Recent Publications

The following are general descriptions of recent publications from McNab Lab. You may also view a complete list of publications from McNab Lab.

Generalized Diffusion Spectrum Magnetic Resonance Imaging (GDSI) for model-free reconstruction of the ensemble average propagator

Description: Diffusion spectrum MRI (DSI) provides model-free estimation of the diffusion ensemble average propagator (EAP) and orientation distribution function (ODF) but requires the diffusion data to be acquired on a Cartesian q-space grid. This study proposes a generalized DSI (GDSI) framework to recover the EAP from commonly acquired multi-shell diffusion MRI data.. The EAP is directly calculated in a preferable coordinate system by multiplying the sampling density corrected q-space signals by a discrete Fourier transform matrix, without any need for gridding. The model-free EAP is demonstrated as a way to map diffusion patterns in brain regions where the tissue microstructure is not as well characterized as in white matter. Scalar metrics derived from the EAP provide new diffusion image contrasts that differentiate tissue types and infer axonal microstructure. GDSI formulates the ODF reconstruction as a linear system that elucidates the contribution and combination of q-space signals to a diffusion ODF. The model-free diffusion ODF allows estimation and comparison of fiber orientations from both the model-free and model-based methods on the same multi-shell data.


Citation
: Tian Q, Yang G, Leuze CW, Rokem A, Edlow BL, McNab JA. Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator. NeuroImage, 2019: 10.1016/j.neuroimage.2019.01.038.

Linkhttps://doi.org/10.1016/j.neuroimage.2019.01.038

Eddy current nulled constrained optimization of isotropic diffusion encoding gradient waveforms

Description: Purpose Isotropic diffusion encoding efficiently encodes additional microstructural information in combination with conventional linear diffusion encoding. However, the gradient‐intensive isotropic diffusion waveforms generate significant eddy currents, which cause image distortions. The purpose of this study is to present a method for designing isotropic diffusion encoding waveforms with intrinsic eddy current nulling. Methods Eddy current nulled gradient waveforms were designed using a constrained optimization waveform for a 3T GE Premier MRI system. Encoding waveforms were designed for a variety of eddy current null times and sequence timings to evaluate the achievable b‐value. Waveforms were also tested with both eddy current nulling and concomitant field compensation. Distortion reduction was tested in both phantoms and the in vivo human brain. Results The feasibility of isotropic diffusion encoding with intrinsic correction of eddy current distortion and signal bias from concomitant fields was demonstrated. The constrained optimization algorithm produced gradient waveforms with the specified eddy current null times. The reduction in the achievable diffusion weighting was dependent on the number of eddy current null times. A reduction in the eddy current–induced image distortions was observed in both phantoms and in vivo human subjects. Conclusion The proposed framework allows the design of isotropic diffusion‐encoding sequences with reduced image distortion. 

Citation: Yang G., McNab J.A., Eddy current nulled constrained optimization of isotropic diffusion encoding gradient waveforms, Magnetic Resonance in Medicine, 81(3):1818-1832, 2018

Linkhttps://doi.org/10.1002/mrm.27539

Diffusion MRI Tractography for Improved Transcranial MRI-guided Focused Ultrasound Thalamotomy Targeting for Essential Tremor

Description: Essential tremor (ET) is one of the most common neurological diseases. Lesioning the ventral intermediate nucleus (Vim) of the thalamus using transcranial magnetic resonance imaging (MRI)-guided focused ultrasound (tcMRgFUS) is an effective and non-invasive approach to suppress the tremor. It is challenging, however, to locate the Vim, a small structure with low intrinsic contrast on standard structural MRI. The atlas based targeting method does not account for the anatomical variability between patients and lacks specificity to the hand representation within Vim, which is essential to suppress the hand tremor. Our study utilizes diffusion MRI tractography to locate the Vim by mapping the white matter pathways that conduct the electrical signals governing hand movement. On eight ET patients treated with tcMRgFUS at Stanford, it was found that higher overlap between the tractography-identified Vim location and the tcMRgFUS treatment-induced lesion location selected by neurosurgeons in awake patients highly correlated with better treatment outcome. Our study supports the use of diffusion tractography as a complementary approach to improve the current targeting methods for tcMRgFUS thalamotomy.

Citation: Tian Q., Wintermark M., Elias W.J., Ghanouni P., Halpern C.H., Henderson J.M., Huss D.S., Goubran M., Thaler C., Airan R., Zeineh M., Pauly K.B., McNab J.A., Diffusion MRI Tractography for Improved Transcranial MRI-guided Focused Ultrasound Thalamotomy Targeting for Essential Tremor, NeuroImage: Clinical, 19: 572-580, 2018.

Link: https://doi.org/10.1016/j.nicl.2018.05.010

Characterizing Signals Within Lesions and Mapping Brain Network Connectivity After Traumatic Axonal Injury: A 7 Tesla Resting-State FMRI Study

Description: Resting-state functional magnetic resonance imaging (RS-FMRI) has been widely used to map brain functional connectivity, but it is unclear how to probe connectivity within and around lesions. In this study, we characterize RS-FMRI signal time course properties and evaluate different seed placements within and around hemorrhagic traumatic axonal injury (hTAI) lesions. RS-FMRI was performed on a 7 Tesla scanner in a patient who recovered consciousness after traumatic coma and in three healthy controls. Eleven lesions in the patient were characterized in terms of temporal signal-to-noise ratio (tSNR), physiological noise and seed-based functional connectivity. We showed that resting-state network has been changed in the brain after recovery from traumatic coma and that seed placement within a lesion’s periphery or in the contralesional hemisphere may be necessary for network identification in patients with hTAI.

Citation: Lee S., Polimeni J.R., Price C.M., Edlow B.L., and McNab J.A., Characterizing Signals Within Lesions and Mapping Brain Network Connectivity After Traumatic Axonal Injury: A 7 Tesla Resting-State FMRI Study, Brain Connectivity, 8(5):288-298, 2018.

Linkhttps://doi.org/10.1089/brain.2017.0499

Double diffusion encoding MRI for the clinic.

Description: Microscopic diffusion anisotropy measurements from DDE promise greater specificity to changes in tissue microstructure compared to conventional diffusion tensor imaging, but implementation of DDE sequences on whole-body MRI scanners is challenging due to the limited gradient strengths and lengthy acquisition times. A custom single-refocused DDE sequence was implemented on a 3T whole-body scanner. The DDE gradient orientation scheme and sequence parameters were optimized based on a Gaussian diffusion assumption. Using an optimized five minute DDE acquisition, microscopic fractional anisotropy (mFA) maps were acquired for the first time in multiple sclerosis (MS) patients. Based on simulations and in vivo human measurements, six parallel and six orthogonal diffusion gradient pairs were found to be the minimum number of diffusion gradient pairs necessary to produce a rotationally invariant measurement of mFA. Simulations showed that optimal precision and accuracy of mFA measurements were obtained using b-values between 1500-3000 s/mm2. The mFA maps showed improved delineation of MS lesions compared to conventional FA and distinct contrast from T2-FLAIR and T1-weighted imaging.

Citation: Yang G., Tian Q., Leuze C., Wintermark M., McNab J.A., Double Diffusion Encoding MRI for the Clinic, Magnetic Resonance in Medicine, 80(2):507-520, 2017.

Linkhttps://doi.org/10.1002/mrm.27043

The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing

Description: Despite the widespread use of magnetic resonance imaging (MRI) of the brain, the relative contribution of different biological components (e.g. lipids and proteins) to structural MRI contrasts (e.g., T1, T2, T2*, proton density, diffusion) remains incompletely understood. This limitation can undermine the interpretation of clinical MRI and hinder the development of new contrast mechanisms. Here, we determine the respective contribution of lipids and proteins to MRI contrast by removing lipids and preserving proteins in mouse brains using the tissue clearing method CLARITY. Our results show that although lipids and proteins account for approximately the same percentage of brain matter by weight, the lipids are by far the dominant source of tissue contrast. While proteins may influence relaxivity, we observed minimal T2 contrast and no contrast at all for all the other MRI sequences when the lipids are removed by clearing (Fig.1). Our data also provides evidence that hindered water diffusion in brain tissue is mainly caused by lipids and bi-lipid cell membranes, while the preserved cytoskeleton has no observable effect on water diffusion rates or anisotropy (Fig.2).

Citation: Leuze C., Aswendt M., Ferenczi E., Liu C., Hsueh B., Goubran M., Tian Q., Steinberg G., Zeineh M., Deisseroth K., McNab J.A., The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing, NeuroImage, 156:412-422, 2017.

Link: https://doi.org/10.1016/j.neuroimage.2017.04.021

Q-Space truncation and sampling in diffusion spectrum imaging

Description: Diffusion spectrum imaging (DSI) is a type of diffusion-weighted magnetic resonance imaging that provides a map of the structural connections in the brain. The way in which the DSI data is sampled affects the ability to resolve the structural connections. In this paper, we studied the impact of DSI sampling density and extent and proposed sampling guidelines and analysis approaches to mitigate artifacts.

Citation: Tian Q., Rokem A., Folkerth R.D, Nummenmaa A., Fan Q., Edlow B.L., McNab J.A., Q-Space truncation and sampling in diffusion spectrum imaging. Magnetic Resonance in Medicine, 76(6):1750-1763, 2016.

Linkhttps://doi.org/10.1002/mrm.26071