Quantitative CT and 19F-MRI tracking of perfluorinated encapsulated mesenchymal stem cells to assess graft immunorejection.

OBJECTIVES: Peripheral artery disease (PAD) affects 12-14% of the world population, and many are not eligible for conventional treatment. For these patients, microencapsulated stem cells (SCs) offer a novel means to transplant mismatched therapeutic SCs to prevent graft immunorejection. Using c-arm CT and 19F-MRI for serial evaluation of dual X-ray/MR-visible SC microcapsules (XMRCaps) in a non-immunosuppressed rabbit PAD model, we explore quantitative evaluation of capsule integrity as a surrogate of transplanted cell fate. MATERIALS AND METHODS: XMRCaps were produced by impregnating 12% perfluorooctylbromine (PFOB) with rabbit or human SCs (AlloSC and XenoSC, respectively). Volume and 19F concentration measurements of XMRCaps were assessed both in phantoms and in vivo, at days 1, 8 and 15 after intramuscular administration in rabbits (n = 10), by 3D segmenting the injection sites and referencing to standards with known concentrations. RESULTS: XMRCap volumes and concentrations showed good agreement between CT and MRI both in vitro and in vivo in XenoSC rabbits. Injected capsules showed small variations over time and were similar between AlloSC and XenoSC rabbits. Histological staining revealed high cell viability and intact capsules 2 weeks after administration. CONCLUSIONS: Quantitative and non-invasive tracking XMRCaps using CT and 19F-MRI may be useful to assess graft immunorejection after SC transplantation.

High-resolution and accelerated multi-parametric mapping with automated characterization of vessel disease using intravascular MRI.

BACKGROUND: Atherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention. This study tests whether intravascular magnetic resonance imaging (IVMRI) measures of relaxation times (T1, T2) and proton density (PD) in a clinical 3 Tesla scanner could characterize vessel disease, and evaluates a practical strategy for accelerated quantification. METHODS: IVMRI was performed in fresh human artery segments and swine vessels in vivo, using fast multi-parametric sequences, 1-2 mm diameter loopless antennae and 200-300 µm resolution. T1, T2 and PD data were used to train a machine learning classifier (support vector machine, SVM) to automatically classify normal vessel, and early or advanced disease, using histology for validation. Disease identification using the SVM was tested with receiver operating characteristic curves. To expedite acquisition of T1, T2 and PD data for vessel characterization, the linear algebraic method ('SLAM') was modified to accommodate the antenna's highly-nonuniform sensitivity, and used to provide average T1, T2 and PD measurements from compartments of normal and pathological tissue segmented from high-resolution images at acceleration factors of R ≤ 18-fold. The results were validated using compartment-average measures derived from the high-resolution scans. RESULTS: The SVM accurately classified ~80% of samples into the three disease classes. The 'area-under-the-curve' was 0.96 for detecting disease in 248 samples, with T1 providing the best discrimination. SLAM T1, T2 and PD measures for R ≤ 10 were indistinguishable from the true means of segmented tissue compartments. CONCLUSION: High-resolution IVMRI measures of T1, T2 and PD with a trained SVM can automatically classify normal, early and advanced atherosclerosis with high sensitivity and specificity. Replacing relaxometric MRI with SLAM yields good estimates of T1, T2 and PD an order-of-magnitude faster to facilitate IVMRI-based characterization of vessel disease.

Acceleration and motion-correction techniques for high-resolution intravascular MRI.

PURPOSE: High-resolution intravascular (IV) MRI is susceptible to degradation from physiological motion and requires high frame-rates for true endoscopy. Traditional cardiac-gating techniques compromise efficiency by reducing the effective scan rate. Here we test whether compressed sensing (CS) reconstruction and ungated motion-compensation using projection shifting, could provide faster motion-suppressed, IVMRI. THEORY AND METHODS: CS reconstruction is developed for undersampled Cartesian and radial imaging using a new IVMRI-specific cost function to effectively increase imaging speed. A new motion correction method is presented wherein individual IVMRI projections are shifted based on the IVMRI detector's intrinsic amplitude and phase properties. The methods are tested at 3 Tesla (T) in fruit, human vessel specimens, and a rabbit aorta in vivo. Images are compared using structural-similarity and "spokal variation" indices. RESULTS: Although some residual artifacts persisted, CS acceleration and radial motion compensation strategies reduced motion artifact in vitro and in vivo, allowing effective accelerations of up to eight-fold at 200-300 µm resolution. CONCLUSION: The 3T IVMRI detectors are well-suited to CS and motion correction strategies based on their intrinsic radially-sparse sensitivity profiles and high signal-to-noise ratios.

Highly Accelerated, Intravascular T1, T2, and Proton Density Mapping with Linear Algebraic Modeling and Sensitivity Profile Correction at 3T.

Vessel wall MRI with intravascular (IV) detectors can produce superior local signal-to-noise ratios (SNR) and generate high-resolution T1, T2, and proton density (PD) maps that could be used to automatically classify atherosclerotic lesion stage. However, long acquisition times potentially limit multi-parametric mapping. Here, for the first time, spectroscopy with linear algebraic modeling (SLAM) is applied to yield accurate compartment-average T1, T2 and PD measures at least 10 times faster compared to a standard full k-space reconstructed MIX-TSE sequence at 3T. Simple phase and magnitude sensitivity corrections are incorporated into the SLAM reconstruction to compensate for IV detector non-uniformity.