Erik Gudmundson

A robust methodology for in vivo T1 mapping

In this article, a robust methodology for in vivo T1 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 T1 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 T1 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. T1 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. Magn Reson Med, 2010.

Blood velocity estimation using ultrasound and spectral iterative adaptive approaches

This paper proposes two novel iterative data-adaptive spectral estimation techniques for blood velocity estimation using medical ultrasound scanners. The techniques make no assumption on the sampling pattern of the emissions or the depth samples, allowing for duplex mode transmissions where B-mode images are interleaved with the Doppler emissions. Furthermore, the techniques are shown, using both simplified and more realistic Field II simulations as well as in vivo data, to outperform current state-of-the-art techniques, allowing for accurate estimation of the blood velocity spectrum using only 30% of the transmissions, thereby allowing for the examination of two separate vessel regions while retaining an adequate updating rate of the B-mode images. In addition, the proposed methods also allow for more flexible transmission patterns, as well as exhibit fewer spectral artifacts as compared to earlier techniques.

Spectral estimation of irregularly sampled exponentially decaying signals with applications to RF spectroscopy

The problem of estimating the spectral content of exponentially decaying signals from a set of irregularly sampled data is of considerable interest in several applications, for example in various forms of radio frequency spectroscopy. In this paper, we propose a new nonparametric iterative adaptive approach that provides a solution to this estimation problem. As opposed to commonly used methods in the field, the damping coefficient, or linewidth, is explicitly modeled, which allows for an improved estimation performance. Numerical examples using both simulated data and data from NQR experiments illustrate the benefits of the proposed estimator as compared to currently available nonparametric methods.

Robust Nuclear Quadrupole Resonance Signal Detection Allowing for Amplitude Uncertainties

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency spectroscopic technique that can be used to detect compounds which contain quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. Unfortunately, the low signal-to-noise ratio (SNR) of the observed signals currently inhibits the widespread use of the technique, thus highlighting the need for intelligent processing algorithms. In earlier work, we proposed a set of maximum likelihood-based algorithms enabling detection of even very weak NQR signals. These algorithms are based on derived realistic NQR data models, assuming that the (complex) amplitudes of the NQR signal components are known to within a multiplicative constant. However, these amplitudes, which are obtained from experimental measurements, are typically prone to some level of uncertainty. For such cases, these algorithms will experience a loss in performance. Herein, we develop a set of robust algorithms, allowing for uncertainties in the assumed amplitudes, showing that these offer a significant performance gain over the current state-of-the art techniques.

Parameter estimation approach to banding artifact reduction in balanced steady-state free precession

The balanced steady‐state free precession (bSSFP) pulse sequence has shown to be of great interest due to its high signal‐to‐noise ratio efficiency. However, bSSFP images often suffer from banding artifacts due to off‐resonance effects, which we aim to minimize in this article.

Computationally Efficient Time-Recursive IAA-Based Blood Velocity Estimation

High-resolution spectral Doppler is an important and powerful noninvasive tool for estimation of velocities in blood vessels using medical ultrasound scanners. Such estimates are typically formed using an averaged periodogram technique, resulting in well-known limitations in the resulting spectral resolution. Recently, we have proposed techniques to instead form high-resolution data-adaptive estimates exploiting measurements along both depth and emission. The resulting estimates gives noticeably superior velocity estimates as compared to the standard technique, but suffers from a high computational complexity, making it interesting to formulate computationally efficient implementations of the estimators. In this work, by exploiting the rich structure of the iterative adaptive approach (IAA) based estimator, we examine how these estimates can be efficiently implemented in a time-recursive manner using both exact and approximate formulations of the method. The resulting algorithms are shown to reduce the necessary computational load with several orders of magnitude without noticeable loss of performance.

Detection and classification of liquid explosives using NMR

In this work, we present a novel method for non-invasive identification of liquids, for instance to allow for the detection of liquid explosives at airports or border controls. The approach is based on a nuclear magnetic resonance technique with an inhomogeneous magnetic field, forming estimates of the liquids spin-spin relaxation time, T2, and diffusion constant, D, thereby allowing for a unique classification of the liquid. The proposed detectors are evaluated using both simulated and measured data sets.

Automatic Smoothing of Periodograms

Thresholding the cepstrum associated with the periodogram is a smoothing technique that appears to be very useful for variance reduction. Here the thresholding is performed via the methods SThresh and EbayesThresh. They both work fine in the broadband spectra case, even if some of the data is missing. The SThresh method appears to be more efficient as it shows a smaller variance and is faster computationally. The smoothing methods are also shown to perform well on a real-life broadband signal

An esprit-based parameter estimator for spectroscopic data

The pulse spin-locking sequence is a common excitation sequence for magnetic resonance and nuclear quadrupole resonance signals, with the resulting measurement data being well modeled as a train of exponentially damped sinusoidals. In this paper, we derive an ESPRIT-based estimator for such signals, together with the corresponding Cramér-Rao lower bound. The proposed estimator is computationally efficient and only requires prior knowledge of the number of spectral lines, which is in general available in the considered applications. Numerical simulations indicate that the proposed method is close to statistically efficient, and that it offers an attractive approach for initialization of existing statistically efficient gradient or search based techniques.

NQR-based explosives detection—an overview

Nuclear quadrupole resonance (NQR) is a radio frequency spectroscopic technique that can be used to detect solid-state compounds containing quadrupolar nuclei, a requirement fulfilled by most high explosives (and narcotics). In this paper, we present an overview of recent research in the detection of explosives using this technique. We also present mathematical models for the data for different acquistion techniques and discuss different state of the art detection algorithms. Finally, we evaluate various algorithms on measured and simulated NQR data.