DTI-MR fingerprinting for rapid high-resolution whole-brain T1 , T2 , proton density, ADC, and fractional anisotropy mapping.

PURPOSE: This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1 , T2 , proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time. METHODS: To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design. RESULTS: The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence. CONCLUSION: The proposed method can achieve whole-brain T1 , T2 , diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.

Correlated noise in brain magnetic resonance elastography.

PURPOSE: Magnetic resonance elastography (MRE) uses phase-contrast MRI to generate mechanical property maps of the in vivo brain through imaging of tissue deformation from induced mechanical vibration. The mechanical property estimation process in MRE can be susceptible to noise from physiological and mechanical sources encoded in the phase, which is expected to be highly correlated. This correlated noise has yet to be characterized in brain MRE, and its effects on mechanical property estimates computed using inversion algorithms are undetermined. METHODS: To characterize the effects of signal noise in MRE, we conducted 3 experiments quantifying (1) physiomechanical sources of signal noise, (2) physiological noise because of cardiac-induced movement, and (3) impact of correlated noise on mechanical property estimates. We use a correlation length metric to estimate the extent that correlated signal persists in MRE images and demonstrate the effect of correlated noise on property estimates through simulations. RESULTS: We found that both physiological noise and vibration noise were greater than image noise and were spatially correlated across all subjects. Added physiological and vibration noise to simulated data resulted in property maps with higher error than equivalent levels of Gaussian noise. CONCLUSION: Our work provides the foundation to understand contributors to brain MRE data quality and provides recommendations for future work to correct for signal noise in MRE.

Polyethylenimine-Spherical Nucleic Acid Nanoparticles against Gli1 Reduce the Chemoresistance and Stemness of Glioblastoma Cells.

Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults, with nearly 100% of patients ultimately succumbing to the disease. Median patient survival is 15 months, and no standard of care currently exists for recurrent cases. Glioma stem cells (GSCs), a rare and highly aggressive subpopulation of cells within these tumors, have recently emerged as drivers of tumor initiation and recurrence, and a growing body of evidence suggests that they must be completely eradicated to prevent relapse. Toward this goal, we have developed polyethylenimine-wrapped spherical nucleic acid nanoparticles (PEI-SNAs) targeting Gli1, a transcription factor within the Hedgehog signaling pathway that is crucial for the maintenance of GSCs. Here, we demonstrate that Gli1 PEI-SNAs bind scavenger receptors on GBM cells to undergo endocytosis in a caveolae/lipid raft/dynamin-dependent manner. They further achieve ∼30% silencing of tumor-promoting Hedgehog pathway genes and downstream target genes that promote the aggressive, chemoresistant phenotype of GBM. This produces a 30% decrease in proliferation that correlates with a robust onset of GBM cell senescence as well as an ∼60% decrease in metabolic activity with or without cotreatment with temozolomide (TMZ), the frontline chemotherapy for GBM. Most importantly, Gli1 PEI-SNAs impair the self-renewal capacity of GBM cells as indicated by a 30-40% reduction in the expression of stemness genes and further impair the formation of stem-like neurospheres. They also substantially improve neurosphere chemosensitivity as demonstrated by a 2-fold increase in the fraction of cells undergoing apoptosis in response to low doses of TMZ. These results underscore the potential for siRNA therapeutics targeting Gli1 to reduce GBM resistance to therapy and warrant further development of PEI-SNAs and Gli1-targeted therapies to alleviate drug resistance and recurrence for GBM patients.

Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI.

Cardiac tagged MR images allow for deformation fields to be measured in the heart by tracking the motion of tag lines throughout the cardiac cycle. Machine learning (ML) algorithms enable accurate and robust tracking of tag lines. Herein, the use of a massive synthetic physics-driven training dataset with known ground truth was used to train an ML network to enable tracking any number of points at arbitrary positions rather than anchored to the tag lines themselves. The tag tracking and strain calculation methods were investigated in a computational deforming cardiac phantom with known (ground truth) strain values. This enabled both tag tracking and strain accuracy to be characterized for a range of image acquisition and tag tracking parameters. The methods were also tested on in vivo volunteer data. Median tracking error was <0.26mm in the computational phantom, and strain measurements were improved in vivo when using the arbitrary point tracking for a standard clinical protocol.