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Developingnovelimagefusionregistrationfortargetedbiopsiesfor earlyprostatecancerdetection LeadInvestigator: VincentGnanapragasam AcademicUrologyGroup,DepartmentofSurgeryandOncology http://www.cambridgecancercentre.org.uk/users/vjg29 CoInvestigators: Carola-BibianeSchoenlieb DepartmentofAppliedMathematicsandTheoreticalPhysics http://www.damtp.cam.ac.uk/user/cbs31/Home.html ProfessorJAston ProfessorofStatistics,StatisticalLaboratory DrGrahamTreece ReaderinInformationEngineering,DepartmentofEngineering This PhD Studentship within the Urological Malignancies Programme offers a highly unique opportunity to work across a clinical, mathematical and engineering interface, and bring together three key disciplines to address a current conundrum in clinical practice and patient safety. The strong background of each of the co-supervisors in their respective disciplines in addressing the different aspects of the project will ensure its success and provide excellent supervision to the student.Inaddition,theprojecthasstrongpotentialtobeextendedfurthertofuturestudies,and rapidclinicalapplicationforpatientbenefit. Thisprojectmightappealtostudentswithaninterestinappliedmathematics,statistics,urologyand computational imaging. A strong background in one of these subjects as well as programming experienceisessential. ProjectDescription Background ProstatecanceristheprevalentmalecancerintheWesternworldanditsincidenceisrisingrapidly (http://www.cancerresearchuk.org/about-cancer/type/prostate-cancer/). The most common methodofdiagnosingprostatecanceriswithaneedlebiopsyoftheprostateguidedbyatransrectal ultrasound (US) probe inserted into the rectum (https://www.nice.org.uk/guidance/). It is now known however that over 30% of cancers will be missed using this method because ultrasound cannotvisualisewherealesionisintheprostate(Nelson,2014). Pre-biopsyimagingisthereforefastbecomingastandardofcareinplanningthebiopsyapproachto reduce the risk of missing cancers. Currently, pre-biopsy imaging is done by a magnetic resonance imaging(MRI)scanoftheprostatewhichisthenusedtomapontoanultrasoundpicturetoguide thebiopsy.Thiscanbedoneusingcognitive(orvisual)estimationofanMRIlesionandestimated correlationwithanUSimage(performedbytheoperator)orbyimagefusiontechniques(whichis moreaccurate).ThemostcommonmethodofimagefusionistosuperimposeanMRIimageontoan ultrasoundpictureinaformofstaticfusion(Nienke,2016).Pre-markedareasintheprostateonthe MRI representing tumour are then assumed to be concurrent with the areas of ultrasound and samplesaretakenaccordingly.Thismethod,however,requiresboththepatientandtheultrasound probetobeverystill(necessitatingageneralanaesthetictokeepthepatientstationary)aswellas expensiveequipmentforthebiopsyguidingmethod(typicallyafixedgantryandstraightlineneedle insertion).Analternativemethodistousemagneticfieldgeneratingsoftware(e.g.Uronavsystem) todetectwhereabiopsyprobemightbeinrelationtotheprostate,andtousethistofuseMRIand USimages.Thisagainrequiresinconvenientandexpensiveequipmentandisnotsuitableasadayto daytoolfortheover80,000prostatebiopsiescarriedoutannuallyintheUK(1millionintheUSand Europe). Currentmethods,althougheffective,arethereforenotsuitableforgeneraluseacrossthethousands ofunitsthatundertakeprostatebiopsies.Acriticalneedisasimplifiedtoolthatcanperformreal time image fusion without the use of specific hardware or expensive clinical equipment. In this proposal, we are seeking to address this by using computation and mathematical modelling to develop novel anatomical registration techniques for real time image fusion guided biopsies and which can be delivered as proprietary software. The student (who will be based in the Centre for Mathematical Sciences) appointed to this project will benefit from collaborative working and joint supervisioninurologicalsurgery(VJG),computationalscience/mathematics(CS/JA)andengineering (GT) to deliver a clinic-ready application. Engineering already has a strong history of producing clinically useful solutions and software (Gee, 2003). This project will also strongly complement existingworklookingatnovelbiopsytechniques. Hypothesis Mathematicalandcomputationalmodellingtechniquescanbeusedtoaddresstheneedof anatomicalbasedreal-timeimageregistrationforguidedprostatebiopsies. Objectives 1.Tousereal-timeclinicalimagingfrompatientsundergoingbiopsiestoexploreanatomicalor fiducialco-registrationtechniqueswithMRIandultrasoundimages. 2.Todevelopandtestnovelregistrationalgorithmstodeterminetheoptimummethodofimage fusion. 3.Todevelopsoftwarethatwillallowreal-timeacquisitionofimagesandpresentableinaformat thataclinicianundertakingprostatebiopsiescanusetoguidebiopsies,withouttheuseof extrinsichardwareorsensor-guidingtechnology. Proposedmethods Image registration is the task of aligning two images. In the present case we are aiming for a registrationthatalignsapre-recordedstaticMRimagewithframesofadynamicUSimagesequence recorded during the biopsy. For developing a clinically practical image-guided prostate biopsy pipeline,atailor-maderegistrationframeworkisnecessary,whichistunedtowardsfindingmutual structuresinMRIandUSimagesthatcanbeusedforaligningtheminacomputationallycheapway. Toaddressthis,thestudentwillundertakeresearchcoveringthefollowingthemes: Theme 1: Novel US/MRI co-registration (Yr 1-2) (VJG/GT/CS): The formalisation and automated detectionofmutualfeaturesofMRandUSimagesarelikelytorequiresophisticatedmathematical concepts and clinical insight. In particular, the student will be expected to go beyond standard intensity-based registration towards more complex local- and non-local feature-based registration (Modersitzki, 2009, Lee 2015, Lotz 2015, Maass J 2015), especially tuned towards the properties encodedinMRandUSimages.Toachievethis,thestudentwillusedatageneratedfromaclinical series of men undergoing biopsies and who will have MRI and US images taken at procedure (throughtheclinicalcollaborationwiththeurologydepartment).Usingthese,thestudentwilltest anddevelopnewalgorithmsandapproachestoimageco-registration. Theme 2: Rapid real-time computing registration (Yr 2-3) (CS/JA/GT): The registration needs to be computable in real-time and on low-level hardware. However sophistication in the registration techniqueusuallygoestogetherwithincreaseincomputationalcosts.Therefore,anotherchallenge will be to build a comprehensive, yet computationally manageable registration framework by enhanced engineering of derived mathematical principles. In particular, we will aim for low-level imageregistrationwhichlinksthemostcharacteristicmutualfeaturesinbothimagestoeachother, whileneglectingsomeofthecomputationallycostlyaspects.Here,computationaltimeneedstobe balanced with registration accuracy in a reliable and statistically controllable manner. This step requires engineering as well as statistical and mathematical expertise (collaboration between engineeringandmathematics). Theme 3: Translation to clinical application (Yr 3) (VJG/GT/CS): A unique aspect and deliverable of this project is that the outputs from the work above will be developed into a tool that can be simulated in the clinic to test potential application (i.e. the ability to see where a tumour is on a fusedrealtimeimageanddirectabiopsyneedletothislesion).Todothisthestudentwillbegiven anhonorarycontracttoworkalongsidetheurologyteam.Softwaretoolsdevelopedfromcombining themes1and2abovewillbeusedinparallelwithstandardimagefusionsoftware(e.g.e.g.Biopsee fusion system) already being used in clinics. Model simulated biopsies from areas identified using thesoftwarewillbecomparedtoyieldsfromtruebiopsiestotestifthetoolcanaccuratelyidentify areas of interest. This will also permit an iterative improvement in the design and performance of thetool. Thetacklingofallthreechallengesaboverequiresacontinuousfeedback-loopcheckingthevalidity ofthehypothesislaidoutanditsclinicalrealisation.Assuchthisprojectwillprovideauniquenexus ofmathematical,engineeringandclinicalsupervisiontosupportthestudenttoachievethegoalsof thisproject References NelsonA(2014)Repeatprostatebiopsystrategiesafterinitialnegativebiopsy:meta-regression comparingcancerdetectionoftransperineal,transrectalsaturationandMRIguidedbiopsy.PLoS One.2013;8(2):e57480 A.H.Gee,R.W.Prager,G.M.TreeceandL.Berman,(2003),Engineeringafreehand3Dultrasound system.PatternRecognitionLetters.24(4-5). Modersitzki,J.(2009).FAIR:flexiblealgorithmsforimageregistration(Vol.6).SIAM. J.Lee,X.Cai,C.-B.Schönlieb,andD.Coomes,(2015),Non-parametricImageRegistrationofAirborne LiDAR,HyperspectralandPhotographicImageryofWoodedLandscapes,GeoscienceandRemote Sensing,IEEETransactionson,53(11),6073-6084. Lotz,J.,JanineOlesch,BenediktMuller,ThomasPolzin,PatriciaGaluschka,JudithLotz,Stefan Heldmannetal.(2015)"Patch-BasedNonlinearImageRegistrationforGigapixelWholeSlide Images.". Maas,J.,Rumpf,M.,Schönlieb,C.,&Simon,S.(2015).Ageneralizedmodelforoptimaltransportof imagesincludingdissipationanddensitymodulation.ESAIM:MathematicalModellingandNumerical Analysis,49(6),1745-1769. Applications Toapplyforthisstudentshippleaseseehttp://www.cambridgecancercentre.org.uk/studentships ForgeneralenquiriespleasecontactTinaThorn [email protected] Forfurtherinformationorquestionsrelatingtothisprojectpleasecontactbothsupervisors: VincentGnanapragasam [email protected] Carola-BibianeSchoenlieb [email protected]