NIMG-45. 3D SINGLE-SCAN FOR MULTI-CONTRAST-WEIGHTED MRI FOR LESION DETECTION IN PATIENTS WITH GLIOMAS. (11th November 2019) - Liu, Jing; Jakary, Angela; Lafontaine, Marisa; Wang, Yan; Xu, Duan; Saloner, David; Butowski, Nicholas; Hess, Christopher; Villanueva-Meyer, Javier; Chang, Susan; Lupo, Janine
Abstract: INTRODUCTION: Multi-contrast-weighted (MCW) MRI allows derivation of T1-weighted, T2-weighted, and T2-FLAIR images and their parametric maps. Under current practice, these high-resolution 3D MR images are acquired separately with a total scan time is approximately 30 minutes. The goal of this study was to develop a novel sequence and processing strategy for MCW MRI and lesion detection of gliomas with 1mm isotropic resolution in 6 minutes, which can significantly improve clinical imaging workflow. The proposed whole brain, single-scan method enables the generation of segmented tumor regions that correspond the hyperintense lesion on T2-weighted or T2-FLAIR images (T2L). METHODS: Our strategy involves using a highly accelerated 3D MRI sequence to achieve whole brain, MCW images that can be jointly segmented with modified k-means clustering. Six patients with enhancing gliomas were scanned with this sequence before the injection of gadolinium, 4 of which also received a repeat scan post-gadolinium. Our automatic segmentation of the T2L and manual definition of the contrast-enhancing lesion (CEL) was compared against manually segmented lesions drawn from conventional anatomical imaging. RESULTS: Whole-brain MCW MRI was achieved with 1mm isotropic resolution in 6 mins. Grey-matter, white-matter, CSF, and the T2L could be automatically segmented with our clustering method. The resulting automatically-segmented T2L volume was not significantly different between our new sequence and clinically defined regions (average Dice Coefficient=0.81(+/-0.05); p=0.95). CONCLUSIONS: Our novel, 6-minute scan and processing strategy was able to generate MCW anatomical images and automatically segment grey-matter, white-matter, and T2L volumes that were similar to manual segmentation of conventional anatomical imaging. We are currently extending the automatic segmentation to the CEL. This approach has the potential to improve clinical workflow by allowing time for therapy-specific advanced imaging and providing a noninvasive tool for monitoring tumor evolution. Validation is currently being performed in a larger cohort.