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Rapid Diagnosis of Acute Heart Disease Rapid Diagnosis of Acute Heart Disease by Cloud-based High Performance by Cloud-based High Performance Computing for Computer VisionComputing for Computer Vision
Oleksii MorozovPhysics in Medicine Research GroupUniversity Hospital of BaselSwitzerland
April 8, 2010
The HeartThe Heart
Life-sustaining pump: 2’500’000 L/year of vital blood
Coronary artery disease (CAD) is most frequent cause of heart malfunction and death
World largest killer (WHO)~29% of global death17’100’000 lives/year
Cardiology todayCardiology today
More tools, more informationSubjective decision mostly relying on experience of a doctor
Cardiology tomorrowCardiology tomorrowMore advanced technologies
Multidimensional informationHigh quality, high resolution dataMultimodal informationQuantitative, objective, integrative computer based analysisWorldwide-networked standards and databases
ProblemsNeed for high performance computing in a distributed environment but only for a fraction of the timeGlobal storage network for storing large datasets
Cardiac UltrasoundCardiac Ultrasound
One of the modern tools for evaluation of the heart function
HF sound waves : No Radiation/IonizationSafe, Non-invasive, Fast, Portable, Cheap“-” Rather low signal to noise ratio
3D Cardiac Ultrasound3D Cardiac Ultrasound
Explore heart in 3DFreehand ultrasound (Manual sweeping)Mechanical sweeping ultrasound (Motor driven)
3D Cardiac Ultrasound3D Cardiac Ultrasound
Explore heart in 3DFreehand ultrasound (Manual sweeping)Mechanical sweeping ultrasound (Motor driven)Live 3D ultrasound (2D arrays with electrical sweeping)
Cardiac UltrasoundCardiac Ultrasound
Ultrasound machine = a transducer + a supercomputer
50’000 – 500’000 USD
Idle 90% of the time
Computational problems Computational problems in Cardiac Ultrasoundin Cardiac Ultrasound
Signal reconstruction
Non-uniformly sampled measurements
Complete gridded or continuous data representation
3D+time signal reconstruction3D+time signal reconstruction
Inherent non-uniformity of scanningSpatial non-uniformitySerialism in scanning
3D+time signal reconstruction3D+time signal reconstruction
Inherent non-uniformity of scanningSpatial non-uniformitySerialism in scanning
Non-uniformity in synchronization (ECG)
3D+time signal reconstruction3D+time signal reconstruction
Inherent non-uniformity of scanningSpatial non-uniformitySerialism in scanning
Non-uniformity in synchronization (ECG)Body motion artifacts (breathing)
4D non-uniform data
I(x,y,z,t) ?
3D+time signal reconstruction3D+time signal reconstructionA spline solutionA spline solution
B-spline non-uniform interpolation by Arigovindan, Unser (EPFL, Switzerland 2005)
Robust global interpolation: handles oversampling and undersampling (gaps) in the dataSparse and well-conditioned alternative to the optimal RBF solutionEnjoys multiresolution properties (way to fast solving)Parallelizability of solving processSuccessfully applied to 2D problems
3D+time signal reconstruction3D+time signal reconstructionA spline solutionA spline solution
Obstacles in 3D/4DComplexity is exponentially dependent on the data size 128 x 128 x 128 x 18 –> 78’752’009’856 non-zeros (312 Gbyte in single precision)
Tensor based approach by Morozov, Hunziker, Unser 2009
Tensor decomposition of the problemRelaxed storage requirementsFeasibility on standard workstations
~9 millions of measurements with size 128 x 128 x 128 x 18 -> 30 minutes on my dual core laptop
3D+time signal reconstruction3D+time signal reconstructionA spline solutionA spline solution
Tensor based approach applied to ultrasound data from continuously rotating transducer
Computational problems Computational problems in Cardiac Ultrasoundin Cardiac Ultrasound
Tissue/blood motion estimationDoppler Ultrasound imaging (State of the art)
Semi-quantitative measurements
Full motion reconstructionGeneralization of B-spline reconstruction to vector valued data (Arigovindan, Unser 2005)Employing additional constraints from physics of fluids (incompressibility, Navier-Stokes equations)
Computational problems Computational problems in Cardiac Ultrasoundin Cardiac Ultrasound
B-spline based tissue motion reconstructionContinuousFully quantifiableCan be combined with Doppler for better robustness
Computational problems Computational problems in Cardiac Ultrasoundin Cardiac Ultrasound
Blood flow reconstructionResolves ambiguity of Doppler measurementsContinuousFully quantifiable
Pathway to distributed supercomputingPathway to distributed supercomputing
Multicore (IBM Power7) claimed 260 GFLOP/chipCluster (UniBasel) 34’500 GFLOP/400 coresGPGPU (ATI 4870X2) 2’000 GFLOP/card GPGPU arrayFPGA accelerator cards: dozens of GFLOP/chip, up to 512 chips per system, low power
In exploration within ICES Microsoft projectCloud - Microsoft Azure
Cloud Ultrasound Processing ServiceCloud Ultrasound Processing Service
ReasonsProcessing of large multidimensional multimodal medical data requires vast computational powerBuilding/maintaining own HPC infrastructure is overly expensiveRelatively rare use of HPC power (few times per day)Availability at multiple points of care (medical practices and hospital emergency rooms)Unified storage/access of the multimodal medical data
Cloud
Cloud Ultrasound Processing ServiceCloud Ultrasound Processing Service
4D acquisition with real-time on board
visualization
4D acquisition with real-time on board
visualization
Interactive web-based visualization of the
result
Interactive web-based visualization of the
resultRendered images and quantitative
information
Visualization/Analysis parameters
Record dataUser
Cloud Ultrasound Processing ServiceCloud Ultrasound Processing Service
Record dataRaw data
180 beams x 500 samples x 100 frames x 10 sec -> 85 MbAdditional information (geometry) -> few Kb
Lossless compressed DICOMLow latency response to the user by sending first a subpart of the data for coarser resolution reconstruction
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Problem is very large for solving using direct solvers ->
use iterative solver Ci+1 = Ci + OP(Ci)OP – linear operator
2 iterations 50 iterations 80 iterations
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Iteration can be distributed relative to the grid
C{1,1} C{1,2}
C{2,1} C{2,2}
C{1,1}i+1 = C{1,1}i + OP{1,1}(Ci)C{1,2}i+1 = C{1,2}i + OP{1,2}(Ci)C{2,1}i+1 = C{2,1}i + OP{2,1}(Ci)C{2,2}i+1 = C{2,2}i + OP{2,2}(Ci)
C{k,m} – solution subpart dedicated to a compute unit
OP{k,m}() – operator applied by a {k,m}’s compute unit
dx
dy
dx, dy – grid spacing
Completely independent output
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Data dependency
OP{k,m}() uses data outside the bounds of C{k,m}
C{k,m}
Extents of dependent input data: 3 samples for cubic splineAt each iteration this data is transferred among adjacent unitsPerformance limiting factor
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Data dependencyData size 512 x 512 x 512 x 64Single precision: 32 GBInfiniband QDR 12X ( ~12GB/s )
Number of units Size of dependent data per unit, MB
Total data transfers for single iteration, MB
Maximal number of iterations/s (excluding CPU time)
64 72 4608 166
128 48 6144 250
256 30 7680 400
512 18 9216 666
1024 12 12288 1000
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Computational loadData size 512 x 512 x 512 x 64Intel® Quad Core 2.67 GHz (~30 GFLOP/s in single precision)PC3-10600 DDR3-SDRAM (30 GB/s)115’000’000 data samplesTotal requirements: ~5000 GFLOP, ~3000 GB of memory transfers
Number of units Maximal number of iterations/s (including inter-unit communication)
64 0.24
128 0.48
256 0.96
512 1.92
1024 3.84
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
MultiresolutionCoarse to scale propagation – getting general from coarser scales and improving details on finer scalesInherent spline inter-scale relation
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Multiresolution in solving algorithmCoarse to scale propagation – getting general from coarser scales and improving details on finer scalesInherent spline inter-scale relationMultigrid solving algorithm
Iterate
Iterate
Iterate
Direct solve
Iterate
Scale 1
Scale 2
Scale N
Projection to coarser scale
Projection to finer scale
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Multiresolution in solving algorithmsCoarse to scale propagation – getting general from coarser scales and improving details on finer scalesInherent spline inter-scale relationMultigrid solving algorithm
Few iterations needed at each scale to get reasonably good solutionWith each coarser scale the cost of iteration decreases exponentiallyIn total requires much less computational load than pure iteration
Cloud Ultrasound Processing Service Cloud Ultrasound Processing Service Signal reconstructionSignal reconstruction
Total requirements per compute unitData size 512 x 512 x 512 x 64 6 scales including finest scale16 iterations at each scale
Motion reconstruction algorithms require ~9 times more computational load
Number of units Memory, GB CPU time, s
64 2.7 62
128 1.36 31.2
256 0.68 15.6
512 0.34 7.8
1024 0.17 3.9
Cloud Ultrasound Processing ServiceCloud Ultrasound Processing Service
Costs estimation Data size 512x512x512x64
Storage, GB 32
Number of instances
1024
Compute hours
0.0011
hour instance∙ 1.13
Switzerland (1/1000 of world population)700 cardiologists/7’000’000 population 1000 echocardiograms per year per cardiologistMultiple views(3) per patientMultiple analyses (3) per view
7’000’000 use cases/year 3043 use cases/hour
Full loaded 3 x 1024 instances(Uniform load in Switzerland)
Collaborative Research enabled by Collaborative Research enabled by the Cloudthe Cloud
Globally available service for processing, storing and accessing medical dataStandardized DICOM interface for unifying data accessInvolve all interested parties around the worldWorld-wide large scale trials are possibleGetting more statistics for rare casesBuilding reference datasets for known cases
ConclusionConclusion
An approach to rapid diagnosis of heart disease using cloud based distributed computing
Replace ultrasound machine’s supercomputer by a cloud service for remote processing and storageMiniaturization of the medical equipment and decrease of its costsAvailability of advanced analysis technologies for objective analysisAvailability at multiple points of careUnified storage and access of medical dataEnables collaborative research