Literature DB >> 27714566

Acquisition models in intraoperative positron surface imaging.

Frédéric Monge1, Dzhoshkun I Shakir2, Florence Lejeune3, Xavier Morandi4,5, Nassir Navab6, Pierre Jannin4.   

Abstract

PURPOSE: Intraoperative imaging aims at identifying residual tumor during surgery. Positron Surface Imaging (PSI) is one of the solutions to help surgeons in a better detection of resection margins of brain tumor, leading to an improved patient outcome. This system relies on a tracked freehand beta probe, using [Formula: see text]F-based radiotracer. Some acquisition models have been proposed in the literature in order to enhance image quality, but no comparative validation study has been performed for PSI.
METHODS: In this study, we investigated the performance of different acquisition models by considering validation criteria and normalized metrics. We proposed a reference-based validation framework to perform the comparative study between acquisition models and a basic method. We estimated the performance of several acquisition models in light of four validation criteria: efficiency, computational speed, spatial accuracy and tumor contrast.
RESULTS: Selected acquisition models outperformed the basic method, albeit with the real-time aspect compromised. One acquisition model yielded the best performance among all according to the validation criteria: efficiency (1-Spe: 0.1, Se: 0.94), spatial accuracy (max Dice: 0.77) and tumor contrast (max T/B: 5.2). We also found out that above a minimum threshold value of the sampling rate, the reconstruction quality does not vary significantly.
CONCLUSION: Our method allowed the comparison of different acquisition models and highlighted one of them according to our validation criteria. This novel approach can be extended to 3D datasets, for validation of future acquisition models dedicated to intraoperative guidance of brain surgery.

Entities:  

Keywords:  Freehand beta probe; Functional imaging; Intraoperative imaging; Neuronavigation; Radiation detection physics; Reconstruction; Validation assessment

Mesh:

Year:  2016        PMID: 27714566     DOI: 10.1007/s11548-016-1487-z

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


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