Clinical Neuroscience

[Correction of the physiological artefacts at pre-surgical clinical functional MR]

KISS Máté1,2,3,4, GÁL Andor Viktor1, KOZÁK Lajos Rudolf3, MARTOS János2, NAGY Zoltán2,4

JANUARY 30, 2020

Clinical Neuroscience - 2020;73(01-02)

DOI: https://doi.org/10.18071/isz.73.0035

[Introduction/aim of the study - Pre-surgical functional MRI (fMRI) is an important modality of examinations before brain surgery. There are several artefacts (e.g. motion, susceptibility) which may hinder the evaluation of fMRI data. Physiological artefacts (breathing, pulsation) also affect the sensitivity and specificity of anatomical localization. The aim of this study is to demonstrate the efficiency of physiological artefact identification and removal methods for presurgical evaluation. Materials and methods - Siemens Magnetom Verio 3T MRI scanner was used to collect data. The physiological parameters (breathing, pulse) were recorded with the MRI system’s built-in devices. Data from fourteen patients - with primary brain tumour - were evaluated with SPM12 utilizing the RETROICOR/RVHR tool to detect and decrease the effect of physiological artefacts. We compared the statistical maps obtained with and without the physiological correction using the Jaccard similarity coefficient, and ROI analyses. Results - Significant differences were found in the mean ROI values (p<0.0016) and the extensions of eloquent activations (p<0.0013), when using the physiological correction (RETORICOR/RVHR) based on convolution method. On the other hand, no significant differences were found between the ROIs’ standard deviations (F=0.28). The RETROICOR/ RVHR method helps to define the precise localisation of eloquent areas (p<0.009). The number of irrelevant (non-significant) voxels were increased (p<0.001). Conclusions - Minimising of physiological artefacts in fMRI data calculations, the (RETROICOR/RVHR) method based on convolution has been successfully adapted. This algorithm could be helpful before neurosurgical intervention. The activity pattern became more reliable. ]

AFFILIATIONS

  1. Magyar Tudományos Akadémia, Természettudományi Kutatóközpont, Agyi Képalkotó Központ, Agyi Szerkezet és Dinamika Kutatócsoport, Budapest
  2. Országos Klinikai Idegtudományi Intézet, Neuroradiológiai Osztály, Budapest
  3. Semmelweis Egyetem, MR Kutató Központ, Budapest
  4. Pannon Egyetem, Képalkotó Laboratórium, Veszprém

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