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Performance in Medical Image Computing. Dr Daniel Rueckert Department of Computing Imperial College London. Introduction. IXI project is about application of e-science to medical imaging research. Distributed image acquisition. Distributed data storage. Distributed image analysis.

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Performance in medical image computing

Performance in Medical Image Computing

Dr Daniel Rueckert

Department of Computing

Imperial College London


Introduction
Introduction

IXI project is about application of e-science to medical imaging research

  • Distributed image acquisition

Distributed

data storage

Distributed

image analysis

Workflow


Aims

  • To build a grid infrastructure for medical image analysis

  • Apply it to exemplars relevant to:

    • biomedical research

    • drug discovery

    • healthcare

  • Use e-science as a driver for novel algorithm development


Ixi system overview
IXI System: Overview

  • How does IXI work?

  • What sort of images?

  • How big are images?

  • How long do the algorithms take?

  • What are the end-user applications?


Ixi system aims
IXI System: Aims

  • Make large remote compute resources available via Grid Services

    • Dedicated service for each algorithm

    • Be able to compose one service with another to form a workflow

  • Hide the complexity from the user

    • Seamless integration with interface

    • Invisible and secure transfer of files

  • Make the results easily available

    • Store in a database


Ixi system architecture
IXI System: Architecture

  • Local network

    • Web portal

    • Relational database (image and meta data are directly imported from DICOM)

    • XML database (stored workflows)

    • File system (image files)

    • Locally hosted grid service (reinsertion)

  • Remotely

    • Registry Service (index)

    • Workflow Service (workflow execution)

    • IXI Core Service (delegation)


How it works

Reinsertion Service

Relational Database

File transfer via Grid FTP

Workflow Service

Insert derived data

and copy to file system

Initiate reinsertion

Retrieve workflow and

local file locations

Matching subjects

located

Query

File transfer via Grid FTP

Co-ordinate

and delegate

Web Portal

Submit workflow

Retrieve workflow

& parse XML

E-mail user

IXI Core

Service

Workbench

XML Database

Select workflow

template

Clicks link in

e-mail

Review

results

User enters

search criteria

Instantiate workflow

and submit

Select files to keep

User

Condor

GT3 GRAM

Execute

locally

How it works

Finished


Ixi dynamic brain atlas demonstrator
IXI: Dynamic brain atlas demonstrator

Age 16-35

rigid/non-rigid registration

Age 35-65

Age 65+

classification

Database of

medical images

Statistical or

probabilistic atlas


How it works problems

Relational Database

Workflow Service

Submit workflow

Web Portal

Workbench

XML Database

E-mail user

User

How it works: Problems

?


What sorts of images
What sorts of images?

2D images (ie x-ray)

3D images (ie CT, MR, PET)

4D images (ie CT, MR)


How big are images
How big are images?

  • Current clinical routine:

    • MRI examination: 200 – 300 slices of 256 x 256 pixels x 2 bytes per pixel ~ 30Mbytes

    • CT examination: 10 – 30 slices of 512 x 512 pixels, 2 bytes per pixel ~10Mbytes

    • Digital x-ray: 512 x 512 pixels x 2 bytes x 8 -25fps x 100 – 500 seconds ~1.5Gbytes

      • but only small fraction of this used for measurement or archive


How big will images be soon
How big will images be soon?

  • Latest technology:

    • MRI examination: 300 – 500 slices of 512 x 512 at 2 bytes per pixel ~150Mbytes

    • CT examination: 100 – 300 slices at 512 x 512 at 2 bytes per pixel: ~100Mbytes

    • And can be dynamic, eg: 10 – 50 cardiac phases

  • The raw data problem:

    • Latest techniques manipulate raw data eg: 32 complex channels, which is 128x larger than reconstructed data ~20Gbytes


How long do the algorithms take to run
How long do the algorithms take to run?

  • Segmentation

    • tissue segmentation: between 30 secs and 10 minutes

    • anatomical segmentation: between several minutes and hours

  • Registration

    • rigid and affine: between 30 secs and 5 minutes

    • non-rigid: between 10 minutes and 24 hours

  • Visualisation:

    • rendering: near real-time even on standard PCs


Broad categories of ixi applications

Accessing, Collecting and Mining Image Data:

Genomics, proteomics, Gene expression

Drug discovery

Clinical Trials

Large Scale Simulation and Analysis

Simulation of cardiac blood flow using CFD

Large image based databases

Interpretation, training

Support of multidisciplinary and collaborative environments requiring complex planning and guidance tasks

Diagnosis

Treatment planning

Treatment verification

Broad categories of IXI applications

Biomedical Research

Healthcare


Why does performance matter
Why does performance matter?

  • Performance is mainly dependent on:

    • Computing time

    • Data transfer time

    • Reliability and availability of services

  • Performance has different priority for different applications:

    • Drug discovery study with 100 subjects

    • Computer assisted surgery


Biomedical research drug discovery
Biomedical Research: Drug discovery

  • Image mining:

    • Statistical parametric maps of volume change in patients with schizophrenia undergoing drug treatment

population

time t = 1

population

time t = 2

intrasubject

registration

intersubject

registration

TBM

reference


Why does performance matter1
Why does performance matter?

  • Drug discovery study with 100 subjects

    • End user: Researcher

    • Computing time for each job: ca. 8 hours

    • Total computing time: 100 x 8 hours, but jobs can run in parallel

    • Data transfer time for each job: ca. 1-2 minutes

    • Total transfer time: 100 x 2 minutes, however transfers can’t run in parallel (complications: firewalls slow data transfer down significantly)

  • Reliability is more important than run-time


Healthcare computer assisted interventions
Healthcare: Computer-assisted interventions

Use non-rigid registration to update pre-

operative plan

Ideally real-time, however 10-20 minutes

are acceptable


Why does performance matter2
Why does performance matter?

  • Computer-assisted surgery

    • End user: Clinicians & Surgeons

    • Computing time: ca. 1 – 8 hours on a workstation

    • Total computing time: Depending on available machine between 10 mins (cluster) and several hours (single workstation)

    • Data transfer time: Can be neglected

  • Reliability is important, but performance prediction is far more important:

    • Which machine should I run the job?

    • How long will it take on that machine?


Performance modelling for image registration
Performance modelling for image registration

source

Rueckert et al IEEE TMI 1999

target



Performance modelling for image registration2

Non-linear

optimization

Performance modelling for image registration

Initial trans-

formation T

Calculate cost function

C for transformation T

Generate new estimate

of T by minimizing C

Update trans-

formation T

Final trans-

formation T

Is new transformation

an improvement ?


Performance modelling

Analytical performance modelling:

Seems impossible

Not desirable since as it often takes more time than developing the algorithms

Experimental performance modelling:

Run algorithms with different parameters and datasets

Performance modelling

Work by Stephen Jarvis, Dan Spooner, Brian Foley

University of Warwick


Performance modelling1

Highly variable runtime - a factor of 16 between fastest and slowest at the same image size

Two classes of registration. Depends on destination image.

Self registration is fast.

Significant speedup using MPI cluster implementation

Prediction based on timing of subsampled images

Performance modelling

Work by Stephen Jarvis, Dan Spooner, Brian Foley

University of Warwick


Performance modelling2

Highly variable runtime - a factor of 16 between fastest and slowest at the same image size

Two classes of registration. Depends on destination image.

Self registration is fast.

Prediction based on timing of subsampled images

Significant speedup using MPI cluster implementation

Performance modelling

Work by Stephen Jarvis, Dan Spooner, Brian Foley

University of Warwick


Performance modelling3

Highly variable runtime - a factor of 16 between fastest and slowest at the same image size

Two classes of registration. Depends on destination image.

Self registration is fast.

Significant speedup using MPI cluster implementation

Prediction based on timing of subsampled images

Performance modelling

Work by Stephen Jarvis, Dan Spooner, Brian Foley

University of Warwick


Performance modelling4

Highly variable runtime - a factor of 16 between fastest and slowest at the same image size

Two classes of registration. Depends on destination image.

Self registration is fast.

Prediction based on timing of subsampled images

Significant speedup using MPI cluster implementation

Performance modelling

Work by Stephen Jarvis, Dan Spooner, Brian Foley

University of Warwick


Modelling systems and applications

Highly variable runtime - a factor of 16 between fastest and slowest at the same image size

Two classes of registration. Depends on destination image.

Self registration is fast.

Prediction based on timing of subsampled images

Significant speedup using MPI cluster implementation

Modelling systems and applications


What next
What next? slowest at the same image size

  • Incorporate performance modelling and predication into the IXI workflow (with help from S. Jarvis, Warwick):

    • to enable the user to tune parameters of the workflow with respect to the predicted performance

    • to enable the user to specify performance constraints

    • to inform the user about progress of workflow and provide updated measures of predicted performance

    • to implement different policies for scheduling for different IXI applications and end users


What next challenges
What next: Challenges slowest at the same image size

  • Data transfer can affect performance significantly:

    • Model data transfer times

    • Model bottlenecks such as firewalls or database servers

  • Performance modelling for different algorithms is a time-consuming tedious task:

    • Large number of different algorithms and different implementations

    • Can this be automated?

  • Reliability and availability is generally more important than performance, however this will change as the grid middleware and infrastructure becomes more mature

  • Future projects require near real-time performance

    • Analyze data while patient is inside the scanner (Neurogrid)


Acknowledgements
Acknowledgements slowest at the same image size

  • IXI team

    • Imperial College: Jo Hajnal, Andrew Rowland, Raj Chandrashekara, Michael Burns, Dimitrios Perperidis

    • University College: Derek Hill, Kelvin Leung, Bea Sneller

    • University of Oxford: Steve Smith, John Vickers

  • Stephen Jarvis, Dan Spooner, Brian FoleyHigh Performance Systems GroupDepartment of Computer ScienceUniversity of Warwick


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