Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi

Abstract

Beyin tümörlerinde doğru tedavi yönteminin belirlenebilmesinde tümör tipinin kesin olarak bilinmesi kritiktir. Bu çalışma, beyin biyopsisi operasyon bölgesi hacmi içindeki mekansal risk faktörlerini gözeten hacimsel yerel risk analizi modeli sunmaktadır. Bu risk modeli, operasyon bölgesinin genel riskini değerlendirmek için kullanılabilir. Risk faktörlerinin genel katkısını tahmin etmek amacıyla, parça bazlı Gauss olasılık fonksiyonu önerilmektedir. Önerilen yaklaşımın mevcut benzerlerinden üstünlüğü, beyin yüzeyindeki tüm aday giriş noktalarının giriş risk haritasını oluştururken, pin-yörüngesinin merkezinde bulunan silindirik bir yapı kullanmasıdır. Pin-yörüngelerinin risk değeri, silindir içindeki damar voksellerini mesafesi değerlendirilerek hesaplanır. Bu modeli kullanarak beyin yüzeyindeki noktalardan hedefe olan risk değerlendirmesinin ardından beyin yüzeyi risk değerlerine göre renklendirilmektedir. Uygulamada, beyin yüzeyindeki noktaların hacimsel yerel riskleri hesaplanarak damar hasarlarının en düşük riskine sahip biyopsi noktalarını tahmin etmek için bir risk haritası elde edilmiştir. Doğrulama amacıyla, 2 beyin cerrahı tarafından işaretlenmiş 15 hasta verisi kullanılmıştır. Yörünge riskleri tablolarda sunulmuştur. Sonraki çalışmada, fMRI bilgilerinin risk modeline entegrasyonu planlanmaktadır.

Keywords

• Beyin biyopsisi
• Mekansal risk analizi
• Risk atama
• Risk haritalama

A New Targeting System for Calculating a Trajectory Risk Map According to the Cerebral Vascular Tree

Abstract

Determining the precise type of tumor is crucial for selecting the correct treatment method for brain tumors. This study presents a volumetric local risk analysis model that considers spatial risk factors within the volume of the brain biopsy operation region. This risk model can be used to assess the overall risk of the operation region. To estimate the general contribution of risk factors, a piecewise Gaussian probability function is proposed. The superiority of the proposed approach over existing ones lies in its use of a cylindrical structure centered on the pin trajectory to create an entry risk map for all potential entry points on the brain surface. The risk value of pin trajectories is calculated by evaluating the distance of vessel voxels within the cylinder. Using this model, after assessing the risk from surface points to the target, the brain surface is color-coded according to risk values. In practice, by calculating the volumetric local risks of surface points using the ITKTUBETK dataset, a risk map is obtained to predict biopsy points with the lowest risk of vascular damage. For validation purposes, data from 15 patients, marked by two neurosurgeons, were used. Trajectory risks are presented in tables. According to the obtained risk analysis results, the proposed method can identify significantly lower-risk paths, 46.66% and 53.33% more effectively compared to Surgeon1 and Surgeon2, respectively. In future work, integration of fMRI information into the risk model is planned.

Keywords

• Brain biopsy
• Risk assessment
• Spatial risk analysis
• Risk mapping

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