Purpose To develop a fast and accurate method for 3D T2 mapping of prostate cancer using undersampled acquisition and dictionary‐based fitting. Methods 3D high‐resolution T2‐weighted images (0.9 × 0.9 × 3 mm3) were obtained with a multishot T2‐prepared balanced steady‐state free precession (T2‐prep‐bSSFP) acquisition sequence using a 3D variable density undersampled Cartesian trajectory. Each T2‐weighted image was reconstructed using total variation regularized sensitivity encoding. A flexible simulation framework based on extended phase graphs generated a dictionary of magnetization signals, which was customized to the proposed sequence. The dictionary was matched to the acquired T2‐weighted images to retrieve quantitative T2 values, which were then compared to gold‐standard spin echo acquisition values using monoexponential fitting. The proposed approach was validated in simulations and a T1/T2 phantom, and feasibility was tested in 8 healthy subjects. Results The simulation analysis showed that the proposed T2 mapping approach is robust to noise and typically observed T1 variations. T2 values obtained in the phantom with T2prep‐bSSFP and the acquisition‐specific, dictionary‐based matching were highly correlated with the gold‐standard spin echo method (r = 0.99). Furthermore, no differences were observed with the accelerated acquisition compared to the fully sampled acquisition (r = 0.99). T2 values obtained in prostate peripheral zone, central gland, and muscle in healthy subjects (age, 26 ± 6 years) were 97 ± 14, 76 ± 7, and 36 ± 3 ms, respectively. Conclusion 3D quantitative T2 mapping of the whole prostate can be achieved in 3 minutes.

Magnetic resonance spectroscopic imaging (MRSI) is an important technique for assessing the spatial variation of metabolites in vivo. The long scan times in MRSI limit clinical applicability due to patient discomfort, increased costs, motion artifacts, and limited protocol flexibility. Faster acquisition strategies can address these limitations and could potentially facilitate increased adoption of MRSI into routine clinical protocols with minimal addition to the current anatomical and functional acquisition protocols in terms of imaging time. Not surprisingly, a lot of effort has been devoted to the development of faster MRSI techniques that aim to capture the same underlying metabolic information (relative metabolite peak areas and spatial distribution) as obtained by conventional MRSI, in greatly reduced time. The gain in imaging time results, in some cases, in a loss of signal‐to‐noise ratio and/or in spatial and spectral blurring. This review examines the current techniques and advances in fast MRSI in two and three spatial dimensions and their applications. This review categorizes the acceleration techniques according to their strategy for acquisition of the k‐space. Techniques such as fast/turbo‐spin echo MRSI, echo‐planar spectroscopic imaging, and non‐Cartesian MRSI effectively cover the full k‐space in a more efficient manner per TR. On the other hand, techniques such as parallel imaging and compressed sensing acquire fewer k‐space points and employ advanced reconstruction algorithms to recreate the spatial‐spectral information, which maintains statistical fidelity in test conditions (ie no statistically significant differences on voxel‐wise comparisions) with the fully sampled data. The advantages and limitations of each state‐of‐the‐art technique are reviewed in detail, concluding with a note on future directions and challenges in the field of fast spectroscopic imaging.

Understanding of the mechanisms by which systemically administered nanoparticles achieve delivery across biological barriers remains incomplete, due in part to the challenge of tracking nanoparticle fate in the body. Here, we develop a new approach for "barcoding" nanoparticles composed of poly(lactic-co-glycolic acid) (PLGA) with bright, spectrally defined quantum dots (QDs) to enable direct, fluorescent detection of nanoparticle fate with subcellular resolution. We show that QD labeling does not affect major biophysical properties of nanoparticles or their interaction with cells and tissues. Live cell imaging enabled simultaneous visualization of the interaction of control and targeted nanoparticles with bEnd.3 cells in a flow chamber, providing direct evidence that surface modification of nanoparticles with the cell-penetrating peptide TAT increases their biophysical association with cell surfaces over very short time periods under convective current. We next developed this technique for quantitative biodistribution analysis in vivo. These studies demonstrate that nanoparticle surface modification with the cell penetrating peptide TAT facilitates brain-specific delivery that is restricted to brain vasculature. Although nanoparticle entry into the healthy brain parenchyma is minimal, with no evidence for movement of nanoparticles across the blood-brain barrier (BBB), we observed that nanoparticles are able to enter to the central nervous system (CNS) through regions of altered BBB permeability - for example, into circumventricular organs in the brain or leaky vasculature of late-stage intracranial tumors. In sum, these data demonstrate a new, multispectral approach for barcoding PLGA, which enables simultaneous, quantitative analysis of the fate of multiple nanoparticle formulations in vivo.

Purpose: Magnetic resonance electrical impedance tomography (MREIT) sequences typically use conventional spin or gradient echo-based acquisition methods for reconstruction of conductivity and current density maps. Use of MREIT in functional and electroporation studies requires higher temporal resolution and faster sequences. Here, single and multishot echo planar imaging (EPI) based MREIT sequences were evaluated to see whether high-quality MREIT phase data could be obtained for rapid reconstruction of current density, conductivity, and electric fields. Methods: A gel phantom with an insulating inclusion was used as a test object. Ghost artifact, geometric distortion, and MREIT correction algorithms were applied to the data. The EPI-MREIT-derived phase-projected current density and conductivity images were compared with simulations and spinecho images as a function of EPI shot number. Results: Good agreement among measures in simulated, spin echo, and EPI data was achieved. Current density errors were stable and below 9% as the shot number decreased from 64 to 2, but increased for single-shot images. Conductivity reconstruction relative contrast ratios were stable as the shot number decreased. The derived electric fields also agreed with the simulated data. Conclusions: The EPI methods can be combined successfully with MREIT reconstruction algorithms to achieve fast imaging of current density, conductivity, and electric field. Magn Reson Med 79:71-82,

Cell therapy represents a promising therapeutic for a myriad of medical conditions, including cancer, traumatic brain injury, and cardiovascular disease among others. A thorough understanding of the efficacy and cellular dynamics of these therapies necessitates the ability to non-invasively track cells in vivo. Magnetic resonance imaging (MRI) provides a platform to track cells as a non-invasive modality with superior resolution and soft tissue contrast. We recently reported a new nanoprobe platform for cell labeling and imaging using fluorophore doped siloxane core nanoemulsions as dual modality (1H MRI/Fluorescence), dual-functional (oximetry/detection) nanoprobes. Here, we successfully demonstrate the labeling, dual-modality imaging, and oximetry of neural progenitor/stem cells (NPSCs) in vitro using this platform. Labeling at a concentration of 10 μl/104 cells with a 40%v/v polydimethylsiloxane core nanoemulsion, doped with rhodamine, had minimal effect on viability, no effect on migration, proliferation and differentiation of NPSCs and allowed for unambiguous visualization of labeled NPSCs by 1H MR and fluorescence and local pO2 reporting by labeled NPSCs. This new approach for cell labeling with a positive contrast 1H MR probe has the potential to improve mechanistic knowledge of current therapies, and guide the design of future cell therapies due to its clinical translatability.