Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent

Abstract

A targeted Gd 3؉ -based contrast agent has been developed that detects tumor cell death by binding to the phosphatidylserine (PS) exposed on the plasma membrane of dying cells. Although this agent has been used to detect tumor cell death in vivo, the differences in signal intensity between treated and untreated tumors was relatively small. As cell death is often spatially heterogeneous within tumors, we investigated whether an image analysis technique that parameterizes heterogeneity could be used to increase the sensitivity of detection of this targeted contrast agent. Two-dimensional (2D) Minkowski functionals (MFs) provided an automated and reliable method for parameterization of image heterogeneity, which does not require prior assumptions about the number of regions or features in the image, and were shown to increase the sensitivity of detection of the contrast agent as compared to simple signal intensity analysis. Magn Reson Med 61:1218 -1224, 2009.

Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

“…[14] Cyst-like structures larger than 1 cm in diameter were excluded to prevent analysis of surgical resection cavities or large necrotic cavities rather than tissue. Tumour images were converted into binary datasets by thresholding, where ten threshold steps were chosen to sample the grey scale, giving 11 thresholded images per slice (pre-clinical studies [13;14] have shown that there is no benefit in using more than 11 thresholds). Pixels were assigned as either black (below threshold) or white (above threshold) (Fig 1).…”

“…Analysis of T 2 -weighted images to determine treatment response is therefore now routine in the clinic, although the detail of how to determine response from these images is a topic of ongoing research. [2;8–11] Our approach exploits the fact that tissue morphology can be a sensitive marker of underlying tissue biology[12] and that morphological information can be extracted from an MR image[13] using image descriptors called Minkowski functionals (MFs). MFs can be used to parameterize the heterogeneous distribution of hyper- and hypointense foci in T 2 -weighted tumour images and have been shown recently to be capable of detecting treatment response through changes in the size and distribution of these foci in pre-clinical MR images, including T 2 -weighted images.…”

“…MFs can be used to parameterize the heterogeneous distribution of hyper- and hypointense foci in T 2 -weighted tumour images and have been shown recently to be capable of detecting treatment response through changes in the size and distribution of these foci in pre-clinical MR images, including T 2 -weighted images. [13;14] Previously applied to describe the complex morphology of galaxies,[15] MFs can capture underlying tumour heterogeneity not apparent to the reporting radiologist. The purpose of this study was to extend the use of MFs to clinical MRI data and develop an imaging technique that can distinguish pseudoprogression from true progression by analysing heterogeneity in T 2 -weighted MR images.…”

Watching tumours gasp and die with MRI: the promise of hyperpolarised13C MR spectroscopic imaging

“…Analysing regions of interest, which is what is done routinely in analysing data of this sort, exacerbates the problem by effectively further reducing the resolution of the MR image and discarding important information on contrast agent distribution. We recaptured this information, and thus improved the sensitivity of cell death detection, by parameterising the distribution of C2A in a treated tumour using an image analysis technique that had been used previously by the astrophysics community to analyse images of galaxies [44]. The distribution of C2A in a treated tumour was much more heterogeneous than in an untreated tumour, reflecting the heterogeneous distribution of cell death that was evident in the microscopy images ( Figure 5).…”

ibex: An open infrastructure software platform to facilitate collaborative work in radiomics

“…20,21 Previous work has shown that radiomics can be used to create improved prediction algorithms for various clinically relevant metrics and endpoints. [22][23][24][25] The lack of an open infrastructure software platform, however, has made previous radiomics research difficult to share and validate between institutions. Image features with the same name may be implemented differently by different groups.…”