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在图像引导治疗实践中进行超快速锥形束计算机断层扫描成像及后处理数据。

Ultrafast cone-beam computed tomography imaging and postprocessing data during image-guided therapeutic practice.

作者信息

Paul Jijo, Mbalisike Emmanuel C, Vogl Thomas J

机构信息

Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt/Main, Germany,

出版信息

Eur Radiol. 2014 Nov;24(11):2866-75. doi: 10.1007/s00330-014-3321-8. Epub 2014 Aug 10.

Abstract

OBJECTIVE

Our objective was to evaluate ultrafast cone-beam computed tomography (u-CBCT) image data using cross-sectional images, perfusion blood volume (PBV), and image fusion during tumour detection at the course of transarterial chemoembolization.

METHODS

One hundred and fifty patients (63 ± 20 years; 33-82) were examined from February to October 2013 with u-CBCT. Tumour delineation and conspicuity were determined using u-CBCT cross-sectional PBV and u-CBCT-magnetic resonance imaging (MRI) fused data sets for hyperenhanced (HYET), heterogeneously enhanced (HEET), and unenhanced (UET) tumour categories. Catheter localisation and tumour feeding vessels were assessed using all data sets. Quantitative and qualitative analyses were performed using appropriate statistical tests.

RESULT

Qualitative and quantitative tumour delineation showed significant difference (all P < 0.05) among tumour categories. Mean tumour-liver-contrast was higher in HYET than in HEET, and UET; moreover, differences between tumour categories were statistically significant (all P < 0.0001). Fused data showed higher value with statistical significance (P < 0.05) compared with other data sets during catheter localisation and feeding-vessel identification.

CONCLUSION

Tumour delineation was clearly possible using u-CBCT cross sections with contrast material. PBV uses color-coded images to increase detection and produces good tumour differentiation. Image fusion helps accurately identify tumour and feeding vessels and locate contrast material injection sites and catheter tips without additional data acquisition.

KEY POINTS

• Ultrafast CBCT cross-sectional data provide good tumour delineation with contrast material • Postprocessed PBV using u-CBCT increased detectability and tumour differentiation • u-CBCT cross-sectional PBV and u-CBCT-MRI data helps image guidance during chemoembolization • u-CBCT-MRI can identify tumours and feeding vessels and locate catheter tip accurately.

摘要

目的

我们的目的是在经动脉化疗栓塞过程中,利用横断面图像、灌注血容量(PBV)以及图像融合技术对超快速锥形束计算机断层扫描(u-CBCT)图像数据进行肿瘤检测评估。

方法

2013年2月至10月,对150例患者(63±20岁;33 - 82岁)进行了u-CBCT检查。使用u-CBCT横断面PBV以及u-CBCT与磁共振成像(MRI)融合数据集,对强化(HYET)、不均匀强化(HEET)和未强化(UET)肿瘤类别进行肿瘤勾画和显影评估。使用所有数据集评估导管定位和肿瘤供血血管。采用适当的统计检验进行定量和定性分析。

结果

肿瘤类别的定性和定量肿瘤勾画显示出显著差异(所有P<0.05)。HYET组的平均肿瘤-肝脏对比度高于HEET组和UET组;此外,肿瘤类别之间的差异具有统计学意义(所有P<0.0001)。在导管定位和供血血管识别过程中,融合数据与其他数据集相比显示出更高的价值且具有统计学意义(P<0.05)。

结论

使用含对比剂的u-CBCT横断面图像能够清晰地进行肿瘤勾画。PBV利用彩色编码图像提高检测率并实现良好的肿瘤分化。图像融合有助于准确识别肿瘤和供血血管,定位对比剂注射部位和导管尖端,无需额外的数据采集。

关键点

• 超快速CBCT横断面数据结合对比剂可提供良好的肿瘤勾画 • 使用u-CBCT后处理的PBV提高了可检测性和肿瘤分化程度 • u-CBCT横断面PBV和u-CBCT - MRI数据有助于化疗栓塞过程中的图像引导 • u-CBCT - MRI能够准确识别肿瘤和供血血管并定位导管尖端

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