Optimal pulse sequences for magnetic resonance imaging-computing accurate t1, t2, and proton density images

An information theory model for optimizing quantitative magnetic resonance imaging acquisitions

Acquisition parameter selection is currently performed empirically for many quantitative MRI (qMRI) acquisitions. Tuning parameters for different scan times, tissues, and resolutions requires some amount of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to minimize variability of quantitative maps and post-processing techniques such as synthetic image generation. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for qMRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of potential acquisitions and allowing informed parameter selection. The information theory framework was tested on artificial data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The phantom measurements showed that higher mutual information calculated by the model correlated with smaller coefficient of variation in the reconstructed parametric maps, and in vivo measurements demonstrated that information-based calibration of acquisition parameters resulted in a decrease in parametric map variability consistent with model predictions.

Flip-angle profile of slice-selective excitation and the measurement of the MR longitudinal relaxation time with steady-state magnetization

In MRI, the flip angle (FA) of slice-selective excitation is not uniform across the slice-thickness dimension. This work investigates the effect of the non-uniform FA profile on the accuracy of a commonly-used method for the measurement, in which the T1 value, i.e., the longitudinal relaxation time, is determined from the steady-state signals of an equally-spaced RF pulse train. By using the numerical solutions of the Bloch equation, it is shown that, because of the non-uniform FA profile, the outcome of the T1 measurement depends significantly on T1 of the specimen and on the FA and the inter-pulse spacing τ of the pulse train. A new method to restore the accuracy of the T1 measurement is described. Different from the existing approaches, the new method also removes the FA profile effect for the measurement of the FA, which is normally a part of the T1 measurement. In addition, the new method does not involve theoretical modeling, approximation, or modification to the underlying principle of the T1 measurement. An imaging experiment is performed, which shows that the new method can remove the FA-, the τ-, and the T1-dependence and produce T1 measurements in excellent agreement with the ones obtained from a gold standard method (the inversion-recovery method).

Imaging sequences in cardiovascular magnetic resonance: current role, evolving applications, and technical challenges

Cardiovascular magnetic resonance (CMR) has been established as a powerful and comprehensive imaging modality for studying the cardiovascular (CV) system. Shortly after invention of magnetic resonance imaging, CMR applications and developments started to emerge, and they continue to evolve up to the present day. CMR has the advantages of high spatial resolution, enhanced tissue contrast, superior safety profile, and the plethora of physiological parameters that can be obtained. In the near future, CMR is expected to be the gold standard modality for comprehensive CV imaging. Specifically, CMR imaging sequences are increasingly growing in parallel with advancements in scanner hardware. Not only do CMR imaging sequences provide detailed anatomical information, but they also provide functional, perfusion, viability, hemodynamic, and metabolic information about the CV system. In this article, an up-to-date review of different CMR imaging sequences is presented. Each sequence is described along with typical imaging parameters, necessary image processing steps, derived CV parameters, and potential applications. The article then addresses advanced CMR imaging techniques and emerging applications. Finally, the challenges facing CMR imaging are discussed along with its expected future role.

Multi-level adaptive segmentation of multi-parameter MR brain images

MR brain image segmentation into several tissue classes is of significant interest to visualize and quantify individual anatomical structures. Traditionally, the segmentation is performed manually in a clinical environment that is operator dependent and may be difficult to reproduce. Though several algorithms have been investigated in the literature for computerized automatic segmentation of MR brain images, they are usually targeted to classify image into a limited number of classes such as white matter, gray matter, cerebrospinal fluid and specific lesions. We present a novel model-based method for the automatic segmentation and classification of multi-parameter MR brain images into a larger number of tissue classes of interest. Our model employs 15 brain tissue classes instead of the commonly used set of four classes, which were of clinical interest to neuroradiologists for following-up with patients suffering from cerebrovascular deficiency (CVD) and/or stroke. The model approximates the spatial distribution of tissue classes by a Gauss Markov random field and uses the maximum likelihood method to estimate the class probabilities and transitional probabilities for each pixel of the image. Multi-parameter MR brain images with T(1), T(2), proton density, Gd+T(1), and perfusion imaging were used in segmentation and classification. In the development of the segmentation model, true class-membership of measured parameters was determined from manual segmentation of a set of normal and pathologic brain images by a team of neuroradiologists. The manual segmentation was performed using a human-computer interface specifically designed for pixel-by-pixel segmentation of brain images. The registration of corresponding images from different brains was accomplished using an elastic transformation. The presented segmentation method uses the multi-parameter model in adaptive segmentation of brain images on a pixel-by-pixel basis. The method was evaluated on a set of multi-parameter MR brain images of a twelve-year old patient 48h after suffering a stroke. The results of classification as compared to the manual segmentation of the same data show the efficacy and accuracy of the presented methods as well as its capability to create and learn new tissue classes.

Optimization of MRI protocols and pulse sequence parameters for eigenimage filtering

The eigenimage filter generates a composite image in which a desired feature is segmented from interfering features. The signal-to-noise ratio (SNR) of the eigenimage equals its contrast-to-noise ratio (CNR) and is directly proportional to the dissimilarity between the desired and interfering features. Since image gray levels are analytical functions of magnetic resonance imaging (MRI) parameters, it is possible to maximize this dissimilarity by optimizing these parameters. For optimization, the authors consider four MRI pulse sequences: multiple spin-echo (MSE); spin-echo (SE); inversion recovery (IR); and gradient-echo (GE). The authors use the mathematical expressions for MRI signals along with intrinsic tissue parameters to express the objective function (normalized SNR of the eigenimage) in terms of MRI parameters. The objective function along with a set of diagnostic or instrumental constraints define a multidimensional nonlinear constrained optimization problem, which the authors solve by the fixed point approach. The optimization technique is demonstrated through its application to phantom and brain images. The authors show that the optimal pulse sequence parameters for a sequence of four MSE and one IR images almost doubles the smallest normalized SNR of the brain eigenimages, as compared to the conventional brain protocol.

Parameter estimation and tissue segmentation from multispectral MR images

A statistical method is developed to classify tissue types and to segment the corresponding tissue regions from relaxation time T(1 ), T(2), and proton density P(D) weighted magnetic resonance images. The method assumes that the distribution of image intensities associated with each tissue type can be expressed as a multivariate likelihood function of three weighted signal intensity values (T(1), T(2), P(D)) at each location within that tissue regions. The method further assumes that the underlying tissue regions are piecewise contiguous and can be characterized by a Markov random field prior. In classifying the tissue types, the method models the likelihood of realizing the images as a finite multivariate-mixture function. The class parameters associated with the tissue types (i.e. the weighted intensity means, variances and correlation coefficients of the multivariate function, as well as the number of voxels within regions of the tissue types of are estimated by maximum likelihood. The estimation fits the class parameters to the image data via the expectation-maximization algorithm. The number of classes associated with the tissue types is determined by the information criterion of minimum description length. The method segments the tissue regions, given the estimated class parameters, by maximum a posteriori probability. The prior is constructed by the tissue-region membership of the first- and second-order neighborhood. The method is tested by a few sets of T(1), T(2), and P(D) weighted images of the brain acquired with a 1.5 Tesla whole body scanner. The number of classes and the associated class parameters are automatically estimated. The regions of different brain tissues are satisfactorily segmented.