Delineation of gliomas using radial metabolite indexing

(1) H MRSI has demonstrated the ability to characterise and delineate brain tumours, but robust data analysis methods are still needed. In this study, we present an objective analysis method for MRSI data to delineate tumour abnormality regions. The presented method is a development of the choline-to-N-acetylaspartate index (CNI), which uses perpendicular distances in a choline versus N-acetylaspartate plot as a measure of abnormality. We propose a radial CNI (rCNI) method that uses the choline to N-acetylaspartate ratio directly as an abnormality measure. To avoid problems with small or zero denominators, we perform an arctangent transformation. CNI abnormality contours were evaluated using a z-score threshold of 2 (CNI2) and 2.5 (CNI2.5) and compared with rCNI2. Simulations modelling low-grade (LGG) and high-grade (HGG) gliomas with different tissue compartments and partial volume effects suggest improved specificity of rCNI2 (LGG 92%/HGG 91%) over CNI2 (LGG 69%/HGG 69%) and CNI2.5 (LGG 74%/HGG 75%), whilst retaining a similar sensitivity to both CNI2 and CNI2.5. Our simulation results also confirm a previously reported increase in specificity of CNI2.5 over CNI2 with little penalty in sensitivity. The analysis of MRSI data acquired from 10 patients with low-grade glioma at 3 T suggests a more robust delineation of the lesions using rCNI with respect to conventional imaging compared with standard CNI. Further analysis of 29 glioma datasets acquired at 1.5 T, together with previously published estimated tumour proportions, suggests that rCNI has higher sensitivity and specificity for the identification of abnormal MRSI voxels.