A Review of the Automated Detection of Change in Serial Imaging Studies of the Brain

Abstract

Serial imaging is frequently performed on patients with diseases of the brain, to track and observe changes. Magnetic resonance imaging provides very detailed and rich information, and is therefore used frequently for this application. The data provided by MR can be so plentiful; however, that it obfuscates the information the radiologist seeks. A system which could reduce the large quantity of primitive data to a smaller and more informative subset of data, emphasizing change, would be useful. This article discusses motivating factors for the production of an automated process to this effect, and reviews the approaches of previous authors. The discussion is focused on brain tumors and multiple sclerosis, but many of the ideas are applicable to other disease processes, as well.

Assessment of Progression-Free-Survival in Glioblastomas by Intratreatment Dynamic Contrast-Enhanced MRI

“…Another possible source of bias may be the registration method, including the baseline ROI template that, under conditions of significant tumour oedema or early therapy failure with increasingly enhancing tumour tissue, may not reflect the true disease burden. Such cases were not observed in our study population, nevertheless, nonlinear registration (warping) methods where, for example, elastic image registration algorithms may be used to match the serial acquisitions or vector displacement field from the serial imaging studies, may be acquired [31].…”

Part 1. Automated Change Detection and Characterization in Serial MR Studies of Brain-Tumor Patients

“…The algorithm proposed in this study embodies the beneficial aspects of many of the prior algorithms, and it has advantages which previous algorithms do not possess. 3 The algorithm attempts to separate acquisitionrelated changes from pathology-related changes through feature extraction and spatially adaptive noise reduction. The algorithm is able to highlight very subtle changes (like subtraction) and produce localized descriptions of those changes (like subtraction and warping).…”

“…This difficulty results from the method of data presentation, which is not well suited to our cognitive capabilities, 1,2 as well as factors related to image acquisition and processing. 3 The issue of change detection has been one of interest in fields of image processing beyond those of medical imaging. 4 Within medical imaging, various methods have been used to effect serial comparisons, including manual inspection, measurement sampling (such as maximum diameter methods), 5Y11 volumetrics, 12Y14 warping, 15Y18 and temporal analysis.…”

“…19,20 It is widely recognized that each of these methods possesses both merits and shortcomings; a thorough description of these considerations may be found elsewhere. 3 We hypothesized that a system could be developed to detect and characterize changes in serial magnetic resonance (MR) studies of brain-tumor patients. It was desired that this system be highly automated, produce quantitative metrics, and be resistant to acquisition-related changes.…”

A method to analyze the evolution of malignant gliomas using MRI

“…However there is a need for objective methods that provide for the quantification of changes in lesions. Change detection is an interesting investigation area of image processing: literature presents several methods that aim to compare serial images in order to track disease modifications [4].…”