Frequency-dependent diffusion kurtosis imaging in the human brain using an oscillating gradient spin echo sequence and a high-performance head-only gradient.

Measuring the time/frequency dependence of diffusion MRI is a promising approach to distinguish between the effects of different tissue microenvironments, such as membrane restriction, tissue heterogeneity, and compartmental water exchange. In this study, we measure the frequency dependence of diffusivity (D) and kurtosis (K) with oscillating gradient diffusion encoding waveforms and a diffusion kurtosis imaging (DKI) model in human brains using a high-performance, head-only MAGNUS gradient system, with a combination of b-values, oscillating frequencies (f), and echo time that has not been achieved in human studies before. Frequency dependence of diffusivity and kurtosis are observed in both global and local white matter (WM) and gray matter (GM) regions and characterized with a power-law model ∼Λ*fθ. The frequency dependences of diffusivity and kurtosis (including changes between fmin and fmax, Λ, and θ) vary over different WM and GM regions, indicating potential microstructural differences between regions. A trend of decreasing kurtosis over frequency in the short-time limit is successfully captured for in vivo human brains. The effects of gradient nonlinearity (GNL) on frequency-dependent diffusivity and kurtosis measurements are investigated and corrected. Our results show that the GNL has prominent scaling effects on the measured diffusivity values (3.5∼5.5% difference in the global WM and 6∼8% difference in the global cortex) and subsequently affects the corresponding power-law parameters (Λ, θ) while having a marginal influence on the measured kurtosis values (<0.05% difference) and power-law parameters (Λ, θ). This study expands previous OGSE studies and further demonstrates the translatability of frequency-dependent diffusivity and kurtosis measurements to human brains, which may provide new opportunities to probe human brain microstructure in health and disease.

Correcting image distortions from a nonlinear B 1 + $$ {\boldsymbol{B}}_{\mathbf{1}}

PURPOSE: To correct image distortions that result from nonlinear spatial variation in the transmit RF field amplitude ( B 1 + $$ {B}_1^{+} $$ ) when performing spatial encoding with the method called frequency-modulated Rabi encoded echoes (FREE). THEORY AND METHODS: An algorithm developed to correct image distortion resulting from the use of nonlinear static field (B0 ) gradients in standard MRI is adapted herein to correct image distortion arising from a nonlinear B 1 + $$ {B}_1^{+} $$ -gradient field in FREE. From a B 1 + $$ {B}_1^{+} $$ -map, the algorithm performs linear interpolation and intensity scaling to correct the image. The quality of the distortion correction is evaluated in 1.5T images of a grid phantom and human occipital lobe. RESULTS: An expanded theoretical description of FREE revealed the symmetry between this B 1 + $$ {B}_1^{+} $$ -gradient field spatial-encoding and standard B0 -gradient field spatial-encoding. The adapted distortion-correction algorithm substantially reduced image distortions arising in the spatial dimension that was encoded by the nonlinear B 1 + $$ {B}_1^{+} $$ gradient of a circular surface coil. CONCLUSION: Image processing based on straightforward linear interpolation and intensity scaling, as previously applied in conventional MRI, can effectively reduce distortions in FREE images acquired with nonlinear B 1 + $$ {B}_1^{+} $$ -gradient fields.

Impact of the Choice of Native T1 in Pixelwise Myocardial Blood Flow Quantification.

BACKGROUND: Quantification of myocardial blood flow (MBF) from dynamic contrast-enhanced (DCE) MRI can be performed using a signal intensity model that incorporates T1 values of blood and myocardium. PURPOSE: To assess the impact of T1 values on pixelwise MBF quantification, specifically to evaluate the influence of 1) study population-averaged vs. subject-specific, 2) diastolic vs. systolic, and 3) regional vs. global myocardial T1 values. STUDY TYPE: Prospective. SUBJECTS: Fifteen patients with chronic coronary heart disease. FIELD STRENGTH/SEQUENCE: 3T; modified Look-Locker inversion recovery for T1 mapping and saturation recovery gradient echo for DCE imaging, both acquired in a mid-ventricular short-axis slice in systole and diastole. ASSESSMENT: MBF was estimated using Fermi modeling and signal intensity nonlinearity correction with different T1 values: study population-averaged blood and myocardial, subject-specific systolic and diastolic, and segmental T1 values. Myocardial segments with perfusion deficits were identified visually from DCE series. STATISTICAL TESTS: The relationships between MBF parameters derived by different methods were analyzed by Bland-Altman analysis; corresponding mean values were compared by t-test. RESULTS: Using subject-specific diastolic T1 values, global diastolic MBF was 0.61 ± 0.13 mL/(min·g). It did not differ from global MBF derived from the study population-averaged T1 (P = 0.88), but the standard deviation of differences was large (0.07 mL/(min·g), 11% of mean MBF). Global diastolic and systolic MBF did not differ (P = 0.12), whereas global diastolic MBF using systolic (0.62 ± 0.13 mL/(min·g)) and diastolic T1 values differed (P < 0.05). If regional instead of global T1 values were used, segmental MBF was lower in segments with perfusion deficits (bias = -0.03 mL/(min·g), -7% of mean MBF, P < 0.05) but higher in segments without perfusion deficits (bias = 0.01 mL/(min·g), 1% of mean MBF, P < 0.05). DATA CONCLUSION: Whereas cardiac phase-specific T1 values have a minor impact on MBF estimates, subject-specific and myocardial segment-specific T1 values substantially affect MBF quantification. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

The adverse effect of gradient nonlinearities on diffusion MRI: From voxels to group studies.

Nonlinearities of gradient magnetic fields in diffusion MRI (dMRI) can introduce systematic errors in estimates of diffusion measures. While there are correction methods that can compensate for these errors, as presented in the Human Connectome Project, such nonlinear effects are often assumed to be negligible for typical applications, and as a result, gradient nonlinearities are mostly left uncorrected. In this work, we perform a systematic analysis to investigate the effect of gradient nonlinearities on dMRI studies, from voxel-wise estimates to group study outcomes. We present a novel framework to quantify and visualize these effects by decomposing them into their magnitude and angle components. Mean magnitude deviation and fractional gradient anisotropy are introduced to quantify the distortions in the size and shape of gradient vector distributions. By means of Monte-Carlo simulations and real data from the Human Connectome Project, the errors on dMRI measures derived from the diffusion tensor imaging and diffusional kurtosis imaging are highlighted. We perform a group study to showcase the alteration in the significance and effect size due to ignoring gradient nonlinearity correction. Our results indicate that the effect of gradient field nonlinearities on dMRI studies can be significant and may complicate the interpretation of the results and conclusions.

Fixed Pattern Noise Reduction and Linearity Improvement in Time-Mode CMOS Image Sensors.

In the paper, a digital clock stopping technique for gain and offset correction in time-mode analog-to-digital converters (ADCs) has been proposed. The technique is dedicated to imagers with massively parallel image acquisition working in the time mode where compensation of dark signal non-uniformity (DSNU) as well as photo-response non-uniformity (PRNU) is critical. Fixed pattern noise (FPN) reduction has been experimentally validated using 128-pixel CMOS imager. The reduction of the PRNU to about 0.5 LSB has been achieved. Linearity improvement technique has also been proposed, which allows for integral nonlinearity (INL) reduction to about 0.5 LSB. Measurements confirm the proposed approach.

Sensitivity of quantitative myocardial dynamic contrast-enhanced MRI to saturation pulse efficiency, noise and t1 measurement error: Comparison of nonlinearity correction methods.

PURPOSE: To compare methods designed to minimize or correct signal nonlinearity in quantitative myocardial dynamic contrast-enhanced (DCE) MRI. METHODS: DCE-MRI studies were simulated and data acquired in eight volunteers. Signal nonlinearity was corrected using either a dual-bolus approach or model-based correction using proton-density weighted imaging (conventional or dual-sequence acquisition) or T1 data (native or bookend). Scanning of healthy and infarcted myocardium at 3 T was simulated, including noise, saturation imperfection and T1 measurement error. Data were analyzed using model-based deconvolution with a one-compartment (mono-exponential) model. RESULTS: Substantial variation between methods was demonstrated in volunteers. In simulations the dual-bolus method proved stable for realistic levels of saturation efficiency but demonstrated bias due to residual nonlinearity. Model-based methods performed ideally in the absence of confounding error sources and were generally robust to noise or saturation imperfection, except for native T1 based correction which was highly sensitive to the latter. All methods demonstrated large variation in accuracy above an over-saturation level where baseline signal was nulled. For the dual-sequence approach this caused substantial bias at the saturation efficiencies observed in volunteers. CONCLUSION: The choice of nonlinearity correction method in myocardial DCE-MRI impacts on accuracy and precision of estimated parameters, particularly in the presence of nonideal saturation.

A time-correlated single photon counting SPAD array camera with a bespoke data-processing algorithm for lightsheet fluorescence lifetime imaging (FLIM) and FLIM videos.

A wide-field microscope with epi-fluorescence and selective plane illumination was combined with a single-photon avalanche diode (SPAD) array camera to enable live-cell fluorescence lifetime imaging (FLIM) using time-correlated single-photon counting (TCSPC). The camera sensor comprised of 192 × 128 pixels, each integrating a single SPAD and a time-to-digital converter. Jointly, they produced a stream of single-photon images of photon arrival times with ≈ 38 ps accuracy. The photon arrival times were subject to systematic delays and nonlinearities, which were corrected by a Monte-Carlo algorithm. The SPAD camera was then applied to FLIM where histogramming the resulting photon arrival times in each pixel resulted in decays compatible with common data processing pipelines for fluorescence lifetime analysis. The capabilities of the TCSPC camera-based FLIM microscope were demonstrated by imaging living unicellular photosynthetic algae and artificial lipid vesicles. Epi-fluorescence illumination enabled rapid fluorescence lifetime imaging of living cells and selective-plane illumination enabled 3-dimensional FLIM of stationary samples.

Design of multi-axis micromotion system in TERS and its nonlinearity and crosstalk correction method.

Tip-Enhanced Raman Spectroscopy (TERS) is an advanced analytical measurement technology combining Raman spectroscopy with Scanning Probe Microscopy, which can detect the molecular structure and chemical composition in micro-nano-scale. As an indispensable part, the micromotion system directly determines TERS spatial resolution. The existing multi-axis system is often composed of several single-axis nonlinear systems, which solves whole problems with a superposition idea of single-axis part. But the multi-axis crosstalk under an overall idea is not fully considered and will cause system uncooperative and even oscillational. Therefore, a multi-axis micromotion system in TERS and its correction method are proposed. The improved Duhem model, simple calculation without inversion, accurate matching and fast response, has been built for nonlinearity. And the feedforward decoupling method is designed for crosstalk, having a favorable multi-axis coordination, good error tracking and simplified controllers. Experimental results show that it can greatly correct the nonlinearity and crosstalk of multi-axis system simultaneously.

Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications.

PURPOSE: To empirically corroborate vendor-provided gradient nonlinearity (GNL) characteristics and demonstrate efficient GNL bias correction for human brain apparent diffusion coefficient (ADC) across 3T MR systems and spatial locations. METHODS: Spatial distortion vector fields (DVF) were mapped in 3D using a surface fiducial array phantom for individual gradient channels on three 3T MR platforms from different vendors. Measured DVF were converted into empirical 3D GNL tensors and compared with their theoretical counterparts derived from vendor-provided spherical harmonic (SPH) coefficients. To illustrate spatial impact of GNL on ADC, diffusion weighted imaging using three orthogonal gradient directions was performed on a volunteer brain positioned at isocenter (as a reference) and offset superiorly by 10-17 cm (>10% predicted GNL bias). The SPH tensor-based GNL correction was applied to individual DWI gradient directions, and derived ADC was compared with low-bias reference for human brain white matter (WM) ROIs. RESULTS: Empiric and predicted GNL errors were comparable for all three studied 3T MR systems, with <1.0% differences in the median and width of spatial histograms for individual GNL tensor elements. Median (±width) of ADC (10-3mm2/s) histograms measured at isocenter in WM reference ROIs from three MR systems were: 0.73 ± 0.11, 0.71 ± 0.14, 0.74 ± 0.17, and at off-isocenters (before versus after GNL correction) were respectively 0.63 ± 0.14 versus 0.72 ± 0.11, 0.53 ± 0.16 versus 0.74 ± 0.18, and 0.65 ± 0.16 versus 0.76 ± 0.18. CONCLUSION: The phantom-based spatial distortion measurements validated vendor-provided gradient fields, and accurate WM ADC was recovered regardless of spatial locations and clinical MR platforms using system-specific tensor-based GNL correction for routine DWI.