up to the sub-sub-segmental and sub-segmental branch levels, respectively, on accelerated spiral 3D UTE. The overall image quality was evaluated as good and excellent for 70.7% of accelerated spiral 3D UTE images (reviewer [R]1, 72.4% [21/29]; R2, 69.0% [20/29]) and acceptable for 20.7% (both R1 and R2, 20.7% [6/29]). Five patients on CT revealed 141 pulmonary metastatic nodules (5.3 ± 2.6 mm); the overall nodule detection rate of accelerated spiral 3D UTE was sensitivity of 90.8% (128/141), accuracy of 87.7%, and positive predictive value of 96.2%. In the Bland-Altman plot analysis comparing nodule size between CT and MRI, 132/141 nodules (93.6%) were inside the limits of agreement.

Conclusion

Accelerated free-breathing spiral 3D UTE using the SPIRiT algorithm could be a potential alternative to CT for oncology patients.

Prior ensemble learning : Theory and application to MR image priors

PURPOSE

Compressed sensing (CS) reduces the measurement time of magnetic resonance (MR) imaging, where the use of regularizers or image priors are key techniques to boost reconstruction precision. The optimal prior generally depends on the subject and the hand-building of priors is hard. A methodology of combining priors to create a better one would be useful for various forms of image processing that use image priors.

METHODS

We propose a theory, called prior ensemble learning (PEL), which combines many weak priors (not limited to images) efficiently and approximates the posterior mean (PM) estimate, which is Bayes optimal for minimizing the mean squared error (MSE). The way of combining priors is changed from that of an exponential family to a mixture family. We applied PEL to an undersampled (10%) multicoil MR image reconstruction task.

RESULTS

We demonstrated that PEL could combine 136 image priors (norm-based priors such as total variation (TV) and wavelets with various regularization coefficient (RC) values) from only two training samples and that it was superior to the CS-SENSE-based method in terms of the MSE of the reconstructed image. The resulting combining weights were sparse (18% of the weak priors remained), as expected.

CONCLUSION

By the theory, the PM estimator was decomposed into the sparse weighted sum of each weak prior's PM estimator, and the exponential computational complexity for RCs was reduced to polynomial order w.r.t. the number of weak priors. PEL is feasible and effective for a practical MR image reconstruction task.

Pulmonary Imaging of Immunocompromised Patients during Hematopoietic Stem Cell Transplantation using Non-Contrast-Enhanced Three-Dimensional Ultrashort Echo Time (3D-UTE) MRI

PURPOSE

 To evaluate the feasibility of non-contrast-enhanced three-dimensional ultrashort echo time (3D-UTE) MRI for pulmonary imaging in immunocompromised patients during hematopoietic stem cell transplantation (HSCT).

METHODS

 MRI was performed using a stack-of-spirals 3D-UTE sequence (slice thickness: 2.34mm; matrix: 256 × 256; acquisition time: 12.7-17.6 seconds) enabling imaging of the entire thorax within single breath-holds. Patients underwent MRI before HSCT initiation, in the case of periprocedural pneumonia, before discharge, and in the case of re-hospitalization. Two readers separately assessed the images regarding presence of pleural effusions, ground glass opacities (GGO), and consolidations on a per lung basis. A T2-weighted (T2w) multi-shot Turbo Spin Echo sequence (BLADE) was acquired in coronal orientation during breath-hold (slice thickness: 6.00mm; matrix: 320 × 320; acquisition time: 3.1-5.5 min) and read on a per lesion basis. Low-dose CT scans in inspiration were used as reference and were read on a per lung basis. Only scans performed within a maximum of three days were included in the inter-method analyses. Interrater agreement, sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of 3D-UTE MRI were calculated.

RESULTS

 67 MRI scans of 28 patients were acquired. A reference CT examination was available for 33 scans of 23 patients. 3D-UTE MRI showed high sensitivity and specificity regarding pleural effusions (n = 6; sensitivity, 92 %; specificity, 100 %) and consolidations (n = 22; sensitivity 98 %, specificity, 86 %). Diagnostic performance was lower for GGO (n = 9; sensitivity, 63 %; specificity, 84 %). Accuracy rates were high (pleural effusions, 98 %; GGO, 79 %; consolidations 94 %). Interrater agreement was substantial for consolidations and pleural effusions (κ = 0.69-0.82) and moderate for GGO (κ = 0.54). Compared to T2w imaging, 3D-UTE MRI depicted the assessed pathologies with at least equivalent quality and was rated superior regarding consolidations and GGO in ~50 %.

CONCLUSION

 Non-contrast 3D-UTE MRI enables radiation-free assessment of typical pulmonary complications during HSCT procedure within a single breath-hold. Yet, CT was found to be superior regarding the identification of pure GGO changes.

KEY POINTS

  · 3D-UTE MRI of the thorax can be acquired within a single breath-hold.. · 3D-UTE MRI provides diagnostic imaging of pulmonary consolidations and pleural effusions.. · 3D-UTE sequences improve detection rates of ground glass opacities on pulmonary MRI.. · 3D-UTE MRI depicts pulmonary pathologies at least equivalent to T2-weighted Blade sequence..

CITATION FORMAT

· Metz C, Böckle D, Heidenreich JF et al. Pulmonary Imaging of Immunocompromised Patients during Hematopoietic Stem Cell Transplantation using Non-Contrast-Enhanced Three-Dimensional Ultrashort Echo Time (3D-UTE) MRI. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1535-2341.

Free-breathing simultaneous T1 , T2 , and T2∗ quantification in the myocardium

PURPOSE

To implement a free-breathing sequence for simultaneous quantification of T 1 , T 2 , and T 2 ∗ for comprehensive tissue characterization of the myocardium in a single scan using a multi-gradient-echo readout with saturation and T 2 preparation pulses.

METHODS

In the proposed Saturation And T 2 -prepared Relaxometry with Navigator-gating (SATURN) technique, a series of multi-gradient-echo (GRE) images with different magnetization preparations was acquired during free breathing. A total of 35 images were acquired in 26.5 ± 14.9 seconds using multiple saturation times and T 2 preparation durations and with imaging at 5 echo times. Bloch simulations and phantom experiments were used to validate a 5-parameter fit model for accurate relaxometry. Free-breathing simultaneous T 1 , T 2 , and T 2 ∗ measurements were performed in 10 healthy volunteers and 2 patients using SATURN at 3T and quantitatively compared to conventional single-parameter methods such as SASHA for T 1 , T 2 -prepared bSSFP, and multi-GRE for T 2 ∗ .

RESULTS

Simulations confirmed accurate fitting with the 5-parameter model. Phantom measurements showed good agreement with the reference methods in the relevant range for in vivo measurements. Compared to single-parameter methods comparable accuracy was achieved. SATURN produced in vivo parameter maps that were visually comparable to single-parameter methods. No significant difference between T 1 , T 2 , and T 2 ∗ times acquired with SATURN and single-parameter methods was shown in quantitative measurements (SATURN T 1 = 1573 ± 86 ms , T 2 = 33.2 ± 3.6 ms , T 2 ∗ = 25.3 ± 6.1 ms ; conventional methods: T 1 = 1544 ± 107 ms , T 2 = 33.2 ± 3.6 ms , T 2 ∗ = 23.8 ± 5.5 ms ; P > . 2 ) CONCLUSION: SATURN enables simultaneous quantification of T 1 , T 2 , and T 2 ∗ in the myocardium for comprehensive tissue characterization with co-registered maps, in a single scan with good agreement to single-parameter methods.

High-dimensional fast convolutional framework (HICU) for calibrationless MRI

PURPOSE

To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging.

THEORY AND METHODS

Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast convolutional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points. For demonstration, HICU is applied to 6 2D T2-weighted brain, 7 2D cardiac cine, 5 3D knee, and 1 multi-shot diffusion weighted imaging (MSDWI) datasets.

RESULTS

The 2D imaging results show that HICU can offer 1-2 orders of magnitude computation speedup compared to other Cl-MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE-based compressed sensing methods with up to 3 dB improvement in signal-to-error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi-shot echo-planar imaging application.

CONCLUSIONS

The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.

Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization

Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning. However, most of these methods rely on estimates of the coil sensitivity profiles, or on calibration data for estimating model parameters. Prior work has shown that these methods degrade in performance when the quality of these estimators are poor or when the scan parameters differ from the training conditions. Here we introduce Deep J-Sense as a deep learning approach that builds on unrolled alternating minimization and increases robustness: our algorithm refines both the magnetization (image) kernel and the coil sensitivity maps. Experimental results on a subset of the knee fastMRI dataset show that this increases reconstruction performance and provides a significant degree of robustness to varying acceleration factors and calibration region sizes.

Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults

Background Obtaining ventricular volumetry and mass is key to most cardiac MRI but challenged by long multibreath-hold acquisitions. Purpose To assess the image quality and performance of a highly accelerated, free-breathing, two-dimensional cine cardiac MRI sequence incorporating deep learning (DL) reconstruction compared with reference standard balanced steady-state free precession (bSSFP). Materials and Methods A DL algorithm was developed to reconstruct custom 12-fold accelerated bSSFP cardiac MRI cine images from coil sensitivity maps using 15 iterations of separable three-dimensional convolutions and data consistency steps. The model was trained, validated, and internally tested in 10, two, and 10 adult human volunteers, respectively, based on vendor partner-supplied fully sampled bSSFP acquisitions. For prospective external clinical validation, consecutive children and young adults undergoing cardiac MRI from September through December 2019 at a single children's hospital underwent both conventional and highly accelerated short-axis bSSFP cine acquisitions in one MRI examination. Two radiologists scored overall and volumetric three-dimensional mesh image quality of all short-axis stacks on a five-point Likert scale and manually segmented endocardial and epicardial contours. Scan times and image quality were compared using the Wilcoxon rank sum test. Measurement agreement was assessed with intraclass correlation coefficient and Bland-Altman analysis. Results Fifty participants (mean age, 16 years ± 4 [standard deviation]; range, 5-30 years; 29 men) were evaluated. The mean prescribed acquisition times of accelerated scans (non-breath-held) and bSSFP (excluding breath-hold time) were 0.9 minute ± 0.3 versus 3.0 minutes ± 1.9 (P < .001). Overall and three-dimensional mesh image quality scores were, respectively, 3.8 ± 0.6 versus 4.3 ± 0.6 (P < .001) and 4.0 ± 1.0 versus 4.4 ± 0.8 (P < .001). Raters had strong agreement between all bSSFP and DL measurements, with intraclass correlation coefficients of 0.76 to 0.97, near-zero mean differences, and narrow limits of agreement. Conclusion With slightly lower image quality yet much faster speed, deep learning reconstruction may allow substantially shorter acquisition times of cardiac MRI compared with conventional balanced steady-state free precession MRI performed for ventricular volumetry. © RSNA, 2021 Online supplemental material is available for this article.

Autocalibrating segmented diffusion-weighted acquisitions

PURPOSE

Segmented echo-planar imaging enables high-resolution diffusion-weighted imaging (DWI). However, phase differences between segments can lead to severe artifacts. This work investigates an algorithm to enable reconstruction of interleaved segmented acquisitions without the need of additional calibration or navigator measurements.

METHODS

A parallel imaging algorithm is presented that jointly reconstructs all segments of one DWI frame maintaining their phase information. Therefore, the algorithm allows for an iterative improvement of the phase estimates included in the joint reconstruction. Given a limited number of interleaves, the initial-phase estimates can be calculated by a traditional parallel-imaging reconstruction, using the unweighted scan of the DWI measurement as a reference.

RESULTS

Reconstruction of phantom data and g-factor simulations show substantial improvement (up to 93% reduction in root mean square error) compared with a generalized auto-calibrating partially parallel-acquisition reconstruction. In vivo experiments show robust reconstruction outcomes in critical imaging situations, including small numbers of receiver channels or low signal-to-noise ratio.

CONCLUSION

An algorithm for the robust reconstruction of segmented DWI data is presented. The method requires neither navigator nor calibration measurements; therefore, it can be applied to existing DWI data sets.

Optimized 64-channel array configurations for accelerated simultaneous multislice acquisitions in 3T cardiac MRI

PURPOSE

Three 64-channel cardiac coils with different detector array configurations were designed and constructed to evaluate acceleration capabilities in simultaneous multislice (SMS) imaging for 3T cardiac MRI.

METHODS

Three 64-channel coil array configurations obtained from a simulation-guided design approach were constructed and systematically evaluated regarding their encoding capabilities for accelerated SMS cardiac acquisitions at 3T. Array configuration A_Uni-sized consists of uniformly distributed equally sized loops in an overlapped arrangement, B_Gapped uses a gapped array design with symmetrically distributed equally sized loops, and C_Dense has non-uniform loop density and size, where smaller elements were centered over the heart and larger elements were placed surrounding the target region. To isolate the anatomic variation from differences in the coil configurations, all three array coils were built with identical semi-adjustable housing segments. The arrays' performance was compared using bench-level measurements and imaging performance tests, including signal-to-noise ratio (SNR) maps, array element noise correlation, and SMS acceleration capabilities. Additionally, all cardiac array coils were evaluated on a healthy volunteer.

RESULTS

The array configuration C_Dense with the non-uniformly distributed loop density showed the best overall cardiac imaging performance in both SNR and SMS encoding power, when compared to the other constructed arrays. The diffusion weighted cardiac acquisitions on a healthy volunteer support the favorable accelerated SNR performance of this array configuration.

CONCLUSION

Our results indicate that optimized highly parallel cardiac arrays, such as the 64-channel coil with a non-uniform loop size and density improve highly accelerated SMS cardiac MRI in comparison to symmetrically distributed loop array designs.

Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times

BACKGROUND

Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters.

METHODS

In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen-Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson's correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer.

RESULTS

The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson's correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L.

CONCLUSIONS

Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.

Real-Time Multifrequency MR Elastography of the Human Brain Reveals Rapid Changes in Viscoelasticity in Response to the Valsalva Maneuver

Modulation of cerebral blood flow and vascular compliance plays an important role in the regulation of intracranial pressure (ICP) and also influences the viscoelastic properties of brain tissue. Therefore, magnetic resonance elastography (MRE), the gold standard for measuring in vivo viscoelasticity of brain tissue, is potentially sensitive to cerebral autoregulation. In this study, we developed a multifrequency MMRE technique that provides serial maps of viscoelasticity at a frame rate of nearly 6 Hz without gating, i.e., in quasi-real time (rt-MMRE). This novel method was used to monitor rapid changes in the viscoelastic properties of the brains of 17 volunteers performing the Valsalva maneuver (VM). rt-MMRE continuously sampled externally induced vibrations comprising three frequencies of 30.03, 30.91, and 31.8 Hz were over 90 s using a steady-state, spiral-readout gradient-echo sequence. Data were processed by multifrequency dual elasto-visco (MDEV) inversion to generate maps of magnitude shear modulus | G^∗| (stiffness) and loss angle φ at a frame rate of 5.4 Hz. As controls, the volunteers were examined to study the effects of breath-hold following deep inspiration and breath-hold following expiration. We observed that | G^∗| increased while φ decreased due to VM and, less markedly, due to breath-hold in inspiration. Group mean VM values showed an early overshoot of | G^∗| 2.4 ± 1.2 s after the onset of the maneuver with peak values of 6.7 ± 4.1% above baseline, followed by a continuous increase in stiffness during VM. A second overshoot of | G^∗| occurred 5.5 ± 2.0 s after the end of VM with peak values of 7.4 ± 2.8% above baseline, followed by 25-s sustained recovery until the end of image acquisition. φ was constantly reduced by approximately 2% during the entire VM without noticeable peak values. This is the first report of viscoelasticity changes in brain tissue induced by physiological maneuvers known to alter ICP and detected by clinically applicable rt-MMRE. Our results show that apnea and VM slightly alter brain properties toward a more rigid-solid behavior. Overshooting stiffening reactions seconds after onset and end of VM reveal rapid autoregulatory processes of brain tissue viscoelasticity.

Real-time dynamic vocal tract imaging using an accelerated spiral GRE sequence and low rank plus sparse reconstruction

PURPOSE

To develop a real-time dynamic vocal tract imaging method using an accelerated spiral GRE sequence and low rank plus sparse reconstruction.

METHODS

Spiral k-space sampling has high data acquisition efficiency and thus is suited for real-time dynamic imaging; further acceleration can be achieved by undersampling k-space and using a model-based reconstruction. Low rank plus sparse reconstruction is a promising method with fast computation and increased robustness to global signal changes and bulk motion, as the images are decomposed into low rank and sparse terms representing different dynamic components. However, the combination with spiral scanning has not been well studied. In this study an accelerated spiral GRE sequence was developed with an optimized low rank plus sparse reconstruction and compared with L1-SPIRiT and XD-GRASP methods. The off-resonance was also corrected using a Chebyshev approximation method to reduce blurring on a frame-by-frame basis.

RESULTS

The low rank plus sparse reconstruction method is sensitive to the weights of the low rank and sparse terms. The optimized reconstruction showed advantages over other methods with reduced aliasing and improved SNR. With the proposed method, spatial resolution of 1.3*1.3 mm² with 150 mm field-of-view (FOV) and temporal resolution of 30 frames-per-second (fps) was achieved with good image quality. Blurring was reduced using the Chebyshev approximation method.

CONCLUSION

This work studies low rank plus sparse reconstruction using the spiral trajectory and demonstrates a new method for dynamic vocal tract imaging which can benefit studies of speech disorders.

Accelerated MR spectroscopic imaging-a review of current and emerging techniques

Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.

Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.

Acceleration of Brain TOF-MRA with Compressed Sensitivity Encoding: A Multicenter Clinical Study

BACKGROUND AND PURPOSE

The clinical practice of three-dimensional TOF-MRA, despite its capability in brain artery assessment, has been hampered by the relatively long scan time, while recent developments in fast imaging techniques with random undersampling has shed light on an improved balance between image quality and imaging speed. Our aim was to evaluate the effectiveness of TOF-MRA accelerated by compressed sensitivity encoding and to identify the optimal acceleration factors for routine clinical use.

MATERIALS AND METHODS

One hundred subjects, enrolled at 5 centers, underwent 8 brain TOF-MRA sequences: 5 sequences using compressed sensitivity encoding with acceleration factors of 2, 4, 6, 8, and 10 (CS2, CS4, CS6, CS8, and CS10), 2 using sensitivity encoding with factors of 2 and 4 (SF2 and SF4), and 1 without acceleration as a reference sequence (RS). Five large arteries, 6 medium arteries, and 6 small arteries were evaluated quantitatively (reconstructed signal intensity, structural similarity, contrast ratio) and qualitatively (scores on arteries, artifacts, overall image quality, and diagnostic confidence for aneurysm and stenosis). Comparisons were performed among the 8 sequences.

RESULTS

The quantitative measurements showed that the reconstructed signal intensities of the assessed arteries and the structural similarity consistently decreased as the compressed sensitivity encoding acceleration factor increased, and no significant difference was found for the contrast ratios in pair-wise comparisons among SF2, CS2, and CS4. Qualitative evaluations showed no significant difference in pair-wise comparisons among RS, SF2, and CS2 (P > .05). The visualization of all the assessed arteries was acceptable for CS2 and CS4, while 2 small arteries in images of CS6 were not reliably displayed, and the visualization of large arteries was acceptable in images of CS8 and CS10.

CONCLUSIONS

CS4 is recommended for routine brain TOF-MRA with balanced image quality and acquisition time; CS6, for examinations when small arteries are not evaluated; and CS10, for fast visualization of large arteries.

Highly accelerated parallel MRI using wave encoding and virtual conjugate coils

PURPOSE

To propose a novel model incorporating virtual conjugate coil (VCC) reconstruction and wave encoding (Wave) for improved parallel MRI.

THEORY AND METHODS

A novel model (VCC-Wave) incorporating VCC and Wave is proposed. The correlation matrix of the encoding operator is introduced to analyze the encoding capability. In addition, simulation experiments are conducted to gain insights into VCC-Wave. In vivo experiments are performed to compare VCC-Wave with alternative methods.

RESULTS

The correlation matrix and the simulation experiments show that the proposed VCC-Wave can utilize more priors of Wave under the VCC framework. In vivo experiments show that the proposed VCC-Wave can achieve good image quality at a 6-fold acceleration in high-resolution and high-bandwidth cases, indicating an improvement over the original Wave technique.

CONCLUSION

The proposed VCC-Wave can not only combine the advantages of both the VCC and Wave but also exploit more priors of Wave under the VCC framework. The improvement in VCC-Wave alleviates the limitation of Wave in high-resolution and high-bandwidth cases.

MRIReco.jl: An MRI reconstruction framework written in Julia

PURPOSE

The aim of this work is to develop a high-performance, flexible, and easy-to-use MRI reconstruction framework using the scientific programming language Julia.

METHODS

Julia is a modern, general purpose programming language with strong features in the area of signal/image processing and numerical computing. It has a high-level syntax but still generates efficient machine code that is usually as fast as comparable C/C++ applications. In addition to the language features itself, Julia has a sophisticated package management system that makes proper modularization of functionality across different packages feasible. Our developed MRI reconstruction framework MRIReco.jl can therefore reuse existing functionality from other Julia packages and concentrate on the MRI-related parts. This includes common imaging operators and support for MRI raw data formats.

RESULTS

MRIReco.jl is a simple to use framework with a high degree of accessibility. While providing a simple-to-use interface, many of its components can easily be extended and customized. The performance of MRIReco.jl is compared to the Berkeley Advanced Reconstruction Toolbox (BART) and we show that the Julia framework achieves comparable reconstruction speed as the popular C/C++ library.

CONCLUSIONS

Modern programming languages can bridge the gap between high performance and accessible implementations. MRIReco.jl leverages this fact and contributes a promising environment for future algorithmic development in MRI reconstruction.

High spatial resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage at 3 T

PURPOSE

To develop and evaluate a high spatial resolution (1.25 × 1.25 mm² ) spiral first-pass myocardial perfusion imaging technique with whole-heart coverage at 3T, to better assess transmural differences in perfusion between the endocardium and epicardium, to quantify the myocardial ischemic burden, and to improve the detection of obstructive coronary artery disease.

METHODS

Whole-heart high-resolution spiral perfusion pulse sequences and corresponding motion-compensated reconstruction techniques for both interleaved single-slice (SS) and simultaneous multi-slice (SMS) acquisition with or without outer-volume suppression (OVS) were developed. The proposed techniques were evaluated in 34 healthy volunteers and 8 patients (55 data sets). SS and SMS images were reconstructed using motion-compensated L1-SPIRiT and SMS-Slice-L1-SPIRiT, respectively. Images were blindly graded by 2 experienced cardiologists on a 5-point scale (5, excellent; 1, poor).

RESULTS

High-quality perfusion imaging was achieved for both SS and SMS acquisitions with or without OVS. The SS technique without OVS had the highest scores (4.5 [4, 5]), which were greater than scores for SS with OVS (3.5 [3.25, 3.75], P < .05), MB = 2 without OVS (3.75 [3.25, 4], P < .05), and MB = 2 with OVS (3.75 [2.75, 4], P < .05), but significantly higher than those for MB = 3 without OVS (4 [4, 4], P = .95). SMS image quality was improved using SMS-Slice-L1-SPIRiT as compared to SMS-L1-SPIRiT (P < .05 for both reviewers).

CONCLUSION

We demonstrated the successful implementation of whole-heart spiral perfusion imaging with high resolution at 3T. Good image quality was achieved, and the SS without OVS showed the best image quality. Evaluation in patients with expected ischemic heart disease is warranted.

Parallel magnetic resonance imaging acceleration with a hybrid sensing approach

In magnetic resonance imaging (MRI), the scan time for acquiring an image is relatively long, resulting in patient uncomfortable and error artifacts. Fortunately, the compressed sensing (CS) and parallel magnetic resonance imaging (pMRI) can reduce the scan time of the MRI without significantly compromising the quality of the images. It has been found that the combination of pMRI and CS can better improve the image reconstruction, which will accelerate the speed of MRI acquisition because the number of measurements is much smaller than that by pMRI. In this paper, we propose combining a combined CS method and pMRI for better accelerating the MRI acquisition. In the combined CS method, the under-sampled data of the K-space is performed by taking both regular sampling and traditional random under-sampling approaches. MRI image reconstruction is then performed by using nonlinear conjugate gradient optimization. The performance of the proposed method is simulated and evaluated using the reconstruction error measure, the universal image quality Q-index, and the peak signal-to-noise ratio (PSNR). The numerical simulations confirmed that, the average error, the Q index, and the PSNR ratio of the appointed scheme are remarkably improved up to 59, 63, and 39% respectively as compared to the traditional scheme. For the first time, instead of using highly computational approaches, a simple and efficient combination of CS and pMRI is proposed for the better MRI reconstruction. These findings are very meaningful for reducing the imaging time of MRI systems.

Highly accelerated subtractive femoral non-contrast-enhanced MRA using compressed sensing with k-space subtraction, phase and intensity correction

PURPOSE

To develop an improved reconstruction method, k-space subtraction with phase and intensity correction (KSPIC), for highly accelerated, subtractive, non-contrast-enhanced MRA.

METHODS

The KSPIC method is based on k-space subtraction of complex raw data. It applies a phase-correction procedure to restore the polarity of negative signals caused by subtraction and an intensity-correction procedure to improve background suppression and thereby sparsity. Ten retrospectively undersampled data sets and 10 groups of prospectively undersampled data sets were acquired in 12 healthy volunteers. The performance of KSPIC was compared with another improved reconstruction based on combined magnitude subtraction, as well as with conventional k-space subtraction reconstruction and magnitude subtraction reconstruction, both using quantitative metrics and using subjective quality scoring.

RESULTS

In the quantitative evaluation, KSPIC had the best performance in terms of peak SNR, structural similarity index measure, contrast-to-noise ratio of artery-to-background and sharpness, especially at high acceleration factors. The KSPIC method also had the highest subjective scores for all acceleration factors in terms of vessel delineation, image noise and artifact, and background contamination. The acquisition can be accelerated by a factor of 20 without significant decreases of subjective scores. The optimal size of the phase-correction region was found to be 12-20 pixels in this study.

CONCLUSION

Compared with combined magnitude subtraction and conventional reconstructions, KSPIC has the best performance in all of the quantitative and qualitative measurements, permitting good image quality to be maintained up to higher accelerations. The KSPIC method has the potential to further reduce the acquisition time of subtractive MRA for clinical examinations.

Variable flip angle echo planar time-resolved imaging (vFA-EPTI) for fast high-resolution gradient echo myelin water imaging

Myelin water imaging techniques based on multi-compartment relaxometry have been developed as an important tool to measure myelin concentration in vivo, but are limited by the long scan time of multi-contrast multi-echo acquisition. In this work, a fast imaging technique, termed variable flip angle Echo Planar Time-Resolved Imaging (vFA-EPTI), is developed to acquire multi-echo and multi-flip-angle gradient-echo data with significantly reduced acquisition time, providing rich information for multi-compartment analysis of gradient-echo myelin water imaging (GRE-MWI). The proposed vFA-EPTI method achieved 26 folds acceleration with good accuracy by utilizing an efficient continuous readout, optimized spatiotemporal encoding across echoes and flip angles, as well as a joint subspace reconstruction. An approach to estimate off-resonance field changes between different flip-angle acquisitions was also developed to ensure high-quality joint reconstruction across flip angles. The accuracy of myelin water fraction (MWF) estimate under high acceleration was first validated by a retrospective undersampling experiment using a lengthy fully-sampled data as reference. Prospective experiments were then performed where whole-brain MWF and multi-compartment quantitative maps were obtained in 5 min at 1.5 mm isotropic resolution and 24 min at 1 mm isotropic resolution at 3T. Additionally, ultra-high resolution data at 600 μm isotropic resolution were acquired at 7T, which show detailed structures within the cortex such as the line of Gennari, demonstrating the ability of the proposed method for submillimeter GRE-MWI that can be used to study cortical myeloarchitecture in vivo.

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

PURPOSE

To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC).

METHODS

APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment.

RESULTS

Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm³ isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results.

CONCLUSION

Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.

Improved simultaneous multislice cardiac MRI using readout concatenated k-space SPIRiT (ROCK-SPIRiT)

PURPOSE

To develop and evaluate a simultaneous multislice (SMS) reconstruction technique that provides noise reduction and leakage blocking for highly accelerated cardiac MRI.

METHODS

ReadOut Concatenated k-space SPIRiT (ROCK-SPIRiT) uses the concept of readout concatenation in image domain to represent SMS encoding, and performs coil self-consistency as in SPIRiT-type reconstruction in an extended k-space, while allowing regularization for further denoising. The proposed method is implemented with and without regularization, and validated on retrospectively SMS-accelerated cine imaging with three-fold SMS and two-fold in-plane acceleration. ROCK-SPIRiT is compared with two leakage-blocking SMS reconstruction methods: readout-SENSE-GRAPPA and split slice-GRAPPA. Further evaluation and comparisons are performed using prospectively SMS-accelerated cine imaging.

RESULTS

Results on retrospectively three-fold SMS and two-fold in-plane accelerated cine imaging show that ROCK-SPIRiT without regularization significantly improves on existing methods in terms of PSNR (readout-SENSE-GRAPPA: 33.5 ± 3.2, split slice-GRAPPA: 34.1 ± 3.8, ROCK-SPIRiT: 35.0 ± 3.3) and SSIM (readout-SENSE-GRAPPA: 84.4 ± 8.9, split slice-GRAPPA: 85.0 ± 8.9, ROCK-SPIRiT: 88.2 ± 6.6 [in percentage]). Regularized ROCK-SPIRiT significantly outperforms all methods, as characterized by these quantitative metrics (PSNR: 37.6 ± 3.8, SSIM: 94.2 ± 4.1 [in percentage]). The prospectively five-fold SMS and two-fold in-plane accelerated data show that ROCK-SPIRiT and regularized ROCK-SPIRiT have visually improved image quality compared with existing methods.

CONCLUSION

The proposed ROCK-SPIRiT technique reduces noise and interslice leakage in accelerated SMS cardiac cine MRI, improving on existing methods both quantitatively and qualitatively.

Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework

PURPOSE

To jointly reconstruct highly undersampled multicontrast two-dimensional (2D) datasets through a low-rank Hankel tensor completion framework.

METHODS

A multicontrast Hankel tensor completion (MC-HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k-space datasets into a single block-wise Hankel tensor. Subsequent low-rank tensor approximation via higher-order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k-space kernels. The missing k-space data are then recovered by iteratively performing such low-rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T₁ -weighted, T₂ -weighted, fluid-attenuated inversion recovery, and T₁ -weighted-inversion recovery) of identical image geometry with random and uniform undersampling schemes.

RESULTS

The proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single-contrast SAKE (simultaneous autocalibrating and k-space estimation) and multicontrast J-LORAKS (joint parallel-imaging-low-rank matrix modeling of local k-space neighborhoods) low-rank reconstruction. Furthermore, the MC-HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k-space sampling strategy where the phase-encoding direction among different contrasts is orthogonally alternated.

CONCLUSION

The proposed MC-HTC approach presents an effective tensor completion framework to jointly reconstruct highly undersampled multicontrast 2D datasets without coil-sensitivity calibration.

A guaranteed convergence analysis for the projected fast iterative soft-thresholding algorithm in parallel MRI

Sparse sampling and parallel imaging techniques are two effective approaches to alleviate the lengthy magnetic resonance imaging (MRI) data acquisition problem. Promising data recoveries can be obtained from a few MRI samples with the help of sparse reconstruction models. To solve the optimization models, proper algorithms are indispensable. The pFISTA, a simple and efficient algorithm, has been successfully extended to parallel imaging. However, its convergence criterion is still an open question. Besides, the existing convergence criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter - step size. In this work, we provide the guaranteed convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the convergence criterion.

Fully Balanced SSFP Without an Endorectal Coil for Postimplant QA of MRI-Assisted Radiosurgery (MARS) of Prostate Cancer: A Prospective Study

PURPOSE

To investigate fully balanced steady-state free precession (bSSFP) with optimized acquisition protocols for magnetic resonance imaging (MRI)-based postimplant quality assessment of low-dose-rate (LDR) prostate brachytherapy without an endorectal coil (ERC).

METHODS AND MATERIALS

Seventeen patients at a major academic cancer center who underwent MRI-assisted radiosurgery (MARS) LDR prostate cancer brachytherapy were imaged with moderate, high, or very high spatial resolution fully bSSFP MRIs without using an ERC. Between 1 and 3 signal averages (NEX) were acquired with acceleration factors (R) between 1 and 2, with the goal of keeping scan times between 4 and 6 minutes. Acquisitions with R >1 were reconstructed with parallel imaging and compressed sensing (PICS) algorithms. Radioactive seeds were identified by 3 medical dosimetrists. Additionally, some of the MRI techniques were implemented and tested at a community hospital; 3 patients underwent MARS LDR prostate brachytherapy and were imaged without an ERC.

RESULTS

Increasing the in-plane spatial resolution mitigated partial volume artifacts and improved overall seed and seed marker visualization at the expense of reduced signal-to-noise ratio (SNR). The reduced SNR as a result of imaging at higher spatial resolution and without an ERC was partially compensated for by the multi-NEX acquisitions enabled by PICS. Resultant image quality was superior to the current clinical standard. All 3 dosimetrists achieved near-perfect precision and recall for seed identification in the 17 patients. The 3 postimplant MRIs acquired at the community hospital were sufficient to identify 208 out of 211 seeds implanted without reference to computed tomography (CT).

CONCLUSIONS

Acquiring postimplant prostate brachytherapy MRI without an ERC has several advantages including better patient tolerance, lower costs, higher clinical throughput, and widespread access to precision LDR prostate brachytherapy. This prospective study confirms that the use of an ERC can be circumvented with fully bSSFP and advanced MRI scan techniques in a major academic cancer center and community hospital, potentially enabling postimplant assessment of MARS LDR prostate brachytherapy without CT.

RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors

PURPOSE

To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning.

METHODS

An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation.

RESULTS

The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts.

CONCLUSION

A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.

A 4-minute solution for submillimeter whole-brain T1ρ quantification

PURPOSE

To develop a robust, accurate, and accelerated T_1ρ quantification solution for submillimeter in vivo whole-brain imaging.

METHODS

A multislice T_1ρ mapping solution (MS-T_1ρ ) was developed based on a two-acquisition scheme using turbo spin echo with RF cycling to allow for whole-brain coverage with 0.8-mm in-plane resolution. A compressed sensing-based fast imaging method, SCOPE, was used to accelerate the MS-T_1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS-T_1ρ by comparing the T_1ρ value obtained using MS-T_1ρ with the reference value obtained using the standard single-slice T_1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS-T_1ρ .

RESULTS

In the phantom study, the T_1ρ values obtained with MS-T_1ρ were in good agreement with the reference T_1ρ values (R² = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T_1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T_1ρ values between MS-T_1ρ acquisitions and fully sampled acquisitions for all brain tissues (p-value > .05). In the intraclass correlation coefficient and Bland-Altman analyses, the accelerated T_1ρ measurements show moderate to good agreement to the fully sampled reference values.

CONCLUSION

The proposed MS-T_1ρ solution allows for high-resolution whole-brain T_1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases.

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an "end-to-end" reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10-5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training

Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network - a cascade of convolutional and data consistency layers. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available for the input types of interest, or when the amount of labels is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training schemes with pixel-wise loss.

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction

PURPOSE

To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.

METHODS

We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as l 1 -ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R = 12) with respect to standard image quality metrics as well as automatic deep learning-based segmentations of left ventricular volumes. Feasibility of DL-ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice.

RESULTS

The (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to l 1 -ESPIRiT reconstructions with respect to standard image quality metrics (P < .001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from l 1 -ESPIRiT images.

CONCLUSIONS

DL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.