BACKGROUND: The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more complex electrode leads. OBJECTIVE: Providing a method to guide clinicians on DBS assessment and parameter tuning by automatically integrating patient individual data. METHODS: We present a fully automatic method for visualization of individual deep brain structures in relation to a DBS lead by combining precise electrode recovery from post-operative imaging with individual estimates of deep brain morphology utilizing a 7T-MRI deep brain atlas. RESULTS: The method was evaluated on 20 STN DBS cases. It demonstrated robust automatic creation of 3D-enabled PDF reports visualizing electrode to brain structure relations and proved valuable in detecting miss placed electrodes. DISCUSSION: Automatic DBS assessment is feasible and can conveniently provide clinicians with relevant information on DBS contact positions in relation to important anatomical structures

Gemmar, P

Goncalves, J

Hertel, F

Husch, A

Petersen, MV

Sunde, N

Husch, Andreas

Petersen, Mikkel V

Gemmar, Peter

Goncalves, Jorge

Sunde, Niels

Hertel, Frank

Apollo (Cambridge)

lable at ScienceDirectBrain Stimulation xxx (2018) 1e4Contents lists avaiBrain Stimulationjournal homepage: http : / /www.journals .elsevier .com/brain-st imulat ionPost-operative deep brain stimulation assessment: Automatic dataintegration and report generationAndreas Husch a, b, *, Mikkel V. Petersen c, Peter Gemmar b, Jorge Goncalves b,Niels Sunde d, Frank Hertel a, ba National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg (City), Luxembourgb Systems Control Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourgc Department of Clinical Medicine - Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmarkd Department of Neurosurgery, Aarhus University Hospital, Aarhus, Denmarka r t i c l e i n f oArticle history:Received 23 October 2017Received in revised form23 January 2018Accepted 26 January 2018Available online xxxKeywords:Deep brain stimulationPost-operative assessmentImage registrationComputer aided surgeryBrain atlasSubthalamic nucleus* Corresponding author. National Department oftalier de Luxembourg, 4 Rue Ernest Barble, L-1210, LuE-mail addresses: andreas.husch@uni.lu, mail@andhttps://doi.org/10.1016/j.brs.2018.01.0311935-861X/© 2018 The Author(s). Published by ElseviePlease cite this article in press as: Husch A,generation, Brain Stimulation (2018), https:a b s t r a c tBackground: The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is amonopolar review of all stimulation contacts, a strategy being challenged by recent developments ofmore complex electrode leads.Objective: Providing a method to guide clinicians on DBS assessment and parameter tuning by auto-matically integrating patient individual data.Methods: We present a fully automatic method for visualization of individual deep brain structures inrelation to a DBS lead by combining precise electrode recovery from post-operative imaging with in-dividual estimates of deep brain morphology utilizing a 7T-MRI deep brain atlas.Results: The method was evaluated on 20 STN DBS cases. It demonstrated robust automatic creation of3D-enabled PDF reports visualizing electrode to brain structure relations and proved valuable indetecting miss placed electrodes.Discussion: Automatic DBS assessment is feasible and can conveniently provide clinicians with relevantinformation on DBS contact positions in relation to important anatomical structures.© 2018 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).BackgroundThe effectiveness of deep brain stimulation (DBS) is related tothe precise placement of electrode leads with respect to speciﬁcbrain structures. Therapeutic outcome for DBS in Parkinson's dis-ease has been directly linked to precise contact placement withinthe subthalamic nucleus (STN) [1e3]. In the absence of integratedvisual information about the placement of electrode contactscompared to individual deep brain structures of a subject, sys-tematically accessing stimulation effects of all electrode contactsafter surgery - the monopolar review strategy [4] - is still the goldstandard.With the introduction of novel lead designs featuringsegmented contact areas the number of possible contact combi-nations increases exponentially. Consequently, there is a need forNeurosurgery, Centre Hospi-xembourg.reashusch.de (A. Husch).r Inc. This is an open access articleet al., Post-operative deep b//doi.org/10.1016/j.brs.2018.01automatic methods to guide the clinician in post-operativeassessment of electrode placement with respect to the patient in-dividual anatomy to enable effective parameter tuning.Automated segmentation of basal ganglia structures from MRIwas reported by Haegelen et al. [5] in 2012. They concluded that asoftware solution that is usable by clinicians without the aid ofengineers is desired in the future.Recently Wang et al. [6] released a 7 TMR imaging based deepbrain structure atlas. The so called Deep7T-atlas consist of unbiasedhigh resolution T1-and T2-weighted average templates with asso-ciated labels. The labels include the STN, the substantia nigra (SN),the red nucleus (RN) and the globus pallidus (GP). The atlas provedexcellent performance in their initial technical study. Lau et al. [7]suggested to non-linearly transform the high contrast T2 templateof this atlas to standard patient imaging data for improved DBStargeting of the STN.In this report, we introduce DBS auto report (DBSAR), a softwaremethod that fully automatically integrates individual neuroana-tomical information with highly accurate DBS lead reconstructions.under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).rain stimulation assessment: Automatic data integration and report.031A. Husch et al. / Brain Stimulation xxx (2018) 1e42Anatomical information is derived by adopting the Deep7T-atlas tothe particular subject and automatic lead reconstruction is utilizingthe recently introduced PaCER algorithm [8]. In contrast to otherapproaches DBSAR does not require any user interaction. Theoutput is a report in form of a PDF ﬁle encapsulating interactive 3Dgraphics objects enabling clinical usage with any 3D capable PDFreader (e.g. Adobe Acrobat) and not requiring any additional soft-ware on the clinical side.Material and methodsAn overview of the automatic workﬂow is presented in Fig. 1.The inputs are conventional pre-operative 3D-T1 and T2 MRI and apost-operative CT. By this means the workﬂow is solely based onwide spread standard modalities in clinical imaging for DBS.A key point of the processing is that eventually all data arerepresented in one coordinate space that is related to the nativescans of the patient's anatomy by only a rigid transformation. Thisrestriction to rotations and translations guarantees that allgeometrical features of the patient are preserved. Thus DBS tra-jectories can be accurately represented in this space.Multi-stage atlas registrationTo integrate 3D anatomical structure information the Deep7T-atlas is registered to subject space. A key feature of this atlas isthat the atlas labels are consistently placed with respect to therespective signal intensities in the atlas template. In example, thelabels for the subthalamic nucleus exactly match the hypointenssubthalamic region visible in the associated T2 template image ofthis atlas. This is not the case for many other deep brain structureatlases (cf. [9]) but considered a key feature for meaningfulregistration-based estimates.The non-linear atlas-to-subject mapping is based on a multi-stage approach using a chain of registrations with increasing de-grees of freedom. In particular: center-of-gravity pre-alignment(three degrees of freedom), rigid registration (six degrees offreedom), afﬁne registration (nine degrees of freedom) and a ﬁnalFig. 1. DBSAR Workﬂow. The PaCER algorithm is used to extract precise lead and contact locare aligned using rigid registration followed by a multi-stage multi-modality registration ostructure information from the atlas are fused in one patient individual space, preservingrendering of the individual deep brain structures combined with the DBS leads as well asPlease cite this article in press as: Husch A, et al., Post-operative deep bgeneration, Brain Stimulation (2018), https://doi.org/10.1016/j.brs.2018.01diffeomorphic registration (non-linear). The registrations are car-ried out using the free ANTS software package [10].This multi-stage-registration pipeline is using both the T1 aswell as the T2 modality. T1 imaging, which provides decentdelineation of brain boundaries is used for the initial stages thusensuring a robust alignment. T2 imaging, providing contrast of theSTN and SN, is used for the ﬁnal non-linear stage to yield best ac-curacy in the region of interest.Lead and contact detectionLead reconstruction using the PaCER algorithm is carried outusing the native space data of the post-op CT before any trans-formation. This ensures best signal-to-noise ratio and accuracy. Inthe data-integration phase, the extracted leads are transformed tomatch the MRI based imaging applying previously found trans-formations from CT to MRI data to the lead objects.Result representation and clinical applicationThe DBSAR results are converted to a PDF ﬁle with embedded 3Dobjects utilizing the ﬁg2u3d toolbox (https://github.com/johnyf/ﬁg2u3d). This PDF can be displayed using standard ofﬁce soft-ware retaining full 3D rotation capabilities, allowing clinicians toassess electrode placement from arbitrary angles (cf.supplementary example PDF). Additionally, metrics that are verycumbersome to assess manually could be computed automatically.For example distances of the stimulation contacts to the center-of-gravity of the STN or any other labeled brain structures.Subject dataThe method was validated on 20 bilateral STN DBS datasets (40electrodes) from two centers. The anatomical structures automat-ically estimated by the atlas registration where manually inspectedfor each case by assessing the transformed atlas labels overlayed tothe pre-OP T2 images.ations from post-op CT. Subject image datasets (post-op CT, pre-op T1 and T2 imaging)f a 7 T imaging based atlas to the patient space. Finally both DBS leads and anatomicalpatient geometry. The result is outputted as a PDF ﬁle integrating an interactive 3Dcontact to STN metrics (refer to supplementary material for example PDF).rain stimulation assessment: Automatic data integration and report.031Fig. 2. Example of DBSAR electrode and brain structure visualizations for three caseswith sub-optimal electrode placement. STN displayed in green, SN in blue, RN in red,GP in yellow. Contacts highlighted in yellow are predicted as closest to the center-of-gravity of the STN. A1 and A2 show a case with the right lead placed to deep before andafter minor lead revision. B shows a case with a sub-optimal placement on the left side.C demonstrates a case with placement of the left electrode in the anterio-lateralsubstantia nigra. (For interpretation of the references to colour in this ﬁgure legend,the reader is referred to the Web version of this article.)A. Husch et al. / Brain Stimulation xxx (2018) 1e4 3ResultsAutomatic electrode lead reconstruction as well as atlas regis-tration was successful in all 20 datasets evaluated. Automaticallyestimated brain structures demonstrated a qualitatively excellentalignment with T2 hypointens STN regions in manual comparisons(cf. supplementary Figure). For 12 datasets manual 3D segmenta-tions of the STN from T2 imaging were available for comparisons.The mean dice-agreement computed to 0.66 ± 0.05 which is su-perior to inter-rater agreements reported for manual raters in theliterature (0.55± 0.08 [5]). In general relatively low dice-kappaagreements are to expect for the STN due to its small volume (cf.[6]).Clinical potentialThe proposed method offers considerable potential for moreefﬁcient DBS programming. However, larger clinical studies out ofscope of this report are needed to prove this. A ﬁeld where 3Dvisualization of DBS lead and individual anatomymight be instantlyhelpful is the assessment in cases of sub-optimally placed leads. Todemonstrate this clinical potential three DBS cases with knownsub-optimal electrode placement were selected and assessed usingDBSAR in a post-hoc analysis.DBSAR visualizations of the three cases are shown in Fig. 2. In(A1) the automatic reconstruction revealed a DBS lead placedsigniﬁcantly to deep, a second reconstruction (A2) after minorelectrode revision (pull without trajectory change) demonstratesthe effect of the revision. The DBSAR report suggest the mostproximal contact as best after revision. Indeed this contact turnedout to be most effective in monopolar review. In Case (B), the sec-ond most proximal contact was chosen as stimulation contact forthe well placed right lead bymonopolar review, which is congruentto the prediction by DBSAR. The left electrode is predicted asslightly misplaced by DBSAR, with only the most proximal contactclose to the STN. Again this contact was found most effective by thegold standard method too.Please cite this article in press as: Husch A, et al., Post-operative deep bgeneration, Brain Stimulation (2018), https://doi.org/10.1016/j.brs.2018.01Case (C) demonstrates a case that exhibited psychiatric sideeffects when enabling stimulation on the left electrode. No effectivecontact could be found for the left lead in this case. DBSARconsistently reports a placement outside the STN with all electrodecontacts located in the anterior-lateral part of the substantia nigra.DiscussionWe introduced a method to automatically integrate neuroana-tomical data on electrode location and deep brain structuremorphology solely based on freely available tools. Evaluation on 20datasets from two centers demonstrated that automatic applicationwithout any user interaction is possible on clinical imaging data.The output is a 3D enabled PDF ﬁle, which was deemed as veryefﬁciently usable by clinicians. While the used electrode extractionmethod was previously shown to be very accurate, the accuracy ofthe atlas based individual deep brain structure estimation is moredifﬁcult to assess. The underlying ground truth is unknown and tosome extend open to debate due do very large variability betweenhuman raters [5]. However, the method already proved useful inthe rapid identiﬁcation of electrodes placed sub-optimal demon-strating congruent results to conventional monopolar review.Future clinical studies will evaluate the predictive power of thepresented method on clinical outcome to enable objective judg-ment. Future methodological improvements might integrate sub-regions of the STN, e.g. the sensorimotor region. The greatest po-tential for improvement is expected when applying DBSAR for theanalysis of cases implanted with novel segmented leads.The straightforward integration of recent algorithms to recoversegmented contacts (for example as in Ref. [11]) thus constitutesthe next step for the near future.AcknowledgmentThis work was support by the Fonds National de la Recherche(FNR), Luxembourg, Grant AFR 5748689 and Grant CORE C14/BM/8231540 as well as by the Danish Parkinson's Association.Appendix A. Supplementary dataSupplementary data related to this article can be found athttps://doi.org/10.1016/j.brs.2018.01.031.References[1] Welter ML, Schüpbach M, Czernecki V, Karachi C, Fernandez-Vidal S,Golmard JL, et al. Optimal target localization for subthalamic stimulation inpatients with Parkinson disease. Neurology 2014;82:1352e61.[2] Wodarg F, Herzog J, Reese R, Falk D, Pinsker MO, Steigerwald F, et al. Stim-ulation site within the MRI-deﬁned STN predicts postoperative motoroutcome. Mov Disord 2012;27:874e9.[3] Schlaier JR, Hanson C, Janzen A, Fellner C, Hochreiter A, Proescholdt M, et al.Deep brain stimulation in Parkinson's disease: motor effects relative to theMRI- deﬁned STN. Neurosurg Rev 2014;37(3):461e71.[4] Volkmann J, Herzog J, Kopper F, Deuschl G. Introduction to the programmingof deep brain stimulators. Mov Disord 2002;17(S3):S181e7.[5] Haegelen C, Coupe P, Fonov V, Guizard N, Jannin P, Morandi X, et al. Auto-mated segmentation of basal ganglia and deep brain structures in MRI ofParkinson's disease. Int J Comput Ass Rad 2013;8(1):99e110.[6] Wang BT, Poirier S, Guo T, Parrent AG, Peters TM, Khan AR. Generation andevaluation of an ultra-high-ﬁeld atlas with applications in DBS planning. In:Styner M, Angelini E, editors. SPIE medical imaging: image processing; 2016,97840H.[7] Lau JC, MacDougall KW, Arango MF, Peters TM, Parrent AG, Khan AR. Ultra-high ﬁeld template-assisted target selection for deep brain stimulation sur-gery. World Neurosurg 2017;103:531e7.[8] Husch A, Petersen MV, Gemmar P, Goncalves J, Hertel F. PaCER - a fullyautomated method for electrode trajectory and contact reconstruction in deepbrain stimulation. Neuroimage-Clin 2018;17:80e9.[9] Ewert S, Plettig P, Li N, Chakravarty MM, Collins DL, Herrington TM, et al.Toward deﬁning deep brain stimulation targets in MNI space: a subcorticalrain stimulation assessment: Automatic data integration and report.031A. Husch et al. / Brain Stimulation xxx (2018) 1e44atlas based on multimodal MRI, histology and structural connectivity. Neu-roimage 2017 [Epub ahead of print].[10] Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducibleevaluation of ANTs similarity metric performance in brain image registration.Neuroimage 2011;54(3):2033e44.Please cite this article in press as: Husch A, et al., Post-operative deep bgeneration, Brain Stimulation (2018), https://doi.org/10.1016/j.brs.2018.01[11] Sitz A, Hoevels M, Hellerbach A, Gierich A, Luyken K, Dembek TA, et al.Determining the orientation angle of directional leads for deep brain stimu-lation using computed tomography and digital x-ray imaging: a phantomstudy. Med Phys 2017;44(9):4463e73.rain stimulation assessment: Automatic data integration and report.031

Post-operative deep brain stimulation assessment: Automatic data integration and report generation.

peer reviewedBackground
The gold standard for post-operative deep brain stimulation (DBS) parameter tuning is a monopolar review of all stimulation contacts, a strategy being challenged by recent developments of more complex electrode leads.
Objective
Providing a method to guide clinicians on DBS assessment and parameter tuning by automatically integrating patient individual data.
Methods
We present a fully automatic method for visualization of individual deep brain structures in relation to a DBS lead by combining precise electrode recovery from post-operative imaging with individual estimates of deep brain morphology utilizing a 7T-MRI deep brain atlas.
Results
The method was evaluated on 20 STN DBS cases. It demonstrated robust automatic creation of 3D-enabled PDF reports visualizing electrode to brain structure relations and proved valuable in detecting miss placed electrodes.
Discussion
Automatic DBS assessment is feasible and can conveniently provide clinicians with relevant information on DBS contact positions in relation to important anatomical structures

Petersen, Mikkel V.

Open Repository and Bibliography - Luxembourg

Post-operative deep brain stimulation assessment: Automatic data integration and report generation

CUED - Cambridge University Engineering Department

Andreas Husch

Mikkel V. Petersen

Peter Gemmar

Jorge Goncalves

Niels Sunde

Frank Hertel

Crossref

Brain Stimulation

https://www.repository.cam.ac.uk/bitstream/1810/274739/4/1-s2.0-S1935861X18300603-main.pdf

Post-operative deep brain stimulation assessment: Automatic data integration and report generation.

Abstract

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Apollo (Cambridge)

Open Repository and Bibliography - Luxembourg

CUED - Cambridge University Engineering Department

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