Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics

The untapped potential of 3D virtualization using high resolution scanner-based and photogrammetry technologies for bone bank digital modeling

Three-dimensional (3D) scanning technologies could transform medical practices by creating virtual tissue banks. In bone transplantation, new approaches are needed to provide surgeons with accurate tissue measurements while minimizing contamination risks and avoiding repeated freeze-thaw cycles of banked tissues. This study evaluates three prominent non-contact 3D scanning methods-structured light scanning (SLG), laser scanning (LAS), and photogrammetry (PHG)-to support tissue banking operations. We conducted a thorough examination of each technology and the precision of the 3D scanned bones using relevant anatomical specimens under sterile conditions. Cranial caps were scanned as separate inner and outer surfaces, automatically aligned, and merged with post-processing. A colorimetric analysis based on CIEDE2000 was performed, and the results were compared with questionnaires distributed among neurosurgeons. The findings indicate that certain 3D scanning methods were more appropriate for specific bones. Among the technologies, SLG emerged as optimal for tissue banking, offering a superior balance of accuracy, minimal distortion, cost-efficiency, and ease of use. All methods slightly underestimated the volume of the specimens in their virtual models. According to the colorimetric analysis and the questionnaires given to the neurosurgeons, our low-cost PHG system performed better than others in capturing cranial caps, although it exhibited the least dimensional accuracy. In conclusion, this study provides valuable insights for surgeons and tissue bank personnel in selecting the most efficient 3D non-contact scanning technology and optimizing protocols for modernized tissue banking. Future work will advance towards smart healthcare solutions, explore the development of virtual tissue banks.

Machine learning segmentation of core and penumbra from acute stroke CT perfusion data

Introduction

Computed tomography perfusion (CTP) imaging is widely used in cases of suspected acute ischemic stroke to positively identify ischemia and assess suitability for treatment through identification of reversible and irreversible tissue injury. Traditionally, this has been done via setting single perfusion thresholds on two or four CTP parameter maps. We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously.

Methods

We used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images.

Results

XGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model demonstrated moderate agreement in segmenting core and penumbra on test images. Dice similarity coefficients for core and penumbra were 0.38 ± 0.26 and 0.50 ± 0.21, respectively, demonstrating moderate agreement. Skull-related image artefacts contributed to lower accuracy.

Discussion

Further development may enable us to move beyond the current overly simplistic core and penumbra definitions using single thresholds where a single error or artefact may lead to substantial error.

How precise is PreSize Neurovascular? Accuracy evaluation of flow diverter deployed-length prediction

OBJECTIVE

The use of flow-diverting stents has been increasingly important in intracranial aneurysm treatment. However, accurate sizing and landing zone prediction remain challenging. Inaccurate sizing can lead to suboptimal deployment, device waste, and complications. This study presents stent deployment length predictions offered in medical software (PreSize Neurovascular) that provides physicians with real-time planning support, allowing them to preoperatively "test" different devices in the patient's anatomy in a safe virtual environment. This study reports the software evaluation methodology and accuracy results when applied to real-world data from a wide range of cases and sources as a necessary step in demonstrating its reliability, prior to impact assessment in prospective clinical practice.

METHODS

Imaging data from 138 consecutive stent cases using the Pipeline embolization device were collected from 5 interventional radiology centers in the United Kingdom and retrospectively analyzed. Prediction accuracy was calculated as the degree of agreement between stent deployed length measured intraoperatively and simulated in the software.

RESULTS

The software predicted the deployed stent length with a mean accuracy of 95.61% (95% confidence interval [CI] 94.87%-96.35%), the highest reported accuracy in clinical stent simulations to date. By discounting 4 outlier cases, in which events such as interactions with coils and severe push/pull maneuvers impacted deployed length to an extent the software was not able to simulate or predict, the mean accuracy further increases to 96.13% (95% CI 95.58%-96.69%). A wide discrepancy was observed between labeled and measured deployed stent length, in some cases by more than double, with no demonstrable correlation between device dimensions and deployment elongation. These findings illustrate the complexity of stent behavior and need for simulation-assisted sizing for optimal surgical planning.

CONCLUSIONS

The software predicts the deployed stent length with excellent accuracy and could provide physicians with real-time accurate device selection support.

Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations

Motivation

Astrocytes, the most abundant glial cells in the mammalian brain, have an instrumental role in developing neuronal circuits. They contribute to the physical structuring of the brain, modulating synaptic activity and maintaining the blood–brain barrier in addition to other significant aspects that impact brain function. Biophysically, detailed astrocytic models are key to unraveling their functional mechanisms via molecular simulations at microscopic scales. Detailed, and complete, biological reconstructions of astrocytic cells are sparse. Nonetheless, data-driven digital reconstruction of astroglial morphologies that are statistically identical to biological counterparts are becoming available. We use those synthetic morphologies to generate astrocytic meshes with realistic geometries, making it possible to perform these simulations.

Results

We present an unconditionally robust method capable of reconstructing high fidelity polygonal meshes of astroglial cells from algorithmically-synthesized morphologies. Our method uses implicit surfaces, or metaballs, to skin the different structural components of astrocytes and then blend them in a seamless fashion. We also provide an end-to-end pipeline to produce optimized two- and three-dimensional meshes for visual analytics and simulations, respectively. The performance of our pipeline has been assessed with a group of 5000 astroglial morphologies and the geometric metrics of the resulting meshes are evaluated. The usability of the meshes is then demonstrated with different use cases.

Availability and implementation

Our metaball skinning algorithm is implemented in Blender 2.82 relying on its Python API (Application Programming Interface). To make it accessible to computational biologists and neuroscientists, the implementation has been integrated into NeuroMorphoVis, an open source and domain specific package that is primarily designed for neuronal morphology visualization and meshing.

Supplementary information

Supplementary data are available at Bioinformatics online.

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.

Contralateral MRI scan can be used reliably for three-dimensional meniscus sizing - Retrospective analysis of 160 healthy menisci

BACKGROUND

Meniscus allograft transplantation is a valuable surgical option for post-meniscectomy syndrome. For best results, the selected allograft should be as similar as possible to the original meniscus. Three-dimensional meniscus sizing could be a new approach to improve the accuracy of meniscus matching. The contralateral anatomy might therefore be a suitable reconstruction template. The purpose of this study was to compare the three-dimensional shape of the right and left menisci by bi-planar segmentation of magnetic resonance imaging (MRI) scans.

METHODS

Three-dimensional surface models of healthy menisci were created based on 40 bilateral MRI scans. Manual segmentation was performed on the MRI data in sagittal and coronal planes. For side-to-side comparison, each left meniscus model was mirrored and then superimposed to its corresponding right meniscus model. Differences between the meniscus pairs were assessed by width, length, height and surface distances. Inter-reader reliability, as well as accuracy of bi-planar segmentation was assessed by two different readers.

RESULTS

The meniscus pairs were not significantly different in terms of width, length and height (P = at least 0.138). Side difference of mean surface distances was 0.76 mm (±0.13 standard deviation (SD)) for medial and 0.78 mm (±0.15 SD) for lateral menisci. Inter-reader reliability was good to excellent (0.828-0.987).

CONCLUSION

The three-dimensional shapes of the left and right menisci are very similar. Therefore, the contralateral meniscus can be used as a template for three-dimensional meniscus allograft sizing. Three-dimensional meniscus segmentation and sizing can be performed accurately by combination of sagittal and coronal planes.

An efficient full space-time discretization method for subject-specific hemodynamic simulations of cerebral arterial blood flow with distensible wall mechanics

A computationally inexpensive mathematical solution approach using orthogonal collocations for space discretization with temporal Fourier series is proposed to compute subject-specific blood flow in distensible vessels of large cerebral arterial networks. Several models of wall biomechanics were considered to assess their impact on hemodynamic predictions. Simulations were validated against in vivo blood flow measurements in six human subjects. The average root-mean-square relative differences were found to be less than 4.3% for all subjects with a linear elastic wall model. This discrepancy decreased further in a viscoelastic Kelvin-Voigt biomechanical wall. The results provide support for the use of collocation-Fourier series approach to predict clinically relevant blood flow distribution and collateral blood supply in large portions of the cerebral circulation at reasonable computational costs. It thus opens the possibility of performing computationally inexpensive subject-specific simulations that are robust and fast enough to predict clinical results in real time on the same day.

Cerebral arteriovenous malformation venous stenosis is associated with hemodynamic changes at the draining vein-venous sinus junction

Cerebral arteriovenous malformations (AVMs) are an uncommon vascular anomaly that carry the risk of rupture and hemorrhage. Several factors have been implicated in the propensity of an AVM to bleed. One such factor is stenosis of AVM draining veins, as impairment of the AVM venous drainage system is associated with increased risk of intracranial hemorrhage. Currently, our understanding of the pathogenesis of AVM venous outflow stenosis is limited, as there is insufficient data on the blood flow patterns and local hemodynamic parameters of these draining veins. The angioarchitecture of AVMs features a nidus lacking a high resistance capillary network. Accordingly, our previous studies on AVM arterial feeders have demonstrated an abnormally high flow volume rate along with low pulsatility and resistance indices on quantitative magnetic resonance angiography. As such, AVM vessels endure high, non-physiologic levels of flow that may partially contribute to ectasia or stenosis depending on whether wall shear stress (WSS) is high or low, respectively. We hypothesize that AVM venous outflow stenosis occurs most commonly near the junction of the draining vein and the dural venous sinus. Increased flow volume rate through the AVM circuit coupled with the variation in compliance and rigidity between the walls of the draining vein and the dural venous sinus likely create turbulence of blood flow. The resulting flow separation, low WSS, and departure from axially aligned, unidirectional flow may create atherogenic conditions that can be implicated in venous intimal hyperplasia and outflow stenosis. We have previously found there to be a significant association between intimal hyperplasia risk factors and venous outflow stenosis. Additionally, we have found a significant association between age and likelihood as well as degree of stenosis, suggesting a progressive disease process. Similar conditions have been demonstrated in the pathophysiology of stenosis of the carotid artery and dialysis arteriovenous fistulas. In both of these conditions, the use of computational fluid dynamics (CFD) has been employed to characterize the local hemodynamic features that contribute to the pathogenesis of intimal hyperplasia and stenosis. We recommend the utilization of CFD to characterize the anatomic and hemodynamic features of AVM venous outflow stenosis. An improved understanding of the possible causative features of venous outflow stenosis may impact how clinicians choose to manage the treatment of patients with AVMs.