Automatic Brain Tissue and Lesion Segmentation and Multi-Parametric Mapping of Contrast-Enhancing Gliomas without the Injection of Contrast Agents: A Preliminary Study

Author:

Liu Jing1ORCID,Jakary Angela1,Villanueva-Meyer Javier E.12,Butowski Nicholas A.2,Saloner David13,Clarke Jennifer L.24,Taylor Jennie W.24,Oberheim Bush Nancy Ann24,Chang Susan M.2,Xu Duan15,Lupo Janine M.15

Affiliation:

1. Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA

2. Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA

3. Radiology Service, VA Medical Center, San Francisco, CA 94121, USA

4. Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA

5. UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco and Berkeley, San Francisco, CA 94143, USA

Abstract

This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.

Funder

National Institutes of Health

Publisher

MDPI AG

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