Temporal/spatial resolution improvement of in vivo DCE-MRI with compressed sensing-optimized FLASH

Dynamic MR image reconstruction based on total generalized variation and low-rank decomposition

PURPOSE

Propose a novel decomposition-based model employing the total generalized variation (TGV) and the nuclear norm, which can be used in compressed sensing-based dynamic MR reconstructions.

THEORY AND METHODS

We employ the nuclear norm to represent the time-coherent background and the spatiotemporal TGV functional for the sparse dynamic component above. We first design an algorithm using the classical first-order primal-dual method for solving the proposed model and then give the norm estimation for the convergence condition. The proposed model is compared with the state-of-the-art methods on different data sets under different sampling schemes and acceleration factors.

RESULTS

The proposed model achieves higher SERs and SSIMs than kt-SLR, kt-RPCA, L+S, and ICTGV on cardiac perfusion and breast DCE-MRI data sets under both the pseudoradial and the Cartesian sampling schemes. In addition, the proposed model better suppresses the spatial artifacts and preserves the edges.

CONCLUSIONS

The proposed model outperforms the state-of-the-art methods and generates high-quality reconstructions under different sampling schemes and different acceleration factors.

Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction

Purpose

To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI).

Methods

A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction.

Results

Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20.

Conclusions

Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.

Dynamic Contrast-Enhanced MRI

Dynamic contrast-enhanced MRI in pre-clinical imaging allows the in-vivo monitoring of vascular, physiological properties in normal and diseased tissue. There is considerable variation in the methods employed owing to the different questions that can be asked and answered about the physiologic alterations as well as morphologic changes in tissue. Here we review the typical decisions in the design and execution of a dynamic contrast-enhanced MRI study in mice although the findings can easily be transferred to other species. Emphasis is placed on highlighting the many pitfalls that wait for the unaware pre-clinical MRI practitioner and that go often unmentioned in the abundant literature dealing with dynamic contrast-enhanced MRI in animal models.

Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast

PURPOSE

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast.

METHODS

We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV _α²), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters K^trans (volume transfer constant) and v_e (extravascular-extracellular volume fraction) across a population of random sampling schemes.

RESULTS

NN produced the lowest image error (SER: 29.1), while TV/TGV _α² produced the most accurate K^trans (CCC: 0.974/0.974) and v_e (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate K^trans (CCC: 0.842) and v_e (CCC: 0.799).

CONCLUSION

TV/TGV _α² should be used as temporal constraints for CS DCE-MRI of the breast.

Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints

Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.

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.

Ultrafast Bilateral DCE-MRI of the Breast with Conventional Fourier Sampling: Preliminary Evaluation of Semi-quantitative Analysis

RATIONALE AND OBJECTIVES

The study aimed to evaluate the feasibility and advantages of a combined high temporal and high spatial resolution protocol for dynamic contrast-enhanced magnetic resonance imaging of the breast.

MATERIALS AND METHODS

Twenty-three patients with enhancing lesions were imaged at 3T. The acquisition protocol consisted of a series of bilateral, fat-suppressed "ultrafast" acquisitions, with 6.9- to 9.9-second temporal resolution for the first minute following contrast injection, followed by four high spatial resolution acquisitions with 60- to 79.5-second temporal resolution. All images were acquired with standard uniform Fourier sampling. A filtering method was developed to reduce noise and detect significant enhancement in the high temporal resolution images. Time of arrival (TOA) was defined as the time at which each voxel first satisfied all the filter conditions, relative to the time of initial arterial enhancement.

RESULTS

Ultrafast images improved visualization of the vasculature feeding and draining lesions. A small percentage of the entire field of view (<6%) enhanced significantly in the 30 seconds following contrast injection. Lesion conspicuity was highest in early ultrafast images, especially in cases with marked parenchymal enhancement. Although the sample size was relatively small, the average TOA for malignant lesions was significantly shorter than the TOA for benign lesions. Significant differences were also measured in other parameters descriptive of early contrast media uptake kinetics (P < 0.05).

CONCLUSIONS

Ultrafast imaging in the first minute of dynamic contrast-enhanced magnetic resonance imaging of the breast has the potential to add valuable information on early contrast dynamics. Ultrafast imaging could allow radiologists to confidently identify lesions in the presence of marked background parenchymal enhancement.

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.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

PURPOSE

To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging.

METHODS

Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K^trans in the extended Tofts model and blood flow F_B and blood volume V_B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated.

RESULTS

The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data.

CONCLUSION

With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

Highly accelerated dynamic contrast enhanced imaging

PURPOSE

Dynamic contrast-enhanced imaging provides unique physiological information, notably the endothelial permeability (K(trans)), and may improve the diagnosis and management of multiple pathologies. Current acquisition methods provide limited spatial-temporal resolution and field-of-view, often preventing characterization of the entire pathology and precluding measurement of the arterial input function. We present a method for highly accelerated dynamic imaging and demonstrate its utility for dynamic contrast-enhanced modeling.

METHODS

We propose a novel Poisson ellipsoid sampling scheme and enforce multiple spatial and temporal l1-norm constraints during image reconstruction. Retrospective and prospective analyses were performed to validate the approach.

RESULTS

Retrospectively, no mean bias or diverging trend was observed as the acceleration rate was increased from 3× to 18×; less than 10% error was measured in K(trans) at any individual rates in this range. Prospectively accelerated images at a rate of 36× enabled full brain coverage with 0.94 × 0.94 × 1.9 mm(3) spatial and 4.1 s temporal resolutions. Images showed no visible degradation and provided accurate K(trans) values when compared to a clinical population.

CONCLUSION

Highly accelerated dynamic MRI using compressed sensing and parallel imaging provides accurate permeability modeling and enables full brain, high resolution acquisitions.

Optimized time-resolved imaging of contrast kinetics (TRICKS) in dynamic contrast-enhanced MRI after peptide receptor radionuclide therapy in small animal tumor models

Anti-tumor efficacy of targeted peptide-receptor radionuclide therapy (PRRT) relies on several factors, including functional tumor vasculature. Little is known about the effect of PRRT on tumor vasculature. With dynamic contrast-enhanced (DCE-) MRI, functional vasculature is imaged and quantified using contrast agents. In small animals DCE-MRI is a challenging application. We optimized a clinical sequence for fast hemodynamic acquisitions, time-resolved imaging of contrast kinetics (TRICKS), to obtain DCE-MRI images at both high spatial and high temporal resolution in mice and rats. Using TRICKS, functional vasculature was measured prior to PRRT and longitudinally to investigate the effect of treatment on tumor vascular characteristics. Nude mice bearing H69 tumor xenografts and rats bearing syngeneic CA20948 tumors were used to study perfusion following PRRT administration with (177) lutetium octreotate. Both semi-quantitative and quantitative parameters were calculated. Treatment efficacy was measured by tumor-size reduction. Optimized TRICKS enabled MRI at 0.032 mm(3) voxel size with a temporal resolution of less than 5 s and large volume coverage, a substantial improvement over routine pre-clinical DCE-MRI studies. Tumor response to therapy was reflected in changes in tumor perfusion/permeability parameters. The H69 tumor model showed pronounced changes in DCE-derived parameters following PRRT. The rat CA20948 tumor model showed more heterogeneity in both treatment outcome and perfusion parameters. TRICKS enabled the acquisition of DCE-MRI at both high temporal resolution (Tres ) and spatial resolutions relevant for small animal tumor models. With the high Tres enabled by TRICKS, accurate pharmacokinetic data modeling was feasible. DCE-MRI parameters revealed changes over time and showed a clear relationship between tumor size and Ktrans .

High temporal resolution dynamic contrast-enhanced MRI using compressed sensing-combined sequence in quantitative renal perfusion measurement

PURPOSE

To demonstrate the feasibility of the improved temporal resolution by using compressed sensing (CS) combined imaging sequence in dynamic contrast-enhanced MRI (DCE-MRI) of kidney, and investigate its quantitative effects on renal perfusion measurements.

MATERIALS AND METHODS

Ten rabbits were included in the accelerated scans with a CS-combined 3D pulse sequence. To evaluate the image quality, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between the proposed CS strategy and the conventional full sampling method. Moreover, renal perfusion was estimated by using the separable compartmental model in both CS simulation and realistic CS acquisitions.

RESULTS

The CS method showed DCE-MRI images with improved temporal resolution and acceptable image contrast, while presenting significantly higher SNR than the fully sampled images (p<.01) at 2-, 3- and 4-X acceleration. In quantitative measurements, renal perfusion results were in good agreement with the fully sampled one (concordance correlation coefficient=0.95, 0.91, 0.88) at 2-, 3- and 4-X acceleration in CS simulation. Moreover, in realistic acquisitions, the estimated perfusion by the separable compartmental model exhibited no significant differences (p>.05) between each CS-accelerated acquisition and the full sampling method.

CONCLUSION

The CS-combined 3D sequence could improve the temporal resolution for DCE-MRI in kidney while yielding diagnostically acceptable image quality, and it could provide effective measurements of renal perfusion.

Ultrafast 3D spin-echo acquisition improves Gadolinium-enhanced MRI signal contrast enhancement

Long scan times of 3D volumetric MR acquisitions usually necessitate ultrafast in vivo gradient-echo acquisitions, which are intrinsically susceptible to magnetic field inhomogeneities. This is especially problematic for contrast-enhanced (CE)-MRI applications, where non-negligible T₂* effect of contrast agent deteriorates the positive signal contrast and limits the available range of MR acquisition parameters and injection doses. To overcome these shortcomings without degrading temporal resolution, ultrafast spin-echo acquisitions were implemented. Specifically, a multiplicative acceleration factor from multiple spin echoes (×32) and compressed sensing (CS) sampling (×8) allowed highly-accelerated 3D Multiple-Modulation-Multiple-Echo (MMME) acquisition. At the same time, the CE-MRI of kidney with Gd-DOTA showed significantly improved signal enhancement for CS-MMME acquisitions (×7) over that of corresponding FLASH acquisitions (×2). Increased positive contrast enhancement and highly accelerated acquisition of extended volume with reduced RF irradiations will be beneficial for oncological and nephrological applications, in which the accurate in vivo 3D quantification of contrast agent concentration is necessary with high temporal resolution.

Atherosclerotic plaque inflammation quantification using dynamic contrast-enhanced (DCE) MRI

Inflammation plays an important role in atherosclerosis. Given the increasing interest in using in-vivo imaging methods to study the physiology and treatment effects in atherosclerosis, noninvasive intraplaque inflammation quantitative method is needed. Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) has been proposed and validated to quantitatively characterize atherosclerotic plaque inflammation. Recent studies have optimized the imaging protocol, pharmacokinetic modeling techniques. All of these technical advances further promoted DCE-MRI to clinical investigations in plaque risk assessment and therapeutic response monitor. Although larger clinical studies are still needed, DCE-MRI has been proven to be a promising tool to reveal more about intraplaque inflammation by in vivo quantitative inflammation imaging.

High spatial and temporal resolution dynamic contrast-enhanced magnetic resonance angiography using compressed sensing with magnitude image subtraction

PURPOSE

We propose a compressed-sensing (CS) technique based on magnitude image subtraction for high spatial and temporal resolution dynamic contrast-enhanced MR angiography (CE-MRA).

METHODS

Our technique integrates the magnitude difference image into the CS reconstruction to promote subtraction sparsity. Fully sampled Cartesian 3D CE-MRA datasets from 6 volunteers were retrospectively under-sampled and three reconstruction strategies were evaluated: k-space subtraction CS, independent CS, and magnitude subtraction CS. The techniques were compared in image quality (vessel delineation, image artifacts, and noise) and image reconstruction error. Our CS technique was further tested on seven volunteers using a prospectively under-sampled CE-MRA sequence.

RESULTS

Compared with k-space subtraction and independent CS, our magnitude subtraction CS provides significantly better vessel delineation and less noise at 4× acceleration, and significantly less reconstruction error at 4× and 8× (P < 0.05 for all). On a 1-4 point image quality scale in vessel delineation, our technique scored 3.8 ± 0.4 at 4×, 2.8 ± 0.4 at 8×, and 2.3 ± 0.6 at 12× acceleration. Using our CS sequence at 12× acceleration, we were able to acquire dynamic CE-MRA with higher spatial and temporal resolution than current clinical TWIST protocol while maintaining comparable image quality (2.8 ± 0.5 vs. 3.0 ± 0.4, P = NS).

CONCLUSION

Our technique is promising for dynamic CE-MRA.

Perfusion MRI: the five most frequently asked technical questions

OBJECTIVE

This and its companion article address the 10 most frequently asked questions that radiologists face when planning, performing, processing, and interpreting different MR perfusion studies in CNS imaging.

CONCLUSION

Perfusion MRI is a promising tool in assessing stroke, brain tumors, and patients with neurodegenerative diseases. Most of the impediments that have limited the use of perfusion MRI can be overcome to allow integration of these methods into modern neuroimaging protocols.