Focused ultrasound-induced blood-brain barrier opening: A comparative analysis of permeability quantification based on <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow> <msup><mrow><mi>K</mi></mrow> <mrow><mtext>trans</mtext></mrow> </msup> </mrow> </math> and PS.

Focused ultrasound-induced blood-brain barrier (BBB) opening is a promising method for neurotherapeutic delivery. The standard for quantifying induced BBB permeability is the <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msup><mrow><mi>K</mi></mrow> <mrow><mtext>trans</mtext></mrow> </msup> </mrow> <annotation>$$ {K}^{\mathrm{trans}} $$</annotation></semantics> </math> parameter, which reflects both permeability and plasma flow. The influence of plasma flow can be eliminated by estimating the PS parameter. However, this parameter has been largely unexplored in this application. This study aims to compare permeability estimates based on <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msup><mrow><mi>K</mi></mrow> <mrow><mtext>trans</mtext></mrow> </msup> </mrow> <annotation>$$ {K}^{\mathrm{trans}} $$</annotation></semantics> </math> and PS in focused ultrasound-induced BBB opening experiments. We used the extended Tofts model (ETM) and the two-compartment exchange model (2CXM) to estimate <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msup><mrow><mi>K</mi></mrow> <mrow><mtext>trans</mtext></mrow> </msup> </mrow> <annotation>$$ {K}^{\mathrm{trans}} $$</annotation></semantics> </math> and PS parameters, respectively. Permeability estimates were compared using simulated concentration curves, simulated DCE-MRI data, and real datasets. We explored the influence of spatially-regularized model fitting on the results. For opened BBB, <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msup><mrow><mi>K</mi></mrow> <mrow><mtext>trans</mtext></mrow> </msup> </mrow> <annotation>$$ {K}^{\mathrm{trans}} $$</annotation></semantics> </math> was minimally influenced by plasma flow under the tested conditions. However, fitting the ETM often introduced outliers in <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msup><mrow><mi>K</mi></mrow> <mrow><mtext>trans</mtext></mrow> </msup> </mrow> <annotation>$$ {K}^{\mathrm{trans}} $$</annotation></semantics> </math> estimates in regions with closed BBB. The 2CXM outperformed the ETM at high signal-to-noise ratios, but its higher complexity led to lower precision at low signal-to-noise ratios. Both these issues were successfully compensated by spatially-regularized model fitting. Both <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msup><mrow><mi>K</mi></mrow> <mrow><mtext>trans</mtext></mrow> </msup> </mrow> <annotation>$$ {K}^{\mathrm{trans}} $$</annotation></semantics> </math> and PS seem to be eligible options for the quantification of BBB opening, and the correct choice depends on the specifics of the acquired DCE-MRI data. Additionally, spatial regularization has demonstrated its importance in enhancing the accuracy and reproducibility of results for both models.