Background and objectives: Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [11C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. Methods: A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTVMRI. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians. Results: The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTVMRI enhanced the CTV more accurately than BTV in 25% of cases. Conclusions: The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTVMRI and GTV should be considered for a comprehensive treatment planning.
Rundo, L., Stefano, A., Militello, C., Russo, G., Sabini, M., D'Arrigo, C., et al. (2017). A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 144, 77-96.
|Citazione:||Rundo, L., Stefano, A., Militello, C., Russo, G., Sabini, M., D'Arrigo, C., et al. (2017). A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 144, 77-96.|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning|
|Autori:||Rundo, L; Stefano, A; Militello, C; Russo, G; Sabini, M; D'Arrigo, C; Marletta, F; Ippolito, M; Mauri, G; Vitabile, S; Gilardi, M|
RUNDO, LEONARDO (Primo)
GILARDI, MARIA CARLA (Ultimo)
|Data di pubblicazione:||2017|
|Rivista:||COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.cmpb.2017.03.011|
|Appare nelle tipologie:||01 - Articolo su rivista|