Master of Science Thesis VT2019

Department of Medical Radiation Physics, Faculty of Science

Lund University www.msf.lu.se

Advanced tractography methods: Applied to patients with carpal tunnel syndrome

Tobias Rosholm

Supervisors Jimmy Lätt and Elena Aksyuk, Lund

ABSTRACTIntroduction: Carpal tunnel syndrome (CTS) is a common condition especially for peoplewith diabetes, obesity or women who are pregnant. Diagnosing CTS has primarily been per-formed using electroneuronography. In this thesis the aim was to use an advanced methodof tractography to measure and evaluate diffusion tensor imaging (DTI) parameters betweenanatomical landmarks at the distal radioulnar joint to the hamate and trapezium through themedian nerve to see if these parametric values were different between patients and healthyvolunteers.

Method and material: The amount of subjects which took part in this thesis was 20in total, 10 patients and 10 healthy volunteers. The data acquisition was obtained with aSIEMENS MAGNETOM Prisma 3T scanner using a handwrist 16 channel coil. The sequenceused to acquire these images was a single-shot echo planar imaging (EPI) with 38 transverseslices. The diffusion encoding was performed in 20 separate encoding directions using a b-factor of 1000 s/mm2. The tractogram created from the tractography algorithm was used asa segmentation tool, to find the voxels in each slice for different anatomical landmarks of thewrist which were between the anatomical landmarks at the distal radioulnar joint to the hookof hamate and trapezium. The DTI parameters fractional anisotropy (FA), mean diffusivity(MD), axial diffusivity (AD), radial diffusivity (RD) and functional cross section area (FCSA)were then evaluated using MATLAB [1]. To evaluate the significance of these parameters theMann-Whitney U-test between the patients and healthy volunteers.

Result: The results of this thesis shows a significant difference between patients and healthyvolunteers for the parameter FA, MD, AD and FCSA. The average parametric values for thesignificant parameters were larger for healthy volunteers than for patients.

Discussion: The method used in this thesis could potentially have a better robustness thansome previous methods used with the purpose of diagnosing CTS. The results of this thesisshowed a difference between patients and healthy volunteers for the FCSA-parameter, whichis a new parameter that has not been used in any previous wrist studies and should be testedmore in future work to see if it was reproducible. The parameters FA, MD and AD showedsome similarities with the values obtained from previous studies with the same purpose.

Conclusion: The method used in this thesis was a valid method when evaluating the para-metric DTI-values for patients with CTS. This thesis concluded that DTI can be useful asa supplementing tool to electroneuronography when diagnosing CTS. The results of the newparameter FCSA showed potential of differentiating between patients and healthy volunteersalthough more work is needed before it can be used on a individual level.

ANVÄNDNING AV TRACTOGRAFIMETODER I MAG-NETRESONANSBILDTAGNING MED SYFTE ATT DI-AGNOSTISERA KARPALTUNNELSYNDROMKarpaltunnelsyndrom är en vanlig sjukdom och omkring 3.7-5.8 % av befolkningen har ellerhar drabbats av åkomman. Karpaltunnelsyndrom behöver vanligtvis inte åtgärdas om prob-lemen är lindriga, men vissa får större problem och då åtgärdas det ofta med en operationsom ibland inte lindrar åkomman. Den mest populära metoden för att diagnostira karpaltun-nelsyndrom är idag en metod som heter elektroneurugrafi. Med elektroneurografi undersöksnervbanan m.h.a. svaga elektriska pulser som skickas in genom nervbanan. Denna metodär inte en tillräckligt pålitlig för att diagnostisera karpaltunnelsyndrom, vilket innebär attbättre metoder för att diagnostisera karpaltunnelsyndrom behövs. Karpaltunnelsyndrom är10 gånger vanligare hos kvinnor än för män och drabbar oftare gravida, överviktiga eller per-soner med diabetes. I detta arbete skall bildtagningsmetoden magnetisk resonans tomografitillämpas för att försöka diagnostisera karpaltunnelsyndrom på ett effektivare sätt. Dettasker genom att extrahera information om lämpliga parametrar baserade på vattens rörligheti nervbanan. Med en särskilt avancerad mätmetod kan visuella banor skapas utav att mätarörligheten av vatten i olika riktningar. Ett tractorgram kan då skapa en bild av hur vattnetrör sig inne i nerven.

Bilden visar ett tractogram, som visarvattnets diffusion genom nerven.

Detta arbete visade att vattnets diffusiongenom karpaltunnlen hade olika värden hospatienter med åkomman jämfört med friskavolentärer, vilket innebär att denna teknikkan visa sig vara värdefull vid diagnostiser-ing av karpaltunnelsyndrom. Det ska ävennämnas att osäkerheten utav resultatet ärstor vilket var på grund av ett begränsatantal deltagare i arbetet. I andra arbetenmed samma ändamål har detta också kunnatses, även om resultaten från detta arbete intehelt överensstämde med liknande arbeten såkunde det fortfarande ses en tydlig skillnadmellan patienter och friska volentärer för deparametrar som undersöktes. Det upptäck-tes även att det fanns en stor skillnad mellanmedelvärdet hos patienterna och medelvärdetför de friska friviliga av storleken på den den funktionella arean hos mediannerven. Slutsatsenutav arbetet är att värdena som togs fram kan visa på att de avancerade MR-undersökningarskulle kunna bidra med information som bidrar till en säkrare diagnos av karpaltunnelsyn-drom.

Contents1 INTRODUCTION 5

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.1.1 Carpal Tunnel Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . 51.1.2 MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 THEORY 82.1 Diffusion Weighted Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1.1 Diffusion Tensor Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.2 Mean Diffusivity and Fractional Anisotropy . . . . . . . . . . . . . . . 102.1.3 Tractography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 DTI pitfalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.2.1 Eddy currents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2.2 Motion artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2.3 Partial volume effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3 Functional cross section area . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3 METHOD AND MATERIAL 153.1 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 Data acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.3 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.4 Anatomical levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.5 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.6 Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 RESULT 21

5 DISCUSSION 265.1 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.2 Data acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.3 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.4 Anatomical levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.5 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.6 Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.7 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6 CONCLUSION 30

AbbreviationsAD Axial DiffusivityDT Diffusion TensorDTI Diffusion Tensor ImagingCTS Carpal Tunnel SyndromeDWI Diffusion Weighted ImagingEPI Echo Planar ImagingFA Fractional DiffusivityFCSA Functional Cross Section AreaFOV Field Of ViewLevel 1 hamate and trapeziumLevel 2 pistform and scaphoideumLevel 3 distal radioulnar jointMD Mean DiffusivityMRI Magnetic Resonance ImagingRD Radial DiffusivityROI Region Of IntrestSNR Signal to noise ratioTE echo timeTR repetition time

Symbols

b the encoding strenghD the Diffusion coefficientλ1 the Axial eigenvalueλ2 the first Radial eigenvalueλ3 the second Radial eigenvaluetD the time of diffusionxrms the root mean square displacementS the Signal

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1 INTRODUCTION

1.1 Background

There are several popular methods of diagnosing CTS today such as the Phalen maneuver,electroneuronography and Flick test [2]. The most frequently used method when diagnos-ing more difficult cases of this CTS is electrophysiological examines. Between the years of2009 and 2016 Cirakli et al. studied the role of electrophysiological examination with thepurpose of diagnosing CTS using 3151 hands of 2516 patients who were examined by neurol-ogists and orthopedists [3]. CTS was only confirmed in just above half of the patients whowere expected to have this condition, and this study conclude that the electrophysiologicalexamination method does not have sufficient statistical support to be a definitive diagnosismethod, which could lead to unnecessary surgical interventions using the current method[3].The relatively new method of magnetic resonance imaging (MRI) is still growing and has stillmany unexplored potentials, this technique has previously been tested by several studies withthe purpose of diagnosing CTS but is yet to be accepted as a clinical tool. An article byOnen et al. concludes that patients with suspected CTS can be diagnosed using clinical andelectrophysiological examination, but MRI can be advantages for some cases with doubtfulsymptoms. The combination of MRI and electrophysiological examinations when diagnosingCTS may increase probability of patients benefiting from surgical treatments [4]. Due to thenerves small size relative to the size of the resolution used in studies, there are several issuesthat needs to be solved regarding the partial volume effect. In this an additional diffusion ten-sor imaging (DTI) parameter which has yet to be explored be evaluated, this is the functionalcross section area (FCSA) which have not yet been evaluated with the purpose of diagnosingCTS in previous studies. This parameter may offer information about the area in which theaxons along the nerve are placed and how much they have been compressed or damaged.

1.1.1 Carpal Tunnel Syndrome

The carpal tunnel is a canal which goes through carpus, and the median nerve can be foundinside this tunnel which is shown on the image in figure 1. The most common entrapmentneruopathy in the upper extremities is CTS [3, 5]. The prevalence of people with CTS isranging between 3.7% to 5.8% [3]. Typical symptoms of CTS can consist of nocturnal painand associated with numbness and tingling in the the index finger, thumb, ring finger andthe middle finger [6]. This condition occur more often for middle aged women [6], and isassociated is pregnancy, people who suffers from diabetes or even obesity [7]. Usually thisCTS require no medical attention and lenient symptoms be managed with treatments likelocal steroid injections [6]. For more severe cases this condition can be surgically resolvedusing carpal tunnel release, which is the most effective treatment of CTS [5, 7, 8]. Carpaltunnel release is performed in short by cutting an opening in the carpal tunnel to minimisethe pressure inside the carpal tunnel due to the volume of the carpal tunnel being increased[6, 7].

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Figure 1: The carpal tunnel is displayed on this image as the green tunnel, inside the carpaltunnel is the median nerve which is marked on the drawing. The image was reproducedand adapted from Rodner C, Raissis A, Akelman E: Carpal tunnel syndrome. OrthopaedicKnowledge Online Journal. Rosemont, IL, American Academy of Orthopaedic Surgeons, 2009;7(5). Accessed March 2016.

1.1.2 MRI

MRI is an advanced clinical diagnosis method using tomographic images based on the phe-nomenon of nuclear magnetic resonance and was described experimentally by Bloch and Pur-cell in 1946 [9]. MRI can provide morphological information about a subject but has also theoption to provide functional information. The MRI is a relatively new non-invasive techniquefor diagnosing patients, and with that follows many unexplored fields for the use of today’sMRI. The purpose of researching the possibility’s of new imaging techniques is to potentiallyreplace or complement older methods of diagnosing. MRI has in the passed shown to havea great potential creating images of the brain [10]. The MRI technique of today is rapidlyexpanding as a useful tool to peripheral regions, even though these regions as been delayed dueto technical and practical obstacles. Diffusion weighted imaging (DWI) is a technique usedto in vivo measure apparent diffusion coefficients in different type tissues [11]. The modellingtechnique of diffusion tensor imaging (DTI) can be used to indirect measure anisotropic diffu-sion of the water which is inside nerves [11]. This modelling technique can be used to createtractograms which can be used to track the anisotropic movement of water molecules throughthese nerves. The DWI technique is today the only non-invasive technique used to probethe micro-structure in vivo, which also provides information about connectivity of anatomicalnetworks displayed as tractograms [10].

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1.2 Purpose

In this thesis the MRI method of DTI will be used to create a tractogram which can beused to evaluate different parametric diffusion related values through the median nerve. Theaim of this thesis was to implement and evaluate the potential of MRI-based DTI as a toolfor diagnosing of CTS. These parameters will be evaluated using a method created in thisthesis to semiautomatically segment the median nerve based on the tractography images wasdeveloped and the fractional cross section area (FCSA) of the median nerve was evaluated asa possible new DTI based parameter.

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2 THEORY

2.1 Diffusion Weighted Imaging

Diffusion is the random microscopic movement of molecules in a fluid under Brownian-motion[9, 12]. The displacement of diffusing molecules can be described by Einstein’s diffusionequation by the root mean square distance (xrms), which can be calculated according toequation (1).

xrms =√

2DctD (1)

where tD is the diffusion time and diffusivity of molecules is described by a diffusion coefficient(Dc), this coefficient differs depending on the size of the molecule, the molecule’s surroundingsand also the thermal energy [13].

Diffusion weighted imaging (DWI) is a technique which quantifies the movement of watermolecules using MRI [9]. If the random microscopic movement of water molecules is un-constrained the water molecules will be able to move freely in all directions, this kind ofmovement is denoted isotropic movement [9]. If instead the water molecules are hindered insome direction, for example by cellular structures, then the diffusion will be referred to asanisotropic diffusion [9]. Anisotropic diffusion implies that the water molecule is partially orentirely constrained, which results in reduced diffusivity in some direction [14]. Note that thediffusion can be either isotropic or anisotropic, depending on the time scale of the observation,defined as the diffusion time tD [14]. When a certain diffusion encoding and diffusion timingis used, the diffusion of the water molecules can then be used as an indirect measure of thesurrounding tissues. The properties of the diffusion gives then information about the mobilityof the water molecules, denoted mean diffusivity (MD) as well as information about its spatialconstraints in the parameter fractional anisotropy (FA). The most standard way of measuringis using a spin-echo sequence with two gradients applied after one another as can be seen infigure 2. The basic principle of DWI is that by emitting a gradient pulse (A), the frequencyof the hydrogen nucleus in the water molecules rotation will change differently depending ontheir spatial location in the object. The water molecules will then be displaced (or not) dueto the effect of diffusion during the time tD between the first pulse A and the second gradientpulse (B), which has the same magnitude as A. The gradient pulse B is then applied in theopposite direction of A with the purpose of restoring the effects of A. If the water moleculeswere not spatially displaced during the time tD, the result would be that the B restores fullythe phase of the water molecules back to their original frequency before the gradient A wasapplied. The displaced water molecules would thereby have a different frequencies than theiroriginal frequency depending on the magnitude diffusion.

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Figure 2: This is the schematic layout of the pulse-gradient spin-echo sequence which typicallyuse in DWI. This schematic drawing is represented with a relative timeline on the horizontalplane. This sequence is normal spin echo sequence but with two additional gradient pulses, Aand B. The first gradient pulse A being turned on after the 90o pulse and the second gradientpulse B being turned on after the 180o.The image was reproduced and adapted from DiffusionTensor Imaging in the Cervical Spinal Cord, Filip Szczepankiewicz, 2011.

The effective time tD can be described as tD = ∆ − δ/3, where ∆ is the time in betweenthe gradients being turned on and δ is the time during the gradients are on [15, 16]. Thisparameter is described as such due to the fact that the gradients were not instantaneous [17].This will result in a phase shifting of the spins which will differ depending on their spatiallocation and will give rise to a signal drop in the measured signal, according to equation (2),

S = S0e−b·MD (2)

where MD is mean diffusivity and b is the strength of the encoding and is calculated usingequation (3)

b = γ2 · δ2 ·G2 · tD (3)

where γ is the gyromagnetic ratio.

2.1.1 Diffusion Tensor Imaging

Amore advanced method of DWI is DTI. Instead of only measuring the magnitude of diffusion,the direction of the left image be evaluated by measuring in several spatial directions (>6)by applying a tensor model for description of the diffusion. The mathematical concept isdescribed using the diffusion tensor (DT ) [9, 18]. The DT is defined by equation (4).

DT =

Dxx Dxy Dxz

Dyx Dyy Dyz

Dzx Dzy Dzz

(4)

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where the diffusivity in each direction is Dij and element of the tensor require its own direc-tional gradient to be measured. DT is rationally invariant, which means thatDTij = DTji anddue to this fact the tensor only requires six unique elements instead of nine. The eigenvaluesof DT is representing the magnitude of each eigenvectors direction, which thereby representthe direction of the waters diffusivity. This type of diffusion can be graphically representedby a diffusion ellipsoid [18]. On the left image in figure 3 the isotropic diffusion is graphicallydisplayed [9]. In the case of theoretical perfect isotropic diffusion, all directions of the diffu-sivity tensor are equal in each direction of the tensor, these directions are represented usingeigenvalues. If the eigenvalues were different from each other, it means that the diffusion isanisotropic and is displayed on the right image in figure 3 [19].

Figure 3: The image to the left graphically shows an isotropic diffusion where all eigenval-ues λi inside the tensor was equal as a sphere. The image the to right graphically showsan anisotropic diffusion, which could reflect the diffusion in axons, for instance, where themolecules were more constrained perpendicular the the nerve. Adapted from Groover et al.Magnetic Resonance Imaging: Principles and Techniques: Lessons for Clinicians.

2.1.2 Mean Diffusivity and Fractional Anisotropy

The most common two parameters calculated from the result of DT in equation (4) are theaverage diffusion rate in all directions, which is denoted MD. MD is defined by the threeeigenvalues λ1, λ2 and λ3 which represents each direction inside the tensor. These are allorthogonal to each other which and is described on the right image of figure 3 as previouslymentioned. The MD-value is calculated using equation (5).

MD =λ1 + λ2 + λ3

3(5)

The axial diffusivity AD is described by equation (6),

AD = λ1 (6)

and the radial diffusivity RD is described by equation (7).

RD =λ2 + λ3

2(7)

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Fractional anisotropy (FA) describes how anisotropic the diffusion is, and varies between 0to 1. If the FA-value is closer to 1 it means that the water molecules has a high anisotropicdiffusivity and if it’s closer to 0, it means that the water is more isotropic [20]. To calculatethe FA-value equation (8) is used.

FA =

√3

2

√(λ1 −MD)2 + (λ2 −MD)2 + (λ3 −MD)2√

λ21 + λ22 + λ23(8)

2.1.3 Tractography

Tractography is a 3D technique which is based on the parameters of DTI. The purpose ofthis is to evaluate which regions are connected [21]. This technique adds a better visualexperience for the viewer and thereby an easier method of evaluating structures such as nervefibers. The tractogram is created using the directionality and magnitude of the anisotropictensors FA-value from DTI. The streamlines of the tractogram is created by connecting theaxial directions of the anisotropic tensors [9]. This is done to determine if there are voxelslined up with the same anisotropic direction from one starting point to an end point. The firststep of creating a connecting streamline between two voxels is to use a slice where a region ofinterest (ROI) is placed and in that ROI distributing seeds randomly within the voxels, whichare a part of the ROI. These seeds will then be used by the tractography algorithm to growstreamlines to the surrounding voxels which will be used as the visual connection between thetwo voxels. Two conditions needs to be met to create this connection. The first conditionis that the FA-value needs to be above a ceratain threshold for the two connected voxels tocreate a line between them. The second condition is that the axial diffusivity of the two voxelsneeds to be angled in approximately same direction, which require a threshold values for howmuch the angel of the axial diffusivity between two voxels can differ from each other. If boththese two conditions were met a streamline will be drawn between these voxels. An exampleof a tractogram of the median nerve is displayed in figure 4. The number of streamlines in atractogram is determined by the amount pathways the algorithm can find from a voxel witha seed to the an end point voxel times the amount of seeds per voxel.

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Figure 4: The image show a tractogram using the tractography algorithm. This tractogramhas been created with the condition that only streamlines that travel through all three ROIwill be appearing. This image was used a to create a segmentation of the DTI-values throughto median nerve.

The tractogram is a visual tool created to help the viewer. There are three typical benefitswith creating a tractogram, the first is that the tractogram gives the viewer information aboutthe connectivity based on the DTI modelling technique, this is a visual tool that benefits theviewer, however it should not to be linked to the function of various nerves since that is toambiguous. The second benefit is that it can be used as a region-growing (segmentation) toolbased on the fibers orientation, which is less user dependent as compared to manual regiondrawing on 2D DWI. The last benefiting information which can be provided is streamlines. Thestreamlines can be used for programming purposes such as trying to solve the partial volumeeffect problem by counting the amount of streamlines. This type of information could be usedto delineate altered white matter anatomy when observing developmental abnormalities [22].

2.2 DTI pitfalls

The sequence echo planar imaging (EPI) is the most frequently used sequence for DTI [10].This kind of fast acquisition of k-space has its down sides as well, when creating DTI’s wheremultiple images is needed for the calculation of the tensor which requires relatively long prob-lems like motion in between the images is a problem. To compensate for this time a fastreadout of the k-space is needed which generally leads to a low bandwidth for the phaseencoding direction, which results in high sensitivity to eddy current, susceptibility and off res-onance effects [10]. These types of negative effects can be reduced with the current techniquesof parallel imaging, multi channel arrays and in combination with sequences that as efficientas possible cancels out the eddy current [23, 24, 25, 26]. The outcome of the tractographyalgorithm can also be influenced by susceptibility artifacts where as nonlinear distortions oc-cur, which is some cases can be corrected for [27]. The result of making corrections to limitsusceptibility artifacts from affecting the tractography algorithm, is that the reconstructedstreamlines will appear more anatomically correct than before the correction which could give

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rise to more anatomical features [10].

2.2.1 Eddy currents

A problem with strong gradients is the eddy current. When a strong gradient is applied theoccurrence of electric current called eddy currents will give rise to local magnetic fields. Theeffect of eddy currents will either strengthen or weekend the effect of the gradients over timefor the spatial encoding direction [10]. The distortions from the eddy current will be varyingdepending on what diffusion encoding is being applied. The effect of the eddy current can beseen in figure 5, as a mismatch between images, obtained with different encoding gradient,giving rise to a bright rim, especially at the edges of an object.

Figure 5: This image shows a slice from a DWI image of a wrist. The bright rims on the edgesof the wrist is artifacts created by eddy current.

One method that can be used to limit the effects of eddy currents is by purposely changethe shape of the current emitted by the gradient or by compensating for it in the sequence.However to reduce the effects of eddy currents as much as possible, the most common strategyis to correct for it in the postprocessing [28].

2.2.2 Motion artifacts

Artifacts from macroscopic motion produced by respiratory and cardial motion is a problemfor any MRI sequence, but for DTI this problem can occur even more often [28]. This problemis primarily due to the fact the MRI sequences require a lot of time to read out all the datarequired to produce images. The effects of the motion artifacts can include ghosting andblurring and ghosting of the images. Ghosting occurs in the phase encoding direction takeshape of the object but is shifted [29].

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2.2.3 Partial volume effect

A larger problem with DTI images is the low resolution [10]. The partial volume effect occurswhen voxels contains different kind of tissue. This effect is displayed in figure 6, where asegmentation is made of the tractography through the median nerve, seen on an FA map. Inthe centre of the image (the bright pixels) s the median nerve. These voxels are penetratedby many tracts which make them obvious to include. But for the voxels at the edges of thetractography where only a few tracts penetrate each voxel it is not that easy to determineif these voxels should be included or not in the segmentation. This problem can be lessproblematic for images with higher resolution.

Figure 6: The ’zoom in’ shows a tractogram which travel through a transversal FA slice. Atthe edges of the trajectory the partial volume problem can be observed, when just a few tractstravel through a voxel.

2.3 Functional cross section area

The FCSA parameter is based on the area included by the segmentation of a slice. Theparameter describes the area of where the axons are, provided that the FA-value is largeenough in a voxel for where the axons are to be included by the tractography algorithm. Thisparameter is be calculated using equation (9)

FCSA = A ·N (9)

where A is the radial area of each voxel and N is the amount of voxels included by thetractography algorithm using the tractography conditions as basis for the algorithm.

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3 METHOD AND MATERIAL

3.1 Subjects

All participating subjects gave their informed consent to participate. In this thesis 10 patientsearlier diagnosed with CTS and a control group consisting of 10 healthy volunteers were con-tributed as subjects. The subject’s age and sex was presented in table 1. All 10 patients hasall been remitted to surgery for CTS, but none of them have had any surgery for CTS beforebeing included in this thesis. The inclusion criteria for the healthy volunteers was; no subjec-tive symptoms or clinical signs of CTS could determined and a age between 18-65 years. Theinclusion criteria for the patients were; subjective symptoms of CTS for more than 3 months,clinical signs of CTS, a electroneurography with a fractionated sensory nerve conduction ve-locity for the median nerve across the wrist of 40 m/s or less (which was a finding compatiblewith CTS) and an age between 18-65. The exclusion criteria for all subjects was that theyhad no history of previous CTS or wrist operation, prior wrist or carpal fracture, diabetes,thyroid disease, rheumatoid arthritis, neurological disease, drug abuse, regular exposure tohand-held vibrating tools or MR contraindications.

Table 1: The age (years) and sex was presented for each subject in this thesis.Healthy volunteers: Age Sex Patient group: Age Sex

51 M 58 F55 F 28 F36 F 47 F37 F 41 F49 M 50 F39 F 38 F32 F 43 M31 F 53 M34 F 56 M26 F 38 F

Average: 39 45

3.2 Data acquisition

All subjects in this thesis were scanned using a SIEMENS MAGNETOM Prisma (3T) whichwas in clinical use in Lund. All subjects were scanned using a Handwrist 16 channel coil A3TTin Coil. The subjects lay in a prone position with their arm reaching above their head, ascan be seen in figure 7.

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Figure 7: The image to the left shows the prone position which all subjects had to lay still induring the scan time. The image on the right shows the SIEMENS Handwrist 16 channel coilA3T Tin Coil with a subjects hand inside.

The optimisation of the imaging protocol were based on [30, 31] which included the followingDWI-parameters. All images was acquired with single-shot EPI sequence with 38 transverseslices (2.2 mm thickness with no slice gap), a field of view of 111x111 mm2, an acquisitionmatrix with the size of 96x96 (interpolated to an in-plane resolution of 0.6x0.6 mm2), arepetition time (TR) of 3500 ms, an echo time (TE) of 53 ms and using GRAPPA = 2as a reconstruction method. The diffusion encoding was performed in 20 separate encodingdirections using a factor b of 1000 s/mm2, with an additional of three b0-images. The diffusionsequence were repeated seven times with the purpose to improve signal to noise (SNR) andallowing motion correction for every individual.

3.3 Data processing

The DWI-data were post processed using FSL Diffusion Toolbox (FDT) of the FMRIB freesoftware library (FSL,http://fsl.fmrib.ox.ac.uk/fsl) v5.0.9 (Oxford, UK) (Smith et al. [32]).The acquired b0-images were first processed to reduce susceptibility-induced artefacts usingFSL-topup [33, 32]. Images were corrected for distortions using FSL-eddy [34]. The motioncorrection where performed within the FSL-eddy since a rigid body registration of the data isincluded in the correction algorithm. The tracking of the streamlines was set to a minimumFA-value 0.20 and the threshold for the minimum angle was 45o. Finally, standard DTI-analysis resulting in FA, MD, AD, RD and directional maps were calculated in-house developedsoftware, based on MATLAB. Fibertracking was performed in TrackVis [35]. Further analysisas calculate cross section of the streamlines was performed using Matlab [1].

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3.4 Anatomical levels

Three ROIs were placed in three different anatomical landmarks of the wrist Level 1 whichrepresents the hook of hamate and trapezium, Level 2 which represents the pisiform andscaphoideum and Level 3 the distal radioulnar joint. These landmarks were visually describedby figure 8.

Figure 8: This figure maps out the three anatomical landmarks used to place the ROI:s andcreate the tractography. Adapted from: Imaios - webbsite - Upper limb illustration.

These ROIs were then used to determine where the streamlines seeds from and where thestreamline ends. The placement of these ROIs were performed by an experienced radiologist.The seeding of these ROIs for the tractography were placed in level 2 and level 1 and 3 whereused as AND gates, i.e., the streamlines need to pass through all levels in order to be included.Due to the subjects different wrist size the track length was resampled to 13 data points (30.8mm) which was the most common distance for the subjects, between the anatomical levels 1and 3.

3.5 Data analysis

The FA value was calculated using equation (8), the MD value was calculated using equation(5), the AD-value was calculated using equation (6), the RD-value was calculated using equa-tion (7) and the functional cross section area (FCSA) value. The average for the FA-value,MD-value, AD-value, RD-value and the FCSA has been calculated for each slice between theanatomical landmarks for level 1 to level 3. This was calculated using the tractography as asegmentation tool for which voxels that should be included in the evaluation of the averagevalue for the parameters FA, MD, AD and RD. The FCSA-value was calculated using equation(9) for the average amount of voxels form each slice that were included by the tractography.The area (A) of each voxel was 0.6 x 0.6 mm2 which were then multiplied with the amount ofvoxels in average. The standard deviation of the resulting average value for each parameterwere calculated for each slice. The resulting parametric average values were then evaluated byusing the mean value of values between the slices defined by the anatomical landmarks level 3to level 2 and level 2 to level 1, respectively. This was done with the purpose of preforming a

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statistical test to evaluate if the different locations through the median nerve had a higher orlower significance depending on which interval of landmarks that were evaluated, these resultsare represented as bar diagram. The Mann-Whitney U-test was used in this thesis to testthe significance of the difference between patients and healthy volunteers for each parameterobtained. This was done for the average of each mean value for every slice between differentanatomical landmarks. The level of significance was chosen to (*) p=0.05 and (**) p=0.01.The Mann-Whitney U-test was used instead of a t-test because patient values not being nor-mal distributed, which was shown using a quantile-quantile plot. The reason for the individualpatient values not being normal distributed was probably because of spread of severity amongpatients not being normal distributed. Studies with similar purposes to this one was alsousing the Mann-Whitney U-test to evaluate the significance of the result [36, 37, 38].

3.6 Threshold

The partial volume effect was also something which should be considered when evaluatingwhich voxels should be included for the calculations of the result for FA-, MD-, AD- andRD-values. As seen in figure 6 the partial volume effect can be a problem even in this casewhen estimating how thick the median nerve is. To evaluate which voxels that should be takenin to account for the result a threshold value was determined based on maximum differencebetween patient and control group. A threshold will determine how many streamlines fromthe tractography that at a minimum should penetrate a voxel to include that voxel. Theevaluation was performed using different threshold values ranging from 0 to 8 streamlines.Figures 9 and 10 shows the effects on the parameters when different (0 to 8) streamlines wereused to determine the threshold value on these images.

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Figure 9: These graphs show the parameters values of the patients and volunteers whendifferent thresholds for the number of streamlines which have to penetrate i voxel to beincluded it in the result. These graphs shows these thresholds for the parameters FA, MD,AD and RD.

Figure 10: These graphs shows the relative difference between the values of the patients andhealthy volunteers obtained using the different values for the threshold in figure 9.

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Based on the plots in figure 9 the resulting differences in the parameters can be seen asthe threshold regarding number of tracks that was required to pass through a voxel in orderto include it in the analysis. In figure 10 a relative difference between patient and healthyvolunteers for the different threshold can be seen. Based on these plots a fix threshold of 5was chosen. The choice was based on creating a robust tracking, including the majority of thenerve, as well as avoiding partial volumes effect and erroneously include voxels next to thenerves. Also it can be seen that an decreasing threshold, might better separate the groups,as it selects more and more the core of the nerve.

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4 RESULTThe average value of all patients and healthy volunteers were presented in figure 11 as a red anda blue line. The anatomical landmarks in figure 8 corresponds to the following slice numbersin figure 11 and figure 13: the anatomical landmark level 3 was represented by slice number1, the anatomical landmark at level 1 is represented by slice number 8 and the anatomicallandmark at level 1 is represented by slice number 13. The average values were then linearlyinterpolated between each slice to give the viewer a better visual experience of the result. Theerror bars on these graphs shows one standard deviation for each separate slice and what canbe noted was that the error bars for both groups overlaps the resulting value for the othergroup.

Figure 11: The average value for the patient group and the control group through eachslice between the anatomical level 1 and level 3 presented in figure 8. Where as level 3 wasrepresented by slice 1 and level 1 was represented by slice 13.

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The average values of the slices between level 3 and level 2 from the graphs in figure 11 werethen summarised for patients and healthy volunteers by a mean value of these slices. Thisprocedure was also performed for slices between anatomical landmarks level 2 and level 1.The result from the Mann-Whitney U-test showed a significantly smaller FA-value in patientscompared to healthy volunteers between level 2 and level 1. The MD-value was significantlysmaller in patients compared to healthy volunteers between the anatomical landmarks of level3 and level 2. Finally the AD-value showed a significance between the anatomical landmarkslevel 3 and level 2. This result was displayed as bars in figure 12.

Figure 12: These graphs shows the difference of the average value of FA, MD, AD and RDbetween the anatomical landmarks level 3 to level 2 and level 2 to level 1. The patients weredisplayed as the white bar and the healthy volunteers was displayed as the grey bar. (Theresult of the statistical significance test shows that (*)=p<0.05 and (**)=p<0.01).

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The result of the FCSA-value was displayed in figure 13, it should be noted that the meanFCSA was through all slices greater for the healthy volunteers than for the patients. Thestandard deviation on this result also appears to be large. The mean FCSA-value through allslices were for the patients 4.82 ± 3.02 mm2 voxels and for the healthy volunteers 6.48 ± 2.52mm2 voxels.

Figure 13: This graph shows the difference of the FCSA-value between patients (red) andhealthy volunteers (blue) represented by the amount of included voxels from the tractography.The error bar shows the standard deviation for each slice.

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The result of average cross section area (FCSA) between landmarks level 3 to level 2 whichwas represented by slices 1 to 8 was significant, shown by the Mann-Whitney U-test. Theresult between landmarks level 2 to level 1 which was represented by by slices 8 to 13 wassignificantly, also shown by the Mann-Whitney U-test. This result was displayed by the barsin figure 14.

Figure 14: These graphs shows the difference of the average value of the FCSA between theanatomical landmarks level 3 to level 2 and level 2 to level 1. The patients were displayed asthe white bar and the healthy volunteers was displayed as the grey bar. (The result of thestatistical significance test shows that (*)=p<0.05 and (**)=p<0.01).

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The average value between parametric value between the anatomical landmarks of the distalradioulnar join (level 3), the pisiform (level 2) and hook of hamate (level 1) was displayed intable 2.

Table 2: The average values obtained in this thesis for patients and healthy volunteers betweendifferent anatomical landmarks.

Subject group Patients Healthy volunteersFA level 3 - level 2 0.45 ± 0.10 0.47 ± 0.07FA level 2 - level 1 0.43 ± 0.09 0.46 ± 0.06FA level 3 - level 1 0.44 ± 0.09 0.46 ± 0.07

MD level 3 - level 2 [µm2/ms] 1.09 ± 0.16 1.16 ± 0.10MD level 2 - level 1 [µm2/ms] 1.17 ± 0.14 1.19 ± 0.09MD level 3 - level 1 [µm2/ms] 1.12 ± 0.16 1.17 ± 0.10AD level 3 - level 2 [µm2/ms] 1.69 ± 0.22 1.84 ± 0.13AD level 2 - level 1 [µm2/ms] 1.79 ± 0.21 1.86 ± 0.13AD level 3 - level 1 [µm2/ms] 1.72 ± 0.22 1.84 ± 0.13RD level 3 - level 2 [µm2/ms] 0.78 ± 0.17 0.82 ± 0.12RD level 2 - level 1 [µm2/ms] 0.87 ± 0.15 0.85 ± 0.10RD level 3 - level 1 [µm2/ms] 0.82 ± 0.17 0.83 ± 0.11FCSA level 3 - level 2 [mm2] 4.54 ± 2.63 6.09 ± 2.06FCSA level 2 - level 1 [mm2] 5.24 ± 2.75 7.00 ± 2.96FCSA level 3 - level 1 [mm2] 4.82 ± 3.02 6.48 ± 2.52

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5 DISCUSSIONIn this thesis we have demonstrated that CTS-patients can be significant distinguished fromhealthy controls. This has been performed using a relatively small study of 10 patients and10 healthy volunteers with DWI. This was in accordance with previous publications whichhad similar purpose [39, 36, 40]. In this thesis the FCSA-parameter was introduced for thefirst time with the purpose of investigating the median nerve. It has previously been pro-posed and used for measuring the area of cingulum. This parameter revealed a statisticallydifference between the two groups (p < 0.01). The evaluation method proposed in this thesiswas also new and is based on volume segmentation of the nerve, using the tractograms of thenerve. The tractogram segments the nerve through each slice, ranging from the anatomicallandmarks at the distal radioulnar joint to the hook of hamate and trapezium. The differencebetween the average parametric values can be seen in figure 11 for the parameter FA, MD,AD and RD and figure 13 for the FCSA-parameter.

The results of this thesis indicates that AD was the DTI-parameter that decreases the mostin the patients. A decrease of the AD-value means that the diffusion of water molecules alongthe median nerve is more hindered for patients in comparison to healthy volunteers. Thelarge difference of the AD-value between patients and healthy volunteers (which is includedin equations (5) and (6)) is probably the leading cause for the decrease in both the MD-and FA-values. This was also in accordance with Lindberg et al. who also found a decreaseof AD through the median nerve [40]. The cause of this change is challenging to interpret.Several hypothesis might be proposed to explain the decrease of the AD-value. One hypoth-esis suggests that the emerging edema surrounding the nerve causes the pressure to increaseson the median nerve in the carpal tunnel, which might increase the undulation of the nerveand thereby hinder the diffusion along the axons [41]. Another hypothesis suggests that thedecrease of the AD-value and the unaffected FA value is due to endoneural fibrosis, whichcomes from chronic severe compression or postoperative scared tissue independent of extrinsiccompressive causes [40]. This parametric relation has been studied in models of fibrosis whichimplies that that fibrosis influences the AD-parameter [42]. There could be more direct effectsof partial volumes effect arising from the emerging oedema, which dilute the axonal waterdiffusion and thereby reducing it. The seemingly decrease of FCSA in the patients could po-tentially also be explained by the emerging oedema which also could reduce the extension ofthe anisotropy, which was used to define the cross section area of the nerve. It should also beemphasized that the FCSA-parameter does not correspond with the anatomical area which isincreased in CTS patients [43]. The increasing area of the carpal tunnel appears due to effectsof swollen tissue inside the carpal tunnel, which in turn pressurises the median nerve andreduces the diffusion along the median nerve provided by the axons. The hypothesis is thatthe diffusion of water molecules along the median nerve correlates to function of the mediannerve. A reduction of the area where the axons are positioned in the nerve might indicatethat the axons inside the median nerve have been damaged.

All parametric results were divided in to two separate parts presented as bar diagrams whencomparing the results between the patients and healthy volunteers. These bar diagramsshowed that the average value of the first 8 slices and the last 5 slices were not comparable

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to each other, when determining a representative average value for the entire nerve. Thisresulted in the decision to divide the result of the nerve in to intervals between the anatomicallandmarks level 3 to level 2 and level 2 to level 1, with the purpose of evaluating the resultseparately. The method of dividing the result between different landmarks made it easier tocompare the result of this thesis to similar studies with the same purpose, due to their studiesalso dividing their result in to different parts [36].

5.1 Subjects

The amount of subject used in this project was fewer compared with studies within the metastudy of Wang et al. where the median/average amount of patients where 15/26 and themedian/average amount of healthy volunteers where 20/24 [44]. In this thesis the average ageof the patients was 46 years for healthy volunteers group and 39 years for the patients, and bothgroups mainly consisted of females. The results from the meta-regression analyses suggestedthat age and sex had no significant relationship with FA or MD values [44]. Therefore thesefactors were not considered in this thesis.

5.2 Data acquisition

During the time of the image acquisition all subjects had to lay down in a prone position withtheir hand held above their head and with their hand squeezed by the wrist coil, this positionwas also used by the patients in additional studies [38, 45]. This could be a uncomfortableposition to lay down in for many patients, especially if its a patient who already experiencessevere pain in the hand. This might have consequences for the images and affect the DTI-valuesdue to movement which results in motion artifacts. The sequence was repeated seven separatetimes and averaged first in the post processing to correct for motion artifacts and improvethe SNR in this thesis. The same approach was made by Stein et al. but with repeating thesequence five times instead [36]. Several other studies performed a coregistration by matchingthe FA and MD images with the b0 images [45, 38] and some were just examined for visiblemotion artifacts before analysis [40]. However the image resolution was compromised basedon the limited signal to noise in the images of this thesis.

5.3 Data processing

The different conditions to create the streamlines in the tractogram was set to a minimumFA-value of 0.2 with a maximum angle difference in the axial direction of 45o. The reason fornot using a higher limit of the FA-value condition is that the spread of individual FA-valuesthat would be representing the median nerve is reduced due to exclusion of voxels below thelimit of the FA-value condition. The result of this effect will influence the difference betweengroups average FA-value, due to the fact that the spread of FA-values below the limit waswider for patients than for healthy volunteers. This means that a greater exclusion of voxelswould be made for the patients than for the healthy volunteers, which would result in theaverage FA-value of the patients increasing more than the average FA-value for the healthyvolunteers with a higher limit on FA-value condition. In the study by Stein et al. a streamlinewas created if the FA-value was above 0.35 and ended it if it fell below a FA-value of 0.2

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which is a different approach but with the down side of a excluding of included values in theresult[36]. The study of Barcelo et al. used even higher condition for their streamline witha FA-value of at least 0.3 [39] through the whole streamline which can give rise to an evenbigger exultation. The benefits of using a higher limit is that more voxels that should notbe included as a contributing factor to the median nerve is excluded. The condition of theangle threshold in this thesis was probably not influencing the results because of the relativelystraight anatomy of the median nerve. In Barcelo et. al the condition of the angle was set toa limit of 7o which might exclude some streamlines in their result.

5.4 Anatomical levels

The 3 anatomical landmarks used in this thesis were primarily used to setup the seedingpoints and end points of the streamlines. A problem with the tractography algorithm usedwhen calculating the streamlines through the median nerve, was that it can potentially createa broadening of the tractogram over a distance. This occurs when the algorithm by mis-take includes voxels next to the median nerve due to algorithmic errors. These errors hasa growing effect the further from the seeding point the algorithm gets, this results in moreerror being included. To reduce the effect from this problem, the seeding point was placedin the anatomical landmark of the pisiform and scaphoideum and the endpoints were set tothe distal radioulnar joint and the hook of hamate. The reason for choosing this landmarkwas that it had the shortest distance out of the three landmarks to the other two landmarks.Different anatomical landmarks have been used in other studies with the purpose of diagnos-ing CTS using DTI [39, 36, 45, 40], but all landmarks used in other studies have been set atapproximately the same positions as the landmarks used in this thesis. A few of these studiesalso included an additional landmark which were placed further from the wrist to add moreinformation about CTS [36, 45]. Additional landmarks could have been chosen for this thesisas well, which could have provided more information. This might be added in future work.

5.5 Data analysis

The result of the average parametric value in table 2 from the distal radioulnar joint to thehook of hamate, was for the FA-value on the same scale as the values obtained in Stein et al[36], but considerably lower than the total average value obtained in Barcelo et al. [39]. Thisdifference could be due to different conditions set on the FA-values, where as Barcelo et al.had tougher conditions with higher limits for the streamlines as previously mentioned. Theaverage MD-value in this thesis was on the same scale as the two studies. The FCSA-valuewas displayed in table 2 which shows a great difference, independent on what anatomicallevels that were evaluated. The parameter FCSA was not comparable previous studies, thusit has not been evaluated earlier. The error for the parameters FA, MD, AD, RD and FCSAwas large which can be determined considering the resulting average values for both groupswere within the other groups error bars in figure 11 and 13. It should also be mentioned thatthe standard deviation was larger for the patients than for the healthy volunteers. This couldbe related to the fact that carpal tunnel syndrome has different degrees of severity [46]. Astudy by Bulut et al. that focuses on evaluating different degrees of severity for CTS-patientto see how the FA-value changes with degree of severity. This study shows that DTI of the

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median nerve can provide useful information about the different degrees of severity of CTSdiagnosis using the FA parameter [38]. Another plausible explanation is that the patients hasmore difficulties to lay still and has moved more during the scan.

5.6 Threshold

The solution for limiting the partial volume effect by using a threshold for how many stream-lines at a minimum which had to pass through a voxel for it to be included in the result seemsto be a more long term consistent solution to the issue in comparison to hand made ROIs.The threshold value was set to five streamlines, because it resulted in the largest differencebetween the groups, which were evaluated on the graphs in figure 9 and 10 respectively. Inprevious studies the partial volume effect issue was limited through placing the ROI, until itis completely within the nerve [40, 36]. The method used in this thesis was more objectivecompared to hand made ROIs, which makes the method used in this thesis less reliable on thehuman consistently drawing the ROIs with the same bias through all slices. Another optionto limit the issue of the partial volume effect was to use a higher resolution.

5.7 Future work

There is still a few modification and implements that could further advance the work done inthis thesis, which could be:

• Using a higher resolution should be tried to limit the partial volume effect even further.

• Use different conditions for limiting for the streamlines of the tractography.

• It would be valuable to use new subjects, using the same tractorgraphy method to testthe robustness.

• Further evaluation of the FCSA-parameter to test its robustness.

• The parameters evaluated in this thesis could be used with the purpose of determiningthe different severity levels of CTS.

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6 CONCLUSIONThe segmentation method used in this thesis has many preferable approaches to limit andsolve problems as it is less user dependent when selecting manual ROIs through many slicesof the median nerve. By applying the method developed in this thesis a great significancefor the difference between patients and healthy volunteers was discovered when looking atthe new FCSA-parameter. The DTI-parameters FA, MD and AD also showed a significantdifference between patients and healthy volunteers aswell but not as significant. The resultfor the FA- and MD-value was primarily driven by the result of the AD-value, which possessesa great difference between the patients and healthy volunteers in comparison to the RD-valuewhich showed no significant difference in this study. The DTI-parameters FA, MD and ADshowed similar results to previous studies with same purpose which helped conclude that theseDTI-parameters could contribute with useful information when diagnosing CTS.

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ACKNOWLEDGEMENTSI would like to give my most sincere thanks to my supervisor Jimmy Lätt, who have madethis experience very enjoyable and educational for me. I would also like to say that I am verythankful to my co-supervisor Elena Aksyuk for the opportunity to be a part of this projectand using the data collected in her project.

A great thanks to all my close friends, family and girlfriend for encouraging me during thisexperience and through my education as a whole. I could not have made it this far withoutyou.

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