Your browser doesn't support javascript.
loading
Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195.
Kang, Yeona; Schlyer, David; Kaunzner, Ulrike W; Kuceyeski, Amy; Kothari, Paresh J; Gauthier, Susan A.
Affiliation
  • Kang Y; Department of Radiology/Nuclear Medicine, Weill Cornell Medicine, New York, New York City, NY, United States of America.
  • Schlyer D; Department of Radiology/Nuclear Medicine, Weill Cornell Medicine, New York, New York City, NY, United States of America.
  • Kaunzner UW; Brookhaven National Laboratory, Upton, NY, United States of America.
  • Kuceyeski A; Judith Jaffe Multiple Sclerosis Center, Weill Cornell Medicine, New York City, NY, United States of America.
  • Kothari PJ; Department of Radiology/Nuclear Medicine, Weill Cornell Medicine, New York, New York City, NY, United States of America.
  • Gauthier SA; Department of Radiology/Nuclear Medicine, Weill Cornell Medicine, New York, New York City, NY, United States of America.
PLoS One ; 13(8): e0201289, 2018.
Article in En | MEDLINE | ID: mdl-30091993
ABSTRACT
Chronic active multiple sclerosis (MS) lesions have a rim of activated microglia/macrophages (m/M) leading to ongoing tissue damage, and thus represent a potential treatment target. Activation of this innate immune response in MS has been visualized and quantified using PET imaging with [11C]-(R)-PK11195 (PK). Accurate identification of m/M activation in chronic MS lesions requires the sensitivity to detect lower levels of activity within a small tissue volume. We assessed the ability of kinetic modeling of PK PET data to detect m/M activity in different central nervous system (CNS) tissue regions of varying sizes and in chronic MS lesions. Ten patients with MS underwent a single brain MRI and two PK PET scans 2 hours apart. Volume of interest (VOI) masks were generated for the white matter (WM), cortical gray matter (CGM), and thalamus (TH). The distribution volume (VT) was calculated with the Logan graphical method (LGM-VT) utilizing an image-derived input function (IDIF). The binding potential (BPND) was calculated with the reference Logan graphical method (RLGM) utilizing a supervised clustering algorithm (SuperPK) to determine the non-specific binding region. Masks of varying volume were created in the CNS to assess the impact of region size on the various metrics among high and low uptake regions. Chronic MS lesions were also evaluated and individual lesion masks were generated. The highest PK uptake occurred the TH and lowest within the WM, as demonstrated by the mean time activity curves. In the TH, both reference and IDIF based methods resulted in estimates that did not significantly depend on VOI size. However, in the WM, the test-retest reliability of BPND was significantly lower in the smallest VOI, compared to the estimates of LGM-VT. These observations were consistent for all chronic MS lesions examined. In this study, we demonstrate that BPND and LGM-VT are both reliable for quantifying m/M activation in regions of high uptake, however with blood input function LGM-VT is preferred to assess longitudinal m/M activation in regions of relatively low uptake, such as chronic MS lesions.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Microglia / Radiopharmaceuticals / Multiple Sclerosis, Chronic Progressive / Isoquinolines Type of study: Prognostic_studies Limits: Adult / Female / Humans / Male / Middle aged Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2018 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Microglia / Radiopharmaceuticals / Multiple Sclerosis, Chronic Progressive / Isoquinolines Type of study: Prognostic_studies Limits: Adult / Female / Humans / Male / Middle aged Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2018 Document type: Article Affiliation country: United States