Envisioning Future Radiology Informatics


Published on

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Envisioning Future Radiology Informatics

  1. 1. Nov. 5, 2009 Grinnell College Spectrum of High PerformanceSpectrum of High Performance Medical Imaging InformaticsMedical Imaging Informatics Jun Ni, Ph.D.Jun Ni, Ph.D. Associate Professor, Dept. of RadiologyAssociate Professor, Dept. of Radiology Director, Medical Imaging HPC & Informatics LabDirector, Medical Imaging HPC & Informatics Lab Carver College of MedicineCarver College of Medicine The University of IowaThe University of Iowa
  2. 2. Medical Imaging Digital Radiology
  3. 3. Medical Imaging Software Resources Medical Imaging Hardware Facility
  4. 4. Medical Imaging Workforce is needed for Knowledge based Computer-aided Diagnostics
  5. 5. MII Domain DefinitionMII Domain Definition Medical Imaging Informatics (MII)Medical Imaging Informatics (MII) ==== Radiology Informatics?Radiology Informatics? One of medical informatics disciplinesOne of medical informatics disciplines SubSub--specialty of radiologyspecialty of radiology BoundaryBoundary ------ > medical image data mining?> medical image data mining? Technical driven:Technical driven: teleradiologyteleradiology/telemedicine/telemedicine Job market: 70,000 on demand, education challengesJob market: 70,000 on demand, education challenges IowaIowa ------ Hawkeye Radiology Informatics (HRI)Hawkeye Radiology Informatics (HRI)
  6. 6. Visions: Radiology InformaticsVisions: Radiology Informatics Frontier in cancer diagnosticsFrontier in cancer diagnostics Proliferated applications:Proliferated applications: Oncology, cardiology, dermatology, surgery,Oncology, cardiology, dermatology, surgery, gastroenterology, obstetrics, gynecology andgastroenterology, obstetrics, gynecology and pathology, and other medical fieldspathology, and other medical fields Strong digital requirement and IT engagementStrong digital requirement and IT engagement
  7. 7. Medical Imaging Informatics (MII)Medical Imaging Informatics (MII) ScopeScope What is current scope of MII?What is current scope of MII? A subspecialty of radiology that aims to improveA subspecialty of radiology that aims to improve medicalmedical imaging relatedimaging related discovery and technical services within thediscovery and technical services within the healthcare enterprisehealthcare enterprise Accuracy (methodology)Accuracy (methodology) Efficiency (workflow)Efficiency (workflow) Usability (feasibility or applicability)Usability (feasibility or applicability) Reliability (accessibility)Reliability (accessibility) SustainabilitySustainability Cost/performanceCost/performance Its ultimate goal toIts ultimate goal to improve health care systemsimprove health care systems
  8. 8. Previous Paradigm: Data OrientedPrevious Paradigm: Data Oriented RoadmapRoadmap Study how medical images (within radiology andStudy how medical images (within radiology and throughout medical enterprise) are processed bythroughout medical enterprise) are processed by AcquisitionAcquisition ArchivingArchiving Retrieving/recoveringRetrieving/recovering Image ProcessingImage Processing AnalyzedAnalyzed EnhancedEnhanced VisualizedVisualized Data format conversionData format conversion ……
  9. 9. Current Paradigm: CrossingCurrent Paradigm: Crossing A multidiscipline and interdisciplinaryA multidiscipline and interdisciplinary Intersection with other fields:Intersection with other fields: Medical science (radiology, internal medicine,Medical science (radiology, internal medicine, neuroscience,neuroscience, ……)) Computer and information scienceComputer and information science Biomedical engineeringBiomedical engineering Electrical engineering (signal and data processing)Electrical engineering (signal and data processing) Biological and physiological sciencesBiological and physiological sciences Medical physicsMedical physics ……
  10. 10. hospital registration order exam waiting room exam operation modality send to officePaperwork film packageRadiologist previewFetch report to HIS radiologist review final report on RIS Workflow
  11. 11. Digitization In Medical SciencesDigitization In Medical Sciences and Data Issueand Data Issue Source: UC Berkeley, School of Information Management and Systems. 0 C.E. 2003 40,000 BCE cave paintings bone tools 3500 writing paper 105 1450 printing 1870 electricity, telephone transistor 1947 computing 1950 1993 The Web Digital Cardiology Electronic Medical Record E-Health Initiatives/Linkages Digital Radiology 1999 Late 1960s Internet Petabytes Digital Pathology
  12. 12. NM (128, 128) MRI (256, 256) CT (512, 512) DSA (1024, 1024) CR (2048, 2048) Mammogram (4096, 4096)
  13. 13. PACS ChallengesPACS Challenges Different regional and industrial interpretation,Different regional and industrial interpretation, configuration, and implementationconfiguration, and implementation Different interfaces and prototypesDifferent interfaces and prototypes Different standardizationDifferent standardization DICOM, HL7, Other IT standardsDICOM, HL7, Other IT standards Different image digitalization of modalitiesDifferent image digitalization of modalities Different scopesDifferent scopes
  14. 14. PACSPACS--IT Technical ComponentsIT Technical Components Image acquisition and management technologyImage acquisition and management technology Data visualization or image displayData visualization or image display Network and communicationsNetwork and communications Computer application softwareComputer application software
  15. 15. PACS Technical ConcernsPACS Technical Concerns Data Migration Back-up archive Fault-tolerance Integration with legacy systems Fast wide-area networks Security
  16. 16. PACS Distributed ComputingPACS Distributed Computing ArchitectureArchitecture Large scale (multipleLarge scale (multiple modulemodule--based):based): Module 1 Module 2 Module 3 Distributed multiple- modules within multiple services units; but with single health organization Local networked
  17. 17. PACS ClassificationPACS Classification Super scale (enterpriseSuper scale (enterprise--,, cyberinfrastructurecyberinfrastructure--, heterogeneous,, heterogeneous, distributed griddistributed grid--based, cross organization, or even globally):based, cross organization, or even globally): Module 1 of site A Module 2 of site A Module 1 of Site B Module 2 of site B High speed network
  18. 18. MII Challenges (1)MII Challenges (1) Lack generic MII ontology (Lack generic MII ontology (Philological IssuePhilological Issue)) Systematic identification and classification of domainSystematic identification and classification of domain entities and existences, and entity relationsentities and existences, and entity relations (communications)(communications) No semantic languages for communications orNo semantic languages for communications or workflowsworkflows LooselyLoosely--defined terminologydefined terminology No linkage and leverage to knowledge, artificialNo linkage and leverage to knowledge, artificial intelligent (AI), decision makingintelligent (AI), decision making
  19. 19. Ontological Data Model inOntological Data Model in Radiology Informatics?Radiology Informatics? What is ontology?What is ontology? Existing, Entity and relationshipsExisting, Entity and relationships Domain ontology?Domain ontology? Domain, domain model, scope, boundary, crossing,Domain, domain model, scope, boundary, crossing, machine, object and service model, class, object,machine, object and service model, class, object, services, process,services, process, …… Medical Imaging Informatics ontology?Medical Imaging Informatics ontology? DD--EE--RR--Graph and Machine descriptionsGraph and Machine descriptions
  20. 20. Ontology Wisdom Philology Knowledge Decision MakingCAD Cognitive Sciences Information Science Data Data Data Data Data Metadata MetadataMetadata Information Management Domain Entity Relations Artificial Intelligence
  21. 21. Strategic ChallengesStrategic Challenges Ontological data model (terminologyOntological data model (terminology classification, entity definition, and relationsclassification, entity definition, and relations establishing) (Methodology issue)establishing) (Methodology issue) KnowledgeKnowledge--drivendriven Artificial intelligenceArtificial intelligence--drivendriven Unprecedented capacity for handling massiveUnprecedented capacity for handling massive datadata System integration and interoperation amongSystem integration and interoperation among various hospital/clinic systemsvarious hospital/clinic systems Expansion of RI domain scopeExpansion of RI domain scope
  22. 22. RI Challenges (3)RI Challenges (3) No standard protocols (No standard protocols (Technical issuesTechnical issues)) To facilitate the interoperation and communicationTo facilitate the interoperation and communication among globallyamong globally--distributed MII resourcesdistributed MII resources To deploy concurrent hardware and softwareTo deploy concurrent hardware and software solutionssolutions To utilize cyberTo utilize cyber--enabled highenabled high--speed networksspeed networks Short of education/training programs (Short of education/training programs (BusinessBusiness issueissue)) To foster the next generation in digital health careTo foster the next generation in digital health care systems.systems.
  23. 23. RI Challenges (4)RI Challenges (4) Software DevelopmentSoftware Development ComputerComputer--Aided Detection and Diagnosis (CAD)Aided Detection and Diagnosis (CAD) ComputerComputer--aided interventional radiologyaided interventional radiology Metrics and computing performanceMetrics and computing performance Medical imaging facility and infrastructureMedical imaging facility and infrastructure developmentdevelopment Fundamental research and developmentFundamental research and development Medical Imaging Service Pack (MISP)Medical Imaging Service Pack (MISP) Medical Imaging Informatics Knowledge IntegrationMedical Imaging Informatics Knowledge Integration Toolkit (M2KIT)Toolkit (M2KIT)
  24. 24. Lab MissionLab Mission Establishment of a nationally and globallyEstablishment of a nationally and globally-- recognized research lab in medical imagingrecognized research lab in medical imaging informatics or radiology informaticsinformatics or radiology informatics
  25. 25. Short Term Action TasksShort Term Action Tasks Learning any subjects and shaping knowledgeLearning any subjects and shaping knowledge Develop infrastructure of unprecedented computingDevelop infrastructure of unprecedented computing facility in medical imaging informaticsfacility in medical imaging informatics Collaborating with enterprise IT and health careCollaborating with enterprise IT and health care industrialsindustrials Working with external and internal professionalsWorking with external and internal professionals Seeking for fundsSeeking for funds Develop software solutions for future health careDevelop software solutions for future health care systemssystems Attract more people including you.Attract more people including you.
  26. 26. LongLong--Term GoalTerm Goal Computation (future projects)Computation (future projects) Infrastructure and algorithm developments for dataInfrastructure and algorithm developments for data mining in medical imagemining in medical image Artificial Intelligence in medical imagingArtificial Intelligence in medical imaging LargeLarge--scale image processing and associatedscale image processing and associated modeling and simulationsmodeling and simulations Digitalization of human body (massDigitalization of human body (mass--phantomphantom system)system) Computational radiologyComputational radiology System radiologySystem radiology
  27. 27. MIHI Lab ProjectsMIHI Lab Projects Medical Imaging & Radiology Informatics (MIRI)Medical Imaging & Radiology Informatics (MIRI) Hawkeye Radiology Informatics (HRI)Hawkeye Radiology Informatics (HRI) http://http://www.uiowa.edu/~hriwww.uiowa.edu/~hri// Radiology Informatics Domain Ontology (RIDO)Radiology Informatics Domain Ontology (RIDO) Medical Imaging Informatics Ontology (MIIO)Medical Imaging Informatics Ontology (MIIO) Medical Imaging Informatics Terminology (MIIT)Medical Imaging Informatics Terminology (MIIT) CyberinfrastructureCyberinfrastructure--enabled Radiology Informatics (CIRI)enabled Radiology Informatics (CIRI) Medical Imaging Information System (MIIS)Medical Imaging Information System (MIIS) http://http://www.uiowa.edu/mihpclab/projects_miis.htmlwww.uiowa.edu/mihpclab/projects_miis.html Radiology Informatics Education and Training (RIET)Radiology Informatics Education and Training (RIET) http://http://www.uiowa.edu/~hri/education.htmlwww.uiowa.edu/~hri/education.html
  28. 28. ProjectsProjects Parallel Computing in Medical Imaging (PCMI)Parallel Computing in Medical Imaging (PCMI) http://http://www.uiowa.edu/mihpclab/projects_pcmi.htmlwww.uiowa.edu/mihpclab/projects_pcmi.html Parallelism of Medical Imaging ProcessingParallelism of Medical Imaging Processing CT ReconstructionCT Reconstruction SegregationSegregation RegistrationRegistration Texturing and classificationTexturing and classification EnhancementEnhancement Image compressionImage compression Image data miningImage data mining ……
  29. 29. Parallel CT Medical ImageParallel CT Medical Image ReconstructionReconstruction
  30. 30. LargeLarge--scale Parallel CT Medical Imagescale Parallel CT Medical Image ReconstructionReconstruction CT TechnologyCT Technology Invented by British Engineer G.Invented by British Engineer G. HounsfieldHounsfield in 1971in 1971 Principle: utilizes XPrinciple: utilizes X--ray technology and computers toray technology and computers to create images of crosscreate images of cross--sectionsection ““slicesslices”” through thethrough the bodybody
  31. 31. LargeLarge--scale Parallel CT Medical Imagescale Parallel CT Medical Image ReconstructionReconstruction TodayToday’’s CT Technologys CT Technology Advanced in technology, software applications andAdvanced in technology, software applications and clinical performanceclinical performance CT scanners are fast and patient friendlyCT scanners are fast and patient friendly Expand the role of CT in both diagnosis andExpand the role of CT in both diagnosis and treatment.treatment.
  32. 32. CT Technology BasisCT Technology Basis XX--ray CT technologiesray CT technologies Classification: XClassification: X--ray beamray beam’’ss geometry, motion of Xgeometry, motion of X--ray locusray locus (source), and design of(source), and design of corresponding detectors whichcorresponding detectors which measure the decay of Xmeasure the decay of X--rayray intensity.intensity. Classified by beam geometryClassified by beam geometry Parallel BeamParallel Beam Fan BeamFan Beam Cone BeamCone Beam Classified by the motion of XClassified by the motion of X--ray locusray locus CircleCircle SpiralSpiral Classified by detectorClassified by detector One rowOne row Multiple rowsMultiple rows Tube (X-ray source)
  33. 33. CT Technology BasisCT Technology Basis Generations:Generations: First Generation:First Generation: ParallelParallel--beam, in which abeam, in which a single Xsingle X--ray tube generates aray tube generates a beam passed through thebeam passed through the object in parallel and a singleobject in parallel and a single detector obtains an opticaldetector obtains an optical signal correspondinglysignal correspondingly The whole system is in aThe whole system is in a translationtranslation--thenthen--rotationrotation manner time consumingmanner time consuming X-ray detector source Object or patient Parallel bean with multiple X-ray sources
  34. 34. CT Technology BasisCT Technology Basis Second Generation:Second Generation: Fan beam of XFan beam of X--rays and arays and a linear detector arraylinear detector array (multiple detectors on the(multiple detectors on the plane).plane). The XThe X--ray source emitsray source emits radiation over a large angle,radiation over a large angle, while every detector in thewhile every detector in the group receives the signalsgroup receives the signals (which are called(which are called projection data).projection data). Improved efficiencyImproved efficiency Employs a translateEmploys a translate--rotaterotate scanning motionscanning motion CorrespondingCorresponding reconstruction algorithm isreconstruction algorithm is a little more complexa little more complex Fan Beam with one single X-ray source
  35. 35. CT Technology BasisCT Technology Basis Third GenerationThird Generation CT scanners uses coneCT scanners uses cone-- beam.beam. The detector array in theseThe detector array in these scanners remains stationaryscanners remains stationary while Xwhile X--rays are producedrays are produced by a highby a high--energy electronenergy electron beam, rotating around abeam, rotating around a patient without moving thepatient without moving the CT scanner.CT scanner. This kind of CT scannerThis kind of CT scanner system is sometimessystem is sometimes referred to as a rotatereferred to as a rotate-- stationary or rotatestationary or rotate--onlyonly geometrical system.geometrical system. z x y Cone Beam with single X-ray source and multiple row’s detector
  36. 36. CT Technology BasisCT Technology Basis Fourth generationFourth generation The Helical (Spiral) CT scanner,The Helical (Spiral) CT scanner, first invented in 1989, used anfirst invented in 1989, used an innovative scanning mechanism ininnovative scanning mechanism in which the gantry rotateswhich the gantry rotates continuously with thecontinuously with the simultaneous translation of thesimultaneous translation of the patient table.patient table. With the motion of the patientWith the motion of the patient table, the scanner can reconstructtable, the scanner can reconstruct a large number of slices anda large number of slices and produce a 3D image of the wholeproduce a 3D image of the whole object.object. Spiral Cone Beam with single X-ray source and multiple row’s detector
  37. 37. CT Technology BasisCT Technology Basis Fourth generationFourth generation Helical CT scanners are often cataloged into singleHelical CT scanners are often cataloged into single-- (single(single-- slice and single detector row), dualslice and single detector row), dual-- (dual(dual--slice and dualslice and dual detector row), or multidetector row), or multi-- (multi(multi--slice and multislice and multi--detector, ordetector, or multimulti--row) sections according to the row number of detectorrow) sections according to the row number of detector elements.elements. A MultiA Multi--Section CT (MSCT) scanner deploys a cone beamSection CT (MSCT) scanner deploys a cone beam projection (single Xprojection (single X--ray source and planner arrays ofray source and planner arrays of detectors); thus further speeding up data collections ordetectors); thus further speeding up data collections or acquisitions.acquisitions. PhilipsSiemensGE GE
  38. 38. Image Reconstruction BasisImage Reconstruction Basis Image reconstruction algorithmsImage reconstruction algorithms Construct images based on projection data from scannersConstruct images based on projection data from scanners Associated with the evolution of fourth generations of CTAssociated with the evolution of fourth generations of CT systems (geometrical design and spatial motion)systems (geometrical design and spatial motion)
  39. 39. Image Reconstruction BasisImage Reconstruction Basis A twoA two--step processstep process the transmission of an Xthe transmission of an X--ray beam isray beam is measured through all possible straightmeasured through all possible straight-- line paths in a plane of an objectline paths in a plane of an object the attenuation of an xthe attenuation of an x--ray beam isray beam is estimated at points in the objectestimated at points in the object Attenuation coefficientAttenuation coefficient u is the x-ray linear attenuation coefficient P(xP(x) is a projection function) is a projection function An attenuation function f(x,y) for 2D object; To evaluate f(x), while p(x) is given eII L dxxu t ∫= − 0 )( 0 0 0 ( ) ln( ) ( ) L tI f x dx P x I = − =∫
  40. 40. Image Reconstruction BasisImage Reconstruction Basis Theorem 1Theorem 1 The value of a 2D function at anThe value of a 2D function at an arbitrary point is uniquely obtainedarbitrary point is uniquely obtained by integrals along the lines of allby integrals along the lines of all directions passing the point.directions passing the point. Mathematically inversedMathematically inversed problemproblem P: observed data. X: unknown original image A: non-zero M by N matrix 1 2 1 2 ( , ,..., ) ( , ,..., ) T M T N Ax P P p p p x x x x = = =
  41. 41. Image Reconstruction BasisImage Reconstruction Basis Analytic Algorithms (Filtered BackAnalytic Algorithms (Filtered Back-- Projection, FBP)Projection, FBP) Efficient computationEfficient computation Predominant in commercial marketPredominant in commercial market Sensitive to noise, inaccurate projection dataSensitive to noise, inaccurate projection data Iterative Algorithms (ART, EM)Iterative Algorithms (ART, EM) Tremendous computation and easy implementationTremendous computation and easy implementation HighHigh--quality reconstructed image from noisy or lowquality reconstructed image from noisy or low-- dose and incomplete projection datadose and incomplete projection data Weight or penalty functions to redeem the loss ofWeight or penalty functions to redeem the loss of project dataproject data
  42. 42. Image Reconstruction BasisImage Reconstruction Basis Analytic AlgorithmsAnalytic Algorithms (Filter Back(Filter Back--Projection)Projection) Radon TransformationRadon Transformation (geometrical)(geometrical) Fourier transformationFourier transformation (project(project--data preprocessing)data preprocessing) FilteringFiltering Fourier inverse transformationFourier inverse transformation ( , ) ( cos sin , sin cos ) cos sin cos sin , sin cos sin cos g s f s u s u du s x x s u y y u θ θ θ θ θ θ θ θ θ θ θ θ θ ∞ −∞ = − + −⎡ ⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎤⎡ ⎤ = =⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥−⎣ ⎦ ⎣ ⎦⎣ ⎦ ⎣ ⎦ ⎣ ⎦⎣ ⎦ ∫ Radon Transformation
  43. 43. Image Reconstruction BasisImage Reconstruction Basis Projection TheoremProjection Theorem The One-dimensional Fourier transform of the Radon transform g(θ,s) for s denoted Gθ(ξ) variable, and the cross-section of the two- dimensional Fourier transform of the object f(x,y), sliced by the plane at θ with the fx -axis and perpendicular to the (fx, fy)-plane, denoted F(fx, fy), are identical to Gθ(ξ)=F(ξcosθ,ξsinθ) )sin,cos()( θξθξξθ FG =
  44. 44. Image Reconstruction BasisImage Reconstruction Basis Filter BackFilter Back Projection (FBP)Projection (FBP) )sin,cos()( θξθξξθ FG = θξθ π dsgFFyxf }||)},({{),( 0 1 ∫ − =
  45. 45. ParallelParallel KatsevichKatsevich AlgorithmAlgorithm Deng, Yu, Ni, et al. The Journal of Supercomputing, 38, 35–47, 2006
  46. 46. KasevitchKasevitch AlgorithmAlgorithm Hilbert Filtering, intermediateHilbert Filtering, intermediate funtionfuntion WeightedWeighted BackprojectionBackprojection ( )bs x PI-segment x ( )sy gantry h R U locus ( )ts x detector 1d2d( , , )g s u v β 3d r Geometrical illustration of the helical cone-beam CT system 2 2 ( ) 0 1 1 ( ) ( ( ), ( , , )) 2 ( ) sin( )PI f q s I x d f x D y q s x ds x y s q π γ γ π λ = ∂ = − Θ − ∂∫ ∫ 2 0 ( , ) ( ) ,f D y f y t dt Sβ β β ∞ = + ∈∫ 2 2 2 2 2 2 ( , , ) ( , , ) ( ) g D u v s u v D s u v du D u v u u ψ ∞ −∞ + + = + + − ∫ % % % % % % 2 2 ( , , ) ( , , )g D u uv D s u v g s u v s D u D v ⎛ ⎞∂ + ∂ ∂ = + +⎜ ⎟ ∂ ∂ ∂⎝ ⎠ ( ) ( ) ( ) ( ) 1 2 3 3 ( ) 2 ( ) ( ) ( ) * , * ( ) ( ) 1 1 ( ) ( , *, *) 2 ( ) t b s s D s D s u v s s f s u v ds s ψ π − − = = − − = − −∫ x x x y d x y d x y d x y d x x y
  47. 47. ParallelParallel KatsevichKatsevich AlgorithmAlgorithm Parallel implementationsParallel implementations parallel reconstruction process PE 1 PE 1 PE nPE n Root PE Projection data Filtered data Reconstructed data Collected reconstructe d data Filtering stage Backprojection stage PE’s Initialization Projection Data Generation/Distribution Projection Data Filtration Projection Data Gathering and Distribution Backprojection Gathering Reconstructed Data on Root PE PE’s Finalization
  48. 48. 0 8 16 24 32 0 8 16 24 32 Case I Case II Case III Case IV Ideal Speedup x y 0 10 16 24 32 10 1 10 2 10 3 10 4 Case I Case II Case III Case IV y 0 8 16 24 32 0 0.2 0.4 0.6 0.8 1 1.2 Case I Case II Case III Case IV Ideal Speedup Data: 3-D Shepp-Logan phantom : 1283, 2563, 3843, 5123 time speedup efficiency
  49. 49. 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 2563 5123 Ideal speedup TeraGrid/NCSA cluster
  50. 50. ProjectsProjects Modeling Biotransport in Biophysical System (MBBS)Modeling Biotransport in Biophysical System (MBBS) http://http://www.uiowa.edu/mihpclab/projects_mbbs.htmlwww.uiowa.edu/mihpclab/projects_mbbs.html NanothermotheropyNanothermotheropy ((nanoHyperthmiananoHyperthmia)) http://http://www.uiowa.edu/mihpclab/projects_nmni.htmlwww.uiowa.edu/mihpclab/projects_nmni.html Tumor growth and dynamics (computational oncology)Tumor growth and dynamics (computational oncology) Optical Imaging Tomography and Applications (OITA)Optical Imaging Tomography and Applications (OITA) http://http://www.uiowa.edu/mihpclab/projects_oita.htmlwww.uiowa.edu/mihpclab/projects_oita.html
  51. 51. ProjectsProjects Stereological Analysis and Tumor VolumeStereological Analysis and Tumor Volume Metrics (SATVM)Metrics (SATVM) voluMeasurevoluMeasure Software Development Project (RSNA'05)Software Development Project (RSNA'05) The Effect of the Shape and Orientation of a Mass on the AccuracThe Effect of the Shape and Orientation of a Mass on the Accuracyy Estimating Its Size Using RECIST (RSNA'09)Estimating Its Size Using RECIST (RSNA'09) Tumor volume measurement in MRI breast imagingTumor volume measurement in MRI breast imaging http://http://www.uiowa.edu/mihpclab/projects_isca.htmlwww.uiowa.edu/mihpclab/projects_isca.html StereotacticStereotactic Atlas for the Anatomic TopologyAtlas for the Anatomic Topology (SAAT)(SAAT) http://http://www.uiowa.edu/mihpclab/projects_saat.htmlwww.uiowa.edu/mihpclab/projects_saat.html Couple Diffusions for Image Enhancement (DDIE)Couple Diffusions for Image Enhancement (DDIE) http://http://www.uiowa.edu/mihpclab/projects_cdie.htmlwww.uiowa.edu/mihpclab/projects_cdie.html
  52. 52. ProjectsProjects KnowledgeKnowledge--based CAD for Breast Imagingbased CAD for Breast Imaging (KCBI)(KCBI) architectural distortionarchitectural distortion calcificationcalcification DeformationDeformation http://http://www.uiowa.edu/mihpclab/projects_kcbi.htmlwww.uiowa.edu/mihpclab/projects_kcbi.html New projectsNew projects TomosynthesisTomosynthesis and Molecular Breast Imagingand Molecular Breast Imaging US Medical ImagingUS Medical Imaging 3D Volume Rendering3D Volume Rendering
  53. 53. Sponsorships and CollaborationsSponsorships and Collaborations Current sponsorsCurrent sponsors NIH (HPC medical imaging)NIH (HPC medical imaging) NSF (HPC computations in nanotechnology)NSF (HPC computations in nanotechnology) Intel (HPC)Intel (HPC) MicrosoftMicrosoft Collaborators:Collaborators: Siemens (medical modality, MII software resources)Siemens (medical modality, MII software resources) IBM (Cell/BE)IBM (Cell/BE) NavidaNavida (GPU/CUDA)(GPU/CUDA) Mayo Clinic (projects)Mayo Clinic (projects) You who love to support this missionYou who love to support this mission
  54. 54. Go Hawkeye!Go Hawkeye! Thanks!Thanks! Q & AQ & A