2 min read

ID: 1113486

Short Link: https://gregory-ms.com/articles/1113486/

Discovery Date: 28 November 2022, 06:00:33 UTC

Published Date: 2022-11-27 11:00:00

Source: PubMed

Link: https://pubmed.ncbi.nlm.nih.gov/36436711/?fc=20210216052009&ff=20221128010009&v=2.17.8

Manual Selection: none

Machine Learning Gaussian Naive Bayes Model: false

Abstract

Neuroimage. 2022 Nov 24:119772. doi: 10.1016/j.neuroimage.2022.119772. Online ahead of print.

ABSTRACT

Multiple sclerosis (MS) is an inflammatory demyelinating disease. Current treatments are focussed on immune suppression to modulate pathogenic activity that causes myelin damage. New treatment strategies are needed to prevent demyelination and promote remyelination. Development of such myelin repair therapies require a sensitive and specific biomarker for efficacy evaluation. Recently, it has been shown that quantification of myelin density is possible using [11C]MeDAS PET. This method, however, requires arterial blood sampling to generate an arterial input function (AIF). As the invasive nature of arterial sampling will reduce clinical applicability, the purpose of this study was to assess whether an image-derived input function (IDIF) can be used as an alternative way to facilitate its routine clinical use. Six healthy controls and 11 MS patients underwent MRI and [11C]MeDAS PET with arterial blood sampling. The application of both population-based whole blood-to-plasma conversion and metabolite corrections were assessed for the AIF. Next, summed images of the early time frames (0-70 seconds) and the frame with the highest blood-brain contrast were used to generate IDIFs. IDIFs were created using either the hottest 2, 4, 6 or 12 voxels, or an iso-contour of the hottest 10% voxels of the carotid artery. This was followed by blood-to-plasma conversion and metabolite correction of the IDIF. The application of a population-based metabolite correction of the AIF resulted in high correlations of tracer binding (Ki) within subjects, but variable bias across subjects. All IDIFs had a sharper and higher peak in the blood curves than the AIF, most likely due to dispersion during blood sampling. All IDIF methods resulted in similar high correlations within subjects (r=0.95-0.98), but highly variable bias across subjects (mean slope=0.90-1.09). Therefore, both the use of population based blood-plasma and metabolite corrections and the generation of the image-derived whole-blood curve resulted in substantial bias in [11C]MeDAS PET quantification, due to high inter-subject variability. Consequently, when unbiased quantification of [11C]MeDAS PET data is required, individual AIF needs to be used.

PMID:36436711 | DOI:10.1016/j.neuroimage.2022.119772

Noun Phrases in Title

  • Investigation
  • image-derived input functions
  • non-invasive quantification
  • myelin density
  • <sup>11</sup>C]MeDAS PET
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