Segmentation of Circle of Willis from 7T TOF-MRI data and immersive exploration using VR

Spitz, Lena; Allgaier, Mareen; Mpotsaris, Anastasios; Behme, Daniel; Preim, Bernhard; Saalfeld, Sylvia

doi:10.1515/cdbme-2022-0033

Cited by 3 publications

(3 citation statements)

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“…A vesselness filter, thresholding and masking were applied to both datasets in MeVisLab 3.4.2 [ 18 ] to emphasize the vessels. Due to strong variations in vessel size, we found that a combination of multiple vesselness filters worked best [ 19 ]. TOF-MRI data were segmented according to [ 19 ] via thresholding and masking in MeVisLab as well [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…Due to strong variations in vessel size, we found that a combination of multiple vesselness filters worked best [ 19 ]. TOF-MRI data were segmented according to [ 19 ] via thresholding and masking in MeVisLab as well [ 20 ]. After editing in Blender 2.93.4 (The Blender Foundation, Amsterdam, Netherlands), to account for fusion artifacts from the segmentation, the Vascular Modelling Toolkit VMTK 1.4.0 [ 21 ] was used to generate centerlines.…”

Section: Methodsmentioning

confidence: 99%

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A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data

Spitz

Gaidzik²,

Stucht³

et al. 2023

Int J CARS

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Purpose 7T time-of-flight (TOF) MRI provides high resolution for the evaluation of cerebrovascular vessels and pathologies. In combination with 4D flow fields acquired with phase-contrast (PC) MRI, hemodynamic information can be extracted to enhance the analysis by providing direct measurements in the larger arteries or patient-specific boundary conditions. Hence, a registration between both modalities is required. Methods To combine TOF and PC-MRI data, we developed a hybrid registration approach. Vessels and their centerlines are segmented from the TOF data. The centerline is fit to the intensity ridges of the lower resolved PC-MRI data, which provides temporal information. We used a metric that utilizes a scaled sum of weighted intensities and gradients on the normal plane. The registration is then guided by decoupled local affine transformations. It is applied hierarchically following the branching order of the vessel tree. Results A landmark validation over Monte Carlo simulations yielded an average mean squared error of 184.73 mm and an average Hausdorff distance of 15.20 mm. The hierarchical traversal that transforms child vessels with their parents registers even small vessels not detectable in the PC-MRI. Conclusion The presented work combines high-resolution tomographic information from 7T TOF-MRI and measured flow data from 4D 7T PC-MRI scan for the arteries of the brain. This enables usage of patient-specific flow parameters for realistic simulations, thus supporting research in areas such as cerebral small vessel disease. Automatization and free deformations can help address the limiting error measures in the future.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data

Spitz

Gaidzik²,

Stucht³

et al. 2023

Int J CARS

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the Circle of Willis context, image segmentation techniques have been applied in neurovascular studies. [48] demonstrated the segmentation of the Circle of Willis from 7T TOF‐MRI data, highlighting the relevance of automated segmentation in neurovascular research. Understanding the Circle of Willis and its variations is crucial for assessing blood flow and vascular‐related pathologies and automated segmentation methods can contribute to this endeavor.…”

Section: Related Workmentioning

confidence: 99%

Topology‐aware anatomical segmentation of the Circle of Willis: HUNet unveils the vascular network

Junayed,

Sanjid,

Hossain

et al. 2024

IET Image Processing

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This research investigates the Circle of Willis, a critical vascular structure vital for cerebral blood supply. A modified novel dual‐pathway multi‐scale hierarchical upsampling network (HUNet) is presented, tailored explicitly for accurate segmentation of Circle of Willis anatomical components from medical imaging data. Evaluating both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, HUNet consistently outperforms the convolutional U‐net model, demonstrating superior capabilities and achieving higher accuracy across various classes. Additionally, the HUNet model achieves an exceptional dice similarity coefficient of 98.61 and 97.95, along with intersection over union scores of 73.32 and 85.76 in both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, respectively. These metrics highlight HUNet's exceptional performance in achieving precise and accurate segmentation of anatomical structures within the Circle of Willis, underscoring its robustness in medical image segmentation tasks. Visual representations substantiate HUNet's efficacy in delineating Circle of Willis structures, offering comprehensive insights into its superior performance.

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Assessment of intracranial aneurysm neck deformation after contour deployment

Spitz,

Korte,

Gaidzik

et al. 2024

Int J CARS

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Purpose: The contour neurovascular system (CNS) is a novel device to treat intracranial wide-necked bifurcation aneurysms, with few studies assessing its long-term effects. Particularly its impact on aneurysm morphology has not been explored yet. We present a preliminary study to explore this impact for the first time, focusing on the neck curve and ostium of the aneurysm. Methods: We investigated seven aneurysms treated with the CNS to assess ostium deformation after CNS deployment by comparing models extracted from in vivo medical pre-treatment and follow-up scans via morphological analysis. Time between pre- and follow-up scans was ten months on average. Size and shape indices like area, neck diameter, ellipticity index, undulation index, and more were assessed. Results: Ostium size was reduced after treatment. On average, ostium area was reduced at a rate of $$-$$ - 0.58 (± 4.88) mm2 per year, from 15.52 (± 3.51) mm2 to 13.30 (± 2.27) mm2, and ostium width from 5.01 (± 0.54) mm to 4.49 (± 0.45) mm, with an average reduction of $$-$$ - 0.59 (± 0.87) mm. This shrinking positively correlated with time passing. Shape deformation was low, though notably mean ellipticity index was reduced by 0.06 (± 0.15) on average, indicating ostia were less elongated after treatment. Conclusion: We interpret the shrinking of the ostium as part of the healing process. Shape changes were found to be small enough to conclude no shape deformation of the ostium from CNS deployment, but the analysis of more cases with more parameters and information is necessary.

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Segmentation of Circle of Willis from 7T TOF-MRI data and immersive exploration using VR

Cited by 3 publications

References 0 publications

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data

A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data

Topology‐aware anatomical segmentation of the Circle of Willis: HUNet unveils the vascular network

Assessment of intracranial aneurysm neck deformation after contour deployment

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