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Medical Engineering & Physics 35 (2013) 926– 936

Contents lists available at SciVerse ScienceDirect

Medical Engineering & Physics

j o ur nal homep age : www.elsev ier .com/ locate /medengphy

an subject-specific single-fibre electrically evoked auditory brainstem responseata be predicted from a model?

iaan K. Malherbe, Tania Hanekom ∗, Johan J. Hanekomioengineering, Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa

r t i c l e i n f o

rticle history:eceived 17 February 2012eceived in revised form 3 August 2012ccepted 2 September 2012

eywords:omputer modellingochlear modelubject specificuinea pig cochlea

a b s t r a c t

This article investigates whether prediction of subject-specific physiological data is viable through anindividualised computational model of a cochlear implant. Subject-specific predictions could be particu-larly useful to assess and quantify the peripheral factors that cause inter-subject variations in perception.The results of such model predictions could potentially be translated to clinical application through opti-misation of mapping parameters for individual users, since parameters that affect perception would bereflected in the model structure and parameters. A method to create a subject-specific computationalmodel of a guinea pig with a cochlear implant is presented. The objectives of the study are to developa method to construct subject-specific models considering translation of the method to in vivo humanmodels and to assess the effectiveness of subject-specific models to predict peripheral neural excitation

icro-computed tomographyochlear implantugmented reconstructioninite element method

on subject level. Neural excitation patterns predicted by the model are compared with single-fibre elec-trically evoked auditory brainstem responses obtained from the inferior colliculus in the same animal.Results indicate that the model can predict threshold frequency location, spatial spread of bipolar andtripolar stimulation and electrode thresholds relative to one another where electrodes are located indifferent cochlear structures. Absolute thresholds and spatial spread using monopolar stimulation arenot predicted accurately. Improvements to the model should address this.

. Introduction

Several models of the cochlea have been developed to investi-ate the effect that cochlear implant electrode design and electrodeosition have on nerve fibre excitation [1–6]. These models vary inetail and are based on the generic geometries of guinea pig anduman cochleae. Generic models predict general trends. Examplesf common trends that can be predicted through generic modelsnclude wider neural excitation profiles from electrodes located athe lateral scalar walls relative to excitation profiles from electrodeseighbouring the modiolus [2,4,7–9], spatial restriction of neuralxcitation with the use of ball, half-band or disk electrodes ratherhan banded electrodes [5], and lower threshold currents for stimu-ation with a monopolar (MP) stimulation protocol relative to thoseor bipolar (BP) or tripolar (TP) protocols [10–14].

However, it has been shown that the geometry of the cochlea

aries among subjects of the same species [15,16]. Data for a specificubject, e.g. inferior colliculus (IC) recordings in the auditory brain-tem, may thus not match predictions by a generic model because

∗ Corresponding author at: Bioengineering, Department of Electrical, Electronicnd Computer Engineering, University of Pretoria, Hatfield 0020, South Africa.el.: +27 12 420 2647; fax: +27 12 362 5000.

E-mail address: tania.hanekom@up.ac.za (T. Hanekom).

350-4533/$ – see front matter © 2012 IPEM. Published by Elsevier Ltd. All rights reservettp://dx.doi.org/10.1016/j.medengphy.2012.09.001

© 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

of variability in cochlear anatomy, electrode location, nerve fibresurvival patterns and insertion damage. It would be valuable to beable to predict perceptual outcomes of individual users, since theprocess of model development would provide insight into someof the factors that cause inter-subject variations in perception. Itcould also provide a tool to optimise mapping parameters based onmodel predictions of performance objectively. This study focuseson the effect that geometric details of a subject’s cochlea has on neu-ral excitation, but perceptual outcomes may also be influenced byother subjective factors such as duration of implant use and dura-tion of deafness [17–19], age of implantation [20] and aetiology[17].

The study tests the hypothesis that a subject-specific model canpredict subject-specific data. To achieve this, the first objective ofthis study is to construct a model that reflects individual traits of asubject’s cochlea and implanted electrode in contrast to a genericmodel. This is done by constructing a model that represents geo-metrically accurate bony cochlear structures as well as accurateelectrode locations for a specific subject. An animal model was usedto develop the modelling method, since single-fibre inferior collicu-lus measurements may be done to validate the modelling approach

on a neural level. A single animal subject was used to assess if sub-ject specific modelling is feasible. The second objective is to developthe modelling technique in a way that would be applicable to thedevelopment of human models where visualisation of the inner

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tructures of the cochlea is severely limited by the resolution ofonventional in vivo computed tomography imaging of the cochlea.

To create models that accommodate subject-specific variations,he detailed geometry of the subject’s cochlea has to be known. Con-uctances of the cochlear structures depend on cochlear geometrynd have a direct influence on the current spread inside the cochlea.his in turn influences neural excitation patterns, which dictate theerceptual outcome that can be achieved through electrical stim-lation. Techniques that may be used to obtain the geometry of aochlea include dissection [21], histological sectioning [22], videouoroscopy [23,24], orthogonal-plane fluorescence optical section-

ng [25], conventional radiography [26,27], rotational tomography28], magnetic resonant imaging [29–31], computed tomographyCT) [32] and micro-computed tomography (�-CT) [33].

A CT-based method to obtain geometric parameters to cre-te subject-specific models was preferred since CT is routinelysed to assess electrode placement in cochlear implant users. Itlso provides good contrast for bony structures such as the boneurrounding cochlear channels. The approach was to start withigh-resolution images to construct an initial model so that theypothesis that a subject-specific model could predict subject-pecific data may be validated. �-CT was selected for constructionf the animal model because it produces relatively high-resolutionmages and has been used successfully to image cochleae [34,35].ince the method to construct the animal model is based on aT imaging technique, translation of the method to create humanodels should be viable. Translation of the method using standard

linical CT is required because �-CT cannot be used for in vivo imag-ng, as it may require sample dissection and exposes the sample toigh levels of radiation for prolonged periods of time.

. Materials and methods

To enable prediction of neural excitation data, a volume con-uction model of a specific subject’s cochlea was constructed usinghe finite element (FE) method. Firstly, anatomical data with ade-uate resolution was required. Secondly, a spatial description ofhese data had to be developed. Thirdly, an FE model had to beonstructed. Finally, to verify the model, neural responses from apecific guinea pig subject had to be obtained and compared toeural responses predicted by the model. Neural responses wereeasured in the form of IC recordings in the auditory brainstem.

.1. Origination of anatomical data

To construct a cochlear model, the positions of the internalochlear structures, especially the cochlear nerve fibres, have to beetermined accurately. This is because these structures determinehe current spread inside the cochlea during electrical stimula-ion, which in turn determines the excitation profile of the cochlearerve fibres.

Guinea pig cochlea �-CT data obtained from Bonham et al.ere used.1 These were obtained from the left implanted cochlea

f a guinea pig. A custom electrode array was implanted via aochleostomy made in the lateral cochlear wall where the hook andasal turn meet. The surgical procedure is described by Snyder et al.11]. The cochlea was dissected and placed in phosphate-buffered

aline. No fixative was used. The longest dimension of the sampleas in the order of 7 mm. A �CT402 scanner was used to image

he sample. This is a cone-beam scanner with reconstruction based

1 Data obtained from Ben H. Bonham and Russell Snyder – Epstein Laboratory,epartment of Otolaryngology – HNS, Box 0526, U490, University of California, Sanrancisco, CA 94143-0526, United States.2 Scanco Medical AG, http://www.scanco.ch (date last viewed 12/11/2011).

ng & Physics 35 (2013) 926– 936 927

on the modified Feldkamp algorithm. The acquisition was madeat 70 kV and 114 mA with a field of view of 20.5 mm and recon-structed with a 20 �m isotropic voxel size. The output of the scanwas a 1024 × 1024 × 244 matrix of raw voxels.

Because of the limited resolution of the �-CT data and arte-facts produced by the metal electrode contacts (Fig. 1a), automatedsegmentation programs could not be used to obtain the three-dimensional geometry of the cochlea. A simple segmentationmethod is described here that involves manual segmentation ofonly three �-CT sections through the cochlea and uses interpola-tion techniques to generate the remainder of the geometry. Thistechnique may be used in future to segment and model humancochleae in vivo, as high-resolution �-CT scans require dissectionand preparation of cochleae beforehand.

The raw �-CT matrix was visualised using ImageJ.3 TheImageJ volume viewer plug-in allows planar sections of thethree-dimensional volume to be viewed at any angle (tri-linearinterpolation was used). To simplify analysis, the matrix was orien-tated so that the modiolus of the cochlea lay parallel to the z-axis.This was done by manually rotating the matrix using the volumeviewer plug-in until good visual alignment was found between themodiolus and z-axis. The greater number of turns of the guinea pigcochlea made the centre line of the modiolus relatively easy to esti-mate by visual inspection compared to a human cochlea with fewerturns. The coordinate system was also adjusted so that the centreline of the modiolus intersected the origin.

Since a manual technique was used to segment the cochlearstructures, the visibility of the bony boundaries of the cochlearstructures in the raw CT data (Fig. 1a) was visually enhanced byapplying a colour map using ImageJ (Fig. 1b).

Although the �-CT scanner used in this study has lower resolu-tion than some of the newer �-CT scanners on the market, the lowerresolution was deemed sufficient in the context of the study, wherethe method eventually needs to be translated to human models.Thus, to add fine geometrical details to the model, an augmentedreconstruction method was used where �-CT data were appendedwith data obtained from a photomicrograph of a mid-modiolar his-tologic section of another healthy guinea pig’s cochlea. This alsoaided estimation of the boundaries of cochlear structures whereartefacts caused by metallic electrodes and wires obscured some ofthe geometry (see Fig. 1c).

The ImageJ volume viewer was used to obtain a single section ofthe �-CT data at an angular position that provided the best matchwith the photomicrograph. The photomicrograph transversal viewof the cochlear canals was then divided into separate images, eachcontaining an individual cochlear canal (Fig. 1d). Each image wasthen manually scaled vertically and horizontally and rotated untilthe best visual match was found between the bony boundariesof the cochlear canals in the �-CT section and the photomicro-graph (Fig. 1e). Scaling was done separately for each cochlear canalto minimise the mismatch between cochleae where shape varia-tions might lead to canals being misaligned. Using this method, thegeometry could be estimated even in cases where the �-CT resolu-tion was insufficient or where metal artefacts obscured details.

The positions of Reissner’s membrane, the basilar membrane,organ of Corti and the spiral ligament were obtained mainly fromthe photomicrograph. The osseous spiral lamina is clearly visi-ble on the �-CT images. Since the cochlear nerve fibres protrude

through the osseous spiral lamina, the origins of the fibres couldbe accurately estimated directly from �-CT data. The positions anddimensions of the cochlear structures obtained were used to con-struct a spatial framework for the three-dimensional model.

3 Wayne Rasband, NIH, public domain software, http://rsbweb.nih.gov/ij/ (datelast viewed 12/11/2011).

928 T.K. Malherbe et al. / Medical Engineering & Physics 35 (2013) 926– 936

Fig. 1. Augmented reconstruction method. (a) The original �-CT section. (b) Colour map and enhance Contrast filter applied in ImageJ to enhance clarity of boundariesbetween structures further. (c) Each cochlear canal was isolated and (d) a photomicrograph of the same canal was obtained. (e) The photomicrograph of the canal was scaledand rotated to fit the �-CT canal. (f) The combined image was sectioned into nine cochlear structures. (g) Sectioned sections were placed in 3D space and connected withinterpolated spirals. One spiral is shown here. Each point on the spiral (A) is characterised using cylindrical coordinates (�, ϕ and z). (h) Three-dimensional rendering of all thec eratedo sion o

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ochlear structures with the bone casing removed. (i) Three-dimensional mesh genf the references to color in this figure legend, the reader is referred to the web ver

.2. Construction of model spatial framework

To construct a spatial framework for the model, the mid-odiolar �-CT section with the appended photomicrosection was

from the spatial framework used in the FEM software package. (For interpretationf the article.)

divided into nine cochlear structures. These include the scalavestibuli, scala tympani, scala media, stria vascularis, spiral lig-ament, Reissner’s membrane, basilar membrane, organ of Corti,nerve and osseous labyrinth (Fig. 1f).

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The boundary of each structure was defined by identifying spe-ific landmark points on the boundaries of the cochlear structuresn the mid-modiolar photomicrograph (Fig. 1f). Between 4 and 13oints on the boundary of each structure were chosen as landmarks.his resulted in the boundary of each structure being defined by

piecewise linear curve. Custom Matlab code displayed the pho-omicrograph and asked the user to pick the landmarks using a

ouse. The user had to ensure that the corresponding landmarksf different canals were roughly at the same location on each canal.he code automatically increased the thickness of the basilar mem-rane from 4 �m to 30 �m and Reissner’s membrane from 1.7 �mo 20 �m to ease generation of the mesh required by the finite ele-

ent model. The increased thicknesses were compensated for bydjusting the resistivities of the structures [5,36]. This is expandedn in another section.

To simplify the modelling process, only one mid-modiolar CTection was used. Because of the spiralling nature of the cochlea,he rest of the geometry was generated by interpolating the datarom this section in a spiralling fashion. It was however found thathis single section was insufficient to describe the geometry accu-ately in the basal region. This is because the starting point of thease was at a different angle than the mid-modiolar section. Theid-modiolar section was at an angle where it included the part

f the cochlea just before the helicotrema. The base is not locatedt the same angle because the cochlea has three and three-quarterurns. The basal part also tapers sharply before the hook area. Two

ore CT sections of the basal part of the cochlea were obtainednd segmented to describe the geometry accurately. The first sec-ion was located just before the starting point of the hook area. Theecond section was made 36◦ from the first section, measured inn anti-clockwise direction around the modiolus. Fig. 1g indicatesow the sectioned sections are positioned in grey.

The three-dimensional cochlear geometry was generated byoining the corresponding landmarks of the structures and canals

ith spirals. These spirals were generated using the middle of theodiolus as centre line (assuming that the middle of the modiolus

s perfectly straight). The discrete points on the spirals are describedn cylindrical coordinates. � is the distance from the modiolus cen-re line, ϕ is the angle of the point measured in an anticlockwiseirection and z is the height of the point. Fig. 1g illustrates a sin-le spiral generated through a point belonging to the boundaryetween the scala vestibuli and the spiral ligament. The coordinatesf an arbitrary point on the spiral (A) are shown.

To generate a spiral, the � and z parameters were interpolatedetween the fixed points on the sectioned sections using third orderubic spline interpolation (interp1 function in Matlab). Values werebtained at angles (ϕ) spaced 2.5◦ apart for the full length of theochlea. This resulted in a spiral being defined by 505 points. Thisame interpolation technique was followed to generate spirals forll the identified landmarks.

The dimensional accuracy of the resulting cochlear structuresas verified by comparing the geometries of some of these struc-

ures to data obtained from other studies. The cross-sectional areasf the scala tympani, scala media and scala vestibuli were obtainedrom a study by Thorne et al. [31]. The basilar membrane widthata were obtained by Wada et al. [37]. The cross-sectional areaata of the organ of Corti were obtained from a study by Fernandez21]. It was found that some areas of the modelled geometries didot match the geometries of the cochlear structures found in liter-ture. This may be the case because of inter-subject variations, butecause histologic data from the cochlea that was modelled wereot available, the model geometries were adjusted to match the

ata from literature. This was done by incrementally adjusting theositions of the picked landmarks until good agreement was foundith the data from literature. After adjustments, the cross-sectional

reas of the scala tympani, scala media and scala vestibule, as well

ng & Physics 35 (2013) 926– 936 929

as the basilar membrane width, closely matched the histologicaldata. The modelled organ of Corti has a cross-sectional area thatis on average 2.5 times larger than histological data. The modelledsize of the organ of Corti was estimated from the photomicrographand increasing the cross-sectional area to match the Fernandez datawould cause deformation of other structures. The discrepancy maybe partly because different fixatives were used and also becauseimaging techniques were less accurate in 1952 when that studywas published. Due to its small size relative to the other struc-tures, the effect that the larger modelled size of the organ of Cortimight have on the results was not quantified, but was assumed tobe negligible.

The cochlear nerve stem was added by generating spirals arounda tapering cylinder that fits between the existing nerve stem spirals.The bone around the cochlea was modelled as a cylinder around allcochlear structures. In reality the guinea pig cochlea protrudes intoan air-filled bulla with only a bone layer around the cochlea. Themodel in this study encases the complete cochlea in bone, similarto a human cochlea. This was done in part to simplify the modellingprocess and also since a study by Briaire and Frijns [36] found thata volume conduction model with an air-filled bulla led to almostidentical neural responses compared to a cochlea without the bulla.Fig. 1h shows a three-dimensional rendering of the final modelframework with the bone casing removed to aid visualisation.

2.3. Modelling of electrode array

A custom electrode array, not resembling a commercial array,consisting of a silastic carrier and 12 ball electrodes each witha diameter of 220 �m, was implanted into the subject [38]. Themetallic parts of the electrode array, such as the electrode con-tacts and connecting wires, have the highest exposure on the �-CTscan image because metal has a higher X-ray absorption rate thanthe surrounding cochlear tissue. To estimate the position of theelectrode array, the voxels with the highest numerical values wereextracted. These voxels formed groups that are roughly spherical atthe locations of the electrodes. Each group had a width of about 12voxels (240 �m). The groups were not perfect spheres and slightlylarger than the actual electrodes due to the artefact caused bythe metal. The electrodes were modelled as perfect spheres with220 �m diameters at the centres of these groups resulting in themodelled electrodes being positioned accurately within 1 voxel(20 �m) of the actual electrodes. The average distance between theelectrode centre points is 0.5 mm, with larger spacings of 1 mmbetween electrodes 4 and 5 and electrodes 8 and 9.

The electrode wires were not modelled, as they are encased ina non-conductive silastic electrode carrier and do not contributeto low-frequency current flow in the model. The effect of carriercapacitances between wires in the carrier may come into play whenusing short stimulus pulses but was not investigated in this study.The non-conductive silastic electrode carrier was also not modelledbecause it is not distinguishable on the �-CT image. The omission ofthe carrier is a simplification of the electrode array and it should beadded in future models, as it influences current flow in the cochlea.

Because of the interpolation of the spirals that define the bound-aries of the cochlear ducts, it can be assumed that the modelledgeometry will not match the actual cochlear geometry perfectly.This is especially true at angles far from where the image sec-tions were obtained. To validate that the electrode contacts werein the correct positions relative to the cochlear neurons and othercochlear structures, sections were made through the model atthe locations of the electrode contacts and these were visually

compared to �-CT image sections through the modiolus at corre-sponding cochlear angles. It was found that some of the modelledelectrodes did not match their positions relative to the walls ofthe cochlear structures adequately. Their modelled positions were

9 ineering & Physics 35 (2013) 926– 936

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Table 1Conductivities of cochlear structures used in model [3]. Note that the conductivitiesof Reissner’s membrane and the basilar membrane were scaled up relative to theirincreased model thicknesses.

Cochlear structure Conductivity [S/m]

Scala tympani 1.43Scala vestibuli 1.43Scala media 1.67Bone 0.156Stria vascularis 0.0053Spiral ligament 1.67Reissner’s membrane 0.00115297Basilar membrane 0.09375Organ of Corti 0.012

30 T.K. Malherbe et al. / Medical Eng

hen adjusted to match their positions relative to the cochlear wallshown in the �-CT images. The cochlear walls were chosen as ref-rence points firstly because they are clearly distinguishable inhe �-CT data and secondly since the neurons were located nearhe inner cochlear wall and neural excitability is influenced byeuron–electrode separation. Appendix A describes the procedurehat was used to adjust the electrode positions.

To reduce geometric errors introduced by using a single mid-odiolar section through the cochlea to base the model geometry

n, more mid-modiolar sections at different cochlear angles may besed. However, this will increase modelling complexity and effort.he number of sections required to achieve optimal accuracy needsurther investigation.

.4. Generation of three-dimensional mesh

To perform an FE analysis on the model, the frameworkeveloped in the previous section had to be converted into a three-imensional volume and meshed into smaller tetrahedra.

Because of the complexity of the geometry, meshing was notone using the FE software package, but was performed using theeshing program GMSH [39]. To model the geometry in GMSH,

he generated spirals and electrode spheres were reconstructedsing GMSH code. The size of the mesh was adjusted to be smallerear the apex to maintain the same geometric resolution, sincehe geometry narrows from the base to the apex. The full unstruc-ured mesh consisted of 88 210 linear tetrahedral elements. The

esh was saved in Nastran4 format. Fig. 1i shows the resultinghree-dimensional mesh.

The mesh was refined to approximately 800 000 elements andolved (see next section) to investigate the effect of mesh resolu-ion on the accuracy of potential field predictions. The resultingotential fields had a maximum error of 0.33% when compared tohe coarser mesh. The refined mesh, however, took 5.6 h to solveompared to 45 s for the coarser mesh. The coarser mesh was thussed in all simulations. All simulations were performed on a com-uter with a 2.6 GHz AMD 64 processor with 8 cores and 8.2 GB ofAM.

.5. Predicting current distribution inside the cochlea

.5.1. Comsol analysisThe three-dimensional mesh was imported into the finite ele-

ent modelling software package Comsol 3.5a5 to determine theurrent distribution inside the cochlea. The model was set up inomsol using the conductive media DC toolbox in the AC/DC mod-le. Next, the material properties of the cochlear structures wereefined. Table 1 lists the conductivities of the cochlear structuressed in the model [3]. To avoid mesh elements that are deformed,he thicknesses of Reissner’s membrane and the basilar membraneere increased from their actual values by factors of 11.8 and 7.5

espectively. The resistivities were scaled by the same values toompensate for this (adjusted values are shown in Table 1) [5,36].

Comsol was set up to use the PARDISO direct6 solver. Becausef the large mesh size, the out-of-core memory option of the solver

as selected, which allows it to use hard disk memory when all the

vailable primary memory is used.Potential distributions as a result of stimulation with MP, BP and

P electrode protocols were investigated. This was done because

4 MSC Software, http://www.mscsoftware.com (date last viewed 12/11/2011).5 COMSOL Multiphysics modeling and simulation, http://www.comsol.com (date

ast viewed 12/11/2011).6 The PARDISO version developed by Olaf Schenk and collaborators is used by

OMSOL, http://www.pardiso-project.org/ (date last viewed 12/11/2011).

Nerve tissue 0.3Metal electrodes 1 × 107

measured data for these protocols were available from the guineapig subject. The outer boundaries of the bone cylinder were setto electric ground for MP stimulation to simulate a return elec-trode that is far away from the stimulating electrodes and to electricshielding for BP and TP stimulation protocols where no MP returnelectrode is present. The electric shielding condition was used tolimit the excitation current to the cochlea and approximated theair-filled bulla of the guinea pig even though it was not modelledexplicitly in the geometry.

Current was then applied to various electrodes and the solverwas run to obtain the potential distribution inside the cochlea. Theelectrodes to which current were applied varied for different stim-ulation protocols. Stimulation protocols using specific electrodecontacts were named using the electrode number, e.g. MP 6 refersto monopolar stimulation on electrode 6 (counted from the apex)and BP 6-7 refers to bipolar stimulation employing electrodes 6 and7.

2.5.2. Location of data pointsThe potentials at the locations of the nerve fibre nodes were

extracted from the potential distribution solution, which was calcu-lated over the complete model space. The nodes correspond to thenodes of Ranvier of a computational model implementation of anauditory nerve fibre, i.e. the sites where current will enter the mod-elled nerve fibre to evoke a response. The first node was positionedat the dendritic part of the three-dimensional nerve fibre region (atthe tip of the bony shelf) and the rest of the nodes were positionedto fall within the modelled three-dimensional nerve fibre regionwhile maintaining the internodal lengths specified in the neuralmodel described below. Complete nerve fibres, each consisting ofa dendrite, soma and axon, were modelled. A total of 505 nervefibres were modelled to reduce the computation time of the neuralmodel. The fibres extended radially from the modiolus spaced 2.5◦

apart and covered the entire length of the cochlea from the base tothe apex.

2.6. Neural model

The Generalised Schwarz–Eikhof–Frijns (GSEF) [4] model wasused to predict the behaviour of the cochlear nerve fibres. Themodel approximates the behaviour of a guinea pig high sponta-neous rate fibre. The model has 16 nodal compartments to whichthe potentials calculated in the previous section were applied.

The amplitude of the stimulus was varied iteratively to deter-mine the threshold at which a nerve fibre would fire. Thresholdswere determined for the 505 modelled nerve fibres to predict

spatial excitation profiles for different stimulation protocols. Acathodic-first biphasic current pulse with a 0.2 ms phase durationand 0.02 ms interphase gap was used for all the simulations. Thispulse shape was chosen to be the same as the pulse shape used

T.K. Malherbe et al. / Medical Engineeri

Fig. 2. Estimation of the width of the spatial tuning curve. The stimulus current isgiven in dB with a reference of 100 �A. The width is determined at 2 dB above thethreshold current. The width was measured as a range between the minimum andmo

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aximum widths to account for the low frequency resolution of the IC data. The barn the right indicates the amplitude of the normalised response.

o obtain the IC data (IC data were used for verification of theodel and are described in Section 2.7). Similar pulse shapes have

lso been used by other research groups for stimulating guinea pigochleae [3].

.7. IC data

Neural responses in the form of IC recordings were obtainedrom the specific live guinea pig subject and were used to verify theesponses predicted by the model. Bonham and colleagues obtainedhese data in a similar way as described in Snyder et al. [12]. A briefverview of the method is provided below.

A 16-channel recording electrode array was inserted into theonotopic section of the central nucleus of the IC of a live guineaig with normal hearing. Before cochlear implantation was per-ormed, pure audio tones with varying amplitudes were presentedo the guinea pig via an audio speaker. The resulting brainstemesponse was recorded via the IC electrodes and subsequently usedo determine the characteristic frequencies at which the recordinglectrodes were located in the IC. These characteristic frequenciesere found to cover the range from 2.8 kHz to 29.3 kHz. The guineaig was then deafened and implanted with a custom cochlear

mplant. The spike rate responses to different stimulation proto-ols of the cochlear implant were recorded in the central nucleusf the IC and mapped as a function of insertion depth and currentevel to the characteristic frequencies. Such a response is called

spatial tuning curve (STC). The spike rate was recorded over a–30 ms interval after stimulation. Spontaneous neural activity wasstimated as the average spike rate of the period before stimulation.he responses were normalised to the maximum spike rate.

The threshold current of a recording site is the lowest stimulusurrent that elicits a neural response. Because of the interferencef spontaneous neural activity and the low spectral resolution ofhe IC recordings, this threshold can be difficult to determine. Toimplify this process, the threshold was defined as the minimumtimulus current that induces a normalised response of 0.2.

The width of the STC was determined at a stimulus level 2 dBbove this minimum threshold current. The width is the frequencyange over which the normalised response exceeds the threshold.he width was measured at 2 dB to allow the comparison of widthsetween stimulation protocols. When determining the width at

igher current levels, MP stimulation caused spatially wider exci-ation than the measurable frequency range between 2.8 kHz and9.3 kHz. The widths are given as frequency ranges (between min.

ng & Physics 35 (2013) 926– 936 931

width and max. width in Fig. 2) to account for the low-frequencyresolution of the IC data.

The frequency resolution of the IC data is approximately 0.2octaves per site for most of the 16 IC recording sites. Each responseobtained is the response of a small population of neurons withsimilar, but not identical, characteristic frequencies. The STC widthwas characterised by two values describing the spread of excitationconsistent with the measured response described in Fig. 2.

2.8. Threshold frequency location

The minimum stimulation current at which any neuron firesin a stimulation protocol is called the threshold current of thatprotocol. The location along the basilar membrane at which thatthreshold occurs is mapped to frequency using Greenwood’s equa-tion, F = A(10ax − k) (with generic guinea pig parameters fromGreenwood A = 0.35, a = 2.1/18.5 and k = 0.85) [40] and called thethreshold frequency. The threshold frequencies of the model shouldideally match the characteristic frequencies associated with eachelectrode in the IC data.

Because the original cochlea from which the model was con-structed was damaged near the apex, the position of the mostapical part of the model was estimated. This had an influence onthe measured length of the basilar membrane used in Greenwood’sequation. To compensate for this, an offset was added to the mea-sured length of the basilar membrane. This offset was varied until agood fit occurred between the predicted threshold frequencies andthe characteristic frequencies associated with each electrode in theEABR data. An offset of 1.87 mm was subtracted from the distancemeasured from the modelled apex to facilitate a good fit.

3. Results

Recorded IC data were compared to the output data of themodel to assess the subject-specific model’s predictive perfor-mance. Responses were predicted for available IC data only.

3.1. Threshold frequency location

The threshold frequencies obtained from the model are com-pared to the characteristic frequencies of the IC data in Table 2. Theratios between the IC frequencies and the model frequencies areshown. The mean and standard deviation of each protocol are alsoshown. The mean ratios are close to 1, suggesting that the modelaccurately predicts the threshold frequency as a result of a specificstimulus. This is to be expected, as the offset that was added tothe basilar length to account for the damaged apex of the cochleawas chosen to minimise the error between the measured and pre-dicted threshold frequencies. The slight deviation from unity thatoccurs may be due to the low-frequency resolution of the IC dataand inaccuracies occurring when applying Greenwood’s equationto this specific cochlea.

3.2. Spatial tuning curve width

The width of the spatial tuning curve at a specific stimulus inten-sity provides an indication of the current spread in the cochlea. Thewidths of the threshold curves obtained from the model were com-pared to the spatial tuning curves obtained from the IC data. Thewidths of excitation of the various stimulation protocols are listedin Table 3. Errors are also indicated and were calculated by dividingthe difference between the minimum IC width and the predicted

the predicted width fell within the range of the IC width.The errors suggest that about half of the predicted BP and most

of the TP STC widths are predicted to be within the width ranges

932 T.K. Malherbe et al. / Medical Engineering & Physics 35 (2013) 926– 936

Table 2Comparison of threshold frequencies between modelled and EABR data.

Protocol Distance fromapex [mm]

Modelfrequency[kHz]

EABRfrequency[kHz]

Ratio(EABR/model)

MonopolarMP 1 10.88 5.72 7.30 1.28MP 2 11.52 6.81 7.30 1.07MP 3 12.01 7.78 7.30 0.94MP 4 12.45 8.76 8.00 0.91MP 5 13.36 11.20 8.00 0.71MP 6 13.83 12.69 8.00 0.63MP 7 14.36 14.62 14.70 1.01MP 10 16.10 23.20 29.30 1.26MP 11 16.37 24.95 29.30 1.17

Mean ± S.D. 1.00 ± 0.23

BipolarBP 1-2 12.59 5.46 4.80 0.88BP 2-3 13.57 7.15 6.20 0.87BP 3-4 14.20 8.48 7.30 0.86BP 4-5 15.12 10.86 12.30 1.13BP 5-6 15.42 11.78 12.30 1.04BP 6-7 16.28 14.83 14.70 0.99BP 9-10 17.58 20.93 22.60 1.08BP 10-11 17.74 21.83 24.70 1.13BP 11-12 18.12 24.17 24.70 1.02

Mean ± S.D. 1.00 ± 0.11

TripolarTP 1-2-3 13.43 6.65 – –TP 2-3-4 13.88 7.51 – –TP 3-4-5 14.72 9.76 12.30 1.26TP 4-5-6 15.32 11.47 12.30 1.07TP 5-6-7 15.47 11.93 12.30 1.03TP 10-11-12 18.99 23.19 24.70 1.07

Mean ± S.D. 1.11 ± 0.10

Table 3Comparison of predicted widths to EABR widths. BP and TP widths closely matchEABR data whereas predicted MP widths are narrower than EABR widths.

Protocol EABR widthrange [kHz]

Predictedwidth [kHz]

Deviation [%]

MonopolarMP 1 11.2–14.6 1.75 84.33MP 2 7.5–11.9 1.85 75.34MP 3 24.5–26.5 1.67 93.20MP 4 11.2–14.6 1.08 90.35MP 5 11.2–14.6 0.86 92.36MP 6 11.2–14.6 1.22 89.09MP 7 12.6–17.9 1.46 88.39MP 10 6.7–8.6 2.47 63.19MP 11 4.6–6.7 2.26 50.93

BipolarBP 1-2 2.5–5.2 2.63 0.00BP 2-3 2.5–5.2 2.79 0.00BP 3-4 – 1.81 –BP 4-5 1.4–4.5 2.47 0.00BP 5-6 0–4.3 2.16 0.00BP 6-7 3.7–7 2.73 26.18BP 9-10 6.7–8.6 3.13 53.33BP 10-11 6.7–8.6 8.78 2.07BP 11-12 11.9–13.3 3.22 72.91

TripolarTP 1-2-3 – 1.47 –TP 2-3-4 – 1.98 –TP 3-4-5 1.9–5.2 2.98 0.00TP 4-5-6 0–4.3 1.63 0.00TP 5-6-7 2.4–5.6 2.63 0.00TP 10-11-12 6.7–8.6 6.01 10.33

Table 4Comparison of thresholds of stimulation protocols. MP stimulation has the lowestthresholds on average compared to BP and TP stimulation.

Protocol Predictedthreshold [dBre 1 �A]

EABR threshold[dB re 1 �A]

Difference(predicted/EABR)[dB]

Monopolar 44.56 ± 3.4709 31.23 ± 3.24 13.1 ± 3.5Bipolar 50.45 ± 8.07 45.33 ± 2.69 7.28 ± 5.2Tripolar 48.23 ± 8.58 47.25 ± 2.50 6.54 ± 5.2

of the IC data. MP widths obtained with the model are narrowerthan the IC widths. Several factors may contribute to this and willbe discussed later.

3.3. Prediction of neural thresholds

The thresholds of the predicted and IC responses are comparedin Fig. 3. For MP stimulation protocol (Fig. 3a and b) there is anotable overall offset in the predicted data relative to the measureddata, signifying the omission of a global modelling parameter. Theclosest prediction is for electrode 5 where the prediction is 7 dBhigher than the measured data. For the other electrodes, this offsetvaries between 11 and 18 dB with a trend for threshold estimatesto improve towards the apex.

For BP stimulation protocol (Fig. 3c and d) predicted data areaccurate within 5 dB in the central part of the array (electrode pairs2-3 to 6-7), suggesting that local characteristics that determinecurrent spread are adequately represented in the model. This isalso reflected in relatively good predictions for electrode sets 1-2-3to 3-4-5 for TP stimulation protocol (Fig. 3e and f) and minimumdeviations for electrodes 4–7 for MP stimulation protocol.

For all three protocols there is a trend for the predicted thresh-olds to decrease from electrode 1 (located apically in the cochlea)to electrode 5 and then increase towards electrode 12 (locatedbasally). A consistent trend between different stimulation protocolsis expected, as the locations of the electrodes involved are fixed. Astudy by Snyder et al. [12] found a trend for thresholds to increasebasally for MP, BP and TP stimulation, supporting the predictedresults for electrodes 1–5. Although the measured IC thresholds forMP stimulation is consistent with this observation, measured ICthresholds do not follow clear trends that are common across allelectrodes in all three stimulation modes. There is a trend for bothpredicted and IC thresholds of electrodes 6–12 to increase basallyover all stimulation protocols, but the thresholds for more apicalelectrodes show no such general trend.

A summary of the mean thresholds within a protocol and ratiosbetween the predicted and IC thresholds are presented in Table 4.

Table 4 shows that the ratios between the averaged measuredand predicted BP and TP threshold currents are comparable. Theaveraged MP threshold ratio shows that the model overestimatesMP threshold currents. These data also show that the mean thresh-olds of the BP and TP protocols are higher than the mean thresholdof the MP protocol. This is true for both the predicted and IC dataand is consistent with literature [10–14,41–43].

4. Discussion

4.1. Modelling approach

Previous models vary in geometrical accuracy and are all genericin nature, while the method presented here describes the con-struction of a subject-specific cochlear model with high geometric

accuracy. The present study describes a method to construct amodel with nine cochlear structures from images obtained witha 20 �m voxel �-CT scanner. This resolution was adequate toprovide subject-specific geometric parameters. The positions of

T.K. Malherbe et al. / Medical Engineeri

Fig. 3. (a) Predicted model and IC thresholds (in dB re 1 �A) of each electrode usingMP, (c) BP and (e) TP stimulation. The difference between the thresholds predictedby the model and measured IC thresholds for (b) MP, (d) BP and (f) TP stimulation.Electrode 1 is located more apically with electrode 12 located near the base.

ng & Physics 35 (2013) 926– 936 933

fine structures were estimated by appending the �-CT data withphotomicrographs.

Other methods used to describe detailed cochlear geometrydivide the entire cochlea into sections (up to 720 sections). Thecochlear canals are then segmented on each of these sections[34,44]. The method described in the present study involves thesegmentation of only three sections. The rest of the cochlear geom-etry is then generated by interpolating spirals between thesesections around the modiolus. This reduces the time and complex-ity required to construct a model with a high degree of geometricdetail. This also renders this method suitable for translation to mod-elling of human cochlea in vivo where only a few low-resolution CTsections that are free of distortions may be obtained.

A number of factors affect the accuracy of the geometric param-eters that are used to construct the model. Histological sectionsof the imaged subject were not available in the present studyand a photomicrograph of another subject’s cochlea was used.Although the geometric accuracy of the model may be improvedby using photomicrographs from the cochlea from which the �-CT data were obtained, using a template photomicrograph fromanother subject contributes to the translational process of the ani-mal study to human studies. The geometric description may also beimproved by modelling the osseous labyrinth around the cochleamore accurately. The present model encases the entire cochlea inbone, whereas an actual guinea pig cochlea is surrounded by athin layer of bone inside an air-filled bulla. In addition, the hookregion may be included in the model to improve accuracy, as mostimplanted electrodes are usually located in the basal area, wherethe hook region may influence local electric fields. Cochlear damagesuch as the array perforating the upper wall of the scala tympani,which could influence the local electric field, may be incorporatedin future models to improve the accuracy of results.

4.2. Model performance

Model performance varies depending on electrode location andstimulation protocol. In general, results indicate that the modelis capable of predicting threshold frequency location and spatialspread, as well as electrode thresholds relative to each other withinBP and TP stimulation protocols. However, absolute thresholds andthe spatial spread using MP stimulation are not predicted accu-rately.

Electrode location affects the predicted thresholds in two ways:(i) through the distance between the electrode and the targetneurons (as established by generic models) and (ii) through theconductive properties of the structure in which the electrode islocated (a subject-specific model parameter). In the present model,all cochlear structures were modelled intact and in cases wherethe electrode was located outside the scala tympani, it was sim-ply placed inside the structure that corresponds to the electrodelocation on the CT scan and photomicrograph. Damage that mighthave been caused to cochlear structures and neurons during inser-tion and that may affect neural excitation is not explicitly modelled.This is an obvious simplification and should be addressed in futuremodels. An example is electrode 4 that is located in the scala media(Fig. A1) where it is assumed that the electrode array perforatedthe upper wall of the scala tympani during insertion. Perforationmay have caused a change in resistance of the structures near thedamaged area. Also, potassium-rich endolymph, which is neuro-toxic [45], may have leaked from the scala media, damaging thenerve fibres. It is also likely that damage was caused to the organ ofCorti and the neurons near electrode 4. Neural damage could cause

the measured response to differ from the response predicted by themodel, since it was not incorporated in the model.

Relatively good performance is obtained with threshold pre-dictions for stimulation protocols that involve local current

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34 T.K. Malherbe et al. / Medical Eng

istributions, i.e. BP and TP stimulation protocols. This suggestshat the model provides an adequate description of the geometrynd thus impedance characteristics of the implanted cochlea in theicinity of the electrode contacts. Predictions of excitation widthor BP and TP electrode sets support this observation for the apicalart of the model, since predicted widths in this area are accurate.owever, inaccurate excitation width predictions in the basal partf the model suggest that the model structure might be inadequatet this location.

On a local scale BP electrode pairs 4-5, 5-6 and 6-7 have theowest thresholds for both IC and predicted data. This is expected,s they are located closer to the neurons relative to the other elec-rode pairs. IC thresholds of BP and TP electrode sets containinglectrode 4 are mostly higher than the modelled thresholds. Thisay be caused by neuron damage that could have occurred near

hese electrodes during insertion and that was not incorporatedn the model. However, the threshold predicted for MP 4 is higherhan the IC threshold, consistent with an overall overestimation of

P threshold currents. This suggests that the effect of local neuronamage on thresholds is less pronounced for MP stimulation whereurrent spread is wider than for BP and TP stimulation with localurrent distributions.

IC thresholds of BP electrode pairs containing electrodes 10 and1 are lower than the predicted thresholds. This may be causedy the location of these electrodes inside the spiral ligament inhe model (Fig. A1), which is probably not a true reflection of theocation of the electrode in the animal. The spiral ligament couldave been pushed aside by the electrode carrier rather than sur-ounding the carrier and electrodes. Because the spiral ligamentas a lower resistance than perilymph, the stimulation current ishunted between the electrodes in the model instead of spreadingo the target neurons.

Poorer performance is achieved for stimulation protocols whereider current distributions are involved, i.e. MP stimulation pro-

ocol. MP IC thresholds increase towards the base. The fact thathis is not reflected in the predicted data could be attributed to aumber of reasons. Firstly, the MP return electrode was not mod-lled as a single electrode at a specific location but rather by settinghe external cylindrical boundary of the entire bone volume toround. Secondly, the cochlea was modelled inside a cylindricalone volume with edges located at an infinite distance instead ofeing surrounded by a thin bone casing protruding into an air-filledpace. Thirdly, the high-resistivity electrode carrier that displaces

significant portion of the cochlear fluid was not included in theodel. The carrier has been shown to affect current paths inside the

ochlea because of its insulating properties. In combination, thesehree factors are expected to have a significant influence on thehreshold predictions for MP stimulation protocol mainly becausehey affect the current paths on an overall scale. For example,f the apical electrodes are considered and the MP return elec-rode is located towards the base of the cochlea, current would beirected through the target neurons, thereby decreasing the thresh-ld prediction instead of being shunted to the external boundaryf the bone volume in the proximity of the apex as is the casen the present model. A thin bone casing would also restrict cur-ent flow around the cochlea, thus directing it towards the cochlearanals and target neurons. The electrode carrier will direct currentowards the side of the carrier onto which electrodes are mountednstead of allowing a radial current distribution from an unshieldedlectrode contact. These factors have to be addressed in subsequentodels.Predictions for BP and TP stimulation protocols will mainly be

ffected by the inclusion of the electrode carrier (and not so muchy location of the MP return electrode or an accurate descriptionf the air-filled bulla) since the current distributions are localisedo within the cochlea. Since the locations of electrodes 3–6 are all

ng & Physics 35 (2013) 926– 936

relatively close to the neurons, threshold predictions for electrodepairs or sets containing these electrodes would be less affected thanelectrode pairs or sets that contain electrodes that are further awayfrom the target neurons. The implication is that the inclusion of theelectrode carrier could improve predictions at the base and apex ofthe model where predictions are currently less accurate.

Additional aspects that could also affect model predictions andshould be investigated in future work include the choice of materialproperties, boundary conditions and the neural model that is usedto predict neural excitation.

5. Conclusion

The hypothesis that subject-specific data may be predicted bya subject-specific model is supported by the outcomes of thisstudy. A method was presented to construct a model of a guineapig cochlea from �-CT image data with adequate accuracy tobe subject-specific. By making use of low-resolution images themethod facilitates translation to construction of in vivo humancochlear models using standard clinical low-resolution CT data.

Results indicate that the model is capable of predicting thresh-old frequency location, spatial spread of BP and TP stimulationprotocols as well as electrode thresholds relative to one another forelectrodes that are located in different cochlear structures. Absolutethresholds and spatial spread of excitation using MP stimulationare generally not predicted accurately by the model, while BP andTP threshold predictions for the central part of the electrode arrayare accurate. Improvements to the model should address insertiondamage, inclusion of the electrode carrier, accurate representationof the MP return electrode and associated factors that could affectthe MP current return path. As this is single animal study, it is neces-sary to extend this study to multiple animals to assess if the resultsobtained here are generally applicable.

The study demonstrated that general trends are not necessarilyapplicable to a specific subject, e.g. it cannot always be assumed thatthresholds increase towards the base of the cochlea. Aspects suchas the location of an electrode inside other cochlear structures andinsertion damage with consequent neural degeneration may leadto different results. It is therefore essential to model the morphol-ogy, electrode position and neural survival patterns of a cochleaaccurately to be able to model these differences. This shows thatsubject-specific models are valuable research tools and are neces-sary to model detailed aspects of the workings between cochleaand implanted array.

The animal nature of the model currently prohibits its use ina clinical setting. Clinical application of the model will require itto be translated to an in vivo human model which may be usedto predict thresholds (perceptual or physiological, e.g. electricallyevoked compound action potentials) for a specific implant user.This involves translation of the modelling technique to incorporatestandard clinical imaging techniques instead of the �-CT used andrevision of the neural model to predict perceptual or physiologicalthresholds instead of single fibre responses. Based on typical per-ceptual threshold variations measured over time ranging between1 and 2 dB [46,47], an ideal human model should be able to predictthresholds to within this range. Such an accurate model could beused to objectively predict person specific device parameters whichcould be a valuable clinical tool to predict maps for users that arenot able to participate in interactive mapping procedures such asinfants and young children.

Acknowledgements

The authors wish to thank Ben H. Bonham, RusselSnyder and colleagues from the Epstein Laboratory, Department of

T.K. Malherbe et al. / Medical Engineering & Physics 35 (2013) 926– 936 935

F in ade tact. (be

Od

A

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1

2

3

E

E

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ig. A1. Electrode positioning. (a) �-CT section through an electrode contact usedxtreme edges of the segment. Marker E is placed at the centre of the electrode conlectrode placements. (c) New adjusted positions of some of the electrodes.

tolaryngology, UCSF for providing the �-CT as well as IC recordingata of the guinea pig subject.

ppendix A.

The following procedure was used to adjust the electrode pos-tions:

. A mid-modiolar section was made through a specific electrodecontact (electrode 3) and its surrounding canal in the �-CT dataas well as the model (Fig. A1a and b).

. Markers were placed on the edges of the cochlear walls in x and ydirections (markers A–D). A marker was also placed at the centreof the electrode contact (marker E).

. In (A1) the new vertical position of the electrode in the model(EM,y) was calculated by ensuring that the ratio between theheight of the cochlear segment (Height is the distance differencebetween markers C and D) and the distance that the electrodeis from the top (Top is the distance difference between markersD and E) is the same as it is in the �-CT image. In (A2) the newhorizontal position of the model electrode (EM,y) was calculatedby ensuring that the ratio between the width (Width is the dis-tance between markers A and B) of the cochlear segment and thedistance that the electrode is from the right (Right is the distancebetween markers B and E) is the same as it is in the �-CT image.

M,y = DM,y − TopCT

HeightCT× HeightM (A1)

M,x = BM,x − RightCT × WidthM (A2)

WidthCT

In (A1) and (A2) the CT and M subscripts denote the positions ofhe markers in the CT and modelled sections respectively. x and yubscripts denote the x and y coordinates of the points respectively.

justing the modelled electrode position. Markers A, B, C and D are placed on the) A section through the model at the angle of the electrode indicating old and new

This procedure was followed to reposition all the modelled elec-trodes. The locations of the electrode contact obtained directly fromthe �-CT scan, as well as the new adjusted position of electrode 3,are shown in Fig. A1b. The new adjusted contact positions for someof the other electrodes are shown in Fig. A1c.

Conflict of interest statement

The authors declare that there are no conflicts of interest.

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