Debra McGivney

SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

Slice profile and B1 corrections in 2D magnetic resonance fingerprinting

The goal of this study is to characterize and improve the accuracy of 2D magnetic resonance fingerprinting (MRF) scans in the presence of slice profile (SP) and B1 imperfections, which are two main factors that affect quantitative results in MRF.

Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer.

Purpose To develop and evaluate an examination consisting of magnetic resonance (MR) fingerprinting-based T1, T2, and standard apparent diffusion coefficient (ADC) mapping for multiparametric characterization of prostate disease. Materials and Methods This institutional review board-approved, HIPAA-compliant retrospective study of prospectively collected data included 140 patients suspected of having prostate cancer. T1 and T2 mapping was performed with fast imaging with steady-state precession-based MR fingerprinting with ADC mapping. Regions of interest were drawn by two independent readers in peripheral zone lesions and normal-appearing peripheral zone (NPZ) tissue identified on clinical images. T1, T2, and ADC were recorded for each region. Histopathologic correlation was based on systematic transrectal biopsy or cognitively targeted biopsy results, if available. Generalized estimating equations logistic regression was used to assess T1, T2, and ADC in the differentiation of (a) cancer versus NPZ, (b) cancer versus prostatitis, (c) prostatitis versus NPZ, and (d) high- or intermediate-grade tumors versus low-grade tumors. Analysis was performed for all lesions and repeated in a targeted biopsy subset. Discriminating ability was evaluated by using the area under the receiver operating characteristic curve (AUC). Results In this study, 109 lesions were analyzed, including 39 with cognitively targeted sampling. T1, T2, and ADC from cancer (mean, 1628 msec ± 344, 73 msec ± 27, and 0.773 × 10-3 mm2/sec ± 0.331, respectively) were significantly lower than those from NPZ (mean, 2247 msec ± 450, 169 msec ± 61, and 1.711 × 10-3 mm2/sec ± 0.269) (P < .0001 for each) and together produced the best separation between these groups (AUC = 0.99). ADC and T2 together produced the highest AUC of 0.83 for separating high- or intermediate-grade tumors from low-grade cancers. T1, T2, and ADC in prostatitis (mean, 1707 msec ± 377, 79 msec ± 37, and 0.911 × 10-3 mm2/sec ± 0.239) were significantly lower than those in NPZ (P < .0005 for each). Interreader agreement was excellent, with an intraclass correlation coefficient greater than 0.75 for both T1 and T2 measurements. Conclusion This study describes the development of a rapid MR fingerprinting- and diffusion-based examination for quantitative characterization of prostatic tissue.

Left and right preconditioning for electrical impedance tomography with structural information

A common problem in computational inverse problems is to find an efficient way of solving linear or nonlinear least-squares problems. For large-scale problems, iterative solvers are the method of choice for solving the associated linear systems, and for nonlinear problems, an additional effective local linearization method is required. In this paper, we discuss an efficient preconditioning scheme for Krylov subspace methods, based on the Bayesian analysis of the inverse problem. The model problem to which we apply this methodology is electrical impedance tomography (EIT) augmented with prior information coming from a complementary modality, such as x-ray imaging. The particular geometry considered here models the x-ray-guided EIT for breast imaging. The interest in applying EIT concurrently with x-ray breast imaging arises from the experimental observation that the impedivity spectra of certain types of malignant and benign tissues differ significantly from each other, thus offering a possibility of diagnosis without more invasive tissue sampling. After setting up the EIT inverse problem within a Bayesian framework, we present an inner and outer iteration scheme for computing a maximum a posteriori estimate. The prior covariance provides a right preconditioner and the modeling error covariance provides a left preconditioner for the iterative method used to solve the linear least-squares problem at each outer iteration of the optimization problem. Moreover, the stopping criterion for the inner iterations is coupled with the progress of the solution of the outer iteration. Besides the preconditioning scheme, the computational efficiency relies on a very efficient method to compute the Jacobian, obtained by carefully organizing the forward computation. Computed examples illustrate the robustness and computational efficiency of the proposed algorithm.

Fast 3D magnetic resonance fingerprinting for a whole‐brain coverage

The purpose of this study was to accelerate the acquisition and reconstruction time of 3D magnetic resonance fingerprinting scans.

Quantitative imaging with electrical impedance spectroscopy.

Electrical impedance spectroscopy (EIS) is a noninvasive modality that can be used to determine the electrical admittivity inside a body given a discrete set of current/voltage measurements made on the surface. Of particular interest is the use of EIS in the diagnosis of breast cancer, as the admittivity spectra of malignant and benign tumors differ significantly. Due to the fact that x-ray mammography is the current standard method of breast imaging to detect tumors, it is natural to see if we can use the admittivity distribution along with the mammogram image to improve the diagnosis, with the hopes that the specificity of these two methods combined will be greatly improved from using the mammogram image on its own. EIS is a highly ill-posed inverse problem, but regularization, in the form of structural prior information from the mammogram image as well as modeling error, allows for the problem to be solved for in a computationally efficient manner with improved results. To interpret the solution from the EIS inverse problem, a classification scheme is added, providing a quantitative image which maps out the tissue classification of the inside of the breast. The computational methods for solving the EIS inverse problem and the classification scheme are discussed and computed examples are presented to demonstrate the high simulated sensitivity and specificity of the method.

Low rank approximation methods for MR fingerprinting with large scale dictionaries

This work proposes new low rank approximation approaches with significant memory savings for large scale MR fingerprinting (MRF) problems.

Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting

To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori.

Magnetic resonance fingerprinting: a technical review

Multiparametric quantitative imaging is gaining increasing interest due to its widespread advantages in clinical applications. Magnetic resonance fingerprinting is a recently introduced approach of fast multiparametric quantitative imaging. In this article, magnetic resonance fingerprinting acquisition, dictionary generation, reconstruction, and validation are reviewed.