Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.

Purpose: For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical.

A fast MR fingerprinting simulator for direct error estimation and sequence optimization.

Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations.

Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization.

Magnetic resonance fingerprinting (MRF) is a method to extract quantitative tissue properties such as [Formula: see text] and [Formula: see text] relaxation rates from arbitrary pulse sequences using conventional MRI hardware. MRF pulse sequences have thousands of tunable parameters, which can be chosen to maximize precision and minimize scan time. Here, we perform de novo automated design of MRF pulse sequences by applying physics-inspired optimization heuristics.

3D magnetic resonance fingerprinting with quadratic RF phase.

Purpose: To implement 3D magnetic resonance fingerprinting (MRF) with quadratic RF phase (qRF-MRF) for simultaneous quantification of T , T , ΔB , and .

Methods: 3D MRF data with effective undersampling factor of 3 in the slice direction were acquired with quadratic RF phase patterns for T , T , and sensitivity. Quadratic RF phase encodes the off-resonance by modulating the on-resonance frequency linearly in time.

Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.

Purpose: This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort.

Methods: Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices.

Magnetic resonance fingerprinting review part 2: Technique and directions.

Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning.

Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations.

Magnetic resonance fingerprinting (MRF) is a powerful quantitative MRI technique capable of acquiring multiple property maps simultaneously in a short timeframe. The MRF framework has been adapted to a wide variety of clinical applications, but faces challenges in technical development, and to date has only demonstrated repeatability and reproducibility in small studies. In this review, we discuss the current implementations of MRF and their use in a clinical setting.

Partial volume mapping using magnetic resonance fingerprinting.

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T.

Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions.

Background: Conventional MRI can be limited in detecting subtle epileptic lesions or identifying active/epileptic lesions among widespread, multifocal lesions.

Purpose: We developed a high-resolution 3D MR fingerprinting (MRF) protocol to simultaneously provide quantitative T , T , proton density, and tissue fraction maps for detection and characterization of epileptic lesions.

Study Type: Prospective.

Three-dimensional MR Fingerprinting for Quantitative Breast Imaging.

Purpose To develop a fast three-dimensional method for simultaneous T1 and T2 quantification for breast imaging by using MR fingerprinting. Materials and Methods In this prospective study, variable flip angles and magnetization preparation modules were applied to acquire MR fingerprinting data for each partition of a three-dimensional data set. A fast postprocessing method was implemented by using singular value decomposition.

Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting.

Purpose: 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.

Theory: Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown.

Fast 3D magnetic resonance fingerprinting for a whole-brain coverage.

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

Methods: A 3D magnetic resonance fingerprinting scan was accelerated by using a single-shot spiral trajectory with an undersampling factor of 48 in the x-y plane, and an interleaved sampling pattern with an undersampling factor of 3 through plane. Further acceleration came from reducing the waiting time between neighboring partitions.

Low rank approximation methods for MR fingerprinting with large scale dictionaries.

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

Theory And Methods: We introduce a compressed MRF with randomized singular value decomposition method to significantly reduce the memory requirement for calculating a low rank approximation of large sized MRF dictionaries. We further relax this requirement by exploiting the structures of MRF dictionaries in the randomized singular value decomposition space and fitting them to low-degree polynomials to generate high resolution MRF parameter maps.

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.

Slice profile and B corrections in 2D magnetic resonance fingerprinting.

Purpose: 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 B imperfections, which are two main factors that affect quantitative results in MRF.

Methods: The SP and B imperfections are characterized and corrected separately. The SP effect is corrected by simulating the radiofrequency pulse in the dictionary, and the B is corrected by acquiring a B map using the Bloch-Siegert method before each scan.

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.

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.