Restoration of signal polarity in a set of inversion recovery NMR images

Whole-Brain Imaging of Subvoxel T1-Diffusion Correlation Spectra in Human Subjects

T1 relaxation and water mobility generate eloquent MRI tissue contrasts with great diagnostic value in many neuroradiological applications. However, conventional methods do not adequately quantify the microscopic heterogeneity of these important biophysical properties within a voxel, and therefore have limited biological specificity. We describe a new correlation spectroscopic (CS) MRI method for measuring how T1 and mean diffusivity (MD) co-vary in microscopic tissue environments. We develop a clinical pulse sequence that combines inversion recovery (IR) with single-shot isotropic diffusion encoding (IDE) to efficiently acquire whole-brain MRIs with a wide range of joint T1-MD weightings. Unlike conventional diffusion encoding, the IDE preparation ensures that all subvoxel water pools are weighted by their MDs regardless of the sizes, shapes, and orientations of their corresponding microscopic diffusion tensors. Accordingly, IR-IDE measurements are well-suited for model-free, quantitative spectroscopic analysis of microscopic water pools. Using numerical simulations, phantom experiments, and data from healthy volunteers we demonstrate how IR-IDE MRIs can be processed to reconstruct maps of two-dimensional joint probability density functions, i.e., correlation spectra, of subvoxel T1-MD values. In vivo T1-MD spectra show distinct cerebrospinal fluid and parenchymal tissue components specific to white matter, cortical gray matter, basal ganglia, and myelinated fiber pathways, suggesting the potential for improved biological specificity. The one-dimensional marginal distributions derived from the T1-MD correlation spectra agree well with results from other relaxation spectroscopic and quantitative MRI studies, validating the T1-MD contrast encoding and the spectral reconstruction. Mapping subvoxel T1-diffusion correlations in patient populations may provide a more nuanced, comprehensive, sensitive, and specific neuroradiological assessment of the non-specific changes seen on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted MRIs (DWIs) in cancer, ischemic stroke, or brain injury.

Fast calculation software for modified Look-Locker inversion recovery (MOLLI) T1 mapping

BACKGROUND

The purpose of this study was to develop a software tool and evaluate different T1 map calculation methods in terms of computation time in cardiac magnetic resonance imaging.

METHODS

The modified Look-Locker inversion recovery (MOLLI) sequence was used to acquire multiple inversion time (TI) images for pre- and post-contrast T1 mapping. The T1 map calculation involved pixel-wise curve fitting based on the T1 relaxation model. A variety of methods were evaluated using data from 30 subjects for computational efficiency: MRmap, python Levenberg-Marquardt (LM), python reduced-dimension (RD) non-linear least square, C++ single- and multi-core LM, and C++ single- and multi-core RD.

RESULTS

Median (interquartile range) computation time was 126 s (98-141) for the publicly available software MRmap, 261 s (249-282) for python LM, 77 s (74-80) for python RD, 3.4 s (3.1-3.6) for C++ multi-core LM, and 1.9 s (1.9-2.0) for C++ multi-core RD. The fastest C++ multi-core RD and the publicly available MRmap showed good agreement of myocardial T1 values, resulting in 95% Bland-Altman limits of agreement of (- 0.83 to 0.58 ms) and (- 6.57 to 7.36 ms) with mean differences of - 0.13 ms and 0.39 ms, for the pre- and post-contrast, respectively.

CONCLUSION

The C++ multi-core RD was the fastest method on a regular eight-core personal computer for pre- or post-contrast T1 map calculation. The presented software tool (fT1fit) facilitated rapid T1 map and extracellular volume fraction map calculations.

A method for longitudinal relaxation time measurement in inhomogeneous fields

The spin-lattice relaxation time (T₁) plays a crucial role in the study of spin dynamics, signal optimization and data quantification. However, the measurement of chemical shift-specific T₁ constants is hampered by the magnetic field inhomogeneity due to poorly shimmed external magnetic fields or intrinsic magnetic susceptibility heterogeneity in samples. In this study, we present a new protocol to determine chemical shift-specific T₁ constants in inhomogeneous fields. Based on intermolecular double-quantum coherences, the new method can resolve overlapped peaks in inhomogeneous fields. The measurement results are in consistent with the measurements in homogeneous fields using the conventional method. Since spatial encoding technique is involved, the experimental time for the new method is very close to that for the conventional method. With the aid of T₁ knowledge, some concealed information can be exploited by T₁ weighting experiments.

A new algebraic method for quantitative proton density mapping using multi-channel coil data

A difficult problem in quantitative MRI is the accurate determination of the proton density, which is an important quantity in measuring brain tissue organization. Recent progress in estimating proton density in vivo has been based on using the inverse linear relationship between the longitudinal relaxation rate T₁ and proton density. In this study, the same type of relationship is being used, however, in a more general framework by constructing 3D basis functions to model the receiver bias field. The novelty of this method is that the basis functions developed are suitable to cover an entire range of inverse linearities between T₁ and proton density. The method is applied by parcellating the human brain into small cubes with size 30mm x 30mm x 30mm. In each cube the optimal set of basis functions is determined to model the receiver coil sensitivities using multi-channel (32 element) coil data. For validation, we use arbitrary data from a numerical phantom where the data satisfy the conventional MR signal equations. Using added noise of different magnitude and realizations, we show that the proton densities obtained have a bias close to zero and also low noise sensitivity. The obtained root-mean-square-error rate is less than 0.2% for the estimated proton density in a realistic 3D simulation. As an application, the method is used in a small cohort of MS patients, and proton density values for specific brain structures are determined.

Improved estimation of MR relaxation parameters using complex-valued data

PURPOSE

In MR image analysis, T₁ , T₂ , and T2* maps are generally calculated using magnitude MR data. Without knowledge of the underlying noise variance, parameter estimates at low signal to noise ratio (SNR) are usually biased. This leads to confounds in studies that compare parameters across SNRs and or across scanners. This article compares several estimation techniques which use real or complex-valued MR data to achieve unbiased estimation of MR relaxation parameters without the need for additional preprocessing.

THEORY AND METHODS

Several existing and new techniques to estimate relaxation parameters using complex-valued data were compared with widely used magnitude-based techniques. Their bias, variance and processing times were studied using simulations covering various aspects of parameter variations. Validation on noise-degraded experimental measurements was also performed.

RESULTS

Simulations and experiments demonstrated the superior performance of techniques based on complex-valued data, even in comparison with magnitude-based techniques that account for Rician noise characteristics. This was achieved with minor modifications to data modeling and at computational costs either comparable to or higher ( ≈two fold) than magnitude-based estimators. Theoretical analysis shows that estimators based on complex-valued data are statistically efficient.

CONCLUSION

The estimation techniques that use complex-valued data provide minimum variance unbiased estimates of parametric maps and markedly outperform commonly used magnitude-based estimators under most conditions. They additionally provide phase maps and field maps, which are unavailable with magnitude-based methods. Magn Reson Med 77:385-397, 2017. © 2016 Wiley Periodicals, Inc.

PERFIDI FILTERS VALIDATION: FROM NUCLEAR MAGNETIC RESONANCE RELAXOMETRY TO MAGNETIC RESONANCE IMAGING

In order to well distinguish different tissues of the human body by magnetic resonance imaging (MRI), it is of great importance to find procedures to improve the image contrast. In particular, a valuable feature is to image only specific parts of organs and/or tissues while ignoring all the others. Dedicated MRI sequences able to filter the 1 H nuclei signals based on the different longitudinal relaxation times (T1) of the tissues have been developed. Standard signal selection/attenuation sequences, such as the Short Time Inversion Recovery and Multiple Inversion Recovery, have the effect to zero the signal for a discrete number of T1 values. Parametrically Enabled Relaxation Filters with Double and multiple Inversion (PERFIDI) sequences act on a range of T1 values and behave as an electronic band-pass or high-pass or low-pass filters. PERFIDI filters are therefore primarily focused on the components that pass through, rather than on those that are blocked. These filters have been developed and tested by nuclear magnetic resonance relaxometry. Here, these sequences have been validated for MRI on phantom samples to mimic T1 distributions present in tissues. Preliminary applications show that PERFIDI filters can effectively work on a range of T1 values to give well contrasted images.

Uncertainty estimations for quantitative in vivo MRI T1 mapping

Mapping the longitudinal relaxation time (T(1)) of brain tissue is of great interest for both clinical research and MRI sequence development. For an unambiguous interpretation of in vivo variations in T(1) images, it is important to understand the degree of variability that is associated with the quantitative T(1) parameter. This paper presents a general framework for estimating the uncertainty in quantitative T(1) mapping by combining a slice-shifted multi-slice inversion recovery EPI technique with the statistical wild-bootstrap approach. Both simulations and experimental analyses were performed to validate this novel approach and to evaluate the estimated T(1) uncertainty in several brain regions across four healthy volunteers. By estimating the T(1) uncertainty, it is shown that the variation in T(1) within anatomic regions for similar tissue types is larger than the uncertainty in the measurement. This indicates that heterogeneity of the inspected tissue and/or partial volume effects can be the main determinants for the observed variability in the estimated T(1) values. The proposed approach to estimate T(1) and its uncertainty without the need for repeated measurements may also prove to be useful for calculating effect sizes that are deemed significant when comparing group differences.

A robust methodology for in vivo T1 mapping

In this article, a robust methodology for in vivo T(1) mapping is presented. The approach combines a gold standard scanning procedure with a novel fitting procedure. Fitting complex data to a five-parameter model ensures accuracy and precision of the T(1) estimation. A reduced-dimension nonlinear least squares method is proposed. This method turns the complicated multi-parameter minimization into a straightforward one-dimensional search. As the range of possible T(1) values is known, a global grid search can be used, ensuring that a global optimal solution is found. When only magnitude data are available, the algorithm is adapted to concurrently restore polarity. The performance of the new algorithm is demonstrated in simulations and phantom experiments. The new algorithm is as accurate and precise as the conventionally used Levenberg-Marquardt algorithm but much faster. This gain in speed makes the use of the five-parameter model viable. In addition, the new algorithm does not require initialization of the search parameters. Finally, the methodology is applied in vivo to conventional brain imaging and to skin imaging. T(1) values are estimated for white matter and gray matter at 1.5 T and for dermis, hypodermis, and muscle at 1.5 T, 3 T, and 7 T.

Robust phase sensitive inversion recovery imaging using a Markov random field model

This paper presents a novel method for phase sensitive inversion recovery (PSIR) imaging for improved T/sub 1/ contrast. This method models the phase of the complex magnetic resonance image using a statistical model based on Markov random fields. A computationally efficient optimization method is developed. Computer simulations and in-vivo brain imaging experiments show that the proposed method can produce PSIR images with enhanced T/sub 1/ contrast and it is robust against high levels of data noise even when rapid phase variations are presented.

Measurements of water diffusion and T1 values in peritumoural oedematous brain

Using quantitative MR imaging, values for the mean water diffusivity (<D>), the diffusion anisotropy and the longitudinal relaxation time (T1) were measured for tumour, oedematous and normal brain in a group of treatment-naive patients with high-grade glioma and low-grade meningioma. The mean values of <D> and T1 for enhancing tumour and oedematous brain were significantly higher in high-grade glioma than meningioma, while the diffusion anisotropy was reduced. Values of <D> and T1 were also positively correlated in oedematous brain in both pathologies. There was, however, no clear evidence of similar correlations in apparently normal contralateral white matter. Such results illustrate the potential of MR imaging to improve not only the characterization of brain oedema, but also the monitoring of treatment response.

Imaging pulmonary blood flow and perfusion using phase-sensitive selective inversion recovery

A technique is described for imaging pulmonary blood flow using a phase-sensitive selective inversion recovery (PS-SIR) sequence. PS-SIR image reconstruction provides excellent contrast, differentiating fully relaxed inflowing blood from inverted blood and lung tissue. The magnetization of the inverted tissues remains negative at any inversion delay less than that at which the magnetization of the lung tissue is nulled, whereas that of the fully relaxed inflowing blood is always positive. Pulmonary blood flow can be observed by tracking the propagation of the pixels with positive values. Five healthy volunteers were imaged. The normal pattern of blood flow advancing from the central arteries toward the peripheries and into the lung parenchyma with return toward the center via draining veins was depicted. The method offers promise for evaluating pulmonary blood flow without the need for image subtraction or contrast administration. Magn Reson Med 43:793-795, 2000.

A method for fast multislice T1 measurement: feasibility studies on phantoms, young children, and children with Canavan's disease

We have developed a multislice protocol for quantitative T1 measurements in which the processing time and the acquisition time are under 2 minutes each for a complete brain study of 15 slices. An echoplanar, inversion-recovery image sequence is designed to collect data suitable for analysis using a linear regression algorithm. The precision is approximately twice the noise to signal ratio of the images. The accuracy of the protocol is better than 1% for T1 in the range 0-2 seconds and deviates slightly for longer T1 values. The protocol is insensitive to B1 field values. If needed, the data can be postprocessed using a slow, nonlinear algorithm to give an accuracy of less than 1% and a precision of approximately the noise to signal ratio throughout a range of T1 values from 0 to 4 seconds.

Parameter estimation from Rician-distributed data sets using a maximum likelihood estimator: application to T1 and perfusion measurements

General expressions are presented to calculate the maximum likelihood (ML) estimator and corresponding Fisher matrix for Rician-distributed data sets. This estimator results in the most precise, unbiased estimations of T1 from magnitude data sets, even when low signal-to-noise ratios (<6) are present. By optimizing the sample point distributions for inversion-recovery experiments, a 32% increase in precision of the estimated T1 is obtained, compared with a linear sampling scheme. Perfusion rates are estimated from combined data sets of the slice- and nonslice-selective inversion-recovery experiments, as obtained with the flow-sensitive alternating inversion recovery (FAIR) technique. The ML estimator for the combined data set results in the most precise, unbiased estimations of the perfusion rate. Error analysis shows that very high signal-to-noise ratios are required for precise estimation of perfusion rates from FAIR experiments.

FAIR excluding radiation damping (FAIRER)

It is shown that the flow-sensitive alternating inversion recovery (FAIR) technique is complicated by the effect of radiation damping, leading to problems in calibrating this method on phantoms and to inaccuracies in measured flows. A modified scheme called FAIRER (FAIR excluding radiation damping) is proposed, which suppresses the damping effects by employing very weak magnetic field gradients (0.06 G/cm) during the inversion recovery, spin-echo, and predelay periods. Results on phantoms and in vivo on cat brain are presented that demonstrate that FAIRER effectively solves these problems.

Inversion recovery image reconstruction with multiseed region-growing spin reversal

A new algorithm is introduced for inversion recovery (IR) image reconstruction. The original complex image is modeled as a product of three factors: magnitude, polarity, and a smoothly changing phase factor. The simple binary polarity factor is first unified by a region-growing spin reversal (RGSR) operation, allowing the phase factor to be extracted. Multiplying the complex conjugate of the phase factor with the original complex data yields the desired IR contrast. The RGSR process is repeated with multiple seeds distributed in the field of view (FOV), and the results are added together, enabling disconnected tissues in the FOV to be handled. The extracted phase factor is filtered to reduce noise and artifacts, without losing useful information. The method is fully automatic and has been used practically in a large number of clinical examinations. The algorithm may also be useful for phase correction in simple proton spectroscopic imaging.

Accurate T1 determination from inversion recovery images: application to human brain at 4 Tesla

It is well known that the signal polarity in inversion-recovery (IR) images changes with inversion time, complicating the determination of T1. To avoid this problem, a simple subtraction method is implemented. In this method, k-space data of the longest inversion time are subtracted from the corresponding data of each inversion time. This subtraction yields IR images of same polarity, making it straightforward to derive T1 using a standard fitting routine. Phantom T1 studies with IR Turbo-FLASH images demonstrate that this technique is robust and accurate. Four Tesla T1 values of the human brain were also determined by this method to demonstrate its in vivo utility.

A simple method for the restoration of signal polarity in multi-image inversion recovery sequences for measuring T1

A simple scheme for the polarity correction of absolute images from inversion recovery multi-image T1 measurement sequences is described and demonstrated. This method is less sensitive to errors from eddy currents than the conventional full phase correction schemes and does not involve an increase in measurement time as it requires no additional scans.

Short TI short TR inversion recovery imaging using reduced flip angles

In this paper the effects of reducing the flip angle of the 90-degree observation pulse in inversion recovery imaging are described and analyzed. When incorporated in an IR sequence with a short inversion time (STIR), reduction to the 90-degree pulse allows a significant shortening of the repetition time without loss in contrast, although at the expense of some signal/noise. The generalized STIR sequence thus combines the previously reported advantages of a conventional STIR sequence--suppression of ghost artifacts from abdominal wall movement, suppression of chemical shift and boundary artifacts, additive effects of N(H), T1 and T2 on image contrast--with reduced power deposition and the advantages resulting from shorter repetition times, viz, single heart-beat triggering, increased number of signal averages for suppression of motion artifacts, acquisition of interleaved contiguous slices without cross-talk or considerable time savings when the number of required slices is limited. The proposed method is demonstrated and experimentally verified by imaging experiments on phantoms and human subjects. In principle the method is applicable to all cases where STIR imaging has been proven to be successful.

Homodyne detection in magnetic resonance imaging

Magnetic detection of complex images in magnetic resonance imaging (MRI) is immune to the effects of incidental phase variations, although in some applications information is lost or images are degraded. It is suggested that synchronous detection or demodulation can be used in MRI systems in place of magnitude detection to provide complete suppression of undesired quadrature components, to preserve polarity and phase information, and to eliminate the biases and reduction in signal-to-noise ratio (SNR) and contrast in low SNR images. The incidental phase variations in an image are removed through the use of a homodyne demodulation reference, which is derived from the image or the object itself. Synchronous homodyne detection has been applied to the detection of low SNR images, the reconstruction of partial k-space images, the simultaneous detection of water and lipid signals in quadrature, and the preservation of polarity in inversion-recovery images.

Regional phase correction of inversion-recovery MR images

Many MR imaging systems are limited in their ability to successfully display inversion-recovery images. The reason is that part of the contrast is encoded as phase differences between pixels, whereas in the more commonly used spin-echo pulse sequence all the information is contained in the pixel magnitude. Inversion-recovery images are often displayed in magnitude form, resulting in loss of potentially useful phase information contained in the data. Before this phase information can be used, phase errors which result from scanner imperfections must be removed. While most of the necessary correction can be accomplished using data obtained by scanning a uniform phantom, this approach has several disadvantages. An alternative method by which phase errors can be readily removed without phantom data is described. This method has been applied to images of the head, knee, and liver with good results. It is concluded that this technique is useful for producing phase corrected inversion-recovery MR images.

A new phase correction method in NMR imaging based on autocorrelation and histogram analysis

A new statistical approach to phase correction in NMR imaging is proposed. The proposed scheme consists of first-and zero-order phase corrections each by the inverse multiplication of estimated phase error. The first-order error is estimated by the phase of autocorrelation calculated from the complex valued phase distorted image while the zero-order correction factor is extracted from the histogram of phase distribution of the first-order corrected image. Since all the correction procedures are performed on the spatial domain after completion of data acquisition, no prior adjustments or additional measurements are required. The algorithm can be applicable to most of the phase-involved NMR imaging techniques including inversion recovery imaging, quadrature modulated imaging, spectroscopic imaging, and flow imaging, etc. Some experimental results with inversion recovery imaging as well as quadrature spectroscopic imaging are shown to demonstrate the usefulness of the algorithm.

Real-value representation in inversion-recovery NMR imaging by use of a phase-correction method

A new technique of real-value representation in inversion-recovery (IR) imaging by use of a phase-correction method is proposed. In this scheme, negative magnetizations at the beginning of the inversion point (T1 approximately equal to 0) are correctly represented as negative values rather than positive. By use of this new scheme, a consistent IR image set as a function of several inversion times (T1) can be obtained. The latter, i.e., consistent image set which represents the real value of T1 weighted images at several inversion times is important in the search for tumors and abnormalities since the inversion time (T1) in the pulse sequence strongly affects T1 contrast.