performance in medical image computing

Performance in Medical Image Computing

Dr Daniel RueckertDepartment of ComputingImperial College London

Introduction

Distributed data storage

Distributed image analysis

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

Distributed image acquisition

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

• 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

• 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

• 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)

Matching subjectslocated

Retrieve workflow

& parse XML

Web Portal

User

Relational Database

XML Database

E-mail user

Workflow Service

Condor GT3 GRAM Executelocally

File transfer via Grid FTP

Insert derived data

and copy to file system

Instantiate workflow

and submit

Clicks link in

e-mail

Retrieve workflow and

local file locations

Select workflow

template

Select files to keep

Reinsertion Service

Initiate reinsertion

IXI Core Service

Co-ordinate

and delegate

File transfer via Grid FTP

Submit workflow

User enters

search criteria

Finished

Workbench

Query

Reviewresults

How it works

IXI: Dynamic brain atlas demonstrator

Database of medical images

rigid/non-rigid registration

Statistical or probabilistic atlas

classification

Age 16-35

Age 35-65

Age 65+

Web Portal

User

Relational Database

XML Database

Workflow Service

Workbench

How it works: Problems

Submit workflow

E-mail user?

What sorts of images?

2D images (ie x-ray) 3D images (ie CT, MR, PET) 4D images (ie CT, MR)

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?

• 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?

• 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

Biomedical Research Healthcare

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

• Image mining:– Statistical parametric maps of volume change in

patients with schizophrenia undergoing drug treatment

populationtime t = 1

populationtime t = 2

reference

intersubject registration

intrasubject registration

TBM


• 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

Use non-rigid registration to update pre-operative plan

Ideally real-time, however 10-20 minutesare acceptable


• 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

source

target Rueckert et al IEEE TMI 1999


Initial trans-formation T

Final trans-formation T

Calculate cost functionC for transformation T

Generate new estimate of T by minimizing C

Is new transformationan improvement ?

Update trans-formation T

Non-linear

optimization

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

Work by Stephen Jarvis, Dan Spooner, Brian FoleyUniversity of Warwick


• 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


Modelling systems and applications






What next?

• 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

• 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

• 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

performance in medical image computing

Documents

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mining image data

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sort of images

sorts of images

medical image analysisapply