Angelini, Elsa D.

Costa, Kevin D.

Duan, Qi

Gerard, Olivier

Herz, Susan L.

Holmes, Jeffrey W.

Homma, Shunichi

Ingrassia, Christopher M.

Laine, Andrew F.

Columbia University Academic Commons

1Dynamic Cardiac Information From Optical Flow Using Four-Dimensional Ultrasound DataQi Duan1, Elsa D. Angelini2, Susan L. Herz1, Christopher M. Ingrassia1, Olivier Gerard3, Kevin D. Costa1, Jeffrey W. Holmes1,  Shunichi Homma4, Andrew F. Laine11Department of Biomedical Engineering, Columbia University, New York, NY, USA;2Ecole Nationale Supérieure des Télécommunications, Département Traitement du Signal et des Images (TSI), Paris, France;3Philips France, Suresnes, France;4Department of Medicine, Columbia University, New York, NY, USA.IEEE EMBC 2005, Shanghai, China, September 3, 20052Outline• Introduction to the clinical problem• Method: Correlation-based optical flow• Data: Real-time 3D ultrasound• Results: Myocardium motion analysis• Conclusion & Future work3Introduction• 3D ultrasound based on matrix phased array transducer provides valuable and unique information.• Abnormal ventricular wall motion can be detected via quantitative 4D analysis.• Such analysis needs tracking and segmentation of ventricular surfacesData Rates: ~250MB/s for raw data4Introduction• Manual segmentation is tedious, time-consuming, and impractical.• Previous research from our group showed that optical flow can effectively track  endocardial and epicardial surfaces.• Optical flow provides a displacement field from which we derive several dynamic cardiac measurements.5Pre-processing• Presence of speckle noise complicates the interpretation of ultrasound images.⇨ Pre-developed anisotropic diffusion filters with time-varying threshold was applied.6Pre-processingBefore denoisingAfter denoising7Processing Flowchart8Initial Segmentation• An expert performs manual tracing of endocardial and epicardial surfaces at end-diastole (1 frame) on :– rotating B-scan views (long-axis views) – parallel C-scan views (short-axis views).9Optical Flow• Optical flow is defined as “displacement of apparent pixel intensity pattern”.⇨ 3D correlation-based method.• “Regularizing” parameters were added:– Variance threshold to avoid “aperture problem”.– Average threshold to avoid tracking dark regions.– Displacement vector field for each point on the surface is averaged within a 6-connected neighborhood to enhance robustness of the estimation.10Surface Tracking• Tracking was initialized at end-diastole (ED) with the manually traced surfaces.• The endocardial and epicardial surface points were automatically tracked by optical flow through the systolic phase.11Motion Field InterpolationGoal: Reconstruction of a continuous motion field based on scattered points.Method: Spatial interpolation with radial basis functions (thin-plate splines).Motion field after myocardial surfaces trackingMotion field interpolated inside the myocardium12Coordinates SystemsxzOjikyrθPixel coordinatesCartesian coordinatesCylindrical coordinates13Dynamic Cardiac Information14Experimental Data• Data acquired with a Philips SONOS 7500 3D ultrasound machine.• Heart-transplant patient data set: (0.8mm)3 and 16 frames per cycle.• Data acquisition was triggered with EKG initialized at ED.• Due to the limits of field of view, the epicardial surface was not visible at the apex.15Experimental Results: Surface Tracking0-0.10.1000.10.20.10.20-0.2-0.50.20.1septumanterior lateral posteriorseptumapex-1.5-1-0.500.5Results at end-systole:– Volume:• Difference = + 2.51%– Ejection Fraction:• EFmanual= 46% • Difference = -1.4%– Surface agreement:• 47% with <5% difference• 73% with <10% difference• Average intra-user manual tracing difference: 17.43%.Lateral view Anterior view16Experimental Results: Surface Error ComparisonRMSE ManualOFLV Volume0 5 10 1500.10.20.30.40.5Frame NumberRMSE0 5 10 15020406080Frame NumberVolume (ml)Maximum RMSE: 0.09 (frame 9)(λ =0.6±0.2 at ED λ = 0.5±0.2 at ES)Maximum error: 10ml17Experimental Results: Motion Field2D short-axis 3D viewFrame ED to ES (contraction phase)18Experimental Results: Radial Displacement-10-8-6-4-20246810SeptumAnteriorWall19Experimental Results: Wall Thickening-3-2-10123SeptumAnteriorWall20Experimental Results: Twist-0.3-0.2-0.100.10.20.3SeptumAnteriorWall21Conclusions• A 4D optical flow algorithm was applied to RT3D ultrasound data for tracking endocardial and epicardial surfaces.• Dense myocardium motion fields were recovered via radial basis function interpolation.• Dynamic cardiac parameters were computed (can provide new diagnostic information).• On one experiment, preliminary results agreed with clinical anatomical facts for the normal part of the heart as well as showed characteristic patterns of decreased motion on the “immobile” septum due to the heart-transplant procedure(“Pure Septal Defect”).22Future Work• Comparison of wall motion analysis with tagged MRI.• Multi-resolution implementation for real-time computation.• Full-field optical flow computation.• Clinical evaluation of abnormal wall motion.23Acknowledgement• National Science Foundation grant BES-02-01617• American Heart Association #0151250T• Philips Medical Systems Research and Philips Marketing• New York State NYSTAR/CAT Technology Program• The Louis Morin Fellowship program• Dr. Andrew McCulloch at the University of California San Diego provided the finite element software “Continuity” through the National Biomedical Computation Resource (NIH P41RR08605)• Dr. Todd Pulerwitz (Department of Medicine, Columbia University).24For additional information please see:http://hbil.bme.columbia.edu2526PersonnelQi DuanGraduate Research AssistantHeffner Biomedical Imaging LabDept. of Biomedical EngineeringColumbia UniversityDr. Olivier GerardSenior Research ScientistMedical Imaging Systems GroupPhilips Research FranceDr. Elsa AngeliniAssistant ProfessorEcole Nationale Supérieure des Télécommunications Dr. Kevin CostaAssistant ProfessorPI of cardiac Biomechanics LabDept. of Biomedical EngineeringColumbia UniversitySusan HerzGraduate Research AssistantCardiac Biomechanics LabDept. of Biomedical EngineeringColumbia UniversityDr. Jeffrey HolmesAssistant ProfessorPI of cardiac Biomechanics LabDept. of Biomedical EngineeringColumbia UniversityChristopher IngrassiaGraduate Research AssistantCardiac Biomechanics LabDept. of Biomedical EngineeringColumbia UniversityDr. Andrew LaineAssociate Professor Director of HBILDept. of Biomedical EngineeringColumbia UniversityDr. Pascal AllainSenior Research ScientistMedical Imaging Systems GroupPhilips Research FranceDr. Shunichi Homma, M.D. Associate Professor of Medicine and Director, Lab. of Echocardiography Service. Dept. of MedicineColumbia University27Correlation MethodFrame 1 Frame 2Small neighborhood(5x5x5)Search range(7x7x7)28Correlation Method( )2 2( , )* ( , )( , ) ( , )I t I t trI t I t t∈Ω∈Ω ∈Ω+∆ +∆=+∆ +∆∑∑ ∑xx xx x xx x xFrame 1 Frame 2Correlation Coefficient29Correlation MethodFrame 1 Frame 2Most similar position30Surface Reconstruction• Surface reconstruction is based on FEM• Each surface was registered with anatomical landmarks.• Each surface was fitted into a 64-element mesh in prolate spheroidal coordinates.• Bicubic Hermite interpolation was used to yield a realistic 3D endocardial surface.31Surface Reconstruction• Quantitative measurements:– Root mean squared errors (RMSE) of the difference of the radial coordinates λat node points for OF and manual tracing result.– Ventricular volume and ejection fraction– Relative error was used to generate local relative error map as a Hammer map.ε = (λmanual –λoptical) /λmanual

Dynamic Cardiac Information From Optical Flow Using Four-Dimensional Ultrasound Data

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Dynamic Cardiac Information From Optical Flow Using Four-Dimensional Ultrasound Data

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