Getting the phase consistent: The importance of phase description in balanced steady-state free precession MRI of multi-compartment systems

PURPOSE

Determine the correct mathematical phase description for balanced steady-state free precession (bSSFP) signals in multi-compartment systems.

THEORY AND METHODS

Based on published bSSFP signal models, different phase descriptions can be formulated: one predicting the presence and the other predicting the absence of destructive interference effects in multi-compartment systems. Numerical simulations of bSSFP signals of water and acetone were performed to evaluate the predictions of these different phase descriptions. For experimental validation, bSSFP profiles were measured at 3T using phase-cycled bSSFP acquisitions performed in a phantom containing mixtures of water and acetone, which replicates a system with two signal components. Localized single voxel MRS was performed at 7T to determine the relative chemical shift of the acetone-water mixtures.

RESULTS

Based on the choice of phase description, the simulated bSSFP profiles of water-acetone mixtures varied significantly, either displaying or lacking destructive interference effects, as predicted theoretically. In phantom experiments, destructive interference was consistently observed in the measured bSSFP profiles of water-acetone mixtures, supporting the theoretical description that predicts such interference effects. The connection between the choice of phase description and predicted observation enables unambiguous experimental identification of the correct phase description for multi-compartment bSSFP profiles, which is consistent with the Bloch equations.

CONCLUSION

The study emphasizes that consistent phase descriptions are crucial for accurately describing multi-compartment bSSFP signals, as incorrect phase descriptions result in erroneous predictions.

An Elaborated Signal Model for Simultaneous Range and Vector Velocity Estimation in FMCW Radar

A rigorous mathematical description of the signal reflected from a moving object for radar monitoring tasks using linear frequency modulated continuous wave (LFMCW) microwave radars is proposed. The mathematical model is based on the quasi-relativistic vector transformation of coordinates and Lorentz time. The spatio-temporal structure of the echo signal was obtained taking into account the transverse component of the radar target speed, which made it possible to expand the boundaries of the range of measuring the range and speed of vehicles using LFMCW radars. An algorithm for the simultaneous estimation of the range, radial and transverse components of the velocity vector of an object from the observation data of the time series during one frame of the probing signal is proposed. For an automobile 77 GHz microwave LFMCW radar, a computer experiment was carried out to measure the range and velocity vector of a radar target using the developed mathematical model of the echo signal and an algorithm for estimating the motion parameters. The boundaries of the range for measuring the range and speed of the target are determined. The results of the performed computer experiment are in good agreement with the results of theoretical analysis.

18Fluorodeoxyglucose Accumulation in Arterial Tissues Determined by PET Signal Analysis

BACKGROUND

Arterial ¹⁸fluorodeoxyglucose (FDG) positron emission tomography (PET) is considered a measure of atherosclerotic plaque macrophages and is used for quantification of disease activity in clinical trials, but the distribution profile of FDG across macrophages and other arterial cells has not been fully clarified.

OBJECTIVES

The purpose of this study was to analyze FDG uptake in different arterial tissues and their contribution to PET signal in normal and atherosclerotic arteries.

METHODS

Wild-type and D374Y-PCSK9 transgenic Yucatan minipigs were fed a high-fat, high-cholesterol diet to induce atherosclerosis and subjected to a clinical FDG-PET and computed tomography scan protocol. Volumes of arterial media, intima/lesion, macrophage-rich, and hypoxic tissues were measured in serial histological sections. Distributions of FDG in macrophages and other arterial tissues were quantified using modeling of the in vivo PET signal. In separate transgenic minipigs, the intra-arterial localization of FDG was determined directly by autoradiography.

RESULTS

Arterial FDG-PET signal appearance and intensity were similar to human imaging. The modeling approach showed high accuracy in describing the FDG-PET signal and revealed comparable FDG accumulation in macrophages and other arterial tissues, including medial smooth muscle cells. These findings were verified directly by autoradiography of normal and atherosclerotic arteries.

CONCLUSIONS

FDG is taken up comparably in macrophage-rich and -poor arterial tissues in minipigs. This offers a mechanistic explanation to a growing number of observations in clinical imaging studies that have been difficult to reconcile with macrophage-selective FDG uptake.

An Event-Driven AR-Process Model for EEG-Based BCIs With Rapid Trial Sequences

Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however, these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models' ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal model for event-related responses in the EEG evoked with a rapid sequence of stimuli in BCI applications. The model describes the EEG as a superposition of impulse responses time-locked to stimuli corrupted with an autoregressive noise process. The performance of the signal model is assessed in the context of RSVP keyboard, a language-model-assisted EEG-based BCI for typing. EEG data obtained for model calibration from 10 healthy participants are used to fit and compare two models: the proposed sequence-based EEG model and the trial-based feature-class-conditional distribution model that ignores temporal dependencies, which has been used in the previous work. The simulation studies indicate that the earlier model that ignores temporal dependencies may be causing drastic reductions in achievable information transfer rate (ITR). Furthermore, the proposed model, with better regularization, may achieve improved accuracy with fewer calibration data samples, potentially helping to reduce calibration time. Specifically, results show an average 8.6% increase in (cross-validated) calibration AUC for a single channel of EEG, and 54% increase in the ITR in a typing task.