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Diffusion Imaging for Mapping Hydraulic White Matter Parameters in Convection Enhanced Delivery
Thomas Lilieholm1, Douglas C Dean III1,2, Jayse M Weaver1,2, Andrew L Alexander1,2,3, Raghu Raghavan4, Martin L Brady4, and Walter F Block1,5,6
1Medical Physics, University of Wisconsin at Madison, Madison, WI, United States, 2Waisman Center, University of Wisconsin at Madison, Madison, WI, United States, 3ImgGyd, LLC, Middleton, WI, United States, 4Therataxis, Baltimore, MD, United States, 5Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, United States, 6Radiology, University of Wisconsin at Madison, Madison, WI, United States

Synopsis

Keywords: Simulation/Validation, Diffusion/other diffusion imaging techniques

Motivation: Intraparenchymal brain infusions using convection enhanced delivery (CED) require predictive modeling to plan catheter locations to visualize end distributions. Currently, diffusion MRI indirectly estimates extracellular volume fraction (ECVF) and creates hydraulic conductivity maps.

Goal(s): To directly measure ECVF parameters using advanced biophysical diffusion models.

Approach: Quantitative brain maps of ECVF from a pre-existing database were generated with diffusion model SANDI. These were compared with prior ECVF estimates determined via invasive physiological techniques.

Results: SANDI modeling predicted ECVF ranges of 0.14-0.26 in white matter across 12 cases, consistent with consensus values around 0.20 estimated from physiology.

Impact: SANDI can quantitatively measure extracellular volume fractions in white matter. Previous methods estimated these parameters indirectly. Direct parametrization could be used for more accurate estimates of end-state biologic distributions in proposed trials of monogenic pediatric neurodegenerative gene therapy trials.

Introduction

Gene therapies for fatal rare neurodegenerative pediatric lysosomal storage diseases are now capable of correcting cellular function but remain limited by drug delivery methods1,2. End distributions of gene therapies using convection enhanced delivery (CED), which uses intra-parenchymal catheter placements to slowly infuse therapeutic agents directly into the brain, are difficult to predict due to heterogeneous hydraulic attributes influencing flow across differing tissues, such as infusate’s preferential flow along white matter tracts2,3. Moving from palliative strategies to one-time cures will require advances in predictive modeling of end-state CED distributions before pharmaceutical companies begin new clinical trials.

Software to predictively model CED distributions and overlay them on anatomical MRI studies has been integrated into presurgical planning tools, as shown in Figure 14 (Brainlab, Munich, Germany). T1, T2, and diffusion tensor imaging (DTI) have been used to segment the adult brain and estimate key determinants of flow, including ECVF and from it, hydraulic conductivity4,5. The extracellular volume fractions (ECVF), the space in which the infusion distributes, is often treated as uniform across all brain tissue using an accepted physiological estimate6. However, ECVF is known to vary across brain regions, though this has not yet been extensively investigated7. Previous attempts to quantitatively measure ECVF with noninvasive imaging were limited by a lack of diffusion imaging techniques8. Soma And Neurite Density Imaging (SANDI), a recent modeling technique, can map ECVF in a manner more relevant to CED due to its ability to delineate cell soma from extracellular space (Figure 2)9. Presented here are measurements made using SANDI to compare its spatially-varying ECVF measurements against prior standards.

Materials and Methods

SANDI:
Many diffusion models, like NODDI, prioritize measuring the orientation and density of neurite axons to map white matter tracts10. This information is less useful in CED where infusates diffuse through and largely remain in the extracellular spaces between neurites. NODDI’s prioritization of axonal information disregards neurite cell bodies, leading to inaccuracies in measurements of extracellular volumes9,10. Alternatively, SANDI uses a different compartment model to account for the intrasomal volumes, providing more relevant measures of the spaces at play in CED, as shown in Figure 2.

Dataset Source:
A set of 12 diffusion MRI (dMRI) acquisitions were sourced from the publicly available Human Connectome Database11. Multishell diffusion-weighted acquisitions with b-values of 1000, 2000, and 3000 s/mm2, and 90 directions were obtained on a Siemens 3T scanner with 1.25mm isotropic resolution.

Data Analysis:
Previous CED planning methodologies have estimated the extracellular volume fraction (ECVF) of regular brain tissue as 20%, based on physiological experimentation5,6. Some regions, like the corpus callosum (CC), are expected to have differing ECVF, though a quantitative estimation is not yet established. Segmentations of the corpus callosum and anterior corona radiata (ACR) were manually generated by an experienced imaging scientist and used to compute the mean ECVF from each region for all 12 cases (Figure 4).

Results

Across the 12 cases, the mean ECVF in the ACR and CC were 0.24±0.09 and 0.18±0.12, respectively. This is generally consistent with prior measurements using invasive physiologic techniques6, earlier, non-diffusion, imaging methods8, and common experience in brain cancer where edema expands preferentially away from the dense corpus callosum.

Discussion

Though estimates of ECVF in white matter are consistent with known physiologic values, gray matter measurements are higher than expected. This is tentatively attributed to glial cells, which differ across gray and white matter, being smaller than the distinguishable soma and thus having their signal contributions attributed to extracellular spaces in SANDI’s compartment model12. This is represented in Figure 5. Using higher b-values can enable greater diffusion sensitivity and potentially delineate these finer compartments. Further work is needed to update the current predictive modeling tools, which are based on stable adult brains, with hydraulic characterizations of the rapidly changing pediatric brain. Intraprocedural dMRI may be beneficial for tracking tissue expansion during infusion, but lies outside this work’s current scope.

Conclusion

In white matter, the proposed application of SANDI can provide quantitative maps of extracellular space consistent with estimates previously determined through invasive physiologic measurements. While previous mapping techniques could only estimate ECVF as a fixed constant of 20%, SANDI can represent spatial variation within white matter. Future work will use altered scan parameters to achieve the same in gray matter and move towards application of predictive infusion flow mapping in animal trials.

Acknowledgements

We acknowledge GE-Healthcare and UW-Madison for research support

Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

References

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[4] Sampson JH, Raghavan R, Brady ML, Provenzale JM, Herndon JE 2nd, Croteau D, Friedman AH, Reardon DA, Coleman RE, Wong T, Bigner DD, Pastan I, Rodríguez-Ponce MI, Tanner P, Puri R, Pedain C. Clinical utility of a patient-specific algorithm for simulating intracerebral drug infusions. Neuro Oncol. 2007 Jul;9(3):343-53. doi: 10.1215/15228517-2007-007. Epub 2007 Apr 13. PMID: 17435179; PMCID: PMC1907410.

[5] Brady M, Raghavan R, Sampson J. Determinants of Intraparenchymal Infusion Distributions: Modeling and Analyses of Human Glioblastoma Trials. Pharmaceutics. 2020 Sep 21;12(9):895. doi: 10.3390/pharmaceutics12090895. PMID: 32967184; PMCID: PMC7559135.

[6] Syková E, Nicholson C. Diffusion in brain extracellular space. Physiol Rev. 2008 Oct;88(4):1277-340. doi: 10.1152/physrev.00027.2007. PMID: 18923183; PMCID: PMC2785730.[7] Nicholson C, Syková E. Extracellular space structure revealed by diffusion analysis. Trends Neurosci. 1998 May;21(5):207-15. doi: 10.1016/s0166-2236(98)01261-2. PMID: 9610885.

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[9] Palombo M, Ianus A, Guerreri M, Nunes D, Alexander DC, Shemesh N, Zhang H. SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage. 2020 Jul 15;215:116835. doi: 10.1016/j.neuroimage.2020.116835. Epub 2020 Apr 11. Erratum in: Neuroimage. 2021 Feb 1;226:117612. PMID: 32289460; PMCID: PMC8543044.

[10] Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012 Jul 16;61(4):1000-16. doi: 10.1016/j.neuroimage.2012.03.072. Epub 2012 Mar 30. PMID: 22484410.

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Figures

Figure 1: Effective CED infusions require sufficient coverage of the infusate across volume. Anisotropy in predicted distributions, as shown in BrainLab’s iPlan Flow pre-surgical planning tool (Munich, Germany), demonstrates how end infusion distributions are seldom spherical regions about catheter tips4. Via MRI, the software infers hydraulic parameters to predict and visualize final distributions of biologics in regular anatomy. Neurosurgeons alter catheter locations, flow rates, and infusion durations in pre-surgical planning to obtain desired end distributions.

Figure 2: Visualization of the compartment models utilized in SANDI as compared against current leading models, sourced from the original work9. Earlier models prioritize probing the orientation and density of the neurite’s axons while largely neglecting the cell bodies. In doing so, signal contributions from the neurons’ soma are inaccurately attributed to extracellular space. In most applications ECVF is not a focus, so this is generally nonproblematic. In CED, ECVF is relevant, so SANDI’s method of delineating the soma adds value to the analysis.

Figure 3: Left is a SANDI-generated map of ECVF in one subject. Intensity corresponds to ECVF on a scale from 0-1. In this way, one can see the spatial variation of extracellular space across differing anatomical features. Notably, the corpus callosum is visibly less intense than the surrounding white matter. The right shows the manually-generated contours of white matter subregions. The anterior corona radiata is overlaid with red, while the corpus callosum is visible in green. The mean and standard deviations of intensities across these contoured volumes are collated in Figure 4.

Figure 4: Recorded extracellular volume fraction in the anterior corona radiata (ACR) and corpus callosum (CC), respectively. Previous standards used a fixed constant value of 0.20 across all brain tissue. The values identified here are consistent with this, but also demonstrate distinctions between different regions within the white matter. The distinctions agree with common neuroradiology experience that edema is diverted from the CC in brain cancers due to its higher neuronal density.

Figure 5: The structure of a neuron, showing the relevant biophysical compartments investigated by diffusion models. Discrimination is based on restriction of diffusion, with long compartments (axon, myelin sheath) being delineated by the very anisotropic diffusion pattern of contained water molecules. More anisotropic diffusion, as may occur in the rounder cell bodies, distinguishes another compartment, though the disparity in their relative sizes may lead to, in gray matter, the smaller compartments among abundant astrocytes being misconstrued as extracellular space.

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
3477
DOI: https://doi.org/10.58530/2024/3477