Abstract
The performance of MRI head coils together with the influence of the prescan normalize filter in different brain regions was evaluated. Functional and structural data were recorded from 26 participants performing motor, auditory, and visual tasks in different conditions: with the 20- and 64-channel Siemens head/neck coil and the prescan normalize filter turned ON or OFF. Data were analyzed with the MRIQC tool to evaluate data quality differences. The functional data were statistically evaluated by comparison of the β estimates and the time-course signal-to-noise ratio (tSNR) in four regions of interest, i.e., the auditory, visual, and motor cortices and the thalamus. The MRIQC tool indicated a better data quality for both functional and structural data with the prescan normalize filter, with an advantage for the 20-channel head coil in functional data and an advantage for the 64-channel head coil in structural measurements. Nevertheless, recommendations for the functional data regarding choice of head coils and prescan normalize filter depend on the brain regions of interest. Higher β estimates and tSNR values occurred in the auditory cortex and thalamus with the prescan normalize filter, whereas the contrary was true for the visual and motor cortices. Due to higher β estimates in the visual cortex in the 64-channel head coil, this head coil is recommended for studies investigating the visual cortex. For most of the research questions, the 20-channel head coil is better suited for functional experiments, with the prescan normalize filter, especially when investigating deep brain areas. For anatomical studies, the 64-channel head coil seemed to be the better choice.
Highlights
For magnetic resonance imaging, a variety of head coils are available with various geometries and different numbers of channels
Results of the MRIQC tool with respect to the functional data indicate a better performance for the 20-channel head coil with the prescan normalize filter ON
The region of interest (ROI) × task interaction indicated that the tasks worked as expected; e.g., there were larger β estimates in the visual cortex during the visual task, and the same holds true for the auditory and motor tasks
Summary
For (functional) magnetic resonance imaging (fMRI), a variety of head coils are available with various geometries and different numbers of channels. One simple method calculates the SNR as the mean signal intensity in a region of interest (ROI) divided by the standard deviation (SD) of the noise in a background ROI (Henkelman, 1985; Kaufman et al, 1989; Constantinides et al, 1997) This method has the limitation that it can only be applied for non-accelerated images, because in parallel image reconstruction, noise is highly variable across the field of view (FOV) and SNR measurements with multichannel coils have to consider the spatial variation in SNR. A more general approach applicable for parallel imaging was suggested by Kellman and McVeigh (2005), who described a method for image reconstruction in SNR units on a pixel base They estimated the noise statistics from a noise-only image (i.e., acquired without radiofrequency (RF) pulses) automatically acquired prior to signal acquisition. The SNR is calculated by dividing the mean intensity in the foreground ROI by the standard deviation of the same region
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