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Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre.

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Presentation on theme: "Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre."— Presentation transcript:

1 Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre Utrecht www.smri.nlwww.smri.nl (h.schnack@umcutrecht.nl)

2 Our recent acquisition: 7 Tesla scanner Officially opened, December 4 th, 2007

3 Our research Investigation of morphological brain abnormalities in psychiatric disorders (schizophrenia) Q: Are brains of schizophrenia patients smaller? Magnetic Resonance Imaging (MRI) scans of patients and healthy comparison subjects Image processing and statistical analyses A: Yes, statistically they are.

4 Schizophrenia patients have less gray matter than healthy subjects N=310 for this result Large variation in brain morphology

5 Heritability of brain changes in schizophrenia

6 Heritable brain changes in schizophrenia P-C N-N N=2x44 for this result (Discordant) twins are sparse

7 We Need More Twins! International collaborations EUTwinsS (European Twin Study Network on Schizophrenia) Germany, the UK, The Netherlands, Spain, Hungary and Switzerland STAR (Schizophrenia Twin and Relatives) consortium Heidelberg, Jena, London, Utrecht, Helsinki

8 Can we combine brain scans from different scanners (machines, manufacturers, acquisition protocols, field strength, in time, …)? What do we mean by ‘Can’? (increase in power; closer to ‘the truth’ – can we know the truth?) Is there a measure of goodness for (processed) MRI scan? Multicenter MRI Goal: combine MRI data from different scanners

9 Scanner Truth Derivatives: Segments, volumes, shapes, fiber tracts, … Processing

10 Another Scanner Now Scanner Two years later Truth Now Truth Two years later Derivatives: Segments, volumes, shapes, fiber tracts, … Derivatives: Segments, volumes, shapes, fiber tracts, … Processing (+2 yr)

11 Multicenter MRI (STAR) STAR multicenter MRI calibration study: Schnack et al. 2004. Human Brain Mapping 22: 312-320. Sitescanner manufacturer and type acquisition summary protocol / orientation / scan time voxel dimensions (mm) / (no. slices) TE (ms) TR (ms) flip angle Utrecht, reference repeated 1 repeated 2 Philips NT 1.5 T 3D-FFE coronal 11 min 1  1  1.2 (180) 4.63030˚ LondonGE Signa 1.5 T 3D-SPGR coronal 19 min 0.781  0.781  1.5 (124) 53535˚ HeidelbergPicker Edge 1.5 T 3D-FLASH sagittal 13 min 1  1  1.5 (128) 33030˚ JenaPhilips ACS II 1.5 T 3D-FFE sagittal 11 min 1  1  1 (256) 51325˚ HelsinkiSiemens Magnetom Impact 1.0 T MPRAGE sagittal 7 min 1  1  1.2 (128) 4.411.412˚

12 Multicenter MRI (STAR) STAR multicenter MRI calibration study: Schnack et al. 2004. Human Brain Mapping 22: 312-320. Sitescanner manufacturer and type acquisition summary protocol / orientation / scan time voxel dimensions (mm) / (no. slices) TE (ms) TR (ms) flip angle Utrecht, reference repeated 1 repeated 2 Philips NT 1.5 T 3D-FFE coronal 11 min 1  1  1.2 (180) 4.63030˚ LondonGE Signa 1.5 T 3D-SPGR coronal 19 min 0.781  0.781  1.5 (124) 53535˚ HeidelbergPicker Edge 1.5 T 3D-FLASH sagittal 13 min 1  1  1.5 (128) 33030˚ JenaPhilips ACS II 1.5 T 3D-FFE sagittal 11 min 1  1  1 (256) 51325˚ HelsinkiSiemens Magnetom Impact 1.0 T MPRAGE sagittal 7 min 1  1  1.2 (128) 4.411.412˚

13 Multicenter MRI 6 healthy subjects scanned in Utrecht (twice), Heidelberg, Jena, London Processed with image processing pipeline in Utrecht 1. Measure reliability (fixed algorithms) 2. Calibrate algorithms (tunable parameters) Goal: combine MRI data from different scanners Calibration study

14 Multicenter MRI Reliability of tissue volumes (ICC) GrayWhite Utrecht repeated0.971.00 Utrecht – London0.940.99 London – Jena 0.850.94 Jena – Utrecht 0.880.94 ICC = true variation / (true variation + error) > 0.7 = “good”

15 Voxelwise reliability (ICC) Utrecht repeated scans: 97% of the voxels has ICC > 0.7 (“good”)

16 Multicenter MRI: Voxelwise reliability (ICC) N eff (gain factor)

17 Challenges Comparability of MR images (scanners) Comparability of analysis tools (software) Comparability of their interactions Creation of gold standards (“truths”) Create better simulated MR images Other “calibration” mechanisms (instead of sending out 6 people around Europe?) How to present / summarize / visualize reliability? (generalizable?) Other modalities…

18 Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre Utrecht www.smri.nlwww.smri.nl (h.schnack@umcutrecht.nl) Contributors Hugo Schnack Neeltje van Haren Rachel Brouwer Hilleke Hulshoff Pol (head Neuroimaging Psychiatry) René Kahn (head Dept. Psychiatry)

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