Constrained Reconstruction Research Articles

Radial simultaneous multi-slice CAIPI for ungated myocardial perfusion

ObjectiveSimultaneous multi-slice (SMS) imaging is a slice acceleration technique that acquires multiple slices in the same time as a single slice. Radial controlled aliasing in parallel imaging results in higher acceleration (radial CAIPIRINHA or CAIPI) is a promising SMS method with less severe slice aliasing artifacts as compared to its Cartesian counterpart. Here we use radial CAIPI with data undersampling and constrained reconstruction to improve the utility of ungated cardiac perfusion acquisitions. We test the proposed framework with a traditional saturation recovery fast low-angle shot (turboFLASH) sequence and also without saturation recovery as a steady-state spoiled gradient echo (SPGR) sequence on animal and human studies. MethodsSimulations and phantom studies were performed for both the turboFLASH and the SPGR radial CAIPI methods. Ungated undersampled golden ratio radial CAIPI data with saturation recovery were acquired in 8 dogs and 2 human subjects. The CAIPI data without saturation pulses were acquired in 4 human subjects. For both methods, slice acceleration factors of two and three were used. A new spatio-temporal reconstruction using total variation and patch-based low rank constraints was used to jointly reconstruct the multi-slice multi-coil images. ResultsPhantom scans and computer simulations showed that ungated SPGR generally provides better contrast to noise ratio (CNR) than the saturation recovery sequence if the saturation recovery time is less than 100ms. Both of the ungated radial CAIPI methods demonstrated promising image quality in terms of preserving dynamics of the contrast agent and maintaining anatomical structures, even with three slices acquired simultaneously. ConclusionUngated simultaneous multi-slice acquisitions with either a saturation recovery turboFLASH sequence or a steady-state gradient echo SPGR sequence are feasible and provide increased slice coverage without loss of temporal resolution. Compared with a sensitivity encoding (SENSE) SMS reconstruction, the constrained reconstruction method provides better image quality for undersampled radial CAIPI data.

Accelerated Time-Resolved Contrast-Enhanced Magnetic Resonance Angiography of Dural Arteriovenous Fistulas Using Highly Constrained Reconstruction of Sparse Cerebrovascular Data Sets.

Time-resolved contrast-enhanced magnetic resonance angiography (MRA) is commonly used to noninvasively characterize vascular malformations. However, the spatial and temporal resolution of current methods often compromises the clinical value of the examinations. Constrained reconstruction is a temporal spatial correlation strategy that exploits the relative sparsity of vessels in space to dramatically reduce the amount of data required to generate fast high-resolution time-resolved contrast-enhanced MRA studies. In this report, we use a novel temporal spatial acceleration method termed HYPRFlow to diagnose and classify dural arteriovenous fistulas (DAVFs). Our hypothesis is that HYPRFlow images are of adequate diagnostic image quality to delineate the arterial and venous components of DAVFs and allow correct classification using the Cognard system. Eight patients with known DAVFs underwent HYPRFlow imaging with isotropic resolution of 0.68 mm and temporal resolution of 0.75 second and 3-dimensional time-of-flight (3DTOF) MRA. The 3DTOF images and HYPRFlow images were evaluated by 2 readers and scored for arterial anatomic image quality. Digital subtraction angiography (DSA) was available for comparison in 7 subjects, and for these patients, each DAVF was classified according to the Cognard system using HYPRFlow and DSA examinations. Digital subtraction angiography was considered the reference examination or criterion standard. HYPRFlow imaging classification was concordant with DSA in all but 1 case. There was no difference in the arterial image quality scores between HYPRFlow and 3DTOF MRA (95% confidence interval). Arterial-to-venous separation was rated excellent (n = 3), good (n = 4), or poor (n = 1), and arteriovenous shunting was easily appreciated. Undersampling artifacts were reduced by using a low pass filter and did not interfere with the diagnostic quality of the examinations. HYPRFlow is a novel acquisition and reconstruction technique that exploits the relative sparsity of intracranial vessels in space to increase temporal and spatial resolution and provides accurate delineation of DAVF vasculature.

Minimum Field Strength Simulator for Proton Density Weighted MRI.

ObjectiveTo develop and evaluate a framework for simulating low-field proton-density weighted MRI acquisitions based on high-field acquisitions, which could be used to predict the minimum B0 field strength requirements for MRI techniques. This framework would be particularly useful in the evaluation of de-noising and constrained reconstruction techniques.Materials and MethodsGiven MRI raw data, lower field MRI acquisitions can be simulated based on the signal and noise scaling with field strength. Certain assumptions are imposed for the simulation and their validity is discussed. A validation experiment was performed using a standard resolution phantom imaged at 0.35 T, 1.5 T, 3 T, and 7 T. This framework was then applied to two sample proton-density weighted MRI applications that demonstrated estimation of minimum field strength requirements: real-time upper airway imaging and liver proton-density fat fraction measurement.ResultsThe phantom experiment showed good agreement between simulated and measured images. The SNR difference between simulated and measured was ≤ 8% for the 1.5T, 3T, and 7T cases which utilized scanners with the same geometry and from the same vendor. The measured SNR at 0.35T was 1.8- to 2.5-fold less than predicted likely due to unaccounted differences in the RF receive chain. The predicted minimum field strength requirements for the two sample applications were 0.2 T and 0.3 T, respectively.ConclusionsUnder certain assumptions, low-field MRI acquisitions can be simulated from high-field MRI data. This enables prediction of the minimum field strength requirements for a broad range of MRI techniques.

Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI.

Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly.

Accelerated cardiac cine MRI using locally low rank and finite difference constraints

PurposeTo evaluate the potential value of combining multiple constraints for highly accelerated cardiac cine MRI. MethodsA locally low rank (LLR) constraint and a temporal finite difference (FD) constraint were combined to reconstruct cardiac cine data from highly undersampled measurements. Retrospectively undersampled 2D Cartesian reconstructions were quantitatively evaluated against fully-sampled data using normalized root mean square error, structural similarity index (SSIM) and high frequency error norm (HFEN). This method was also applied to 2D golden-angle radial real-time imaging to facilitate single breath-hold whole-heart cine (12 short-axis slices, 9–13s single breath hold). Reconstruction was compared against state-of-the-art constrained reconstruction methods: LLR, FD, and k-t SLR. ResultsAt 10 to 60 spokes/frame, LLR+FD better preserved fine structures and depicted myocardial motion with reduced spatio-temporal blurring in comparison to existing methods. LLR yielded higher SSIM ranking than FD; FD had higher HFEN ranking than LLR. LLR+FD combined the complimentary advantages of the two, and ranked the highest in all metrics for all retrospective undersampled cases. Single breath-hold multi-slice cardiac cine with prospective undersampling was enabled with in-plane spatio-temporal resolutions of 2×2mm2 and 40ms. ConclusionHighly accelerated cardiac cine is enabled by the combination of 2D undersampling and the synergistic use of LLR and FD constraints.

Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT

Increased noise is a general concern for dual-energy material decomposition. Here, we develop an image-domain material decomposition algorithm for dual-energy CT (DECT) by incorporating an edge-preserving filter into the Local HighlY constrained backPRojection reconstruction (HYPR-LR) framework. With effective use of the non-local mean, the proposed algorithm, which is referred to as HYPR-NLM, reduces the noise in dual-energy decomposition while preserving the accuracy of quantitative measurement and spatial resolution of the material-specific dual-energy images. We demonstrate the noise reduction and resolution preservation of the algorithm with an iodine concentrate numerical phantom by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). We also show the superior performance of the HYPR-NLM over the existing methods by using two sets of cardiac perfusing imaging data. The DECT material decomposition comparison study shows that all four algorithms yield acceptable quantitative measurements of iodine concentrate. Direct matrix inversion yields the highest noise level, followed by HYPR-LR and Iter-DECT. HYPR-NLM in an iterative formulation significantly reduces image noise and the image noise is comparable to or even lower than that generated using Iter-DECT. For the HYPR-NLM method, there are marginal edge effects in the difference image, suggesting the high-frequency details are well preserved. In addition, when the search window size increases from to , there are no significant changes or marginal edge effects in the HYPR-NLM difference images. The reference drawn from the comparison study includes: (1) HYPR-NLM significantly reduces the DECT material decomposition noise while preserving quantitative measurements and high-frequency edge information, and (2) HYPR-NLM is robust with respect to parameter selection.

Abstract B51: Metabolic imaging with hyperpolarized [1-13C]-pyruvate and DCE-MRI

Abstract Purpose: To improve the quality and specificity of metabolic imaging with hyperpolarized pyruvate using prior information from dynamic contrast-enhanced MRI (DCE-MRI). Background: Dynamic nuclear polarization (DNP) and MR spectroscopic imaging of [1-13C]-pyruvate shows tremendous promise for imaging aerobic glycolysis with sensitivity, specificity, and resolution that was previously impossible. Exogeneous, enriched hyperpolarized (HP) pyruvate is injected as a bolus and traverses multiple biological barriers before it is exposed to intracellular enzymes that mediate its fate. Preservation of the spin label through chemical reaction allows visualization of pyruvate and its chemical endpoints (lactate, alanine, and carbon dioxide/bicarbonate). We have previously observed a strong correlation between tumor perfusion and the normalized area under the curve for HP lactate, the chemical endpoint associated with aerobic glycolysis. A pharmacokinetic model of HP substrate evolution that balances physiological accuracy and computational burden includes two physical pools (intravascular/extravascular) and two chemical pools (pyruvate/lactate). In the analytical solution for this model, terms for substrate extravasation and the fractional intravascular volume can be recognized as analogues for KTRANS and Vb that are derived from analysis of DCE-MRI data using the extended Tofts model. Use of these DCE-MRI parameters as prior information could eliminate unknowns in the measurement of HP agents and improve the specificity of quantitative estimates for kPL, the apparent conversion rate for HP pyruvate into lactate. Experimental Procedures: Athymic nude mice bearing orthotopic anaplastic thyroid cancer tumors were anesthetized and scanned using a 7T Biospec small animal MR system (Bruker Biospin Corp.). A dual-tuned 1H/13C volume resonator was used for anatomic imaging and for 13C excitation, and a surface coil was used for 13C detection. HP pyruvate was prepared using a HyperSense DNP system (Oxford Instruments). Dynamic 13C spectroscopic imaging data was acquired using a radial multi-band frequency encoding sequence beginning prior to intravascular administration of HP pyruvate via tail-vein catheter. DCE-MRI data was acquired after HP measurements, using a fast spoiled gradient echo sequence. DCE-MRI data was fit to the extended Tofts model on a pixel-by-pixel basis using software developed in Matlab (The Mathworks). Parametric maps for perfusion and vascular blood volume fraction were resampled to the resolution of HP 13C scans and used as priors in a constrained reconstruction algorithm that enforces consistency between undersampled 13C projection data, the kinetic model, and prior information from 1H MRI. Results: With prior information about extravasation and fractional intravascular blood volume, the constrained reconstruction algorithm needs only solve for a global parameterized vascular input function and values for chemical conversion (kPL) and signal relaxation/loss for each voxel in the imaging volume. Elimination of nuisance parameters with strongly correlated effects reduces the computational burden of constrained reconstruction and clarifies interpretation of changes in biomarkers due to disease progression or response to therapy. Conclusions: The combination of DCE-MRI with constrained reconstruction reduces the number of unknowns in quantitative metabolic imaging using HP pyruvate, eases the computational burden, and decouples correlated effects of perfusion and chemical exchange. Citation Format: James A. Bankson, Christopher M. Walker, Yunyun Chen, Stephen Y. Lai, John D. Hazle. Metabolic imaging with hyperpolarized [1-13C]-pyruvate and DCE-MRI. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B51.

L1- andl2-Norm Joint Regularization Based Sparse Signal Reconstruction Scheme

Many problems in signal processing and statistical inference involve finding sparse solution to some underdetermined linear system of equations. This is also the application condition of compressive sensing (CS) which can find the sparse solution from the measurements far less than the original signal. In this paper, we proposel1- andl2-norm joint regularization based reconstruction framework to approach the originall0-norm based sparseness-inducing constrained sparse signal reconstruction problem. Firstly, it is shown that, by employing the simple conjugate gradient algorithm, the new formulation provides an effective framework to deduce the solution as the original sparse signal reconstruction problem withl0-norm regularization item. Secondly, the upper reconstruction error limit is presented for the proposed sparse signal reconstruction framework, and it is unveiled that a smaller reconstruction error thanl1-norm relaxation approaches can be realized by using the proposed scheme in most cases. Finally, simulation results are presented to validate the proposed sparse signal reconstruction approach.

A Constrained Optimization Reconstruction Model for X-ray Computed Tomography Metal Artifact Suppression

A Constrained Optimization Reconstruction Model for X-ray Computed Tomography Metal Artifact Suppression

Kinetic Modeling and Constrained Reconstruction of Hyperpolarized [1-13C]-Pyruvate Offers Improved Metabolic Imaging of Tumors.

Hyperpolarized [1-(13)C]-pyruvate has shown tremendous promise as an agent for imaging tumor metabolism with unprecedented sensitivity and specificity. Imaging hyperpolarized substrates by magnetic resonance is unlike traditional MRI because signals are highly transient and their spatial distribution varies continuously over their observable lifetime. Therefore, new imaging approaches are needed to ensure optimal measurement under these circumstances. Constrained reconstruction algorithms can integrate prior information, including biophysical models of the substrate/target interaction, to reduce the amount of data that is required for image analysis and reconstruction. In this study, we show that metabolic MRI with hyperpolarized pyruvate is biased by tumor perfusion and present a new pharmacokinetic model for hyperpolarized substrates that accounts for these effects. The suitability of this model is confirmed by statistical comparison with alternates using data from 55 dynamic spectroscopic measurements in normal animals and murine models of anaplastic thyroid cancer, glioblastoma, and triple-negative breast cancer. The kinetic model was then integrated into a constrained reconstruction algorithm and feasibility was tested using significantly undersampled imaging data from tumor-bearing animals. Compared with naïve image reconstruction, this approach requires far fewer signal-depleting excitations and focuses analysis and reconstruction on new information that is uniquely available from hyperpolarized pyruvate and its metabolites, thus improving the reproducibility and accuracy of metabolic imaging measurements.

Musical Onset Detection Using Constrained Linear Reconstruction

This letter presents a multi-frame extension of the well-known spectral flux method for unsupervised musical onset detection. Instead of comparing only the spectral content of two frames, the proposed method takes into account a wider temporal context to evaluate the dissimilarity between a given frame and its previous frames. More specifically, the dissimilarity is measured by using the previous frames to obtain a linear reconstruction of the given frame, and then calculating the rectified, ${l_2}$ -norm reconstruction error. Evaluation on a dataset comprising 2,169 onset events of 12 instruments shows that this simple idea works fairly well. When a non-negativity constraint is imposed in the linear reconstruction, the proposed method can outperform the state-of-the-art unsupervised method SuperFlux by 2.9% in F-score. Moreover, the proposed method is particularly effective for instruments with soft onsets, such as violin, cello, and ney. The proposed method is efficient, easy to implement, and is applicable to scenarios of online onset detection.

A novel shape-similarity-based elastography technique for prostate cancer assessment.

Association between tissue stiffness alteration and pathology is well known. This has formed the basis for prostate elastography imaging techniques where images of prostate tissue mechanical properties are reconstructed. In this paper, the authors present a novel prostate elastography technique which, unlike other techniques, relies on magnitude image data only. This proposed technique works in conjunction with ultrasound or magnetic resonance imaging (MRI) imaging modalities and it requires the prostate's pre- and postdeformation images as input. It uses a constrained reconstruction method where the elastic moduli of the prostate's normal and pathological tissues are determined based on an essential subset of the tissue deformation provided by the images data. The elasticity reconstruction technique uses optimization where similarity between calculated and observed shape features of the postcompression prostate image is maximized. The method was validated with an in silico phantom study followed by studies using ultrasound and MR with tissue-mimicking phantoms. Using the proposed methods, it was observed that the maximum uncertainties of the reconstructed Young's modulus ratios of tumor to normal tissue were 15.6% and 9.7%, which were obtained from the transrectal ultrasound (TRUS) and MR tissue-mimicking phantom studies, respectively. This novel prostate elastography technique relies on prostate TRUS or MRI images that can be routinely acquired without additional imaging hardware. The phantom studies provided evidence that the proposed technique has a good potential to reconstruct prostate stiffness maps noninvasively particularly when applied in conjunction with MRI. Further studies are necessary to evaluate the technique's merits for clinical use.

Atraumatic Dislocation of Constrained Total Scapular Endoprosthesis: A Report of Two Cases.

Two patients undergoing constrained endoprosthetic reconstruction following scapulectomy for the treatment of neoplasm sustained atraumatic glenohumeral dissociation. To our knowledge, this complication had not been observed in association with anatomic-orientation constrained designs prior to 2014. Despite the improved stability associated with constrained total scapular endoprosthesis designs, every effort should be made to reconstruct the joint capsule and consideration should be given to augmentation with use of a synthetic sleeve. However, even when these measures are taken, dislocations of the constrained glenohumeral articulation may occur. Treatment of dislocations should be based on patient-specific factors, and nonoperative treatment may be appropriate. In the setting of dislocation associated with constrained liner wear, limited revision may be considered. Reassessment of periprosthetic tissue-balancing and potential augmentation should be considered.

MO‐FG‐204‐03: Using Edge‐Preserving Algorithm for Significantly Improved Image‐Domain Material Decomposition in Dual Energy CT

Purpose:To significantly improve dual energy CT (DECT) imaging by establishing a new theoretical framework of image‐domain material decomposition with incorporation of edge‐preserving techniques.Methods:The proposed algorithm, HYPR‐NLM, combines the edge‐preserving non‐local mean filter (NLM) with the HYPR‐LR (Local HighlY constrained backPRojection Reconstruction) framework. Image denoising using HYPR‐LR framework depends on the noise level of the composite image which is the average of the different energy images. For DECT, the composite image is the average of high‐ and low‐energy images. To further reduce noise, one may want to increase the window size of the filter of the HYPR‐LR, leading resolution degradation. By incorporating the NLM filtering and the HYPR‐LR framework, HYPR‐NLM reduces the boost material decomposition noise using energy information redundancies as well as the non‐local mean. We demonstrate the noise reduction and resolution preservation of the algorithm with both iodine concentration numerical phantom and clinical patient data by comparing the HYPR‐NLM algorithm to the direct matrix inversion, HYPR‐LR and iterative image‐domain material decomposition (Iter‐DECT).Results:The results show iterative material decomposition method reduces noise to the lowest level and provides improved DECT images. HYPR‐NLM significantly reduces noise while preserving the accuracy of quantitative measurement and resolution. For the iodine concentration numerical phantom, the averaged noise levels are about 2.0, 0.7, 0.2 and 0.4 for direct inversion, HYPR‐LR, Iter‐ DECT and HYPR‐NLM, respectively. For the patient data, the noise levels of the water images are about 0.36, 0.16, 0.12 and 0.13 for direct inversion, HYPR‐LR, Iter‐DECT and HYPR‐NLM, respectively. Difference images of both HYPR‐LR and Iter‐DECT show edge effect, while no significant edge effect is shown for HYPR‐NLM, suggesting spatial resolution is well preserved for HYPR‐NLM.Conclusion:HYPR‐NLM provides an effective way to reduce the generic magnified image noise of dual–energy material decomposition while preserving resolution.This work is supported in part by NIH grants 7R01HL111141 and 1R01‐EB016777. This work is also supported by the Natural Science Foundation of China (NSFC Grant No. 81201091), Fundamental Research Funds for the Central Universities in China, and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.

Constrained X-ray tensor tomography reconstruction.

Quite recently, a method has been presented to reconstruct X-ray scattering tensors from projections obtained in a grating interferometry setup. The original publications present a rather specialised approach, for instance by suggesting a single SART-based solver. In this work, we propose a novel approach to solving the inverse problem, allowing the use of other algorithms than SART (like conjugate gradient), a faster tensor recovery, and an intuitive visualisation. Furthermore, we introduce constraint enforcement for X-ray tensor tomography (cXTT) and demonstrate that this yields visually smoother results in comparison to the state-of-art approach, similar to regularisation.

P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data.

To propose and evaluate P-LORAKS a new calibrationless parallel imaging reconstruction framework. LORAKS is a flexible and powerful framework that was recently proposed for constrained MRI reconstruction. LORAKS was based on the observation that certain matrices constructed from fully sampled k-space data should have low rank whenever the image has limited support or smooth phase, and made it possible to accurately reconstruct images from undersampled or noisy data using low-rank regularization. This paper introduces P-LORAKS, which extends LORAKS to the context of parallel imaging. This is achieved by combining the LORAKS matrices from different channels to yield a larger but more parsimonious low-rank matrix model of parallel imaging data. This new model can be used to regularize the reconstruction of undersampled parallel imaging data, and implicitly imposes phase, support, and parallel imaging constraints without needing to calibrate phase, support, or sensitivity profiles. The capabilities of P-LORAKS are evaluated with retrospectively undersampled data and compared against existing parallel MRI reconstruction methods. Results show that P-LORAKS can improve parallel imaging reconstruction quality, and can enable the use of new k-space trajectories that are not compatible with existing reconstruction methods. The P-LORAKS framewok provides a new and effective way to regularize parallel imaging reconstruction.

Incorporation of image data from a previous examination in 3D serial MR imaging.

We aimed to demonstrate that follow-up scans in longitudinal examinations can be significantly accelerated by using images from previous scans as priors for constrained reconstruction. In this work, we propose a method for incorporating a prior image to improve the reconstruction of a new acquisition with considerable k-space undersampling, which contains a two-level registration scheme with non-parametric transformation, an adaptive synthesis procedure, and a constrained reconstruction with weighted total variation constraint. The performance of the method is evaluated using simulations, as well as results from volunteer and patient examinations. In vivo experiments with both volunteers and patients show that incorporating a prior image into the constrained reconstruction produces many fewer reconstruction errors compared to the conventional reconstruction using only the highly undersampled k-space data. The redundant information in the prior image can be efficiently adopted to improve the reconstruction quality of the new acquisition. When maintaining the image quality, higher acceleration can be achieved with the incorporation of the prior image.

Scan time reduction in 23Na-Magnetic Resonance Imaging using the chemical shift imaging sequence: Evaluation of an iterative reconstruction method

AimTo evaluate potential scan time reduction in 23Na-Magnetic Resonance Imaging with the chemical shift imaging sequence (CSI) using undersampled data of high-quality datasets, reconstructed with an iterative constrained reconstruction, compared to reduced resolution or reduced signal-to-noise ratio. Materials and MethodsCSI 23Na-images were retrospectively undersampled and reconstructed with a constrained reconstruction scheme. The results were compared to conventional methods of scan time reduction. The constrained reconstruction scheme used a phase constraint and a finite object support, which was extracted from a spatially registered 1H-image acquired with a double-tuned coil. The methods were evaluated using numerical simulations, phantom images and in-vivo images of a healthy volunteer and a patient who suffered from cerebral ischemic stroke. ResultsThe constrained reconstruction scheme showed improved image quality compared to a decreased number of averages, images with decreased resolution or circular undersampling with weighted averaging for any undersampling factor. Brain images of a stroke patient, which were reconstructed from three-fold undersampled k-space data, resulted in only minor differences from the original image (normalized root means square error < 12%) and an almost identical delineation of the stroke region (mismatch < 6%). ConclusionThe acquisition of undersampled 23Na-CSI images enables up to three-fold scan time reduction with improved image quality compared to conventional methods of scan time saving.

Accelerated MR parameter mapping with low-rank and sparsity constraints.

To enable accurate magnetic resonance (MR) parameter mapping with accelerated data acquisition, utilizing recent advances in constrained imaging with sparse sampling. A new constrained reconstruction method based on low-rank and sparsity constraints is proposed to accelerate MR parameter mapping. More specifically, the proposed method simultaneously imposes low-rank and joint sparse structures on contrast-weighted image sequences within a unified mathematical formulation. With a pre-estimated subspace, this formulation results in a convex optimization problem, which is solved using an efficient numerical algorithm based on the alternating direction method of multipliers. To evaluate the performance of the proposed method, two application examples were considered: (i) T2 mapping of the human brain and (ii) T1 mapping of the rat brain. For each application, the proposed method was evaluated at both moderate and high acceleration levels. Additionally, the proposed method was compared with two state-of-the-art methods that only use a single low-rank or joint sparsity constraint. The results demonstrate that the proposed method can achieve accurate parameter estimation with both moderately and highly undersampled data. Although all methods performed fairly well with moderately undersampled data, the proposed method achieved much better performance (e.g., more accurate parameter values) than the other two methods with highly undersampled data. Simultaneously imposing low-rank and sparsity constraints can effectively improve the accuracy of fast MR parameter mapping with sparse sampling.

Highly accelerated dynamic contrast-enhanced MRI with temporal constrained reconstruction.

For DCE MRI applications, the images of adjacent time frames are often similar, especially when motion is minimal, in which case temporal TV is a reasonable regularization term. Temporal constraint reconstruction (TCR) has been developed to reconstruct dynamic images from undersampled k-t space data based on such prior information. However, the convergence speed of the algorithm highly depends on the initialization method. In this study, we study initialization using a composite high resolution image based on a jigsaw sampling pattern during pre-contrast frames. The proposed initialization method converges much faster than a conventional initialization method using low resolution images, especially at high reduction factors. In vivo breast imaging experiments were carried out to evaluate the performance of the proposed method. Experiments show the new initialization method allows TCR to achieve a high reduction factor up to 40 without compromising much of the spatial or temporal resolution. The reconstruction errors are much lower than those using the low resolution initialization when the same number of measurements is used.