Bayesian uncertainty quantification for magnetic resonance fingerprinting.

Magnetic Resonance Fingerprinting (MRF) is a promising technique for fast quantitative imaging of human tissue. In general, MRF is based on a sequence of highly undersampled MR images which are analyzed with a pre-computed dictionary. MRF provides valuable diagnostic parameters such as theT1andT2MR relaxation times. However, uncertainty characterization of dictionary-based MRF estimates forT1andT2has not been achieved so far, which makes it challenging to assess if observed differences in these estimates are significant and may indicate pathological changes of the underlying tissue. We propose a Bayesian approach for the uncertainty quantification of dictionary-based MRF which leads to probability distributions forT1andT2in every voxel. The distributions can be used to make probability statements about the relaxation times, and to assign uncertainties to their dictionary-based MRF estimates. All uncertainty calculations are based on the pre-computed dictionary and the observed sequence of undersampled MR images, and they can be calculated in short time. The approach is explored by analyzing MRF measurements of a phantom consisting of several tubes across which MR relaxation times are constant. The proposed uncertainty quantification is quantitatively consistent with the observed within-tube variability of estimated relaxation times. Furthermore, calculated uncertainties are shown to characterize well observed differences between the MRF estimates and the results obtained from high-accurate reference measurements. These findings indicate that a reliable uncertainty quantification is achieved. We also present results for simulated MRF data and an uncertainty quantification for anin vivoMRF measurement. MATLAB®source code implementing the proposed approach is made available.

Compressed FTIR spectroscopy using low-rank matrix reconstruction.

Fourier transform infrared (FTIR) spectroscopy is a powerful technique in analytical chemistry. Typically, spatially distributed spectra of the substance of interest are conducted simultaneously using FTIR spectrometers equipped with array detectors. Scanning-based methods such as near-field FTIR spectroscopy, on the other hand, are a promising alternative providing higher spatial resolution. However, serial recording severely limits their application due to the long acquisition times involved and the resulting stability issues. We demonstrate that it is possible to significantly reduce the measurement time of scanning methods by applying the mathematical technique of low-rank matrix reconstruction. Data from a previous pilot study of Leishmania strains are analyzed by randomly selecting 5% of the interferometer samples. The results obtained for bioanalytical fingerprinting using the proposed approach are shown to be essentially the same as those obtained from the full set of data. This finding can significantly foster the practical applicability of high-resolution serial scanning techniques in analytical chemistry and is also expected to improve other applications of FTIR spectroscopy and spectromicroscopy.

Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization.

Quantification of myocardial perfusion by contrast-enhanced cardiovascular magnetic resonance imaging (CMR) aims for an observer independent and reproducible risk assessment of cardiovascular disease. Currently, the data used for the pixel-wise analysis of cardiac perfusion are either filtered prior to a fitting procedure, which inherently reduces the spatial resolution of data; or all pixels are considered without any regularization or prior filtering, which yields an unstable fit in the presence of low signal-to-noise ratio. Here, we propose a new pixel-wise analysis based on spatial Tikhonov regularization which exploits the spatial smoothness of the data and ensures accurate quantification even for images with low signal-to-noise ratio. The regularization parameter is determined automatically by an L-curve criterion. We study the performance of our method on a numerical phantom and demonstrate that the method reduces significantly the root-mean square error in the perfusion estimate compared to a non-regularized fit. In patient data our method allows us to recover the myocardial perfusion and to distinguish between healthy and ischemic regions.

Compressed sensing FTIR nano-spectroscopy and nano-imaging.

Infrared scattering scanning near-field optical microscopy (IR s-SNOM) provides for spectroscopic imaging with nanometer spatial resolution, yet full spatio-spectral imaging is constrained by long measurement times. Here, we demonstrate the application of compressed sensing algorithms to achieve hyperspectral FTIR-based nano-imaging at an order of magnitude faster imaging speed to achieve the same spectral content compared to conventional approaches. At the example of the spectroscopy of a single vibrational resonance, we discuss the relationship of prior knowledge of sparseness of the employed Fourier base functions and sub-sampling. Compressed sensing nano-FTIR spectroscopy promises both rapid and sensitive chemical nano-imaging which is highly relevant in academic and industrial settings for fundamental and applied nano- and bio-materials research.

Transfer Standard Uncertainty Can Cause Inconclusive Inter-Laboratory Comparisons.

Inter-laboratory comparisons use the best available transfer standards to check the participants' uncertainty analyses, identify underestimated uncertainty claims or unknown measurement biases, and improve the global measurement system. For some measurands, instability of the transfer standard can lead to an inconclusive comparison result. If the transfer standard uncertainty is large relative to a participating laboratory's uncertainty, the commonly used standardized degree of equivalence ≤ 1 criterion does not always correctly assess whether a participant is working within their uncertainty claims. We show comparison results that demonstrate this issue and propose several criteria for assessing a comparison result as passing, failing, or inconclusive. We investigate the behavior of the standardized degree of equivalence and alternative comparison measures for a range of values of the transfer standard uncertainty relative to the individual laboratory uncertainty values. The proposed alternative criteria successfully discerned between passing, failing, and inconclusive comparison results for the cases we examined.

Virtual experiment for near-field goniophotometric measurements.

Near-field goniometric measurements are employed to determine the photometric characteristics of light sources, i.e., the spatial and angular distribution of the emitted light. To this end, a complex measurement system consisting of a goniometer and a CCD-based imaging photometer is employed. In order to gain insight into the measurement system and to enable characterization of the whole measurement setup, we propose to apply a computer model to conduct virtual experiments. Within the computer model, the current state of all parts of the virtual experiment can be easily controlled. The reliability of the computer model is demonstrated by a comparison to actual measurement results. As an example for the application of the virtual experiment, we present an analysis of the impact of axial malpositions of the goniometer and camera.

Comparison and assessment of procedures for calculating the R(12) line strength of the ν1 + 2 ν2 + ν3 band of CO2.

Recently, results for the CO(2) R(12) line strength parameter have been reported, which differ significantly and are inconsistent with respect to quoted uncertainties. We investigate to what extent this inconsistency might be caused by the chosen data analysis methods. To this end, we assess and compare a parametric fitting procedure and a non-parametric approach. We apply the methods to simulated and measured line spectra, and we specify the conditions required for the safe application of the two procedures. For our present data, the corresponding conditions are satisfied for both methods, and consistent results are obtained. However, the simulations reveal that the fitting procedure can show shortcomings when the uncertainty in the wavenumber is large.

Patient safety concept for multichannel transmit coils.

PURPOSE: To propose and illustrate a safety concept for multichannel transmit coils in MRI based on finite-differences time-domain (FDTD) simulations and validated by measurements. MATERIALS AND METHODS: FDTD simulations of specific absorption rate (SAR) distributions in a cylindrical agarose phantom were carried out for various radio frequency (RF) driving conditions of a four-element coil array. Additionally, maps of transmit amplitude, signal phase, and temperature rise following RF heating were measured by MRI. RESULTS: Quantitative agreement was achieved between simulated and measured field distributions, thus validating the numerical modeling. When applying the same RF power to each element of the coil array but systematically varying the RF phase between its elements, the maximum of the SAR distribution was found to vary by a factor of about 15. CONCLUSION: Our results demonstrate that current RF safety approaches are inadequate to deal with the new challenge of multichannel transmit coils. We propose a new concept based on a systematic investigation of the parameter space for RF phases and amplitudes. In this way the driving conditions generating the highest local SAR values per unit power can be identified and appropriately considered in the RF safety concept of a given MRI system.

Neurovascular coupling analyzed non-invasively in the human brain.

Most functional brain imaging methods detect neuronal activations indirectly through the accompanying neurovascular response. Here, we demonstrate that a novel methodological approach, the combination of DC-magnetoencephalography (DC-MEG) and near-infrared spectroscopy (NIRS), allows non-invasive assessment of the dynamics of neurovascular coupling in the human brain: detecting directly slow neuronal processes (with time constants of 30s), DC-MEG revealed, even in unaveraged recordings, sustained neuronal activations at pericentral hand cortices contralateral to repetitive finger movements; these were accompanied by the ensuing local vascular response showing similar dynamical features as quantified by simultaneously recorded NIRS. This non-invasive approach opens a new avenue for the understanding of neurovascular coupling during physiological tasks as well as in diseases involving slow neuronal depolarization shifts and alterations of blood flow, such as stroke or migraine.

Non-invasive magnetic detection of human injury currents.

OBJECTIVE: Injury currents are a hallmark of acute lesions in polarized cells. Our objective was to develop a non-invasive technique for monitoring human near-DC injury currents in vivo. METHODS: Using diagnostic muscle biopsy as controlled paradigm, injury-related magnetic DC-fields were mapped for 60 min postsurgery over leg muscle lesions of 9 subjects. A 49-channel magnetometer was used in combination with a mechanical horizontal modulation of the subject beneath the sensor array. RESULTS: Magnetic DC-field maps showed salient differences between biopsy and contralateral legs in 8/9 patients with a characteristic slowly decaying field in all biopsy legs. A variety of anomalous DC-field patterns was recorded over the biopsy sites, corresponding to theoretically predicted geometric variations of equivalent DC-current dipoles, i.e. wound surfaces, pointing into opposing muscle fibre ends. By contrast, all control measurements showed an elongated dipolar DC-field pattern. Additionally, mean global DC-field strengths were significantly higher over biopsy legs compared to the contralateral site. CONCLUSIONS: Our pilot data illustrate that human injury currents can be detected using non-invasive magnetometry. Thus, DC-magnetometry may provide an essential new tool for clinical monitoring of injury currents, possibly also in brain tissue, e.g. in case of anoxic or peri-infarct depolarizations.

Cardiac artifact subspace identification and elimination in cognitive MEG data using time-delayed decorrelation.

To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis, signal-space projection, regression using secondary information, and independent component analysis. This method has become popular as it does not have constraints such as orthogonality between artifact and signal or the need for a priori information. Applying the time-delayed decorrelation algorithm to raw data from a visual stimulation MEG experiment, we show that several of the independent components can be attributed to the cardiac artifact. Calculating an average cardiac activity shows that physiologically different excitation states of the heart produce similar field distributions in the MEG sensor system. This is equivalent to differing spectral properties of cardiac field distributions in the raw data. As a consequence, the algorithm combines, e.g., the R peak and the T wave of the cardiac cycle into a single component and the one-to-one assignment of each independent component with a physiological source is not justified in this case. To improve the signal quality of visually evoked fields, the multidimensional cardiac artifact subspace is suppressed from the data. To assess the preservation of the evoked signal after artifact suppression, a geometrical and a temporal measure are introduced. The suppression of cardiac and alpha wave artifacts allows, in our experimental setting, the reduction of the number of epochs to one half while preserving the visually evoked signal.