Computational Fluid Dynamic Study for aTAA Hemodynamics: An Integrated Image-Based and Radial Basis Functions Mesh Morphing Approach

We present a novel framework for the fluid dynamics analysis of healthy subjects and patients affected by ascending thoracic aorta aneurysm (aTAA). Our aim is to obtain indications about the effect of a bulge on the hemodynamic environment at different enlargements. Three-dimensional (3D) surface models defined from healthy subjects and patients with aTAA, selected for surgical repair, were generated. A representative shape model for both healthy and pathological groups has been identified. A morphing technique based on radial basis functions (RBF) was applied to mold the shape relative to healthy patient into the representative shape of aTAA dataset to enable the parametric simulation of the aTAA formation. Computational fluid dynamics (CFD) simulations were performed by means of a finite volume solver using the mean boundary conditions obtained from three-dimensional (PC-MRI) acquisition. Blood flow helicity and flow descriptors were assessed for all the investigated models. The feasibility of the proposed integrated approach pertaining the coupling between an RBF morphing technique and CFD simulation for aTAA was demonstrated. Significant hemodynamic changes appear at the 60% of the bulge progression. An impingement of the flow toward the bulge was observed by analyzing the normalized flow eccentricity (NFE) index.

Accelerated PET kinetic maps estimation by analytic fitting method

In this work, we propose and test a new approach for non-linear kinetic parameters' estimation from dynamic PET data. A technique is discussed, to derive an analytical closed-form expression of the compartmental model used for kinetic parameters' evaluation, using an auxiliary parameter set, with the aim of reducing the computational burden and speeding up the fitting of these complex mathematical expressions to noisy TACs. Two alternative algorithms based on numeric calculations are considered and compared to the new proposal. We perform a simulation study aimed at (i) assessing agreement between the proposed method and other conventional ways of implementing compartmental model fitting, and (ii) quantifying the reduction in computational time required for convergence. It results in a speed-up factor of ∼120 when compared to a fully numeric version, or ∼38, with respect to a more conventional implementation, while converging to very similar values for the estimated model parameters. The proposed method is also tested on dynamic 3D PET clinical data of four control subjects. The results obtained supported those of the simulation study, and provided input and promising perspectives for the application of the proposed technique in clinical practice.

Estimation of pancreatic R2* for iron overload assessment in the presence of fat: a comparison of different approaches

OBJECTIVES

To propose a method for estimating pancreatic relaxation rate, R2*, from conventional multi-echo MRI, based on the nonlinear fitting of the acquired magnitude signal decay to MR signal models that take into account both the signal oscillations induced by fat and the different R2* values of pancreatic parenchyma and fat.

MATERIALS AND METHODS

Single-peak fat (SPF) and multi-peak fat (MPF) models were introduced. Single-R2* and dual-R2* assumptions were considered as well. Analyses were conducted on simulated data and 20 thalassemia major patients.

RESULTS

Simulations revealed the ability of the MPF model to correctly estimate the R2* value in a large range of fat fractions and R2* values. From the comparison between the results obtained with a single R2* value for water and fat and the dual-R2* approach, the latter is more accurate in both water R2* and fat fraction estimation. In patient's data analysis, a strong concordance was found between SPF and MPF estimated data with measurements done with manual signal correction and from fat-saturated images. The MPF method showed better reproducibility.

CONCLUSION

The MPF dual-R2* approach improves reproducibility and reduces image analysis time in the assessment of pancreatic R2* value in patients with iron overload.

Biomolecular imaging of 13C-butyrate with dissolution-DNP: Polarization enhancement and formulation for in vivo studies

Magnetic Resonance Spectroscopy of hyperpolarized isotopically enriched molecules facilitates the non-invasive real-time investigation of in vivo tissue metabolism in the time-frame of a few minutes; this opens up a new avenue in the development of biomolecular probes. Dissolution Dynamic Nuclear Polarization is a hyperpolarization technique yielding a more than four orders of magnitude increase in the ¹³C polarization for in vivo Magnetic Resonance Spectroscopy studies. As reported in several studies, the dissolution Dynamic Nuclear Polarization polarization performance relies on the chemico-physical properties of the sample. In this study, we describe and quantify the effects of the different sample components on the dissolution Dynamic Nuclear Polarization performance of [1-¹³C]butyrate. In particular, we focus on the polarization enhancement provided by the incremental addition of the glassy agent dimethyl sulfoxide and gadolinium chelate to the formulation. Finally, preliminary results obtained after injection in healthy rats are also reported, showing the feasibility of an in vivo Magnetic Resonance Spectroscopy study with hyperpolarized [1-¹³C]butyrate using a 3T clinical set-up.

Numerical evaluation of human exposure to WiMax patch antenna in tablet or laptop

The use of wireless communication devices, such as tablets or laptops, is increasing among children. Only a few studies assess specific energy absorption rate (SAR) due to exposure from wireless-enabled tablets and laptops, in particular with Worldwide Interoperability for Microwave Access (WiMax) technology. This paper reports the estimation of the interaction between an E-shaped patch antenna (3.5 GHz) and human models, by means of finite-difference time-domain (FDTD) method. Specifically, four different human models (young adult male, young adult female, pre-teenager female, male child) in different exposure conditions (antenna at different distances from the human model, in different positions, and orientations) were considered and whole-body, 10 and 1 g local SAR and magnetic field value (Bmax) were evaluated. From our results, in some worst-case scenarios involving male and female children's exposure, the maximum radiofrequency energy absorption (hot spots) is located in more sensitive organs such as eye, genitals, and breast. Bioelectromagnetics. 39:414-422, 2018. © 2018 Wiley Periodicals, Inc.

New Imaging Frontiers in Cardiology: Fast and Quantitative Maps from Raw Data

BACKGROUND

Among the novelties in the field of cardiovascular imaging, the construction of quantitative maps in a fast and efficient way is one of the most interesting aspects of the clinical research. Quantitative parametric maps are typically obtained by post processing dynamic images, that is, sets of images usually acquired in different temporal intervals, where several images with different contrasts are obtained. Magnetic resonance imaging, and emission tomography (positron emission and single photon emission) are the imaging techniques best suited for the formation of quantitative maps.

METHODS

In this review article we present several methods that can be used for obtaining parametric maps, in a fast way, starting from the acquired raw data. We describe both methods commonly used in clinical research, and more innovative methods that build maps directly from the raw data, without going through the image reconstruction.

RESULTS

We briefly described recently developed methods in magnetic resonance imaging that accelerate further the MR raw data generation, based on appropriate sub-sampling of k-space; then, we described recently developed methods for generating MR parametric maps. With regard to the emission tomography techniques, we gave an overview of both conventional methods, and more recently developed direct estimation algorithms for parametric image reconstruction from dynamic positron emission tomography data.

CONCLUSION

We have provided an overview of the possible approaches that can be followed to realize useful parametric maps from imaging raw data. We moved from the conventional approaches to more recent and efficient methods for accelerating the raw data generation and the of parametric maps formation.

Measured PET Data Characterization with the Negative Binomial Distribution Model

Accurate statistical model of PET measurements is a prerequisite for a correct image reconstruction when using statistical image reconstruction algorithms, or when pre-filtering operations must be performed. Although radioactive decay follows a Poisson distribution, deviation from Poisson statistics occurs on projection data prior to reconstruction due to physical effects, measurement errors, correction of scatter and random coincidences. Modelling projection data can aid in understanding the statistical nature of the data in order to develop efficient processing methods and to reduce noise. This paper outlines the statistical behaviour of measured emission data evaluating the goodness of fit of the negative binomial (NB) distribution model to PET data for a wide range of emission activity values. An NB distribution model is characterized by the mean of the data and the dispersion parameter α that describes the deviation from Poisson statistics. Monte Carlo simulations were performed to evaluate: (a) the performances of the dispersion parameter α estimator, (b) the goodness of fit of the NB model for a wide range of activity values. We focused on the effect produced by correction for random and scatter events in the projection (sinogram) domain, due to their importance in quantitative analysis of PET data. The analysis developed herein allowed us to assess the accuracy of the NB distribution model to fit corrected sinogram data, and to evaluate the sensitivity of the dispersion parameter α to quantify deviation from Poisson statistics. By the sinogram ROI-based analysis, it was demonstrated that deviation on the measured data from Poisson statistics can be quantitatively characterized by the dispersion parameter α, in any noise conditions and corrections.

A Cross-Correlational Analysis between Electroencephalographic and End-Tidal Carbon Dioxide Signals: Methodological Issues in the Presence of Missing Data and Real Data Results

Electroencephalographic (EEG) irreducible artifacts are common and the removal of corrupted segments from the analysis may be required. The present study aims at exploring the effects of different EEG Missing Data Segment (MDS) distributions on cross-correlation analysis, involving EEG and physiological signals. The reliability of cross-correlation analysis both at single subject and at group level as a function of missing data statistics was evaluated using dedicated simulations. Moreover, a Bayesian-based approach for combining the single subject results at group level by considering each subject’s reliability was introduced. Starting from the above considerations, the cross-correlation function between EEG Global Field Power (GFP) in delta band and end-tidal CO₂ (P_ETCO₂) during rest and voluntary breath-hold was evaluated in six healthy subjects. The analysis of simulated data results at single subject level revealed a worsening of precision and accuracy in the cross-correlation analysis in the presence of MDS. At the group level, a large improvement in the results’ reliability with respect to single subject analysis was observed. The proposed Bayesian approach showed a slight improvement with respect to simple average results. Real data results were discussed in light of the simulated data tests and of the current physiological findings.

A Conway-Maxwell-Poisson (CMP) model to address data dispersion on positron emission tomography

Positron emission tomography (PET) in medicine exploits the properties of positron-emitting unstable nuclei. The pairs of γ- rays emitted after annihilation are revealed by coincidence detectors and stored as projections in a sinogram. It is well known that radioactive decay follows a Poisson distribution; however, deviation from Poisson statistics occurs on PET projection data prior to reconstruction due to physical effects, measurement errors, correction of deadtime, scatter, and random coincidences. A model that describes the statistical behavior of measured and corrected PET data can aid in understanding the statistical nature of the data: it is a prerequisite to develop efficient reconstruction and processing methods and to reduce noise. The deviation from Poisson statistics in PET data could be described by the Conway-Maxwell-Poisson (CMP) distribution model, which is characterized by the centring parameter λ and the dispersion parameter ν, the latter quantifying the deviation from a Poisson distribution model. In particular, the parameter ν allows quantifying over-dispersion (ν<1) or under-dispersion (ν>1) of data. A simple and efficient method for λ and ν parameters estimation is introduced and assessed using Monte Carlo simulation for a wide range of activity values. The application of the method to simulated and experimental PET phantom data demonstrated that the CMP distribution parameters could detect deviation from the Poisson distribution both in raw and corrected PET data. It may be usefully implemented in image reconstruction algorithms and quantitative PET data analysis, especially in low counting emission data, as in dynamic PET data, where the method demonstrated the best accuracy.

Voice quality in patients suffering from bipolar disease

People suffering from bipolar disease are more and more common. Such pathology can severely affect patients' lifestyle by wide, and sometimes extreme, mood swings. Biosignals can be very useful to understand this disease. Specifically, speech-related features have been seen to vary in depressed people with respect to healthy subjects. Usually prosodic, spectral and energy-related features are studied. Some further information, instead, can be provided studying voice quality. According to Laver's model, voice quality is sensitive and depends on both anatomic/physiologic issues and long-term muscular adjustments of the larynx or the supraglottal vocal tract. A pilot study on both bipolar patients and healthy control subjects, performed by means of the Long-Term Average Spectrum (LTAS) is presented. The effects on LTAS estimation of a F0-correction procedure are discussed. Pairwise statistical comparisons between subjects in euthymic and depressed states and euthymic and hypomanic states were performed. Significant differences were found in some frequency intervals in both cases. The F0-correction procedure modified the values of the significant frequency intervals in the euthymic/depressed comparison, that also was characterized by a change of F0. Noticeably, no statistically significant differences were found in control subjects acquired in the same mood state. Though the number of subjects is small, the results are encouraging given their coherence across patients and the lack of differences in the control group. Finally, this work suggests that particular vocal settings might be involved in different mood states.

Analysis of the microcirculatory pulse wave: age-related alterations

Morphological analysis of the pulse wave of central blood pressure signal is commonly used for the study of cardiac and vascular properties, but very few attempts were performed for analyzing the peripheral pulse wave of blood flow. In this work, we analyzed this waveform using classical methods, based on the application of FFT, followed by principal components analysis, for assessing the properties of the blood flow. As a sample problem, we evaluated the capability of the proposed method of assessing the alterations correlated with the aging of the vascular system. Results show a good discrimination between the different age groups, confirming the validity of the approach.

A novel 3D Cartesian random sampling strategy for Compressive Sensing Magnetic Resonance Imaging

In this work we propose a novel acquisition strategy for accelerated 3D Compressive Sensing Magnetic Resonance Imaging (CS-MRI). This strategy is based on a 3D cartesian sampling with random switching of the frequency encoding direction with other K-space directions. Two 3D sampling strategies are presented. In the first strategy, the frequency encoding direction is randomly switched with one of the two phase encoding directions. In the second strategy, the frequency encoding direction is randomly chosen between all the directions of the K-Space. These strategies can lower the coherence of the acquisition, in order to produce reduced aliasing artifacts and to achieve a better image quality after Compressive Sensing (CS) reconstruction. Furthermore, the proposed strategies can reduce the typical smoothing of CS due to the limited sampling of high frequency locations. We demonstrated by means of simulations that the proposed acquisition strategies outperformed the standard Compressive Sensing acquisition. This results in a better quality of the reconstructed images and in a greater achievable acceleration.

Correlational analysis of electroencephalographic and end-tidal carbon dioxide signals during breath-hold exercise

The central mechanism of breathing control is not totally understood. Several studies evaluated the correlation between electroencephalographic (EEG) power spectra and respiratory signals by performing resting state tasks or adopting hypercapnic/hypoxic stimuli. The observation of brain activity during voluntary breath hold tasks, might be an useful approach to highlight the areas involved in mechanism of breath regulation. Nevertheless, studies of brain activity with EEG could present some limitations due to presence of severe artifacts. When artifact rejection methods, as independent component analysis, cannot reliably clean EEG data, it is necessary to exclude noisy segments. In this study, global field power in the delta band and end-tidal CO2 were derived from EEG and CO2 signals respectively in 4 healthy subjects during a breath-hold task. The cross correlation function between the two signals was estimated taking into account the presence of missing samples. The statistical significance of the correlation coefficients at different time lags was assessed using surrogate data. Some simulations are introduced to evaluate the effect of missing data on the correlational analysis and their results are discussed. Results obtained on subjects show a significant correlation between changes in EEG power in the delta band and end-tidal CO2. Moreover, the changes in end-tidal CO2 were found to precede those of global field power. These results might help to better understand the cortical mechanisms involved in the control of breathing.

Technological Innovations in Magnetic Resonance for Early Detection of Cardiovascular Diseases

Most recent technical innovations in cardiovascular MR imaging (CMRI) are presented in this review. They include hardware and software developments, and novelties in parametric mapping. All these recent improvements lead to high spatial and temporal resolution and quantitative information on the heart structure and function. They make it achievable ambitious goals in the field of magnetic resonance, such as the early detection of cardiovascular pathologies. In this review article, we present recent innovations in CMRI, emphasizing the progresses performed and the solutions proposed to some yet opened technical problems.

Variable density randomized stack of spirals (VDR-SoS) for compressive sensing MRI

PURPOSE

To develop a 3D sampling strategy based on a stack of variable density spirals for compressive sensing MRI.

METHODS

A random sampling pattern was obtained by rotating each spiral by a random angle and by delaying for few time steps the gradient waveforms of the different interleaves. A three-dimensional (3D) variable sampling density was obtained by designing different variable density spirals for each slice encoding. The proposed approach was tested with phantom simulations up to a five-fold undersampling factor. Fully sampled 3D dataset of a human knee, and of a human brain, were obtained from a healthy volunteer. The proposed approach was tested with off-line reconstructions of the knee dataset up to a four-fold acceleration and compared with other noncoherent trajectories.

RESULTS

The proposed approach outperformed the standard stack of spirals for various undersampling factors. The level of coherence and the reconstruction quality of the proposed approach were similar to those of other trajectories that, however, require 3D gridding for the reconstruction.

CONCLUSION

The variable density randomized stack of spirals (VDR-SoS) is an easily implementable trajectory that could represent a valid sampling strategy for 3D compressive sensing MRI. It guarantees low levels of coherence without requiring 3D gridding. Magn Reson Med 76:59-69, 2016. © 2015 Wiley Periodicals, Inc.