Quality controls (Pipeline Part 1)

As previously explained in the Introduction section, it is important to evaluate the performance of an MRI acquisition system and the reproducibility of an acquisition sequence, for the interest in en-suring image quality and consistency between sessions. This translates into comparing the level of signal released from the scanned region during an acquisition with the level of noise present in that region (signal to noise ratio - SNR - estimation), as well as comparing sessions to assess the variability of an acquisition (relative percentage difference - RPD - between scans). In the Introduction section, it was raised the concern of signal inhomogeneity in regions close to air cavities such as the nasal and the ear cavities. Considering the proximity of the VOTC to the ear cavity, quality control metrics were estimated in order to quantitatively assess the presence of noise and variability between sessions in occipital-tem-poral regions, before the development of the pipeline that would extract the T1 laminar profile in cate-gory-selective regions of the VOTC using the MP2RAGE sequence.

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The signal-to-noise ratio (SNR) and covariance using the relative percentage difference (RPD) were estimated taking into consideration the raw data acquired, UNIT1, and the T1 map obtained from it, both at 0.75 mm resolution. Figure 2.1 illustrates a schematic representation of the implemented steps to assess quantitatively the acquisition sequence. Each step will be further detailed throughout the sec-tion.

Figure 2.1: Flowchart diagram of the quality controls estimation. This diagram provides a detailed overview of all inputs, processing steps and outputs for the Part 1 of the Pipeline. The rectangle shapes represent data (inputs or outputs), the sta-dium shapes represent processes (orange and green for workflows implemented respectively with bidspm and CPP ROI) and bellow the stadium shapes is a transparent rectangle representing the chosen software or toolbox to perform the process. The

pipeline is performed with each subject.

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2.2.1 T1 map creation and skull removal

T1 maps were created with scripts within bidspm that rely on the MP2RAGE toolbox (https://github.com/benoitberanger/mp2rage)147,155–157, a SPM^158,159 extension for neuroimaging analyses used within MATLAB¹⁵³. The required variables were the path to UNIT1 and the acquisition parameters (B0, MP2RAGE TR, Echo Spacing, TI1 and TI2, FAs, number of slices per slab, partial Fourier in slice and fat saturation pulse)^147,157. Posteriorly, the skull and background were removed with bidsNighres (https://github.com/Remi-Gau/bidsNighres), a BIDS toolbox to help preprocess high-resolution anatom-ical data with Nighres¹⁶⁰.

2.2.2 Spatial preprocessing of UNIT1

As mentioned previously, at high magnetic fields there is an increase in inhomogeneity of B0, and of the transmit 𝐵₁⁺ and receive 𝐵₁⁻ fields, creating variations throughout the image (bias field)¹⁴³. This affects not only the quality of the image but also segmentation¹⁴³. Image segmentation divides the image into a set of regions of similar attributes such as the intensity of image. In case of brain MRI, segmen-tation usually is performed by either performing registration with a template image or using a tissue classification approach in which voxels are assigned to a tissue class according to their intensities¹⁶¹. The corruption of the MRI images by inhomogeneity leads to a modulation of the intensity of the im-age¹⁶¹. Through the correction of bias field, the final segmentation accuracy is improved. The MP2RAGE sequence was introduced to eliminate most of the inhomogeneity, nonetheless, some seg-mentation softwares suggest the correction of this bias before performing segseg-mentation in order to in-crease the accuracy of the results.

The assessment of the introduction of noise during the MP2RAGE acquisition and the variability between sessions must be performed on UNIT1 images without bias correction to ensure that reliable conclusions are taken with SNR and RPD parameters. In order to obtain a brain mask, segmentation was performed and the skull and background were removed from each UNIT1 using bidspm workflows. The skull and background removal returns a brain mask based on the tissue probability maps from the seg-mentation workflow. The threshold used was the default defined in bidspm, 0.75 meaning that the brain mask would only contain voxels where the probability of being grey matter, white matter or CSF was higher than 0.75 ¹⁵². Consequently, an image with the common voxels between the binary brain mask and the UNIT1 was created, resulting in a UNIT1 with the skull removed and no bias correction. This step was performed for sessions 1 and 2.

This spatial preprocessing step performed with bidspm workflows returns two other outputs of interest: the bias corrected UNIT1 and the deformation field. The bias corrected UNIT1 was the input image for segmentation in FreeSurfer since it is of our interest to estimate the SNR for the whole-brain grey matter, white matter and their combination, and for the grey matter within the occipital-temporal regions. FreeSurfer’s wiki page recommends the following bias correction parameters to perform this spatial preprocessing within SPM for 7T data: bias full width at half maximum (FWHM) = 18 and sampling distance = 2¹⁶². The segmentation step performed in FreeSurfer was completed using the recon-all command for high resolution data¹⁶². To run the recon-all pipeline at native resolution, the parameters needed are: a “-hires” flag and an expert options file that contains the maximum number of iterations for surface inflation¹⁶². FreeSurfer wiki page recommends put a very large number since the inflation process will terminate either when FreeSurfer internal criterion has been reached or when the maximum number of iterations is exceeded¹⁶². The expert options file contains “mris_inflate -n 100”¹⁶².

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The second output of interest is the deformation field, which measures the spatial transformation to deform a template of brain anatomy to each individual data. The deformation field allows moving from a common coordinate space, usually referred to as Montreal Neurological Institute (MNI) coordi-nates, to a native space and vice versa.

2.2.3 Creation of masks for occipital-temporal regions from atlases

In order to assess the presence of noise and variability in occipital-temporal regions, functional probabilistic atlases were chosen to identify these regions in the participants. The Wang atlas¹⁶³ was chosen to extract the primary visual cortex dorsal and ventral (V1d and V1v, respectively) and the visual functional atlas (visfAtlas)¹⁰¹ was chosen to extract other occipital-temporal regions: secondary visual cortex dorsal and ventral (V2d and V2v, respectively), third visual cortex dorsal and ventral (V3d and V3v respectively), inferior occipital gyrus (IOG), inferior temporal gyrus (ITG), middle temporal gyrus (MTG), lateral occipital sulcus (LOS), human middle temporal cortex (hMT), posterior lateral fusiform gyrus (pFus), occipital transverse sulcus (OTS), mid-lateral fusiform gyrus (mFus) and collateral sulcus (CoS). Only the ROIs available in both hemispheres were included. V1 was extracted from the proba-bilistic map by Wang et al. (2015)¹⁶³ as it is in our interest to use this region as a control in the second part of the Pipeline and the ROI offered by Wang has a higher cortical volume than in VisfAtlas¹⁰¹.

The first probabilistic atlas was developed and validated by Wang et al. (2015)¹⁶³ and defines twenty-five topographic visual regions in each hemisphere. To generate the atlas, fifty-three participants took part in functional scans at 3T. This atlas comprises 8 ventral–temporal, 9 dorsal–lateral, 7 parietal and one frontal regions¹⁶³. Figure 2.2 shows the parcellation of the twenty-five ROIs for one example subject included during the creation of the atlas, with particular attention for V1d and V1v.

Figure 2.2: Maximum probability map of 25 topographic visual regions displayed in both (a) surface and (b) volume. The primary visual cortex dorsal and ventral (V1d and V1v, respectively) are respectively represented in red and brown. Figure

taken from Wang et al. (2015)¹⁶³.

VisfAtlas¹⁰¹ was the second probabilistic atlas used and it is the result of a study with nineteen participants who underwent functional scans at 3T. In addition to the identification of topographic re-gions in occipital cortex (V1d, V2d, V3d and V1v, V2v, V3v), ventral and lateral category-selective areas were defined as well. These regions are: mFus, pFus, IOG, OTS, ITG, MTG, LOS, pOTS, IOS, TOS, CoS and hMT. A maximum probability map of all these regions is represented in Figure 2.3.

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Figure 2.3: Maximum probability map of occipito-temporal cortex functional regions of interest in volume representation in MNI space. Figure taken from Rosenke et al. (2021)¹⁰¹.

Using these atlases available in the CPP ROI toolbox, the ROIs (V1d, V2d, V3d, V1v, V2v, V3v, mFus, pFus, IOG, OTS, ITG, MTG, LOS, CoS and hMT) were moved from MNI coordinates to each subject’s native space and resolution using the respective deformation field (MNI to individual space).

As a result, for each participant was available a binary mask for each ROI with a resolution of 0.75 mm in native space.

In order to obtain a more comprehensive point of comparison with the results of the quality control metrics obtained in occipital-temporal regions, the SNR and RPD metrics were additionally estimated in the whole-brain WM, GM and the combination of WM and GM, as well as for the GM within the occipital-temporal regions. To complete this step, the white matter and the grey matter masks were extracted from the segmentation performed in FreeSurfer during the spatial preprocessing step. The segmentation maps, in the format MGZ, were converted to NIfTI format running in the command line a bash script that uses a FreeSurfer function to convert MRI files between different formats. The three binary masks were resliced to UNIT1 dimensions and orientation using bidspm.

For some of the subjects it was found that along with the removal of the skull by bidspm work-flows, some brain voxels were not included because the choice of a threshold led to a trade-off between including more or less voxels that are not part of the brain and brain voxels. As a result, some voxels in V1 mask did not have some correspondence in the brain mask. Therefore, only the common voxels between the ROIs binary masks and the brain mask were maintained to ensure that quality control met-rics were not affected by these voxels.

2.2.4 Signal to Noise Ratio estimation

After an MRI procedure, the image signal intensity is the true image intensity (energy released by the hydrogen proton spins during the acquisition) and the superimposed noise¹⁶⁴. The SNR compares the level of signal of interest to the level of noise, becoming an useful metric to characterize the perfor-mance of a MRI system, evaluate the quality of an MRI image and assess acquisition sequences¹⁶⁵. The SNR of an MRI experiment increases with a higher magnetic field strength B0 and a high SNR allows to compromise with increases in spatial resolution and decreases of scanning time¹²².

The loss of SNR can be caused by very low local 𝐵₁⁺, usually in the cerebellum and temporal lobes^166,167. If this issue is not appropriately addressed, these inhomogeneities can compromise the image quality, lead to incorrect segmentation or unreliable alignment (co-registration)155,166–168.

The SNR was estimated in the ROIs mentioned in 2.2.3, using the following expression¹⁶⁷:

SNR =μ

σ × √ n

n − 1 Equation 2.1

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where μ is the average of the signal over the ROI, σ is the standard deviation of the signal over the ROI and n is the number of voxels within the binary mask¹⁶⁷. The higher the standard deviation in a region, the more noise is present, leading to a lower SNR.

2.2.5 Relative percentage difference estimation

In order to perform a voxel-wise estimation of RPD, UNIT1 and T1 map from session 2 were aligned (coregistered) to the respective images from session 1. Only the common voxels in the brain masks were considered for the RPD estimation.

RPD is a measure of precision and it allows an automatic estimate of the variance within-person, independent of the absolute measurement value. Comparing scans for the same participant, the lower the RPD, the more precise and higher the performance of the acquisition¹⁶⁹.

The relative percentage difference (RPD) was estimated as the absolute difference relative to the mean on a voxel-wise basis expressed in percentage for the common voxels between the brain masks of sessions 1 and 2, using the following expression¹⁴⁸:

RPD (%) = |S₁− S2|

mean (S₁, S₂)× 100 Equation 2.2 where S₁ and S₂ are the voxel signals from session 1 and session 2 of a participant, in each voxel¹⁴⁸.

Lastly, the median of RPD was estimated in each occipital-temporal region.

2.3 T1 relaxation profile in the primary visual cortex and VOTC