INTERPRET in a nutshell by Carles Arús

Basic objective of INTERPRET? To develop a decission support system (DSS) to help Radiologists increase their performance when using MRS in the diagnosis of human brain tumours.

The beggining... Project requested on June 1999 (3.05 Meuros). SGHMS (John Griffiths) as coordinator. Project granted (retained) for negotiation on August 1999 (2.0 Meuros). Negotiation phase September- November Incoporation of new industrial partners, PRAXIM, Siemens (2.2 Meuros). UAB (Carles Arús) as coordinator. Contract signed on December Project start January 1st, 2000 (3 years).

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INTERPRET participants (IST ) Universitat Autònoma de Barcelona, ES St George’s Hospital Medical School, UK IDI-Bellvitge, ES Centre Diagnòstic Pedralbes, ES. INSERM U438-Université Joseph Fourier, FR. University Medical Center Nijmegen, NE. Katholieke Universiteit Nijmegen, NE. University of Sussex, UK. PRAXIM, FR Siemens AG, DE. Advisory group data contributors: - Medical University of Lodz, Poland. - FLENI Buenos Aires, Argentina.

Project Co-cordinator EU Comission Project Officer Project Management Committee Clinical Data Validation Committee Advisory Group Project Administrator Scientific Manager Quality Evaluators (QAU-UAB) External Evaluators WP7 - D&I WP1 - PM WP6 - A&E WP4 - GUI WP2 - DB WP3 - PR WP5 - IPCE WP8 - CP Project Administrator Partner 2 SGHMS

Managing related items 4 plenary meetings. 19 PMC meetings. 6 technical meetings. 7 CDVC meetings 3 Annual technical reviews succesfully endured. 1 Consortium agreement signed. 1 Contract amendment (pending of signature at Brussels).

What was needed to produce a good DSS? Automate processing of raw MRS data. Good classifiers. Good Graphical user interface (GUI).

What was needed to produce a good DSS? Automate processing of raw MRS data. Good classifiers. Good Graphical user interface (GUI).

Development of automated raw MRS data processing. Software modules for automated (SV) or semiautomated (CSI) processing from raw fid to spectra in canonical INTERPRET formats.

What was needed to produce a good DSS? Automate processing of raw MRS data. Good classifiers. Good Graphical user interface (GUI).

Requirements for classifier development Large number of MRS compatible spectra originating from clinically validated data. Consensus MRS acquisition protocols, available by June Final version of clinical validation protocols and ICR questionnaire ready by March Protocol for QC of scanners and MRS data available by April 2001 and January 2002, respectively. Database to store and retrieve data, ipDB until september 2001 and iDB since then.

Requirements for classifier development Large number of MRS compatible spectra originating from clinically validated data.. Consensus MRS acquisition protocols, available by June Final version of clinical validation protocols and ICR questionnaire ready by March Protocol for QC of scanners and MRS data available by April 2001 and January 2002, respectively. Database to store and retrieve data, ipDB until september 2001 and iDB since then.

Consensus acquisition protocols for SV and CSI. SV: TE/TR, 30 and 136/2000 ms PRESS. CSI: TE/TR 20/2000 STEAM and 136/2000 ms PRESS. MRS acquired post-contrast administration.

Requirements for classifier development Large number of MRS compatible spectra originating from clinically validated data.. Consensus MRS acquisition protocols, available by June Final version of clinical validation protocols and ICR questionnaire ready by March Protocol for QC of scanners and MRS data available by April 2001 and January 2002, respectively. Database to store and retrieve data, ipDB until september 2001 and iDB since then.

14 yr Anaplastic astrocytoma

Shape TrianglesStars Colour Blue Green PR Good classifiers...

Classifiers developed. SV, supervised approach on short echo time (20-32 msec) spectra. CSI, unsupervised (clustering) and supervised on short echo (20 msec) spectra. CSI supervised on long echo (272 and 135? msec) spectra.

What was needed to produce a good DSS? Automate processing of raw MRS data. Good classifiers. Good Graphical User Interface (GUI).

Graphical User Interface Scientific prototype for SV spectra. Scientific prototype for CSI short echo spectra (Siemens). Scientific prototype for CSI long echo spectra (Philips). Industrial prototype of DSS for SV spectra.

Graphical User Interface Scientific prototype for SV spectra. Scientific prototype for CSI short echo spectra (Siemens). Scientific prototype for CSI long echo spectra (Philips). Industrial prototype of DSS for SV spectra.

Graphical User Interface Scientific prototype for SV spectra. Scientific prototype for CSI short echo spectra (Siemens). Scientific prototype for CSI long echo spectra (Philips). Industrial prototype of DSS for SV spectra.

Graphical User Interface Scientific prototype for SV spectra. Scientific prototype for CSI short echo spectra (Siemens). Scientific prototype for CSI long echo spectra (Philips). Industrial prototype of DSS for SV spectra.

Example - case 6: HGG HGG (3)HGG (2)? Metastasis (1)?

Graphical User Interface Scientific prototype for SV spectra. Scientific prototype for CSI short echo spectra (Siemens). Scientific prototype for CSI long echo spectra (Philips). Industrial prototype of DSS for SV spectra.

Diffusion and Exploitation 17 publications or articles in press related to INTERPRET. 50 communications/posters/conferences related to INTERPRET. Prototype demonstration at ISMRM, ESMRMB, RSNA meetings and smaller workshops. Industrial prototype in certification path to obtain CE marking.

The final rush and the future... A preliminary clinical evaluation of the CSI 272 msec scientific prototype has been carried out and a similar one for SV and short echo time CSI prototypes is in progress. How can we improve what has been accomplished?. Should we do it?.

Acknowledgements. I thank you all for making INTERPRET possible...