Automated multi-parametric segmentation of brain veins from GRE acquisition
Serena Monti1,2, Pasquale Borrelli1, Sirio Cocozza3, Sina Straubb4, Mark Ladd4, Marco Salvatore1, Enrico Tedeschi3, and Giuseppe Palma5

1IRCCS SDN, Naples, Italy, 2Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy, 3Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy, 4Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 5Institute of Biostructure and Bioimaging, National Research Council, Naples, Italy

Synopsis

A new fully automated algorithm, based on structural, morphological and relaxometric information, is proposed to segment the entire brain deep venous system from MR images. The method is tested on brain datasets at different magnetic fields and its inter-scan reproducibility is also assessed. The proposed segmentation algorithm shows good accuracy and reproducibility, outperforming previous methods and becoming a promising candidate for the characterization of venous tree topology.

Purpose

The assessment of the intracranial venous system anatomy provides a fundamental tool to study brain diseases such as neurodegenerative disorders or traumatic brain injury. Venous tree can be proficiently visualized by Susceptibility Weighted Imaging (SWI)1, which allows for detection of vascular abnormalities in different cerebral pathological conditions.

Manual segmentation of brain vessels in a MR dataset is, however, a complex, time-consuming task and therefore automated or semi-automated approaches are actively sought for2-4, as they also improve results reproducibility.

Here we present and assess an algorithm that is, to the best of our knowledge, the first fully automated segmentation tool of the entire brain deep venous system that implements at once several independent criteria (structural, morphological and relaxometric information) of vein characterization to enhance classification accuracy.

Methods

Vein voxels are characterized by:

· low SWI intensity (structural content);

· high Vesselness values (morphology);

· high R2* values (relaxometry).

The tri-parametric segmentation (tPS) algorithm iteratively refines the vein mask by adding newly detected vessel voxels.

The initial condition is provided by a highly selective global thresholding that distinguishes a large number of reliable vein voxels from the surrounding parenchyma. Each of the following criteria has to be satisfied:

· SWI<mean(SWI)–2.5*std_dev(SWI)

· Vesselness>mean(Vesselness)+std_dev(Vesselness)

· R2*>mean(R2*)

Then, a spherical moving window filter (radius of 5mm) is iteratively applied: each voxel that satisfies the above conditions, locally computed on the neighborhood excluding previously marked voxels, is added to the vein mask.

The exit condition for the iteration is verified when no more than 1‰ of the voxels are added in the last step. The result of the last iteration is further refined by applying an additional condition on the size of each classified cluster, as described previously2.

The algorithm has been tested on brain datasets from 6 Healthy Controls (HCs) obtained on different MR scanners at B0 of 1.5, 3 and 7T. The acquired sequences were 3D double-echo spoiled gradient echo (GRE) with acquisition parameters largely dependent from B0 and flip angle close to the parenchyma Ernst angle.

To assess inter-scan reproducibility, one HC was acquired twice at 3T, with head repositioning between the scans (GREpre and GREpost).

SWI images were derived from the second echo of each dataset5; Vesselness maps were computed from these images using an optimized version of the Vessel Enhancing Diffusion filter6 and the R2*-maps were obtained as previously reported6.

Results

The segmentation results obtained at 1.5, 3 and 7T are shown in Fig.1, 2 and 3, respectively, where the obtained voxel classifications are placed side by side with the corresponding SWI and their fusion.

MIPped segmentation maps (thickness: 20mm) were presented to two experienced neuroradiologists that, blindly and in consensus, graded the accuracy of the vascular tree depiction on a 0-5 scale (0 corresponding to the lowest reliability of the voxel classification; 5 reflecting an optimal compromise between sensitivity and specificity) and compared the performance of tPS to previous mono/bi-parametric segmentation (m/bPS) approaches6, which neglect some classification criteria. At all B0, m/bPS maps were never preferred to the corresponding tPS maps in 36 comparisons. The mean accuracy scores were 4.6±0.2 for tPS vs 3.4±0.2 for m/bPS.

To evaluate inter-scan reproducibility, the segmentations (Spre and Spost) were computed, respectively from GREpre and GREpost. The second echo of GREpost was co-registered to the second echo of GREpre and the obtained affine transformation was used to map Spost to Spre coordinate system (Fig.4). A Modified Hausdorff Distance7 (MHD) was used to grade the segmentation matching, resulting in a MHD of 0.33mm.

Discussion

In this work, a fully automated method based on structural, morphological and relaxometric information is proposed to segment the entire brain deep venous system, its performance is evaluated at different B0s, and inter-scan reproducibility is assessed at 3T.

This approach, combining SWI, Vesselness and R2* values, led to a reduction of false positives coupled to an improved detection of true positives, and achieved great accuracy in vessel display, outperforming the m/bPS, which, lacking the structural/relaxometric reference, erroneously identified many voxels as veins.

The segmentation algorithm showed comparable accuracy at all the B0 explored, provided that visible vessel density increases with B0. Moreover, it exhibited excellent inter-scan reproducibility since the measured MHD of 0.33mm was well below the resolution of the used dataset (0.5x0.5x1mm3).

Conclusion

In our opinion, this algorithm represents a promising approach to characterize the topology of the venous tree. However, further studies are required to extend the present method to obtain a more comprehensive tool capable of quantifying parameters that can be of clinical relevance for detection of venous change in neurovascular diseases.

Acknowledgements

No acknowledgement found.

References

[1] Liu J, Xia S, Hanks R et al. Susceptibility Weighted Imaging and Mapping of Micro-hemorrhages and Major Deep Veins after Traumatic Brain Injury. J Neurotrauma. 2015. [Epub ahead of print].

[2] Haacke EM, Reichenbach JR. Susceptibility weighted imaging in MRI: basic concepts and clinical applications. John Wiley & Sons. 2011.

[3] Bériault S, Archambault-Wallenburg M, Sadikot AF et al. Automatic Markov Random Field Segmentation of Susceptibility-Weighted MR Venography. Lecture Notes in Computer Science. 2014; 8361:39-47.

[4] Kuijf HJ, Bouvy WH, Zwanenburg JJM et al. Automated detection of periventricular veins on 7 T brain MRI. SPIE Medical Imaging. 2015; 9413.

[5] Haacke EM, Xu Y, Cheng YCN et al. Susceptibility weighted imaging (SWI). Mag Reson Med. 2004; 52(3):612-618.

[6] Monti S, Palma G, Borrelli P et al. A Multiparametric and Multiscale Approach to Automated Segmentation of Brain Veins. Proc of 37th IEEE EMBc. 2015; 3041-3044.

[7] Dubuisson MP, Anil KJ. A modified Hausdorff distance for object matching. Proceedings of the 12th IAPR. 1994; 1:566-568.

Figures

Figure 1. Segmentation result at 1.5 T. From left to right: tPS-MIP; fusion of SWI-mIP with tPS-MIP; SWI-mIP. Image projections cover 20 mm in the head-foot direction.

Figure 2. Segmentation result at 3 T. From left to right: tPS-MIP; fusion of SWI-mIP with tPS-MIP; SWI-mIP. Image projections cover 20 mm in the head-foot direction.

Figure 3. Segmentation result at 7 T. From left to right: tPS-MIP; fusion of SWI-mIP with tPS-MIP; SWI-mIP. Image projections cover 20 mm in the head-foot direction.

Figure 4. RGB color coded fusion of Spre (red) and Spost (cyan) maps, MIPped over 20 mm in the head-foot direction. White areas correspond to matched vein detections.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
1906