Análisis y Discusión de Resultados

Current clinical imaging protocols and surface EEG techniques for epileptogenic focus

localization may not be sufficient for pre-operative planning due to limited sensitivity to deeper brain structures, and low resolution of source localization techniques [68].

Novel magnetic resonance imaging (MRI) techniques such as diffusion tensor imaging (DTI), relaxometry mapping, high resolution functional MRI (fMRI), voxel-based mor-

phometry (VBM), and cortical thickness analysis can detect abnormalities not identified

with conventional or diagnostic MRI protocols [69, 70, 71]. This is important since it

has been shown that post-operative outcomes can be predicted more accurately in pa-

tients where lesions can be identified [26, 72]. These techniques also have the potential

to improve pre-operative localization of the focus, paving the way towards less invasive

procedures and better surgical outcomes.

Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) is an MRI technique that allows the study of brain microstructure by mapping the diffusion process of water molecules [73]. Water diffu- sion in the brain is a three-dimensional anisotropic process believed to originate from

1.4. Diagnostic techniques for pre-operative evaluation 21

specific organization of fiber bundles, neural axons, membranes and macromolecules.

Diffusion anisotropy patterns in the white matter can therefore reveal tissue microstruc- ture and architecture for both diseased and healthy states [74]. Diffusion is encoded in the MR signal by using diffusion sensitizing magnetic field gradients and only molec- ular diffusion that occurs along the direction of the gradient is visible [73]. If diffusion was isotropic, it would be fully described by a single (scalar) parameter, the diffusion coefficient, D. The effect of diffusion on the MRI signal is an attenuation, A, such as:

A=exp(−bD),

where b is a factor characterizing the gradient pulses (duration (δ), strength (g), time between the gradient pair (∆) and shape) of the MRI sequence, and can be simplified for a rectangular pulse pair as:

b= γ2g2_Dδ2(∆−δ/3)

Since diffusion is anisotropic it requires a tensor D, which fully describes molecular mobility in each direction and the correlation between them, to be characterized.

D=

Dxx Dxy Dxz

Dyx Dyy Dyz,

The tensor is commonly calculated from six or more different diffusion weighted acquisitions, each obtained with a different orientation of the diffusion sensitizing gra- dients [74]. The directional information is encoded as eigen vectors and can be used to

follow, whether in a deterministic or a probabilistic fashion, the orientations of the fiber

tracts through the brain, in a process called DTI tractography. Tractography can be used

to map eloquent fiber tracts for surgical planning. Figure 1.4 demonstrates an example

of the corticospinal tract mapped using deterministic and probabilistic tractography for

surgical planning of a tumour resection case. A standard clinical DTI sequences varies

across centers and has an image resolution on the order of a few millimeters, a b-value

of 1000 and 12 to 30 diffusion directions.

Several diffusion indices (maps) have been proposed to characterize anisotropy or diffusivity within the brain. Fractional anisotropy (FA) and mean diffusivity (MD) are the most commonly used indices in the epilepsy literature: They are described as fol-

lows: FA = p 3[(λ1− hλi)2+(λ2− hλi)2+(λ3− hλi)2] q 2(λ2 1+λ 2 2+λ 2 3) , where hλi= MD= (λ1+λ2+λ3) 3 ,

andλ1,λ2,λ3, are the eigen values of the tensor. Previous studies have demonstrated

1.4. Diagnostic techniques for pre-operative evaluation 23

Figure 1.4: Example visualization of the diffusion maps FA and MD (with the first eigen vector overlaid on the maps), as well as fibers from deterministic (bottom left)

These changes may be due to degeneration of axons, reduced packing, or demyelina-

tion [79]. The measured diffusion weighted (DW) signal in each voxel (estimate of rate of water diffusion at that voxel) combines the signal arising from a variety of hetero- geneous microstructural environments including multiple cell types, sizes, geometries

and orientations and extra-cellular space [80]. Most diffusion MR studies rely on sim- plistic single-compartment models to model the DW signal, such as apparent diffusion coefficient (ADC) or intravoxel incoherent motion (IVIM). Several models have been proposed to characterize the white matter microstructure by modeling the underlying

biophysical mechanisms into multiple compartments. Most notably, the composite hin-

dered and restricted water diffusion (Charmed) model [81] represents the intra-cellular compartment as impermeable parallel cylinders with a gamma distribution of radii;

and the neurite orientation dispersion and density imaging (NODDI) approach [82]

estimates the microstructural complexity using a three compartment (neurite density,

orientation dispersion and extra-cellular volume fraction) with a spherical Watson dis-

tribution. Although these multi-compartment models have the potential to better inves-

tigate the tissue microstructure and supplement standard clinical tractography results,

they require more time-consuming MRI acquisitions (with multiple b-values), which

may limit their efficacy in a clinical setting.

Functional MRI

Functional MRI, or fMRI, is a technique for measuring brain activity through the detec-

tion of changes in blood flow that occur in response to neural activity (hemodynamic

response). fMRI uses the blood-oxygen-level dependent (BOLD) method which re-

1.4. Diagnostic techniques for pre-operative evaluation 25

(deoxyhemoglobin) blood to the magnetic field [83]. Cerebral blood flow and neu-

ronal activation are coupled, in that oxygenated blood flows at a greater rate to inactive

neurons and hence the difference in magnetic susceptibility between oxy and deoxyhe- moglobin leads to signal variations detectable using an MRI scanner [84]. However,

the hemodynamic response lags the neuronal events or the stimulus by a few seconds

[83].

Advances in MRI hardware and post-processing statistical analysis techniques lead

to the possibility of acquiring a high-resolution fMRI sequence (typically on the or-

der of 2 mm resolution). In the standard fMRI paradigm, task and control states are

carefully constructed to isolate one component of brain function, and the analysis then

investigates areas that correlate with the known stimulus [84]. These states are alter-

nated in blocks, with each block also event related having a number of fMRI scans and

within each only one condition is presented [83]. Another paradigm is to map brain

networks correlations between spontaneous fluctuations of the BOLD signal during the

“resting state” of the brain. The idea of resting state fMRI, or rsFMRI, is to allow for

the exploration of functional connectivity between spatially remote areas based on their

synchronous BOLD activity [85]. While not assessed in this thesis, fMRI can have a

role in language mapping or lateralization of the seizure for epileptic patients. It can

also provide higher spatial resolution to complement the high temporal resolution of

EEG in EEG/fMRI studies, where EEG spikes are correlated with the fMRI time series to determine regions in which a change in the BOLD signal resulted from an epileptic

Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) is a novel MRI technique that provides

measurements of the apparent tissue magnetic susceptibility from measurements of the

magnetic field perturbation. It requires filtering of the MR phase data and relies on

regularization techniques to solve the magnetic field to susceptibility ill-posed prob-

lem. QSM provides a different contrast than the traditional susceptibility weighted imaging (SWI) and can be useful in the quantification and identification of magnetic

biomarkers such as iron and calcium. QSM and qualitative SWI have demonstrated

enhanced contrast and sensitivity compared to traditional T2-weighted imaging in sev-

eral clinical applications, including imaging of vascular malformations, calcifications,

and iron deposition [87, 88]. Furthermore, susceptibility-weighted contrast has demon-

strated clinical potential in the assessment of epilepsy [89] and Alzheimer’s disease

[90]. Moreover, QSM can detect cerebral microbleeds with a higher sensitivity than

gradient echo (GRE) magnitude imaging [91] and can be used to accurately quantify

iron content in deep grey matter nuclei [92].

High resolution 3D Relaxometry

Relaxometry is a voxel-wise quantification of intrinsic relaxation times from MR im-

ages. T1, T2 and T₂∗ can be estimated using the appropriate pulse sequence and pa-

rameters. Its advantages over qualitative T1 or T2 −weighted imaging is the relative

insensitivity to acquisition parameters and improved sensitivity to biochemical tissue

changes, and more importantly, the ability to acquire MRI data with consistent tissue

contrast at multiple time points and across different imaging centers, making it a quan- titative mapping technique. Data measured during a conventionalT1−orT2−weighted

1.4. Diagnostic techniques for pre-operative evaluation 27

image acquisition is a function of a combination of numerous properties of the tissue

(T1, T2, and proton density), as well as extraneous effects associated with hardware

such as amplifier gains and RF coil sensitivity [93]. QuantitativeT1 voxel-wise maps

present ‘pure’T1relaxation times that are not contaminated withT2effects as compared

toT1-weighted images. However, conventional relaxometry techniques such as inver-

sion recovery (IR) for T1 mapping and spin-echo forT2, are rarely used in a clinical

setting due to the long scan times. In this work we employed the driven equilibrium

single pulse observation ofT1 andT2, or DES POT1,2 [94, 95], which provide accu-

rate mapping with high image resolution (1 mm isotropic) in clinically feasible time.

The method derives T1 maps from a series of two or more spoiled gradient recalled

echo (SPGR) or spoiled fast low angle shot (FLASH) scans with constant TR and in-

cremented flip angle (α) [96], as follows:

SS PGR =

M0(1−E1)sinαT

1−E1cosαT

,

whereE1= exp(−T R/T1), which results inT1being calculated asT1= −T R/ln(m)

andmbeing the slope ofSspgr/sinαvsSspgr/tanα

and T2 maps from a series of five balanced steady-state free precession (bSSFP)

using flip angles 5◦, 35◦ and 68◦ with phase cycling patternsθRF = 0◦ and 180◦. The

signal from SSFP images is:

SS S FP sinα = SS S FP tanα ( E1−E2 1−E1E2 )+M0 (1−E1)E2 1−E1E2

where E2 = exp(−T R/T2), which allows calculation of T2 as T2 = −T R/ln[(m- E1)/(mE1 -1)]. Figure1.5 demonstrates a visual comparison between weighted images

and relaxometry maps. It should be noted however, that the simple T1 and T2 relaxom-

etry sequences assume a uni-exponential model for the relaxation mechanism in each

case, which may not always be a true representation of the underlying phenomena cre-

ating the relaxation. For this reason, more complex sequences have also been developed

that allow for multi-exponential decay [95].

Figure 1.5: Comparison of MRIT1&T2-weighted images (acquired at 1.5T & 3T)

and relaxometry maps of the same subject.