Faculty Bibliography

Biophysical modeling of macroscopic diffusion-weighted MRI (dMRI) signal in terms of microscopic cellular parameters holds the promise of quantifying the integrity of white matter. Unfortunately, even fairly simple multi-compartment models of proton diffusion in the white matter do not provide a unique, biophysically plausible solution. Here we report a nontrivial diffusion MRI signal dependence on echo time (TE) in human white matter in vivo. We demonstrate that such TE dependence originates from compartment-specific T2 values and that it is a promising "orthogonal measure" able to break the degeneracy in parameter estimation, and to yield important relaxation metrics robustly. We thereby enable the precise estimation of the intra- and extra-axonal water T2 relaxation values, which is precluded by a limited signal-to-noise ratio when using multi-echo relaxometry alone.

We report on an experimental observation of classical diffusion distinguishing between structural universality classes of disordered systems in one dimension. Samples of hyperuniform and short-range disorder were designed, characterized by the statistics of the placement of micrometer-thin parallel permeable barriers, and the time-dependent diffusion coefficient was measured by NMR methods over three orders of magnitude in time. The relation between the structural exponent, characterizing disorder universality class, and the dynamical exponent of the diffusion coefficient is experimentally verified. The experimentally established relation between structure and transport exemplifies the hierarchical nature of structural complexity-dynamics are mainly determined by the universality class, whereas microscopic parameters affect the nonuniversal coefficients. These results open the way for noninvasive characterization of structural correlations in porous media, complex materials, and biological tissues via a bulk diffusion measurement.

OBJECTIVE: To assess the association between peripheral lipid/fat profiles and cerebral gray matter (GM) and white matter (WM) in healthy Old Order Amish (OOA). METHODS: Blood lipids, abdominal adiposity, liver lipid contents, and cerebral microstructure were assessed in OOA (N = 64, 31 males/33 females, ages 18-77). Orthogonal factors were extracted from lipid and imaging adiposity measures. GM assessment used the Human Connectome Project protocol to measure whole-brain average cortical thickness. Diffusion-weighted imaging was used to derive WM fractional anisotropy and kurtosis anisotropy measurements. RESULTS: Lipid/fat measures were captured by three orthogonal factors explaining 80% of the variance. Factor one loaded on cholesterol and/or low-density lipoprotein cholesterol measurements; factor two loaded on triglyceride/liver measurements; and factor three loaded on abdominal fat measurements. A two-stage regression including age/sex (first stage) and the three factors (second stage) examined the peripheral lipid/fat effects. Factors two and three significantly contributed to WM measures after Bonferroni corrections (P < 0.007). No factor significantly contributed to GM. Blood pressure (BP) inclusion did not meaningfully alter the lipid/fat-WM relationship. CONCLUSIONS: Peripheral lipid/fat indicators were significantly and negatively associated with cerebral WM rather than with GM, independent of age and BP level. Dissecting the fat/lipid components contributing to different brain imaging parameters may open a new understanding of the body-brain connection through lipid metabolism.

A diffusion measurement in the short-time surface-to-volume ratio (S/V) limit (Mitra et al., Phys Rev Lett. 1992;68:3555) can disentangle the free diffusion coefficient from geometric restrictions to diffusion. Biophysical parameters, such as the S/V of tissue membranes, can be used to estimate microscopic length scales non-invasively. However, due to gradient strength limitations on clinical MRI scanners, pulsed gradient spin echo (PGSE) measurements are impractical for probing the S/V limit. To achieve this limit on clinical systems, an oscillating gradient spin echo (OGSE) sequence was developed. Two phantoms containing 10 fiber bundles, each consisting of impermeable aligned fibers with different packing densities, were constructed to achieve a range of S/V values. The frequency-dependent diffusion coefficient, D(omega), was measured in each fiber bundle using OGSE with different gradient waveforms (cosine, stretched cosine, and trapezoidal), while D(t) was measured from PGSE and stimulated-echo measurements. The S/V values derived from the universal high-frequency behavior of D(omega) were compared against those derived from quantitative proton density measurements using single spin echo (SE) with varying echo times, and from magnetic resonance fingerprinting (MRF). S/V estimates derived from different OGSE waveforms were similar and demonstrated excellent correlation with both SE- and MRF-derived S/V measures (rho >/= 0.99). Furthermore, there was a smoother transition between OGSE frequency f and PGSE diffusion time when using teffS/V=9/64f, rather than the commonly used teff = 1/(4f), validating the specific frequency/diffusion time conversion for this regime. Our well-characterized fiber phantom can be used for the calibration of OGSE and diffusion modeling techniques, as the S/V ratio can be measured independently using other MR modalities. Moreover, our calibration experiment offers an exciting perspective of mapping tissue S/V on clinical systems.

OBJECTIVE: Prior studies in prostate diffusion-weighted magnetic resonance imaging (MRI) have largely explored the impact of b-value and diffusion directions on estimated diffusion coefficient D. Here we suggest varying diffusion time, t, to study time-dependent D(t) in prostate cancer, thereby adding an extra dimension in the development of prostate cancer biomarkers. METHODS: Thirty-eight patients with peripheral zone prostate cancer underwent 3-T MRI using an external-array coil and a diffusion-weighted image sequence acquired for b = 0, as well as along 12 noncollinear gradient directions for b = 500 s/mm using stimulated echo acquisition mode (STEAM) diffusion tensor imaging (DTI). For this sequence, 6 diffusion times ranging from 20.8 to 350 milliseconds were acquired. Tumors were classified as low-grade (Gleason score [GS] 3 + 3; n = 11), intermediate-grade (GS 3 + 4; n = 16), and high-grade (GS >/=4 + 3; n = 11). Benign peripheral zone and transition zone were also studied. RESULTS: Apparent diffusion coefficient (ADC) D(t) decreased with increasing t in all zones of the prostate, though the rate of decay in D(t) was different between sampled zones. Analysis of variance and area under the curve analyses suggested better differentiation of tumor grades at shorter t. Fractional anisotropy (FA) increased with t for all regions of interest. On average, highest FA was observed within GS 3 + 3 tumors. CONCLUSIONS: There is a measurable time dependence of ADC in prostate cancer, which is dependent on the underlying tissue and Gleason score. Therefore, there may be an optimal selection of t for prediction of tumor grade using ADC. Controlling t should allow ADC to achieve greater reproducibility between different sites and vendors. Intentionally varying t enables targeted exploration of D(t), a previously overlooked biophysical phenomenon in the prostate. Its further microstructural understanding and modeling may lead to novel diffusion-derived biomarkers.

The time dependence of the diffusion coefficient is a hallmark of tissue complexity at the micrometer level. Here we demonstrate how biophysical modeling, combined with a specifically tailored diffusion MRI acquisition performing diffusion tensor imaging (DTI) for varying diffusion times, can be used to determine fiber size and membrane permeability of muscle fibers in vivo. We describe the random permeable barrier model (RPBM) and its assumptions, as well as the details of stimulated echo DTI acquisition, signal processing steps, and potential pitfalls. We illustrate the RPBM method on a few pilot examples involving human subjects (previously published as well as new), such as revealing myofiber size derived from RPBM increase after training in a calf muscle, and size decrease with atrophy in shoulder rotator cuff muscle. Finally, we comment on the potential clinical relevance of our results

Besides amyloid deposition, white matter (WM) changes are involved in the early pathogenesis of Alzheimer's Disease (AD), including inflammation, demyelination and axonal loss. Using simultaneous PET and MRI, we investigated differences in WM microstructural integrity, measured with Diffusion Kurtosis Imaging (DKI), with respect to beta amyloid (Aa) deposition as measured with18F-Florbetapir PET. DKI is a clinically feasible diffusion MRI method that extends beyond Diffusion Tensor Imaging and probes non-Gaussian diffusion properties of nervous tissue, and allows for quantifying the microstructural index for the axonal water fraction (AWF), a specific marker for axonal degeneration and demyelination. Methods: 34 subjects were scanned on a 3T integrated PET-MRI system (Siemens Biograph mMR, VB20). 18FFlorbetapir (9 mCi, Eli Lilly) was injected intravenously and a static 20-minute PET image was reconstructed starting at 40 min post-injection using a UTE-based attenuation map. An anatomical MP-RAGE was acquired for cortical and sub-cortical segmentation using Freesurfer. Hippocampal volume was normalized to the estimated total intracranial volume. The standardized uptake values (SUV) in 5 cortical regions known for pathological uptake of Florbetapir (anterior and posterior cingulate, medial orbito-frontal, parietal and temporal), normalized to the cerebellum, yielded mean cortical relative SUV (SUVr). DKI provided parametric maps for the radial diffusivity (RD), radial kurtosis (RK), and the AWF. Using a lower and higher mean SUVr threshold of 1.0 and 1.1, age- and gender-controlled subjects were categorized into Aa negative (Aa-) (n = 13, 5 females, age = 69.8 +/- 5.1 yrs), Aa intermediate (Aai) (n = 13, 8 females, age = 68.9 +/- 4.8 yrs), or Aa positive (Aa+) (n = 8, 4 females, age = 70.6 +/- 5.3 yrs). Using Tract-Based Spatial Statistics (TBSS), skeletonized voxel-wise analysis was performed to identify areas of differences in the diffusion metrics while covarying for age. Separately, WM regions of interests (ROIs) were automatically segmented using atlas registration over which mean values were extracted. Analysis of covariance covarying for age was used to compare diffusion metrics and hippocampal volume among groups. Results: See figure. Results from both TBSS and ROI analysis demonstrated changes in the fornix and the genu of the corpus callosum. Between the Aa- and Aai groups, RD decreased while RK and AWF increased. Conversely, between the Aai and Aa+ groups, RD increased RD while RK and AWF decreased. A trend towards significantly higher hippocampal volume in the Aai group was observed. Conclusions: We report changes in RD, RK and AWF in opposite directions between Aa- and Aa~, and between Aa~ and Aa+, respectively, suggesting that different mechanisms affect the microstructure during different stages of AD. Early on, mechanisms including microglial activation may restrict diffusion, resulting in the observed decrease in RD and increase in RK and AWF. Later on, neurodegenerative effects such as demyelination and axonal loss may outweigh inflammation, resulting in the observed increase in RD and decrease in RK and AWF. [IMAGE PRESENTED]

Solid tumor microstructure is related to the aggressiveness of the tumor, interstitial pressure and drug delivery pathways, which are closely associated with treatment response, metastatic spread and prognosis. In this study, we introduce a novel diffusion MRI data analysis framework, pulsed and oscillating gradient MRI for assessment of cell size and extracellular space (POMACE), and demonstrate its feasibility in a mouse tumor model. In vivo and ex vivo POMACE experiments were performed on mice bearing the GL261 murine glioma model (n = 8). Since the complete diffusion time dependence is in general non-analytical, the tumor microstructure was modeled in an appropriate time/frequency regime by impermeable spheres (radius Rcell , intracellular diffusivity Dics ) surrounded by extracellular space (ECS) (approximated by constant apparent diffusivity Decs in volume fraction ECS). POMACE parametric maps (ECS, Rcell , Dics , Decs ) were compared with conventional diffusion-weighted imaging metrics, electron microscopy (EM), alternative ECS determination based on effective medium theory (EMT), and optical microscopy performed on the same samples. It was shown that Decs can be approximated by its long time tortuosity limit in the range [1/(88 Hz)-31 ms]. ECS estimations (44 +/- 7% in vivo and 54 +/- 11% ex vivo) were in agreement with EMT-based ECS and literature on brain gliomas. Ex vivo, ECS maps correlated well with optical microscopy. Cell sizes (Rcell = 4.8 +/- 1.3 in vivo and 4.3 +/- 1.4 microm ex vivo) were consistent with EM measurements (4.7 +/- 1.8 microm). In conclusion, Rcell and ECS can be quantified and mapped in vivo and ex vivo in brain tumors using the proposed POMACE method. Our experimental results support the view that POMACE provides a way to interpret the frequency or time dependence of the diffusion coefficient in tumors in terms of objective biophysical parameters of neuronal tissue, which can be used for non-invasive monitoring of preclinical cancer studies and treatment efficacy

Dynamic contrast enhanced (DCE) MRI has emerged as a reliable and diagnostically useful functional imaging technique. DCE protocol typically lasts 3-15 minutes and results in a time series of N volumes. For automated analysis, it is important that volumes acquired at different times be spatially coregistered. We have recently introduced a novel 4D, or volume time series, coregistration tool based on a user-specified target volume of interest (VOI). However, the relationship between coregistration accuracy and target VOI size has not been investigated. In this study, coregistration accuracy was quantitatively measured using various sized target VOIs. Coregistration of 10 DCE-MRI mouse head image sets were performed with various sized VOIs targeting the mouse brain. Accuracy was quantified by measures based on the union and standard deviation of the coregistered volume time series. Coregistration accuracy was determined to improve rapidly as the size of the VOI increased and approached the approximate volume of the target (mouse brain). Further inflation of the VOI beyond the volume of the target (mouse brain) only marginally improved coregistration accuracy. The CPU time needed to accomplish coregistration is a linear function of N that varied gradually with VOI size. From the results of this study, we recommend the optimal size of the VOI to be slightly overinclusive, approximately by 5 voxels, of the target for computationally efficient and accurate coregistration.

We introduce and evaluate a post-processing technique for fast denoising diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements, yielding parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and/or spatial resolution.