Edinburgh Data-Intensive Research - SINAPSE

David Rodrìgues transfers to BRIC

Malcolm.Atkinson — Fri, 03 May 2013 14:38:46 +0000

David Rodrìguez now has a research post in the Brain Research Imaging Centre, http://www.bric.ed.ac.uk, led by Professor Joanna Wardlaw, http://www.ccbs.ed.ac.uk/members/profile.asp?staffID=11. This is a modest change, as he will continue to work closely with the DIR group and drop into Informatics regularly; it is an inversion of his previous modus operandi of the last three years, where he was based in Informatics and visited BRIC at least two days per week. He will continue to work on the DECIPHER project and with Informatics students. We all wish him well in his new post.

Topic of this submission:

Staff leaving

Projects:

SINAPSE

The BraINS databank

David.Rodriguez — Wed, 12 Dec 2012 14:55:39 +0000

NeSC Research Seminar Series

Speaker:

Dominic Job & David Dickie

Brain Images of Normal Subjects (BRAINS) bank and atlases are being developed with >1000 normal subjects from across the lifespan, to be expanded in the future to include subjects with disease. The images have been collected in centres across Scotland and are in a range of magnetic resonance (MR) sequences, including T1, T2, T2*, and fluid attenuated inversion recovery (FLAIR). When BRAINS is released these will be searchable by a wide range of metadata, e.g. blood pressure<140/90; age=85; MMSE>26.
BRAINS atlases are based on calculated distributions of brain structure rather than parametric estimates. These will be used to support image analysis research and clinical reporting of brain images.

Attachment	Size
dir_dickieJob.ppt	13.01 MB

Date and time:

Friday, 14 December, 2012 - 10:00

Length:

45 minutes

Location:

Turing Room (5.42), Informatics Forum

Projects:

SINAPSE

Research topics:

Biological/Medical Image Processing

Congratulations to Fan Zhu

Malcolm.Atkinson — Fri, 19 Oct 2012 16:28:41 +0000

I am delighted to report that Fan Zhu's external examiners were pleased with his thesis and his spirited viva defense. They have recommended a pass with a few minor corrections. A big thank you to Professors Gary Green and Richard Baldock.

Title:
Brain Perfusion Imaging - Performance and Accuracy

Abstract:
Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions.
The purpose of my PhD research is to develop novel methodologies for improving the efficiency and quality of brain perfusion-imaging analysis so that clinical decisions can be made more accurately and in shorter time.
This thesis consists of three parts:

1. My research investigates the possibilities that parallel computing brings to make perfusion-imaging analysis faster in order to deliver results that are used in stroke diagnosis earlier. Brain perfusion analysis using local Arterial Input Functions (AIF) technique takes a long time to execute due to its heavy computational load. As time is vitally important in the case of acute stroke, reducing analysis time and therefore diagnosis time can reduce the number of brain cells damaged and improve the chances for patient recovery. We present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose computing on Graphics Processing Units) using the CUDA programming model. Our method aims to accelerate the process without any quality loss.

2. Specific features of perfusion source images are also used to reduce noise impact, which consequently improves the accuracy of hemodynamic maps. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR) makes use of the temporal information in the perfusion source imges to reduce the noise level. Over the entire image, our noise reduction method based on Gaussian process regression gains a 99% contrast-to-noise ratio improvement over the raw image and also improves the quality of hemodynamic maps, allowing a better identification of edges and detailed information. At the level of individual voxels, GPR provides a stable baseline, helps identify key parameters from tissue time-concentration curves and reduces the oscillations in the curves. Furthermore, the results shows that GPR is superior to the alternative techniques compared in this study.

3. My research also explores automatic segmentation of perfusion images into potentially healthy areas and lesion areas which can be used as additional information that assists in clinical diagnosis. Since perfusion source images contain more information than hemodynamic maps, good utilisation of source images leads to better understanding than the hemodynamic maps alone. Correlation coefficient tests are used to measure the similarities between the expected tissue time-concentration curves (from (reference tissue)) and the measured time-concentration curves (from target tissue). This information is then used to distinguish tissues at risk and dead tissues from healthy tissues. A correlation coefficient based signal analysis method that directly spots suspected lesion areas from perfusion source images is presented. Our method delivers a clear automatic segmentation of healthy tissue, tissue at risk and dead tissue. From our segmentation maps, it is easier to identify lesion boundaries than using traditional hemodynamic maps.

Topic of this submission:

News

Research topics:

Biological/Medical Image Processing

Projects:

SINAPSE

Brain Perfusion Imaging - Performance and Accuracy

David.Rodriguez — Tue, 28 Aug 2012 14:54:37 +0000

NeSC Research Seminar Series

Speaker:

Fan Zhu

I aim to use computer science methodologies to solve brain perfusion imaging (medical imaging) problems in my PhD research.
I focus on three issues. The first issue addressed was the acceleration of the algorithms used to generate hemodynamic maps. It proved possible, using GPGPUs, to achieve substantial speedups while retaining the quality of generated hemodynamic maps. The next two investigations sought to make better use of the information latent in the time-domain of CT perfusion scans. The first of these showed that Gaussian process regression (GPR) could significantly enhance the quality of derived data at modest computational cost. The second of these then used that denoised data to improve the discrimination between healthy, penumbra and dead tissue.

Date and time:

Friday, 12 October, 2012 - 10:30

Length:

90 minutes

Location:

Turing Room (5.42), Informatics Forum

Projects:

SINAPSE

Research topics:

Biological/Medical Image Processing

RapidBrain: Developing a Portal for Brain Research Imaging

David.Rodriguez — Fri, 30 Sep 2011 10:46:02 +0000

Speaker(s):

David.Rodriguez

Presentation Type:

talk

Brain imaging researchers execute complex multistep workflows in their computational analysis. Those workflows often include applications that have very different user interfaces and sometimes use different data formats. A good example is the brain perfusion quantification workflow used at the BRIC (Brain Research Imaging Centre) in Edinburgh.
Rapid provides an easy method for creating portlets for computational jobs, and at the same it is extensible.
We have exploited this extensibility with additions that stretch the functionality beyond the original limits. These changes can be used by other projects to create their own portals, but it should be noted that the development of such portals involve a greater effort than the required in the regular use of Rapid for creating portlets. In our case it has been used to provide a user-friendly interface for perfusion analysis that covers from volume reconstruction to results visualisation.

Date and time:

Wednesday, 28 September, 2011 - 13:40

Location:

All Hands Meeting 2011, York

Projects:

SINAPSE

Research topics:

Intuitive interfaces

Automated MRI Quality Assurance Data Collection and Analysis

Jano.van.Hemert — Thu, 21 Jan 2010 15:03:52 +0000

Speaker(s):

David.Rodriguez

Presentation Type:

talk

Quality Assurance (QA) is used to monitor the performance of MRI scanners and this is particularly important in multicentre imaging studies. In SINAPSE (Scottish Imaging Network a Platform for Scientific Excellence) a common QA protocol was agreed after studying the different QA protocols being used in the seven participant MR scanners in four centres and analysing the needs of multicentre studies. This common protocol provides a framework for monitoring the quality of the data obtained in the different centres to help facilitate the combination of data between centres.

Building on this previous MRI QA work performed in SINAPSE ([1,2]) we have designed a centralised service for collecting and analysing QA data produced by the SINAPSE collaboration centres. The deployment of such automatic service has several advantages that will streamline the adoption of the aforementioned protocol:
• Removes the burden of some manual tasks currently being done in the centres.
• Allows the sequence parameters used to be checked against the agreed acquisition protocol for accuracy and consistency.
• Ensures the consistency of the software used for the analysis and makes it easier the control and update of the software versions.
• Facilitates the reanalysis of the data.
• Enables the pooling of QA data from multiple scanners to help determine appropriate action levels.

This service will use a SINAPSE server deployed at ECDF (Edinburgh Compute and Data Facility). DICOM data containing the scanned phantoms can be directly sent to a DICOM receiver in the server. As QA data contains no patient data there should be no data protection issues; however, we will use PrivacyGuard [3], a DICOM de-identification application developed in SINAPSE, to assure that if any patient data is mistakenly sent it would be automatically removed, and that dataset deleted.
Furthermore, PrivacyGuard provides an extensibility mechanism used in this case to extract relevant QA information from the DICOM header and insert it into SINAPSE’s QA database.

The DICOM data is stored in ECDF and included in SINAPSE data catalogue that facilitates future searches, for instance for reanalysis. Additional non-DICOM information can be sent by SFTP (e.g. MR spectroscopy files) and will be also included in the QA database. Then an analysis job is submitted to the cluster, and the results inserted in the QA database. A web application, using the QA database information, allows monitoring the evolution of the QA parameters from all SINAPSE centres.
The integration of all these components provides a complete system for collecting, analysing and monitoring QA data in multicentre deployments while minimising the work load and the manual input required at each centre.

[1] K. Lymer et al. “Comparison of MRI at 3T and 1.5T – Preliminary Results from a Systematic Review”. British Chapter of ISMRM Meeting, Cardiff September 2009
[2]A. Coombs et al. “Geometric distortion in multicentre MRI: preliminary results of a phantom study”. British Chapter of ISMRM Meeting, Cardiff September 2009.
[3] See http://www.sinapse.ac.uk/dicom.html and http://research.nesc.ac.uk/privacyguard

D. Rodriguez Gonzalez (1,2,3), T. Carpenter (1,3), J.I. van Hemert (1,2), A. Hutchison (3), K. Lymer (1,3), J De Wilde (1) and J. Wardlaw (1,3)
1 SINAPSE collaboration www.sinapse.ac.uk
2 National e-Science Centre, School of Informatics, University of Edinburgh
3 SFC Brain Imaging Research Centre, Division of Clinical Neuroscience, University of Edinburgh

Date and time:

Friday, 26 March, 2010 - 15:40

Location:

Edinburgh Training & Conference Centre, 16 St. Mary's Street, Edinburgh, UK

Web site:

http://www.ipem.ac.uk/ipem_public/article.asp?id=397&did=49&aid=4068&st=&oaid=-1

Projects:

SINAPSE

Research topics:

Collaborative environments

New conceptual models for systems

A Solution to Sharing Clinical Data: Introduction to the Methodology and Applications

Jano.van.Hemert — Mon, 06 Jul 2009 09:58:51 +0000

Speaker(s):

David.Rodriguez

Presentation Type:

poster

Medical imaging acquired primarily for patient diagnosis is also of value for research and teaching. The Data Protection Act (1998), which regulates the use of personal data, indicates that only the identifying data that is required for a purpose should be held. At this moment, no commonly accepted solution exits because the amount of personal data that is required by research and teaching changes case by case.

Based on the analysis of the data provenance, transfer mechanisms and de-identification requirements of research projects and teaching libraries we have developed a DICOM data de-identification toolkit. This toolkit is flexible enabling the implementation of different anonymisation strategies in each case. It also provides a mechanism for forwarding the anonymous output to a remote computer using SFTP. Using the toolkit we have developed applications for anonymising DICOM files contained in a folder (recursively) and objects received via the DICOM protocol. A graphical user interface is provided to guide users in policy writing and configuration. The toolkit was tested by incorporating it into the workflow for a multi-centre trial using modalities in both NHS and Higher Education sectors.

The toolkit was successfully deployed within NHS (Edinburgh) and university departments (Aberdeen and Edinburgh) networks. In the two universities the automatic transfer of anonymous data to a central project repository via SFTP was used. Due to NHS network restrictions this was not possible for data acquired in the NHS; the necessary agreement to overcome this difficulty was not in place.

The toolkit we have developed allows privacy policies to be enforced. It provides a flexible and highly configurable framework that makes no a priori assumptions about the anonymisation strategy. The automated transfer mechanism simplifies the collection of data in multi-centre trials and avoids the use of physical media.

Date and time:

Wednesday, 17 June, 2009 - 11:00

Location:

SINAPSE Annual Scientific Meeting 2009, The Royal College of Physicians of Edinburgh, Edinburgh, UK

Web site:

http://www.sinapse.ac.uk/sciencemeeting.html

Projects:

SINAPSE

SINAPSE: e-Science Activities in a Brain Imaging Research Network.

David.Rodriguez — Mon, 22 Jun 2009 14:17:22 +0000

Speaker(s):

David.Rodriguez

Presentation Type:

talk

Presentation on the status and plans of the eScience activities in the SINAPSE network.

Attachment	Size
SINAPSE-Grids&eScience09.ppt	1.68 MB

Date and time:

Monday, 15 June, 2009 - 12:30

Location:

Grids & e-Science 2009 (UIMP). Palacio de la Magdalena. Santander, Spain

Web site:

http://www.sinapse.ac.uk

Projects:

SINAPSE

Medical Imaging in the SINAPSE project: data sharing infrastructure

Jano.van.Hemert — Thu, 18 Dec 2008 13:06:19 +0000

Speaker(s):

David.Rodriguez

Presentation Type:

talk

Presentation on SINAPSE and its data sharing infrastructure.

Date and time:

Tuesday, 16 December, 2008 - 10:00

Location:

Imagen Medica, Instrumentación y Grids: de la adquisición al diagnóstico

Web site:

http://indico.ifca.es/indico/conferenceDisplay.py?confId=190

Projects:

SINAPSE

MIDAS—Research Medical Imaging Data in the University and the NHS

Jano.van.Hemert — Tue, 02 Dec 2008 15:54:47 +0000

The meeting will be a forum to discuss issues affecting medical imaging data access and sharing of research medical images in Scotland. It would cover various aspects like information governance, data transfer and data anonymisation. There is a key strategic need for a national discussion on these topics between NHS and Universities in Scotland. The aim is to have an intensive discussion after some keynotes and it is hoped that the outcome will be a willingness at a high level to facilitate a solution to this medical imaging data challenge.

SINAPSE, the Scottish Imaging Network - A Platform for Scientific Excellence, is a consortium of six Scottish universities; Aberdeen, Dundee, Edinburgh, Glasgow, St. Andrews, and Stirling. Its aim is to create a strong dynamic network for strategic research development in brain imaging. More information in the web page: www.sinapse.ac.uk

Target audience: This meeting is intended to bring together both NHS and HEIs representatives including experts in the different topics involved (data transfer, data protection, etc.).

Location:

e-Science Institute, 15 South College Street, Edinburgh

Dates:

18 Mar 2009

Web site:

http://www.nesc.ac.uk/esi/events/954/

Projects:

SINAPSE