Fast cardiac T1 mapping in mice using a model-based compressed sensing method

Latest Advances in Image Acceleration: All Dimensions are Fair Game

Magnetic resonance imaging (MRI) is a versatile modality that can generate high-resolution images with a variety of tissue contrasts. However, MRI is a slow technique and requires long acquisition times, which increase with higher temporal and spatial resolution and/or when multiple contrasts and large volumetric coverage is required. In order to speedup MR data acquisition, several approaches have been introduced in the literature. Most of these techniques acquire less data than required and exploit intrinsic redundancies in the MR images to recover the information that was not sampled. This article presents a review of MR acquisition and reconstruction methods that have exploited redundancies in the temporal, spatial, and contrast/parametric dimensions to accelerate image data acquisition, focusing on cardiac and abdominal MR imaging applications. The review describes how each of these dimensions has been separately exploited for speeding up MR acquisition to then discuss more advanced techniques where multiple dimensions are exploited together for further reducing scan times. Finally, future directions for multidimensional image acceleration and remaining technical challenges are discussed. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.

Fast multicomponent 3D-T1ρ relaxometry

NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.

Compressed-Sensing MP2RAGE sequence: Application to the detection of brain metastases in mice at 7T

PURPOSE

To develop a Compressed Sensing (CS)-MP2RAGE sequence to drastically shorten acquisition duration and then detect and measure the T₁ of brain metastases in mice at 7 T.

METHODS

The encoding trajectory of the standard Cartesian MP2RAGE sequence has been modified (1) to obtain a variable density Poisson disk under-sampling distribution along the k_y -k_z plane, and (2) to sample the central part of the k-space exactly at TI₁ and TI₂ inversion times. In a prospective study, the accuracy of the T₁ measurements was evaluated on phantoms containing increasing concentrations of gadolinium. The CS acceleration factors were increased to evaluate their influence on the contrast and T₁ measurements of brain metastases in vivo. Finally, the 3D T₁ maps were acquired with at 4-fold increased spatial resolution. The volumes and T₁ values of the metastases were measured while using CS to reduce scan time.

RESULTS

The implementation of the CS-encoding trajectory did not affect the T₁ measurements in vitro. Accelerating the acquisition by a factor of 2 did not alter the contrast or the T₁ values of the brain metastases. 3D T₁ maps could be obtained in < 1 min using a CS factor of 6. Increasing the spatial resolution enabled more accurately measurement of the metastasis volumes while maintaining an acquisition duration below 5 min.

CONCLUSION

The CS-MP2RAGE sequence could be of great interest in oncology to either rapidly obtain mouse brain 3D T₁ maps or to increase the spatial resolution with no penalty on the scan duration.

Recovery of Damped Exponentials Using Structured Low Rank Matrix Completion

We introduce a structured low rank matrix completion algorithm to recover a series of images from their under-sampled measurements, where the signal along the parameter dimension at every pixel is described by a linear combination of exponentials. We exploit the exponential behavior of the signal at every pixel, along with the spatial smoothness of the exponential parameters to derive an annihilation relation in the Fourier domain. This relation translates to a low-rank property on a structured matrix constructed from the Fourier samples. We enforce the low-rank property of the structured matrix as a regularization prior to recover the images. Since the direct use of current low rank matrix recovery schemes to this problem is associated with high computational complexity and memory demand, we adopt an iterative re-weighted least squares algorithm, which facilitates the exploitation of the convolutional structure of the matrix. Novel approximations involving 2-D fast Fourier transforms are introduced to drastically reduce the memory demand and computational complexity, which facilitates the extension of structured low-rank methods to large scale 3-D problems. We demonstrate our algorithm in the MR parameter mapping setting and show improvement over the state-of-the-art methods.

Quantitative DLA-based compressed sensing for T1-weighted acquisitions

High resolution Manganese Enhanced Magnetic Resonance Imaging (MEMRI), which uses manganese as a T₁ contrast agent, has great potential for functional imaging of live neuronal tissue at single neuron scale. However, reaching high resolutions often requires long acquisition times which can lead to reduced image quality due to sample deterioration and hardware instability. Compressed Sensing (CS) techniques offer the opportunity to significantly reduce the imaging time. The purpose of this work is to test the feasibility of CS acquisitions based on Diffusion Limited Aggregation (DLA) sampling patterns for high resolution quantitative T₁-weighted imaging. Fully encoded and DLA-CS T₁-weighted images of Aplysia californica neural tissue were acquired on a 17.2T MRI system. The MR signal corresponding to single, identified neurons was quantified for both versions of the T₁ weighted images. For a 50% undersampling, DLA-CS can accurately quantify signal intensities in T₁-weighted acquisitions leading to only 1.37% differences when compared to the fully encoded data, with minimal impact on image spatial resolution. In addition, we compared the conventional polynomial undersampling scheme with the DLA and showed that, for the data at hand, the latter performs better. Depending on the image signal to noise ratio, higher undersampling ratios can be used to further reduce the acquisition time in MEMRI based functional studies of living tissues.

Optimization of sparse phase encodings for variable repetition-delay turbo-spin echo (TSE) T1 measurements for preclinical applications

A variable repetition-delay (TR) spin echo sequence with repeated refocusing pulses, i.e., a variable TR turbo-spin echo (TSE), provides an attractive means of acquiring an accurate T₁ map information that is free from gradient echo based artifacts such as magnetic field inhomogeneities particularly for ultra-high field (at 7T and above) preclinical applications. However, the applicability of multi-slice TSE sequences is often limited by signal distortion from T₂ relaxation due to echo-train acquisitions for short T₂ tissues, inter-slice cross talks and magnetization transfer (MT) from repetitive slice-selective 180° pulse, and extended scan times with multiple TR excitations. These TSE shortcomings are difficult to remedy for preclinical applications, where small sizes of target organs usually limit the slice-gap control with restricted parallel imaging capabilities. In this study, compressed-sensing-assisted turbo-spin echo (CS-TSE) acquisitions for variable TR T₁ measurements at 7T preclinical scanner were implemented to reduce the echo-trains by sparse phase encodings. Following the sparse signal simulation and sampling scheme optimization, the measured T₁ values from CS-TSE and TSE were compared for phantoms, ex vivo, and in vivo subjects. The phantom T₁ values from CS-TSE and TSE were identical to those from the inversion recovery spin echo. For both ex vivo and in vivo multi-slice T₁ mapping, the shortened echo-trains of CS-TSE relieved the T₂ relaxation, reduced the inter-slice interferences of multi-slice acquisition, and made room for additional slice encodings while maintaining a shorter scan time than the conventional TSE at the expense of local image smoothness from CS regularizations.

MR Parametric Mapping as a Biomarker of Early Joint Degeneration

Context:

Osteoarthritis (OA) is a common, worldwide disorder. Magnetic resonance (MR) imaging can directly and noninvasively evaluate articular cartilage and has emerged as an essential tool in the study of OA.

Evidence Acquisition:

A PubMed search was performed using the keywords quantitative MRI and cartilage. No limits were set on the range of years searched. Articles were reviewed for relevance with an emphasis on in vivo studies performed at 3 tesla.

Study Design:

Clinical review.

Level of Evidence:

Level 4.

Results:

T2, T2*, T1 (particularly when measured after exogenous contrast administration, such as with the delayed gadolinium-enhanced MR imaging of cartilage [dGEMRIC] technique), and T1ρ are among the most widely utilized quantitative MR imaging techniques to evaluate cartilage and have been implemented in various patient cohorts. Existing challenges include reproducibility of results, insufficient consensus regarding optimal sequences and parameters, and interpretation of values.

Conclusion:

Quantitative assessment of cartilage using MR imaging techniques likely represents the best opportunity to identify early cartilage degeneration and to follow patients after treatment. Despite existing challenges, ongoing work and unique approaches have shown exciting and promising results.

High field magnetic resonance imaging of rodents in cardiovascular research

Transgenic and gene knockout rodent models are primordial to study pathophysiological processes in cardiovascular research. Over time, cardiac MRI has become a gold standard for in vivo evaluation of such models. Technical advances have led to the development of magnets with increasingly high field strength, allowing specific investigation of cardiac anatomy, global and regional function, viability, perfusion or vascular parameters. The aim of this report is to provide a review of the various sequences and techniques available to image mice on 7-11.7 T magnets and relevant to the clinical setting in humans. Specific technical aspects due to the rise of the magnetic field are also discussed.

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

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

Rapid T2 mapping of mouse heart using the carr-purcell-meiboom-gill sequence and compressed sensing reconstruction

PURPOSE

To develop and prove preliminary validation of a fast in vivo T2 mapping technique for mouse heart.

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) experiments were performed on a 7T animal scanner. The standard Carr-Purcell-Meiboom-Gill (CPMG) sequence was modified to minimize the effect of stimulated echoes for accurate T2 quantification. The acquisition was further accelerated with the compressed sensing approach. The accuracy of the proposed method was first validated with both phantom experiments and numerical simulations. In vivo T2 measurement was performed on seven mice in a manganese-enhanced MRI study.

RESULTS

In phantom studies, T2 values obtained with the modified CPMG sequence are in good agreement with the standard spin-echo method (P > 0.05). Numerical simulations further demonstrated that with the application of the compressed sensing approach, fast T2 quantification with a spatial resolution of 2.3 mm can be achieved at a high temporal resolution of 1 minute per slice. With the proposed technique, an average T2 value of 25 msec was observed for mouse heart at 7T and this number decreased significantly after manganese infusion (P < 0.001).

CONCLUSION

A rapid T2 mapping technique was developed and assessed, which allows accurate T2 quantification of mouse heart at a temporal resolution of 1 minute per slice. J. Magn. Reson. Imaging 2016;44:375-382.

Small animal cardiovascular MR imaging and spectroscopy

The use of MR imaging and spectroscopy for studying cardiovascular disease processes in small animals has increased tremendously over the past decade. This is the result of the remarkable advances in MR technologies and the increased availability of genetically modified mice. MR techniques provide a window on the entire timeline of cardiovascular disease development, ranging from subtle early changes in myocardial metabolism that often mark disease onset to severe myocardial dysfunction associated with end-stage heart failure. MR imaging and spectroscopy techniques play an important role in basic cardiovascular research and in cardiovascular disease diagnosis and therapy follow-up. This is due to the broad range of functional, structural and metabolic parameters that can be quantified by MR under in vivo conditions non-invasively. This review describes the spectrum of MR techniques that are employed in small animal cardiovascular disease research and how the technological challenges resulting from the small dimensions of heart and blood vessels as well as high heart and respiratory rates, particularly in mice, are tackled.

Fast and robust 3D T1 mapping using spiral encoding and steady RF excitation at 7 T: application to cardiac manganese enhanced MRI (MEMRI) in mice

Mapping longitudinal relaxation times in 3D is a promising quantitative and non-invasive imaging tool to assess cardiac remodeling. Few methods are proposed in the literature allowing us to perform 3D T1 mapping. These methods often require long scan times and use a low number of 3D images to calculate T1 . In this project, a fast 3D T1 mapping method using a stack-of-spirals sampling scheme and regular RF pulse excitation at 7 T is presented. This sequence, combined with a newly developed fitting procedure, allowed us to quantify T1 of the whole mouse heart with a high spatial resolution of 208 × 208 × 315 µm(3) in 10-12 min acquisition time. The sensitivity of this method for measuring T1 variations was demonstrated on mouse hearts after several injections of manganese chloride (doses from 25 to 150 µmol kg(-1) ). T1 values were measured in vivo in both pre- and post-contrast experiments. This protocol was also validated on ischemic mice to demonstrate its efficiency to visualize tissue damage induced by a myocardial infarction. This study showed that combining spiral gradient shape and steady RF excitation enabled fast and robust 3D T1 mapping of the entire heart with a high spatial resolution.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Accelerated whole-brain multi-parameter mapping using blind compressed sensing

PURPOSE

To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping.

METHODS

BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R).

RESULTS

From 2D retrospective undersampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions.

CONCLUSION

BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques.

Preclinical MR fingerprinting (MRF) at 7 T: effective quantitative imaging for rodent disease models

High-field preclinical MRI scanners are now commonly used to quantitatively assess disease status and the efficacy of novel therapies in a wide variety of rodent models. Unfortunately, conventional MRI methods are highly susceptible to respiratory and cardiac motion artifacts resulting in potentially inaccurate and misleading data. We have developed an initial preclinical 7.0-T MRI implementation of the highly novel MR fingerprinting (MRF) methodology which has been described previously for clinical imaging applications. The MRF technology combines a priori variation in the MRI acquisition parameters with dictionary-based matching of acquired signal evolution profiles to simultaneously generate quantitative maps of T1 and T2 relaxation times and proton density. This preclinical MRF acquisition was constructed from a fast imaging with steady-state free precession (FISP) MRI pulse sequence to acquire 600 MRF images with both evolving T1 and T2 weighting in approximately 30 min. This initial high-field preclinical MRF investigation demonstrated reproducible and differentiated estimates of in vitro phantoms with different relaxation times. In vivo preclinical MRF results in mouse kidneys and brain tumor models demonstrated an inherent resistance to respiratory motion artifacts as well as sensitivity to known pathology. These results suggest that MRF methodology may offer the opportunity for the quantification of numerous MRI parameters for a wide variety of preclinical imaging applications.

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

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

Pre-clinical functional Magnetic Resonance Imaging Part II: The heart

One third of all deaths worldwide in 2008 were caused by cardiovascular diseases (CVD), and the incidence of CVD related deaths rises ever more. Thus, improved imaging techniques and modalities are needed for the evaluation of cardiac morphology and function. Cardiac magnetic resonance imaging (CMRI) is a minimally invasive technique that is increasingly important due to its high spatial and temporal resolution, its high soft tissue contrast and its ability of functional and quantitative imaging. It is widely accepted as the gold standard of cardiac functional analysis. In the short period of small animal MRI, remarkable progress has been achieved concerning new, fast imaging schemes as well as purpose-built equipment. Dedicated small animal scanners allow for tapping the full potential of recently developed animal models of cardiac disease. In this paper, we review state-of-the-art cardiac magnetic resonance imaging techniques and applications in small animals at ultra-high fields (UHF).

Sparse parallel transmission on randomly perturbed spiral k-space trajectory

Combination of parallel transmission and sparse pulse is able to shorten the excitation by using both the coil sensitivity and sparse k-space, showing improved fast excitation capability over the use of parallel transmission alone. However, to design an optimal k-space trajectory for sparse parallel transmission is a challenging task. In this work, a randomly perturbed sparse k-space trajectory is designed by modifying the path of a spiral trajectory along the sparse k-space data, and the sparse parallel transmission RF pulses are subsequently designed based on this optimal trajectory. This method combines the parallel transmission and sparse spiral k-space trajectory, potentially to further reduce the RF transmission time. Bloch simulation of 90° excitation by using a four channel coil array is performed to demonstrate its feasibility. Excitation performance of the sparse parallel transmission technique at different reduction factors of 1, 2, and 4 is evaluated. For comparison, parallel excitation using regular spiral trajectory is performed. The passband errors of the excitation profiles of each transmission are calculated for quantitative assessment of the proposed excitation method.

Rapid measurements of heterogeneity in sandstones using low-field nuclear magnetic resonance

Sandstone rocks can contain microscopic variations in composition that complicate interpretation of nuclear magnetic resonance (NMR) relaxation time measurements. In this work, methods for assessing the degree of sample heterogeneity are demonstrated in three sandstones. A two-dimensional T1-Δχapp correlation (where Δχapp is the apparent solid/liquid magnetic susceptibility contrast) reveals the microscopic heterogeneity in composition, whilst a spatially resolved T1 profile reveals the macroscopic structural heterogeneity. To perform these measurements efficiently, a rapid measure of longitudinal T1 relaxation time has been implemented on a low-field NMR spectrometer with a magnetic field strength B0=0.3 T. The "double-shot" T1 pulse sequence is appropriate for analysis of porous materials in general. Example relaxation time distributions are presented for doped water phantoms to validate the method. The acquisition time of the double-shot T1 sequence is equivalent to the single-shot Carr-Purcell Meiboom-Gill (CPMG) sequence used routinely in petrophysics to measure transverse T2 relaxation. Rapid T1 measurements enable practical studies of core plugs at magnetic field strengths previously considered inappropriate, as T1 is independent of molecular diffusion through pore-scale (internal) magnetic field gradients.

PANDA-T1ρ: Integrating principal component analysis and dictionary learning for fast T1ρ mapping

PURPOSE

Long scanning time greatly hinders the widespread application of spin-lattice relaxation in rotating frame (T1ρ) in clinics. In this study, a novel method is proposed to reconstruct the T1ρ-weighted images from undersampled k-space data and hence accelerate the acquisition of T1ρ imaging.

METHODS

The proposed approach (PANDA-T1ρ) combined the benefit of PCA and dictionary learning when reconstructing image from undersampled data. Specifically, the PCA transform was first used to sparsify the image series along the parameter direction and then the sparsified images were reconstructed by means of dictionary learning and finally solved the images. A variation of PANDA-T1ρ was also developed for the heavy noise case. Numerical simulation and in vivo experiments were carried out with the accelerating factor from 2 to 4 to verify the performance of PANDA-T1ρ.

RESULTS

The reconstructed T1ρ maps using the PANDA-T1ρ method were found to be comparable to the reference at all verified acceleration factors. Moreover, the variation exhibited better performance than the original version when the k-space data were contaminated by heavy noise.

CONCLUSION

PANDA-T1ρ can significantly reduce the scanning time of T1ρ by integrating PCA and dictionary learning and provides better parameter estimation than the state-of-art methods for a fixed acceleration factor.

Estimation of the CSA-ODF using Bayesian compressed sensing of multi-shell HARDI

PURPOSE

Diffusion MRI provides important information about the brain white matter structures and has opened new avenues for neuroscience and translational research. However, acquisition time needed for advanced applications can still be a challenge in clinical settings. There is consequently a need to accelerate diffusion MRI acquisitions.

METHODS

A multi-task Bayesian compressive sensing (MT-BCS) framework is proposed to directly estimate the constant solid angle orientation distribution function (CSA-ODF) from under-sampled (i.e., accelerated image acquisition) multi-shell high angular resolution diffusion imaging (HARDI) datasets, and accurately recover HARDI data at higher resolution in q-space. The proposed MT-BCS approach exploits the spatial redundancy of the data by modeling the statistical relationships within groups (clusters) of diffusion signal. This framework also provides uncertainty estimates of the computed CSA-ODF and diffusion signal, directly computed from the compressive measurements. Experiments validating the proposed framework are performed using realistic multi-shell synthetic images and in vivo multi-shell high angular resolution HARDI datasets.

RESULTS

Results indicate a practical reduction in the number of required diffusion volumes (q-space samples) by at least a factor of four to estimate the CSA-ODF from multi-shell data.

CONCLUSION

This work presents, for the first time, a multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions. It demonstrates improvement of the quality of acquired datasets by means of CS de-noising, and accurate estimation of the CSA-ODF, as well as enables a reduction in the acquisition time by a factor of two to four, especially when "staggered" q-space sampling schemes are used. The proposed MT-BCS framework can naturally be combined with parallel MR imaging to further accelerate HARDI acquisitions.

Suppressing multi-channel ultra-low-field MRI measurement noise using data consistency and image sparsity

Ultra-low-field (ULF) MRI (B₀ = 10–100 µT) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.

Accelerated high-frame-rate mouse heart cine-MRI using compressed sensing reconstruction

We introduce a new protocol to obtain very high-frame-rate cinematographic (Cine) MRI movies of the beating mouse heart within a reasonable measurement time. The method is based on a self-gated accelerated fast low-angle shot (FLASH) acquisition and compressed sensing reconstruction. Key to our approach is that we exploit the stochastic nature of the retrospective triggering acquisition scheme to produce an undersampled and random k-t space filling that allows for compressed sensing reconstruction and acceleration. As a standard, a self-gated FLASH sequence with a total acquisition time of 10 min was used to produce single-slice Cine movies of seven mouse hearts with 90 frames per cardiac cycle. Two times (2×) and three times (3×) k-t space undersampled Cine movies were produced from 2.5- and 1.5-min data acquisitions, respectively. The accelerated 90-frame Cine movies of mouse hearts were successfully reconstructed with a compressed sensing algorithm. The movies had high image quality and the undersampling artifacts were effectively removed. Left ventricular functional parameters, i.e. end-systolic and end-diastolic lumen surface areas and early-to-late filling rate ratio as a parameter to evaluate diastolic function, derived from the standard and accelerated Cine movies, were nearly identical.

Interpolated compressed sensing for 2D multiple slice fast MR imaging

Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR), decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS) method to further enhance the imaging speed or reduce data size without significant sacrifice of image quality and CNR for multi-slice two-dimensional sparse MR imaging in humans. This method utilizes the k-space data of the neighboring slice in the multi-slice acquisition. The missing k-space data of a highly undersampled slice are estimated by using the raw data of its neighboring slice multiplied by a weighting function generated from low resolution full k-space reference images. In-vivo MR imaging in human feet has been used to investigate the feasibility and the performance of the proposed iCS method. The results show that by using the proposed iCS reconstruction method, the average image error can be reduced and the average CNR can be improved, compared with the conventional sparse MRI reconstruction at the same undersampling rate.