Multispectral PCCT and CBCT imaging for high precision radiotherapy through translation of imaging parameters with machine learning validation

通过机器学习验证,利用成像参数转换实现多光谱PCCT和CBCT成像,从而提高放射治疗的精度。

阅读:5

Abstract

Photon-counting CT (PCCT) is the mainstay of multi-spectral imaging, enabling quantitative tissue characterization. In radiation oncology, cone-beam CT is used daily for image-guided and online-adaptive radiotherapy. The novel HyperSight cone-beam CT imaging mode (CBCT), with enhanced image quality due in part to its enlarged detector size and optimized reconstruction modes, further facilitates quantitative image monitoring and high-precision radiotherapy. Integrating spectral PCCT information may further amplify its potential. Therefore, this study investigates whether qualitative and spectral quantitative PCCT-parameters can be translated to CBCT. An inorganic tissue-equivalent anthropomorphic phantom analysis was conducted using CBCT (iCBCT/iCBCT Acuros reconstruction, Pelvis/Pelvis Large preset) and PCCT (T3D (polychromatic reconstruction) with virtual monochromatic imaging (VMI)). Twenty regions with different CT numbers were assessed qualitatively and quantitatively. Image quality was highest for T3D PCCT. Quantitative analysis showed stronger agreement between CBCT (iCBCT Acuros) and PCCT-derived 60 and 67 keV VMI (concordance correlation coefficient (CCC) ≥ 0.595), compared to T3D (CCC ≤ 0.183), with CCC values significantly affected by CBCT presets and reconstruction method (p ≤ 0.001). Machine learning-based hierarchical clustering confirmed alignment between CBCT and PCCT-based VMI, but not T3D. This successful translatability of specific VMI levels paves the way for the integration of multi-spectral imaging into high-precision CBCT-based radiotherapy using PCCT.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。