Real-Time MRI Guidance for Reproducible Hyperosmolar Opening of the Blood-Brain Barrier in Mice

Abstract

The blood-brain barrier (BBB) prevents effective delivery of most therapeutic agents to the brain. Intra-arterial (IA) infusion of hyperosmotic mannitol has been widely used to open the BBB and improve parenchymal targeting, but the extent of BBB disruption has varied widely with therapeutic outcomes often being unpredictable. In this work, we show that real-time MRI can enable fine-tuning of the infusion rate to adjust and predict effective and local brain perfusion in mice, and thereby can be allowed for achieving the targeted and localized BBB opening (BBBO). Both the reproducibility and safety are validated by MRI and histology. The reliable and reproducible BBBO we developed in mice will allow cost-effective studies on the biology of the BBB and drug delivery to the brain. In addition, the IA route for BBBO also permits subsequent IA delivery of a specific drug during the same procedure and obtains high targeting efficiency of the therapeutic agent in the targeted tissue, which has great potential for future clinical translation in neuro-oncology, regenerative medicine and other neurological applications.

Modeling hyperosmotic blood–brain barrier opening within human tissue-engineered in vitro brain microvessels

“…17,18 Advances in image-guided insertion of catheters beyond the Circle of Willis into specific locations in the cerebrovasculature have renewed interest in hyperosmotic BBBO for delivery of therapeutic and diagnostic agents. 15,19–21…”

Iterative versus non-iterative image reconstruction methods for sparse magnetic resonance imaging

“…The aim of the iterative algorithm is to minimize an objective function that consists of one data fidelity term and two Bayesian terms. The objective function, C t , can be expressed as (1) where m is the spatial domain image, F is the twodimensional (2D) Fourier transform, W is the masking matrix depending on the k-space sampling scheme, N is the total number of image pixels, and ε is a small positive constant. The mathematical symbols  x ,  y ,  t are the gradients in the x, y, the time (t) directions, respectively.…”

“…An iterative gradient descent algorithm is used to optimize the objective function, C t , as defined in Eq. (1). The number of iterations is chosen to be 1000 as suggested in reference [19].…”

“…Real-time or dynamic imaging in magnetic resonance imaging (MRI) require fast data acquisition, and the k-space data acquired is incomplete [1,2]. When the data is incomplete, the conventional analytical image reconstruction methods usually produce severe aliasing artifacts.…”

Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

“…This paper considers image reconstruction for undersampled magnetic resonance imaging (MRI) data, which is a typical case for fast imaging such as dynamic imaging and real-time imaging [1,2]. Since the data is incomplete, direct image reconstruction contains severe artifacts.…”