Estimating Brain Lesion Volume Change in Multiple Sclerosis by Subtraction of Magnetic Resonance Images

Abstract

Use of the subtraction algorithm leads to improved reliability and lower operator fatigue in clinical trials and studies of the natural history of MS.

Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method

“…Our results are in accordance with the literature in which different optimized reading techniques have been compared with scrolling through images, as in subtraction techniques 7,9,11,18,19 or with semiautomated 12 or automated 8,10 assistive software plat- forms. These methods have shown improvement in detecting new lesions and reducing radiologists' false-negative errors.…”

“…A few studies have shown that methods such as subtraction or registration could improve the detection of new HST2 lesions, but these approaches to imaging may not be practical in many clinical environments. [7][8][9][10][11][12] The purpose of our study was to evaluate the efficacy of an automated coregistration-fusion (CF) approach during follow-up for patients with MS. tion of Helsinki (institutional review board 2016-A00896-45). Signed informed consent was obtained from all subjects.…”

T1w dark blood imaging improves detection of contrast enhancing lesions in multiple sclerosis

“…Since detection of CEL is always highly rater dependent, it has been suggested to use subtraction imaging to increase sensitivity in lesion detection, especially for lesion with only subtle contrast enhancement. [ 23 – 26 ] However, automatically processed image registration is not provided by all vendors or workstations on-the-fly. Even if available it still has its limitations and subtraction without prior registration is prone to motion artefacts and might lead to a number of false positive results ( Fig 6 ).…”

Detection of Focal Longitudinal Changes in the Brain by Subtraction of MR Images

“…The method is described in detail elsewhere 11 and is illustrated in Fig 1. Briefly, input images are registered to a common frame of reference at the average position of all input images. Then, radiofrequency bias field variations are modeled as a smoothly varying polynomial function; then, individual registered images are corrected by pixel-wise division by the modeled field.…”