Diffusion-encoding Gradient Research Articles

A comparison of diffusion tensor imaging tractography and constrained spherical deconvolution with automatic segmentation in traumatic brain injury

Detection of microstructural white matter injury in traumatic brain injury (TBI) requires specialised imaging methods, of which diffusion tensor imaging (DTI) has been extensively studied. Newer fibre alignment estimation methods, such as constrained spherical deconvolution (CSD), are better than DTI in resolving crossing fibres that are ubiquitous in the brain and may improve the ability to detect microstructural injuries. Furthermore, automatic tract segmentation has the potential to improve tractography reliability and accelerate workflow compared to the manual segmentation commonly used. In this study, we compared the results of deterministic DTI based tractography and manual tract segmentation with CSD based probabilistic tractography and automatic tract segmentation using TractSeg. 37 participants with a history of TBI (with Glasgow Coma Scale 13–15) and persistent symptoms, and 41 healthy controls underwent deterministic DTI-based tractography with manual tract segmentation and probabilistic CSD-based tractography with TractSeg automatic segmentation.Fractional anisotropy (FA) and mean diffusivity of corpus callosum and three bilateral association tracts were measured. FA and MD values derived from both tractography methods were generally moderately to strongly correlated. CSD with TractSeg differentiated the groups based on FA, while DTI did not. CSD and TractSeg-based tractography may be more sensitive in detecting microstructural changes associated with TBI than deterministic DTI tractography. Additionally, CSD with TractSeg was found to be applicable at lower b-value and number of diffusion-encoding gradients data than previously reported.

Abstract P3-03-03: Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

Abstract Background: Early and accurate prediction of response to neoadjuvant therapy (NAT) would empower personalization of breast cancer treatment regimens based on expected response. Noninvasive, quantitative dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), when performed during the course of NAT, can accurately predict the ultimate pathological response. While these techniques have been incorporated into clinical trials in the academic setting, they have not yet been translated to the community setting, where most cancer patients receive their care. Implementation of quantitative MRI in the community setting widely expands the potential impact it can provide by 1) allowing community settings to participate in clinical trials that require quantitative MRI, and 2) advancing quantitative MRI towards standard-of-care for prediction of response in breast cancer. Methods: Women with locally advanced breast cancer (N = 28) were imaged four times during the course of NAT: 1) prior to the start of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after MRI #3. Imaging data was acquired on 3T Siemens Skyra scanners equipped with breast coils and sited in a community hospital and radiology clinic, respectively. DW-MRI and DCE-MRI were acquired over 10 slices of 5 mm thickness. DW-MRI was acquired with diagonal monopolar diffusion-encoding gradients with b-values of 0, 200, and 800 s/mm2 in a total scan time of 1 minute 39 seconds. Voxel wise tumor cellularity was quantified using the apparent diffusion coefficient (ADC). For DCE-MRI, a gadolinium-based contrast agent was administered intravenously at 2 mL/sec after the acquisition of baseline scans. DCE-MRI data was acquired dynamically with a temporal resolution of 7.27 seconds for a total acquisition time of 8 minutes. The volume transfer constant Ktrans was calculated using Patlak analysis of DCE-MRI data to characterize the tumor vasculature. The tumor was semi-automatically segmented using a manually drawn region of interest followed by fuzzy c-means clustering of DCE-MRI data to identify a functional tumor volume. Measurements of tumor volume were combined with both ADC and Ktrans to yield metrics of tumor cellularity and bulk tumor flow, respectively. Results: Women who achieved pathological complete response at the time of surgery (pCR; n=8) displayed significantly different treatment-induced changes in MRI-derived tumor parameters versus women who did not achieve pCR (non-pCR, n=20). After 1 cycle of NAT, women who achieved pCR had smaller functional tumor volume and lower cellularity (p < 0.05) than non-pCR study participants. At the third and fourth MRI, tumor volume, ADC, Ktrans, cellularity, and bulk tumor flow were all significantly different between the pCR and non-pCR cohorts (p < 0.05). Of note, longest tumor diameter was not predictive of pCR at any time point in this study. Conclusions: This study demonstrates that quantitative DCE- and DW-MRI can be implemented successfully in community care facilities within standard-of-care settings for imaging locally advanced breast cancer. Metrics extracted from the change in DW-MRI (ADC) and DCE-MRI (Ktrans) can accurately predict pathological complete response to neoadjuvant therapy and may be more sensitive to tumor response than the RECIST criteria. Furthermore, incorporating quantitative metrics with tumor volume further increases the ability to predict pathological response to NAT in locally advanced breast cancer. While there are still challenges to address to effectively implement these quantitative metrics into the clinical workflow, this study is first in its kind to transition a decade’s worth of quantitative MRI advancements from academic settings into standard-of-care. Citation Format: John Virostko, Anna G Sorace, Kalina P Slavkova, Anum S Kazerouni, Angela M Jarrett, Julie C DiCarlo, Stefanie Woodard, Sarah Avery, Boone W Goodgame, Debra Patt, Thomas E Yankeelov. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-03.

Anisotropy measure from three diffusion-encoding gradient directions

We propose a method that can provide information about the anisotropy and orientation of diffusion in the brain from only 3 orthogonal gradient directions without imposing additional assumptions. The method is based on the Diffusion Anisotropy (DiA) that measures the distance from a diffusion signal to its isotropic equivalent. The original formulation based on a Spherical Harmonics basis allows to go down to only 3 orthogonal directions in order to estimate the measure. In addition, an alternative simplification and a color-coding representation are also proposed. Acquisitions from a publicly available database are used to test the viability of the proposal. The DiA succeeded in providing anisotropy information from the white matter using only 3 diffusion-encoding directions. The price to pay for such reduced acquisition is an increment in the variability of the data and a subestimation of the metric on those tracts not aligned with the acquired directions. Nevertheless, the calculation of anisotropy information from DMRI is feasible using fewer than 6 gradient directions by using DiA. The method is totally compatible with existing acquisition protocols, and it may provide complementary information about orientation in fast diffusion acquisitions.

An Unsupervised Deep Learning Approach for Dynamic-Exponential Intravoxel Incoherent Motion MRI Modeling and Parameter Estimation in the Liver.

Dynamic-exponential intravoxel incoherent motion (IVIM) imaging is a potential technique for prediction, monitoring, and differential diagnosis of hepatic diseases, especially liver tumors. However, the use of such technique at voxel level is still limited. To develop an unsupervised deep learning approach for voxel-wise dynamic-exponential IVIM modeling and parameter estimation in the liver. Prospective. Ten healthy subjects (4 males; age 28 ± 6 years). Single-shot spin-echo echo planar imaging (SE-EPI) sequence with monopolar diffusion-encoding gradients (12 b-values, 0-800 seconds/mm2 ) at 3.0 T. The proposed deep neural network (DNN) was separately trained on simulated and in vivo hepatic IVIM datasets. The trained networks were compared to the approach combining least squares with Akaike information criterion (LSQ-AIC) in terms of dynamic-exponential modeling accuracy, inter-subject coefficients of variation (CVs), and fitting residuals on the simulated subsets and regions of interest (ROIs) in the left and right liver lobes. The ROIs were delineated by a radiologist (H.-X.Z.) with 7 years of experience in MRI reading. Comparisons between approaches were performed with a paired t-test (normality) or a Wilcoxon rank-sum test (nonnormality). P < 0.05 was considered statistically significant. In simulations, DNN gave significantly higher accuracy (91.6%-95.5%) for identification of bi-exponential decays with respect to LSQ-AIC (79.7%-86.8%). For tri-exponential identification, DNN was also superior to LSQ-AIC despite not reaching a significant level (P=0.08). Additionally, DNN always yielded comparatively low root-mean-square error for estimated parameters. For the in vivo IVIM measurements, inter-subject CVs (0.011-0.150) of DNN were significantly smaller than those (0.049-0.573) of LSQ-AIC. Concerning fitting residuals, there was no significant difference between the two approaches (P=0.56 and 0.76) in both the simulated and in vivo studies. The proposed DNN is recommended for accurate and robust dynamic-exponential modeling and parameter estimation in hepatic IVIM imaging. 2 TECHNICAL EFFICACY: Stage 1.

Optimisation of spatially‐encoded diffusion‐ordered NMR spectroscopy for the analysis of mixtures

Diffusion-ordered NMR spectroscopy (DOSY NMR) is a widely used method for the analysis of mixtures. It can be used to separate the spectra of a mixture's components and to analyse interactions. The classic implementation of DOSY experiments, based on an incrementation of the diffusion-encoding gradient area, requires several minutes or more to collect a 2D data set. Spatially-encoded (SPEN) DOSY makes it possible to collect a complete data set in less than 1 s, by spatial parallelisation of the effective gradient area. While several short descriptions of SPEN DOSY experiments have been reported, a thorough characterisation of its features and its practical use is missing, and this hinders the use of the method. Here, we present the unusual principles and implementation of the SPEN DOSY experiment, an understanding of which is useful to make optimal use of the method. The encoding and acquisition steps are described, and the parameter relations that govern the setup of SPEN DOSY experiments are discussed. The influence of key parameters, including on sensitivity, is illustrated experimentally on mixtures of small molecules. This study should be useful for the setup of SPEN DOSY experiments, which are particularly useful for systems that evolve in time.

SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI

The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called ’b-tensor’ encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7μm, while the second, pure Monte Carlo simulations, yielded a lower limit of 3μm and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered.

The Rate of Apparent Diffusion Coefficient Change With Diffusion Time on Breast Diffusion-Weighted Imaging Depends on Breast Tumor Types and Molecular Prognostic Biomarker Expression.

The aim of this study was to investigate the variation of apparent diffusion coefficient (ADC) values with diffusion time according to breast tumor type and prognostic biomarkers expression. A total of 201 patients with known or suspected breast tumors were prospectively enrolled in this study, and 132 breast tumors (86 malignant and 46 benign) were analyzed. Diffusion-weighted imaging scans with 2 diffusion times were acquired on a clinical 3-T magnetic resonance imaging scanner using oscillating and pulsed diffusion-encoding gradients (effective diffusion times, 4.7 and 96.6 milliseconds) and b values of 0 and 700 s/mm2. Diagnostic performances to differentiate malignant and benign breast tumors for ADC values at short and long diffusion times (ADCshort and ADClong), ΔADC (the rate of change in ADC values with diffusion time), ADC0-1000 (ADC value from a standard protocol), and standard reading including dynamic contrast-enhanced magnetic resonance imaging (BI-RADS) were investigated. The correlations of ADCshort, ADClong, and ΔADC values with hormone receptor expression and breast cancer subtypes were also analyzed. The ADC values were lower, and ΔADC was higher in malignant tumors compared with benign tumors. The specificity of ADC values at all diffusion times and ΔADC values for differentiating malignant and benign breast tumors was superior to that of BI-RADS (87.0%-95.7% vs 73.9%), whereas the sensitivity was inferior (87.2%-90.7% vs 100%). Lower ADCshort and ADC0-1000 in ER-positive compared with ER-negative cancers (false discovery rate [FDR]-adjusted P = 0.037 and 0.018, respectively) and lower ADCshort, ADClong, and ADC0-1000 in progesterone receptor-positive compared with progesterone receptor-negative cancers (FDR-adjusted P = 0.037, 0.036, and 0.018, respectively) were found. Ki-67-positive cancers had larger ΔADCs than Ki-67-negative cancers (FDR-adjusted P = 0.018). The ADC values vary with different diffusion time and vary in correlation with molecular biomarkers, especially Ki-67. Those results suggest that the diffusion time, which should be reported, might be a useful parameter to consider for breast cancer management.

Prediction of Gleason Grade Group of Prostate Cancer on Multiparametric MRI using Deep Machine Learning Models

The Gleason Grade (GG) Group system has been introduced recently for more accurate stratification of prostate cancer (PCa). The grading system is based on the histologic patterns which is accessed from needle core biopsy, therefore it could be negatively impacted by the intratumor heterogeneity. We aim to develop a deep learning algorithm to predict GG groups using multiparametric magnetic resonance images (mp-MRI). We studied a retrospective collection of 201 patients with 320 lesions from the SPIE-AAPM-NCI PROSTATEx Challenge (https://doi.org/10.7937/K9TCIA.2017.MURS5CL), among which 98 patients with 110 lesions with GG available from biopsy. And the number of lesions in each subgroup was 36, 39, 20, 8, and 7, respectively, for GG 1-5. The images were acquired on two different types of Siemens 3T MR scanners. T2W images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800 s/mm2), and subsequently, the ADC map was calculated by the scanner software. Image pre-processing included registration and normalization. Image rotation and scaling were also used to increase the sample size and re-balance the number of lesions in various GG. To prevent over-fitting on a small sample size, we implemented a transfer learning model by carrying over the features learned from the malignancy classification of 320 lesions from our previous model into the GG prediction. And we replaced the end-to-end convolutional neural network (CNN) training model with a combination of feature extraction using CNN and classification using weighted extreme learning machine (wELM). Features from the best performing model were extracted to represent each lesion, and those from the last convolutional layer were found constantly better than from all other layers. Based on 3-fold cross validation, the average validation results for sensitivity, specificity, positive predictive value, and negative predictive value for differentiation of each GG (1-5) were (1, 0.99, 0.97, 1), (0.69, 0.85, 0.73, 0.83), (0.9, 0.69, 0.46, 0.97), (0.89, 0.64, 0.16, 0.99), and (1, 0.78, 0.39, 1), respectively. GG4 had the highest false positive values. GG 3 was often misclassified as GG 4. Results of GG3-5 vs. GG1-2 were (0.82, 0.87, 0.76, 0.92). The stratification of GG4-5 vs. GG1-3 was (0.87, 0.81, 0.42, 0.98). This work has made substantial progress tackling the challenging task of GG prediction from mp-MRI due to a smaller and unbalanced data size by transferring knowledge from a malignancy classification task we developed earlier. The combined feature extraction using deep learning model and weighted extreme learning machine classifier has shown promising results for the GG prediction. This work was supported by a Research Scholar Grant, RSG-15-137-01-CCE from the American Cancer Society.

Apparent diffusion coefficient measured by diffusion MRI of moving and deforming domains

The modeling of the diffusion MRI signal from moving and deforming organs such as the heart is challenging due to significant motion and deformation of the imaged medium during the signal acquisition. Recently, a mathematical formulation of the Bloch-Torrey equation, describing the complex transverse magnetization due to diffusion-encoding magnetic field gradients, was developed to account for the motion and deformation. In that work, the motivation was to cancel the effect of the motion and deformation in the MRI image and the space scale of interest spans multiple voxels. In the present work, we adapt the mathematical equation to study the diffusion MRI signal at the much smaller scale of biological cells.We start with the Bloch-Torrey equation defined on a cell that is moving and deforming and linearize the equation around the magnitude of the diffusion-encoding gradient. The result is a second order signal model in which the linear term gives the imaginary part of the diffusion MRI signal and the quadratic term gives the apparent diffusion coefficient (ADC) attributable to the biological cell. We numerically validate this model for a variety of motions and deformations.

A field-monitoring-based approach for correcting eddy-current-induced artifacts of up to the 2nd spatial order in human-connectome-project-style multiband diffusion MRI experiment at 7T: A pilot study

Over the recent years, significant advances in Spin-Echo (SE) Echo-Planar (EP) Diffusion MRI (dMRI) have enabled improved fiber tracking conspicuity in the human brain. At the same time, pushing the spatial resolution and using higher b-values inherently expose the acquired images to further eddy-current-induced distortion and blurring. Recently developed data-driven correction techniques, capable of significantly mitigating these defects, are included in the reconstruction pipelines developed for the Human Connectome Project (HCP) driven by the NIH BRAIN initiative. In this case, however, corrections are derived from the original diffusion-weighted (DW) magnitude images affected by distortion and blurring. Considering the complexity of k-space deviations in the presence of time varying high spatial order eddy currents, distortion and blurring may not be fully reversed when relying on magnitude DW images only. An alternative approach, consisting of iteratively reconstructing DW images based on the actual magnetic field spatiotemporal evolution measured with a magnetic field monitoring camera, has been successfully implemented at 3T in single band dMRI (Wilm et al., 2017, 2015). In this study, we aim to demonstrate the efficacy of this eddy current correction method in the challenging context of HCP-style multiband (MB ​= ​2) dMRI protocol.The magnetic field evolution was measured during the EP-dMRI readout echo train with a field monitoring camera equipped with 16 19F NMR probes. The time variation of 0th, 1st and 2nd order spherical field harmonics were used to reconstruct DW images. Individual DW images reconstructed with and without field correction were compared. The impact of eddy current correction was evaluated by comparing the corresponding direction-averaged DW images and fractional anisotropy (FA) maps.19F field monitoring data confirmed the existence of significant field deviations induced by the diffusion-encoding gradients, with variations depending on diffusion gradient amplitude and direction. In DW images reconstructed with the field correction, residual aliasing artifacts were reduced or eliminated, and when high b-values were applied, better gray/white matter delineation and sharper gyri contours were observed, indicating reduced signal blurring. The improvement in image quality further contributed to sharper contours and better gray/white matter delineation in mean DW images and FA maps.In conclusion, we demonstrate that up-to-2nd-order-eddy-current-induced field perturbation in multiband, in-plane accelerated HCP-style dMRI acquisition at 7T can be corrected by integrating the measured field evolution in image reconstruction.

Cardiac Diffusion: Technique and Practical Applications

The 3D microarchitecture of the cardiac muscle underlies the mechanical and electrical properties of the heart. Cardiomyocytes are arranged helically through the depth of the wall, and their shortening leads to macroscopic torsion, twist, and shortening during cardiac contraction. Furthermore, cardiomyocytes are organized in sheetlets separated by shear layers, which reorientate, slip, and shear during macroscopic left ventricle (LV) wall thickening. Cardiac diffusion provides a means for noninvasive interrogation of the 3D microarchitecture of the myocardium. The fundamental principle of MR diffusion is that an MRI signal is attenuated by the self-diffusion of water in the presence of large diffusion-encoding gradients. Since water molecules are constrained by the boundaries in biological tissue (cell membranes, collagen layers, etc.), depicting their diffusion behavior elucidates the shape of the myocardial microarchitecture they are embedded in. Cardiac diffusion therefore provides a noninvasive means to understand not only the dynamic changes in cardiac microstructure of healthy myocardium during cardiac contraction but also the pathophysiological changes in the presence of disease. This unique and innovative technology offers tremendous potential to enable improved clinical diagnosis through novel microstructural and functional assessment. in vivo cardiac diffusion methods are immediately translatable to patients, opening new avenues for diagnostic investigation and treatment evaluation in a range of clinically important cardiac pathologies. This review article describes the 3D microstructure of the LV, explains in vivo and ex vivo cardiac MR diffusion acquisition and postprocessing techniques, as well as clinical applications to date. Level of Evidence: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:348-368.

Diffusional attenuation during soft pulses: A Zangger-Sterk pure shift iDOSY experiment

Diffusion-ordered spectroscopy experiments in which existing delays in a parent pulse sequence are used for diffusion encoding - iDOSY experiments - are potentially attractive because of their simplicity and sensitivity. However the calculation of diffusional attenuation in Zangger-Sterk pure shift iDOSY experiments is a very difficult problem to attack analytically, and is more easily approached numerically. Numerical simulations show that for typical experimental conditions, the dependence of diffusional attenuation on diffusion-encoding gradient amplitude is well represented by a shifted Gaussian function. The shift in gradient can be calculated analytically for the limiting case where the selective pulse is replaced by a hard 180° pulse at its midpoint; numerical simulations show that the effect of using different shapes of selective pulse is to scale down this limiting gradient shift by a constant factor that depends on the pulse shape used. The practical consequence is that under the experimental conditions appropriate for small molecules, the pure shift iDOSY method should allow good diffusion coefficient measurements to be made if appropriate allowance is made for the change in effective diffusion-encoding gradient. Parallel sets of numerical simulations and experiments are presented, and a practical application of a Zangger-Sterk pure shift iDOSY experiment to a simple test mixture is illustrated.

Effect of flow-encoding strength on intravoxel incoherent motion in the liver.

To study the impact of variable flow-encoding strength on intravoxel incoherent motion (IVIM) liver imaging of diffusion and perfusion. Signal attenuation in DWI arises from (1) intravoxel microvascular blood flow, which depends on the flow-encoding strength α (first gradient moment) of the diffusion-encoding waveform, and (2) intravoxel spin diffusion, which depends on the b-value of the diffusion-encoding gradient waveforms α and b-value. Both are linked to the diffusion-encoding gradient waveform and conventionally are not independently controlled. In this work a convex optimization framework was used to generate gradient waveforms with independent α and b-value. Thirty-six unique α and b-value sample points from 5 different gradient waveforms were used to reconstruct perfusion fraction (f), coefficient of diffusion (D), and blood velocity standard deviation (Vb ) maps using a recently proposed IVIM model. Faster acquisition strategies were evaluated with 1000 random subsampling strategies of 16, 8, and 4 α and b-value. Among the subsampled reconstructions, the sampling schemes that minimized the difference with the fully sampled reconstruction were reported. Healthy volunteers (N = 9) were imaged on a 3T scanner. Liver perfusion and diffusion estimates using the fully sampled IVIM method were f = 0.19 ± 0.06, D = 1.15 ± 0.15 10-3 mm2 /s, and Vb = 5.22 ± 3.86 mm/s. No statistical differences were found between the fully sampled and 2-times undersampled reconstruction (f = 0.2 ± 0.07, D = 1.19 ± 0.15 10-3 mm2 /s, Vb = 5.79 ± 3.43 mm/s); 4-times undersampled (f = 0.2 ± 0.06, D = 1.15 ± 0.17 10-3 mm2 /s, Vb = 4.66 ± 3.61 mm/s), or 8-times undersampled ( f = 0.2 ± 0.06, D = 1.23 ± 0.22 10-3 mm2 /s, Vb = 4.99 ± 3.82 mm/s) approaches. We demonstrate the IVIM signal's dependence on the b-value, the diffusion-encoding time and the flow-encoding strength and observe in vivo the ballistic regime signature of microperfusion in the liver. This work also demonstrates that using an IVIM model and sampling scheme matched to the ballistic regime, pixel-wise IVIM parameter maps are possible when sampling as few as 4 IVIM signals.

Experimental determination of pore shapes using phase retrieval from q-space NMR diffraction.

This paper presents an approach to solving the phase problem in nuclear magnetic resonance (NMR) diffusion pore imaging, a method that allows imaging the shape of arbitrary closed pores filled with an NMR-detectable medium for investigation of the microstructure of biological tissue and porous materials. Classical q-space imaging composed of two short diffusion-encoding gradient pulses yields, analogously to diffraction experiments, the modulus squared of the Fourier transform of the pore image which entails an inversion problem: An unambiguous reconstruction of the pore image requires both magnitude and phase. Here the phase information is recovered from the Fourier modulus by applying a phase retrieval algorithm. This allows omitting experimentally challenging phase measurements using specialized temporal gradient profiles. A combination of the hybrid input-output algorithm and the error reduction algorithm was used with dynamically adapting support (shrinkwrap extension). No a priori knowledge on the pore shape was fed to the algorithm except for a finite pore extent. The phase retrieval approach proved successful for simulated data with and without noise and was validated in phantom experiments with well-defined pores using hyperpolarized xenon gas.

Four-dimensional diffusion-weighted MR imaging (4D-DWI): a feasibility study.

Diffusion-weighted Magnetic Resonance Imaging (DWI) has been shown to be a powerful tool for cancer detection with high tumor-to-tissue contrast. This study aims to investigate the feasibility of developing a four-dimensional DWI technique (4D-DWI) for imaging respiratory motion for radiation therapy applications. Image acquisition was performed by repeatedly imaging a volume of interest (VOI) using an interleaved multislice single-shot echo-planar imaging (EPI) 2D-DWI sequence in the axial plane. Each 2D-DWI image was acquired with an intermediately low b-value (b=500s/mm2 ) and with diffusion-encoding gradients in x, y, and z diffusion directions. Respiratory motion was simultaneously recorded using a respiratory bellow, and the synchronized respiratory signal was used to retrospectively sort the 2D images to generate 4D-DWI. Cine MRI using steady-state free precession was also acquired as a motion reference. As a preliminary feasibility study, this technique was implemented on a 4D digital human phantom (XCAT) with a simulated pancreas tumor. The respiratory motion of the phantom was controlled by regular sinusoidal motion profile. 4D-DWI tumor motion trajectories were extracted and compared with the input breathing curve. The mean absolute amplitude differences (D) were calculated in superior-inferior (SI) direction and anterior-posterior (AP) direction. The technique was then evaluated on two healthy volunteers. Finally, the effects of 4D-DWI on apparent diffusion coefficient (ADC) measurements were investigated for hypothetical heterogeneous tumors via simulations. Tumor trajectories extracted from XCAT 4D-DWI were consistent with the input signal: the average D value was 1.9mm (SI) and 0.4mm (AP). The average D value was 2.6mm (SI) and 1.7mm (AP) for the two healthy volunteers. A 4D-DWI technique has been developed and evaluated on digital phantom and human subjects. 4D-DWI can lead to more accurate respiratory motion measurement. This has a great potential to improve the visualization and delineation of cancer tumors for radiotherapy.

Evaluation and Minimization of the Pseudohepatic Anisotropy Artifact in Liver Intravoxel Incoherent Motion.

The aim of this study was to evaluate the effect of the pseudohepatic anisotropy artifact on liver intravoxel incoherent motion (IVIM) metrics and whether the use of multiple gradient directions in the IVIM acquisition minimizes the artifact. Multiple breath-holding and forced shallow free-breathing IVIM scans were performed on 8 healthy volunteers using 1 and 6 gradient directions. Cluster analysis was carried out to separate motion-contaminated parenchyma from liver parenchyma and vessels. Nonlinear motion analysis was also performed to look for a possible link between IVIM metrics and nonlinear liver motion. On the basis of the resulted clusters, motion-contaminated parenchyma is often noted in the left liver lobe, where the prominent pseudohepatic artifact has previously been identified. A significant reduction in outliers was obtained with the acquisition of 6 noncoplanar gradient directions and when using forced shallow free-breathing. The pseudohepatic anisotropy artifact can be minimized when using multiple diffusion-encoding gradient directions and forced free-breathing during IVIM acquisition.

DTI microstructural abnormalities in adolescent siblings of patients with childhood-onset schizophrenia

DTI microstructural abnormalities in adolescent siblings of patients with childhood-onset schizophrenia

Whole brain white matter integrity measures on patients with moderate to severe traumatic brain injury (TBI). FA and entropy analysis

Introduction Mean Fractional Anisotropy (FA) along with Longitudinal and Transverse Apparent Diffusion Coefficients (ADC) are the established measures in assessing white matter (WM) integrity in TBI patients. A question is raised whether mean FA performs better than Shannon Entropy (SE) as a measure of discrimination between controls and patients with moderate to severe TBI. Purpose To assess WM integrity in moderate to severe TBI patients utilizing the established diffusion tensor measures, and compare FA with Shannon Entropy as measures of assessment of WM integrity. Materials and methods A group of 13 patients with moderate to severe TBI, 2–8 years post-ictus, has been compared with a group of 14 matched healthy controls. Diffusion Tensor Imaging (DTI) data were obtained at 3T with a pulse sequence employing 32 diffusion-encoding gradient directions. After data preprocessing, Tract Based Spatial Statistics (FSL-FMRIB) for FA, whole-brain histogram analysis for SE and Receiver Operating Curve (ROC) analysis for both FA and SE have been performed. Results Higher axial and transverse diffusivity and lower FA have been observed in TBI patients ( p P = 0.007), Global mean FA (0.8, P = 0.0066) and SE (0.8, p = 0.008). Conclusion Axial and transverse diffusivity are increased in TBI patients compared to controls. Diffuse Axonal Injury (DAI) is evident as global reduction of FA in TBI patients, which is driven by increased transverse diffusion changes. No significant difference is observed between FA and SE in TBI patient discrimination. Disclosure The authors declare no conflicts of interest including financial, consultant, institutional, and other relationships that might lead to bias or a any other conflict of interest. This work was supported by the Cyprus Research Promotion Foundation through a grant co-funded by the Cyprus Government and the European Regional Development Fund (F.C., PI; NEW INFRASTRUCTURE/STRATEGIC/0309/37). The authors would like to thank the study volunteers who participated in the project.

Adapting the Kärger model to account for finite diffusion-encoding pulses in diffusion MRI

Diffusion magnetic resonance imaging (dMRI) is an imaging modality that probes the diffusion characteristics of a sample via the application of magnetic field gradient pulses. If the imaging voxel can be divided into different Gaussian diffusion compartments with inter-compartment exchange governed by linear kinetics, then the dMRI signal can be described by the Kärger model, which is a well-known model in Nuclear Magnetic Resonance. However, the Kärger model is limited to the case when the duration of the diffusion-encoding gradient pulses is short compared to the time delay between the start of the pulses. Under this assumption, the time at which to evaluate the Kärger model to obtain the dMRI signal is unambiguously the delay between the pulses. Recently, a new model of the dMRI signal, the Finite-Pulse Kärger (FPK) model, was derived for arbitrary diffusion gradient profiles. Relying on the FPK model, we show that when the duration of the gradient pulses is not short, the time at which to evaluate the Kärger model should be the time delay between the start of the pulses, shortened by one third of the pulse duration. With this choice, we show the sixth order convergence of the Kärger model to the FPK model in the non-dimensionalized pulse duration.

Convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted MRI.

To evaluate convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted imaging (DWI). Diffusion-encoding gradient waveforms were designed for a range of b-values and spatial resolutions with and without motion compensation using the CODE framework. CODE, first moment (M1 ) nulled CODE-M1 , and first and second moment (M2 ) nulled CODE-M1 M2 were used to acquire neuro, liver, and cardiac ADC maps in healthy subjects (n=10) that were compared respectively to monopolar (MONO), BIPOLAR (M1 = 0), and motion-compensated (MOCO, M1 + M2 = 0) diffusion encoding. CODE significantly improved the SNR of neuro ADC maps compared with MONO (19.5 ± 2.5 versus 14.5 ± 1.9). CODE-M1 liver ADCs were significantly lower (1.3 ± 0.1 versus 1.8 ± 0.3 × 10-3 mm2 /s, ie, less motion corrupted) and more spatially uniform (6% versus 55% ROI difference) than MONO and had higher SNR than BIPOLAR (SNR = 14.9 ± 5.3 versus 8.0 ± 3.1). CODE-M1 M2 cardiac ADCs were significantly lower than MONO (1.9 ± 0.6 versus 3.8 ± 0.3 x10-3 mm2 /s) throughout the cardiac cycle and had higher SNR than MOCO at systole (9.1 ± 3.9 versus 7.0 ± 2.6) while reporting similar ADCs (1.5 ± 0.2 versus 1.4 ± 0.6 × 10-3 mm2 /s). CODE significantly improved SNR for ADC mapping in the brain, liver and heart, and significantly improved DWI bulk motion robustness in the liver and heart. Magn Reson Med 77:717-729, 2017. © 2016 International Society for Magnetic Resonance in Medicine.