Accel-RT · Accel-RT Implementation A Ethical framework for collection of patient image data er...

Accel-RT Knowledge management, imaging & RT

Planning

Raj JenaOn behalf of Accel-RT Team

Disclosures

• I am in receipt of funding from

– Cancer Research UK

– National Cancer Action Team (Public Health England)

• Accel-RT is funded through an STFC Innovation Partnership Scheme (IPS) award

Accel-RT | COMPRT Symposium | Cavendish Laboratory | 17th June 2015

Overview

• The clinical problem

• The proposed solution

• Repurposing knowledge from Grid Computing to RT planning

• Accel-RT implementation

• Lessons learned from the collaboration


The clinical problem


DICOM

MRICTPET

RTSTRUCT

RTPLAN

RTDOSE

MVCT

CBCT

EMR

OIS

Experience : the power of precedent

• Experience = I’ve seen this problem before (or one like it)

• I’ll base what I do on what I did the last time …

• That assumes I can find what I did the last time!


The solution : a new kind of search

• Search freely –unconstrained ‘Google’ like searches

• Recall effectively –visualise a previous treatment scenario and transpose it to the one in front of me


The solution : a new kind of search

• No overhead – automate the data capture from DICOM

• Platform agnostic – work with data from any source

• Ubiquitous


REPURPOSING KNOWLEDGE FROM GRID COMPUTING

Grid Computing

• Astrophysics : 100Gb data per night sky

• Particle Physics and LHC : 10% of global internet bandwidth consumed during experiments

• Distribute compute and storage across the world


Grid Computing

• Open source software tools and workflows e.g. Virtual Observatory (Taverna), GridPP, Pathgrid

• Computing paradigms (XML, SOAP, REST)


ACCEL-RT IMPLEMENTATION

Accel-RT ImplementationA

IDA Ethical

framework for collection of patient image data O

bje

ct P

acka

ger

Collect data objects, anonymiseand process for research DB

Dat

abas

e Web based database, with flexible searches and image preview D

eskt

op

ap

plic

atio

n Review CT,structuresand dose

Fuse onto a new patient

RES Approval as a

research database

Python (PyDicom) Postgres SQL back end

Python / Django web

front end

Image Processing on

Amazon EC2 cloud

instance

QT & C++ / VTK / ITK /

Plastimatch

Clinical domain Non-clinical domain


Changing landscape of our RT data•2008

– Paper action sheets & prescriptions

– No templates

– Limited IMRT use cases


•2014– Paperless OIS

– Extensive templating

– Extensive IMRT and IGRT

AIDA & DICOM Push

• AIDA = Anonymous Image Donation Archive

– REC approved NHS research DB

– Allows multiple usage, prospective and retrospective capture of images

• ToAida DICOM node on physics network


Object packager

• Ingest DICOM images, DICOM RT files, planning annotations

• Anonymise data and create unique study identifiers

• Compress data to single file for upload


Drag and drop

Drag & drop upload of data

• Simplifies upload process

• HTML5 canvas – drag archive file from object packager onto canvas


Accel-RT Database

• Works anywhere using any device with a web browser

• Secure login

• Clean UX design (EPIC could learn a thing or two!)

• Developed against synthetic data


Accel-RT Database

• ‘Google like’ search features

• Similar patient search

• ‘Treatment era’ search


Accel-RT Database

• Image preview within the database

• Some of the processing for the DB is performed using Amazon EC2 server


Search across patients

• Retrieve DVH data from RTDose file

• Evaluate same structure across multiple patients in the database


Accel-RT App• Cross platform

• Reconstruct plan data, with images, structures and dose.

• Image registration tools to register your images with those in the database.


Accel-RT App• Plethora of

powerful Image registration tools to register your images with those in the database.

• Wizards simplify parameter choices


LESSONS LEARNED FROM COLLABORATION

• Great questions!

• Challenge conventional ways of thinking

• You don’t know what you don’t know


Encompassing change

• Research partnerships changed to reflect changing business and research priorities

• Opportunities to work with new industry partners (OSL UK)

• New clinical workflows

• New hardware & software technologies


What next?

• Few things to finish up!

• Ramp up data flows and use system in the workplace

• See Accel-RT used at a national level (clinical trials?)

• Accel-RT 2 – link the data warehouse to a simulation engine


My thanks to a very wise team!

Andy ParkerMichael SimmonsNeil BurnetMark HayesMo Al Sa’dAndrew Hoole

Jim DaviesSteve Harris

Charles Crichton

Dean WillemsGrigorios Karangelis

Sue Barley

Himu ShuklaJoseph Stancanello

Barbara CamanziSTFC


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