Utility of zero echo time (ZTE) sequence for assessing bony lesions of skull base and calvarium

The emergence of zero echo time (ZTE) imaging has transformed bone imaging, overcoming historical limitations in capturing detailed bone structures. By minimizing the time gap between radiofrequency excitation and data acquisition, ZTE generates CT-like images. While ZTE has shown promise in various applications, its potential in assessing skull base and calvarium lesions remains unexplored. Hence we aim to introduce a novel perspective by investigating the utility of inverted ZTE images (iZTE) and pseudoCT (pCT) images for studying lesions in the skull base and calvarium.

A total of 35 eligible patients, with an average age of 42 years and a male/female ratio of 1:4, underwent ZTE MRI and images are processed to generate iZTE and pCT images were generated through a series of steps including intensity equalization, thresholding, and deep learning-based pCT generation. These images were then compared to CT scans using a rating scale; inter-rater kappa coefficient evaluated observer consensus while statistical metrics like sensitivity and specificity assessed their performance in capturing bone-related characteristics.

The study revealed excellent interobserver agreement for lesion assessment using both pCT and iZTE imaging modalities, with kappa coefficient of 0.91 (P < 0.0001) and 0.92 respectively (P < 0.0001). Also, pCT and iZTE accurately predicted various lesion characteristics with sensitivity ranging from 84.3% to 95.1% and 82.6%-94.2% (95% CI) with a diagnostic accuracy of 95.56% and 94.44% respectively. Although both of them encountered challenges with ground glassing, hyperostosis, and intralesional bony fragments, they showed good performance in other bony lesion assessments.

The pilot study suggests strong potential for integrating the ZTE imaging into standard care for skull base and calvarial bony lesions assessment. Additionally, larger-scale studies are needed for comprehensive assessment of its efficacy.