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Voxel Based Analysis of Surgical Revascularization for Moyamoya Disease: Pre- and Postoperative SPECT Studies

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Listed:
  • Yasutaka Fushimi
  • Tomohisa Okada
  • Yasushi Takagi
  • Takeshi Funaki
  • Jun C Takahashi
  • Susumu Miyamoto
  • Kaori Togashi

Abstract

Moyamoya disease (MMD) is a chronic, progressive, cerebrovascular occlusive disease that causes abnormal enlargement of collateral pathways (moyamoya vessels) in the region of the basal ganglia and thalamus. Cerebral revascularization procedures remain the preferred treatment for patients with MMD, improving the compromised cerebral blood flow (CBF). However, voxel based analysis (VBA) of revascularization surgery for MMD based on data from pre- and postoperative data has not been established. The latest algorithm called as Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) has been introduced for VBA as the function of statistical parametric mapping (SPM8), and improved registration has been achieved by SPM8 with DARTEL. In this study, VBA was conducted to evaluate pre- and postoperative single photon emission computed tomography (SPECT) images for MMD by SPM8 with DARTEL algorithm, and the results were compared with those from SPM8 without DARTEL (a conventional method). Thirty-two patients with MMD who underwent superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery as the first surgery were included and all patients underwent pre- and postoperative 3D T1-weighted imaging and SPECT. Pre- and postoperative SPECT images were registered to 3D T1-weighted images, then VBA was conducted. Postoperative SPECT showed more statistically increased CBF areas in the bypassed side cerebral hemisphere by using SPM8 with DARTEL (58,989 voxels; P

Suggested Citation

  • Yasutaka Fushimi & Tomohisa Okada & Yasushi Takagi & Takeshi Funaki & Jun C Takahashi & Susumu Miyamoto & Kaori Togashi, 2016. "Voxel Based Analysis of Surgical Revascularization for Moyamoya Disease: Pre- and Postoperative SPECT Studies," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0148925
    DOI: 10.1371/journal.pone.0148925
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    Cited by:

    1. Gyula Gyebnár & Zoltán Klimaj & László Entz & Dániel Fabó & Gábor Rudas & Péter Barsi & Lajos R Kozák, 2019. "Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients’ DTI data – Theory, simulations and example cases," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-30, September.

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