Differential MRI analysis for quantification of low grade glioma growth

A differential analysis framework is proposed to quantify tumoral growth on brain MRI. (1) Two longitudinal FLAIR MRI volumes are compared via: (2) non-linear midway intensity mapping and (3) computation of difference maps, without the need for inhomogeneity correction. Significantly high difference values are selected with parameterization for optimistic and pessimistic growth estimations. (4) A clinical study was performed on 32 longitudinal clinical cases from 13 patients to quantify low-grade glioma growth. Results showed millimetric precision on a specific volumetric radius growth index. [Display omitted] ► We propose an original method to quantify differences between two longitudinal brain MRI from the same patient. ► A non-linear Midway intensity mapping & a statistical thresholding framework are presented to process MRI difference maps. ► Optimistic and pessimistic parameterizations were evaluated on FLAIR MRI for the quantification of low-grade glioma growth. ► A clinical study was performed on 32 longitudinal clinical cases from 13 patients. ► Results demonstrate millimetric precision on a specific volumetric radius growth index measurement. A differential analysis framework of longitudinal FLAIR MRI volumes is proposed, based on non-linear gray value mapping, to quantify low-grade glioma growth. First, MRI volumes were mapped to a common range of gray levels via a midway-based histogram mapping. This mapping enabled direct comparison of MRI data and computation of difference maps. A statistical analysis framework of intensity distributions in midway-mapped MRI volumes as well as in their difference maps was designed to identify significant difference values, enabling quantification of low-grade glioma growth, around the borders of an initial segmentation. Two sets of parameters, corresponding to optimistic and pessimistic growth estimations, were proposed. The influence and modeling of MRI inhomogeneity field on a novel midway-mapping framework using image models with multiplicative contrast changes was studied. Clinical evaluation was performed on 32 longitudinal clinical cases from 13 patients. Several growth indices were measured and evaluated in terms of accuracy, compared to manual tracing. Results from the clinical evaluation showed that millimetric precision on a specific volumetric radius growth index measurement can be obtained automatically with the proposed differential analysis. The automated optimistic and pessimistic growth estimat