Advanced MR vascular Fingerprinting

Maitê Marçal, Thomas Coudert, Aurelien Delphin, Antoine Barrier,
Emmanuel L Barbier, Benjamin Lemasson, Thomas Christen

► To cite this version:

Maitê Marçal, Thomas Coudert, Aurelien Delphin, Antoine Barrier, Emmanuel L Barbier, et al. Advanced MR vascular Fingerprinting. International Society for Magnetic Resonance in Medicine, May 2025, Honolulu (Hawaii), USA, United States. 10.1038/nature11971 . hal-05078184

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Submitted on 21 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Advanced MR vascular Fingerprinting

Maitê Marçal¹, Thomas Coudert¹, Aurélien Delphin², Antoine Barrier¹, Emmanuel L. Barbier¹, Benjamin Lemasson¹, and Thomas Christen¹

¹Univ. Grenoble Alpes, INSERM U1216, Grenoble Institut Neurosciences, GIN, Grenoble, France, ²Univ. Grenoble Alpes, INSERM US17, CNRS, UAR 3552, CHU Grenoble Alpes, IRMaGe, Grenoble, France

## Synopsis

**Motivation:** MRI measurements of microvascular network geometries and functions can help better understand brain functions and pathologies.

**Goal(s):** Improve microvascular network estimates with MR vascular Fingerprinting (MRvF).

**Approach:** We generated 45,000 realistic 3D voxels from mouse brain microscopy datasets using advanced segmentation tools and extracted multiple metrics through graph analysis. Then, we applied data augmentation techniques to generate lesional voxels. Finally, we employed a deep neural network to simulate MR signals and match them to in vivo MR acquisitions on healthy and tumor-bearing animals.

**Results:** Our method improved the MRvF results, allowing the generation of several new microvascular parameter maps.

**Impact:** By combining realistic 3D voxel generation and the MRvF technique, we can achieve a detailed depiction of brain microvascular parameters, potentially improving our understanding of the brain and treatment of brain lesions.

## Introduction

MR vascular Fingerprinting (MRvF)¹ extends the MR Fingerprinting framework² to quantify brain microvascular properties. A recent study improved the quality of MRvF estimates by simulating MR signals from realistic 3D vascular voxels³. However, limitations remained in voxel segmentation robustness, processing time and the limited number of metrics (CBV, R, SO2) used to produce the parametric maps. In this work, we propose to push the MRvF technique to obtain new MRI estimates of brain microvascular networks. To achieve this, we used (1) an automated software capable of quickly segmenting whole mice brain vasculature to create *in silico* 3D MRI-sized voxels, (2) data augmentation to generalize across brain structures and pathologies, (3) advanced microvessel geometry analysis (radius, density, length, orientation,...), and (4) deep learning MR simulations for efficient creation of signal dictionaries. We tested our method on healthy rat brains and two types of brain tumors.

## Materials and methods

**Animals:** control: Wistar rats (n = 8, 268±23g). tumor: 9L cells implanted in Fisher 344 rats (n = 9, 223±8 g). C6 cells implanted in Wistar rats (n = 7, 237±8 g).

**Acquisition:** was made on a 4.7 T scanner (Bruker, Germany). Anatomical T2w and GESFIDE sequences (TR=4000ms, 32 gradient echoes, Spin-Echo=60ms; voxel size=234×234×800µm³, 5 slices) were acquired before and after injection of USPIO P904 (200 µmol/kg; Guerbet, France).

**Microvascular dataset:** We used a public dataset of a whole brain from an adult male C57BL/6 mouse, acquired at 0.65x0.65x2µm³ using light-sheet fluorescence microscopy⁴.

**Segmentation:** We used VesselExpress⁵ for image segmentation and developed an automatic pipeline to download parts of the dataset, cut the large voxels (1.3312×1.3312×4.32mm³) into MRI-sized voxels (0.248×0.744x0.248mm³), segment and analyze the microvascular networks (Figure 1). 20,000 different MRI-sized voxels were eventually obtained. Characterization: Vessel centerlines were extracted using the parallel thinning algorithm and converted into undirected graphs, from which average diameter (R), branch length, and tortuosity were obtained. We also calculated vessel anisotropy as described by Ji et al.⁶, density length (DL), and the blood volume fraction (CBV) in the voxel.

**Augmentation:** To achieve a greater diversity of voxels representing healthy and pathological vascular networks, two types of data augmentation were performed on the original 20,000 voxels: (1) vessel dilation using two different structuring elements, and (2) vessel merging, where two or more segmented MR-size voxels were superimposed (Figure 2). The augmented database contains 25,000 voxels which were also characterized.

**MRvF simulations** were conducted with separate pre- and post-USPIO contrast-agent (CA) computations. Signals were calculated using 3D magnetic field estimations and frequency combinations of single isochromat Bloch simulations. Water diffusion was modeled by a Recurrent Neural Network (ADC= 850µm²/s). From 45,000 voxels with varying SO2 and T2 values, the final MRvF dictionary contained 135,000 entries and (CBV-R-SO2-T2- Length-Tortuosity-Anisotropy-DL) dimensions. In addition, we added an extra dimension (augmentation) that represents the origin of the voxel (Original dataset-vessel_dilation-vessel_merge).

**MRvF reconstruction:** Each GESFIDE voxel signal was matched (inner product) with signals from the MRvF dictionary.

## Results

Figure 3 shows the parameter space for each vascular structure metric. Each point represents a segmented voxel (n = 45,000). Voxels from the original segmentation (blue) cover only a small part of the parameter spaces, while data augmentation (cyan and pink) adds great diversity to the dataset.

Reconstructed MRvF maps are shown in Figure 4. In the healthy rat brains, CBV, R, SO2, and T2 values aligned with those reported in previous studies³. Vessel length, density and anisotropy maps are mostly flat. Matched voxels mainly come from the original dataset except in large vessel areas. For the 9L group, the CBV, R, and T2 maps indicate higher values in the lesion, while SO2 values are stable. Interestingly, we observed lower vessel length, while density length and tortuosity increased. Tumor microvasculature also displays different anisotropic patterns, suggesting disordered organization. Lastly, most voxels matched with augmented data. In the C6 tumor, blood volume and density length map are significantly lower, while anisotropy is higher. Instead of showing parametric maps, Figure 5 shows direct examples of 3D-matched voxels in 3 animals.

## Discussion

We improved the MRvF approach by creating tens of thousands realistic 3D voxels with effective image segmentation, data augmentation, and voxel characterization. Using efficient MR signal simulations, we produced large dictionaries and new maps for quantifying microvascular tissue parameters. These maps could be particularly useful for tumor monitoring, and this approach could now benefit from the addition of other vascular datasets (lesions + humans) that could further improve the results.

## Acknowledgements

All animal procedures conformed to French government guidelines and were performed under permits 380820 and A3851610008 (for experimental and animal care facilities) from the French Ministry of Agriculture (Articles R214-117 to R214-127 published on 7 February 2013), and in compliance
with the ARRIVE guidelines (Animal Research: Reporting in VivoExperiments). All animal preparations were performed by an expert animal technician. The MRI facility IRMaGe is partly funded by the French program "Investissement d'avenir" run by the French National Research Agency, grant "Infrastructure d'avenir en Biologie et Santé". [ANR-11-INBS-0006]. The project is supported by the French National Research Agency. [ANR-20-CE19-0030 MRFUSE, ANR-21-CE17-0028 CMRO2].

## References

1. Christen T, Pannetier NA, Ni WW, et al. MR vascular fingerprinting: A new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain. NeurolImage. 2014;89:262-270.

2. Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, Griswold MA. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192. doi: 10.1038/nature11971. Epub 2013 Mar 6. PMID: 23435080.

3. Delphin A. Development of MR fingerprinting for brain oxygenation mapping [Doctoral thesis]. Université Grenoble Alpes; 2022.

4. Di Giovanna AP, Tibo A, Silvestri L, et al. Whole-brain vasculature reconstruction at the single capillary level. Scientific Reports. 2018;8(1):12573.

5. Spangenberg P, Hagemann N, Squire A, et al. Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs. Cell Reports Methods. 2023;3(3)

6. Xiang JI, Ferreira T, Friedman B, et al. Brain microvasculature has a common topology with local differences in geometry that match metabolic load. Neuron. 2021;109(7):1168-1187.e13.

## Figures

<figure><img src="image_2.png" /></figure>

Illustration of the whole mouse brain, cut into several smaller MRI-sized voxels.

<figure><img src="image_3.png" /></figure>

Microvascular voxel data augmentation. Example of dilation process (top) and merging process (bottom).

<figure><img src="image_4.png" /></figure>

Parameter space with original, dilated and merged segmentations used to quantify our MRI-sized voxels. Mean radius, CBV, Mean branch length, mean tortuosity, density length and anisotropy metrics were used to characterize all the structures.
<figure><img src="image_5.png" /></figure>

All reconstructed MRvF maps on one slice of two healthy subjects, two 9L tumor-bearing and two C6 tumor-bearing. Group averaged values of all metrics in the whole healthy brain, 9L tumor and C6 tumor ROIs are also illustrated.

<figure><img src="image_6.png" /></figure>

Examples of voxels used to produce the best match dictionary entry in three different animals.