Morphometrische Verlaufsuntersuchungen

bei Verdacht auf dementielle Erkrankungen
und Multipler Sklerose

Die Bestimmung des gesamten Hirnvolumens bzw. ausgewählter Bereiche des Gehirns gewinnt in der Diagnostik und bei Verlaufsuntersuchungen von Patienten mit neurodegenerativen (wie der Alzheimer-Demenz) und entzündlichen ZNS-Erkrankungen (wie der Multiplen Sklerose) zunehmend an Bedeutung.

Radiologie am Berliner Platz Würzburg

Hauptmerkmal der Multiplen Sklerose sind zeitlich und örtlich an bevorzugten Stellen verstreut auftretende Entmarkungsherde der weißen, Nervenfasern beinhaltenden Hirnsubstanz. Ein Hirnvolumenverlust begleitet jedoch oft die Erkrankung und ist mit dem Auftreten körperlicher und kognitiver Einschränkungen vergesellschaftet. Wenn es dagegen gelingt, einen Hirnvolumenverlust unter der Gabe von Medikamenten zu stoppen, so kann das die Wirksamkeit der Behandlung für den Patienten belegen. Bei beiden hier gezeigten Fällen handelt es sich um etwa 35jährige Frauen, die seit circa 5 Jahren an Multipler Sklerose erkrankt waren.

Im Fall 1 (MS-Beispielfall 1 Morphometrie) kam es im Verlauf eines Jahres unter der Behandlung mit einem neuen Medikament zu keiner wesentlichen Hirnvolumenänderung. Im Fall 2 (MS-Beispielfall 1 Morphometrie) nahm das Hirnvolumen im gleichen Zeitraum dagegen um mehr als 0.52 % ab. Das zeigt in mehr als 95 % der Fälle einen fortschreitenden Abbauprozeß an (J Neurol Neurosurg Psychiatry 2015;0:1–7) und kann dazu anregen, die weitere Behandlung zu überdenken und zu optimieren. Die Radiologie Bamberg bietet derartige Untersuchungen und komplexe Auswertungen für ausgewählte Fragestellungen in enger Kooperation mit den klinisch-fachärztlichen Spezialisten an und ist auch an verschiedenen multizentrischen Studien beteiligt.

SIENA Report

siena 201503.nii.gz 201603.nii.gz -B -f 0.32 -B -S -R

BET brain extraction results

201503

morphometrie-1

201603

morphometrie-2

FLIRT A-to-B registration results

201503

morphometrie-3

Field-of-view and standard space masking

Red shows the common field-of-view of the two timepoint images and the standard-space-based field-of-view masking (if this was run). Blue shows the brain masks, including standard-space-based brain masking (if this was run). Green shows the intersection of the two.

201503

morphometrie-4

201603

morphometrie-5

FAST tissue segmentation

These images show the tissue segmentation used to find the brain/non-brain boundary. The exact segmentation of grey matter vs. white matter is not important.

201503

mrophometrie-6

201603

mrophometrie-7

Final brain edge movement image
atrophy 0 „growth“

201503

morphometrie-8

Estimated PBVC: .0075250000

.

SIENA Methods

Two-timepoint percentage brain volume change was estimated with SIENA [Smith 2001, Smith 2002], part of FSL [Smith 2004]. SIENA starts by extracting brain and skull images from the two-timepoint whole-head input data [Smith 2002b]. The two brain images are then aligned to each other [Jenkinson 2001, Jenkinson 2002] (using the skull images to constrain the registration scaling); both brain images are resampled into the space halfway between the two. Next, tissue-type segmentation is carried out [Zhang 2001] in order to find brain/non-brain edge points, and then perpendicular edge displacement (between the two timepoints) is estimated at these edge points. Finally, the mean edge displacement is converted into a (global) estimate of percentage brain volume change between the two timepoints.

[Smith 2001] S.M. Smith, N. De Stefano, M. Jenkinson, and P.M. Matthews. Normalised accurate measurement of longitudinal brain change. Journal of Computer Assisted Tomography, 25(3):466-475, May/June 2001.

[Smith 2002] S.M. Smith, Y. Zhang, M. Jenkinson, J. Chen, P.M. Matthews, A. Federico, and N. De Stefano. Accurate, robust and automated longitudinal and cross-sectional brain change analysis. NeuroImage, 17(1):479-489, 2002.

[Smith 2004] S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004.

[Smith 2002b] S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, November 2002.

[Jenkinson 2001] M. Jenkinson and S.M. Smith. A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2):143-156, June 2001.

[Jenkinson 2002] M. Jenkinson, P.R. Bannister, J.M. Brady, and S.M. Smith. Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2):825-841, 2002.

[Zhang 2001] Y. Zhang, M. Brady, and S. Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans. on Medical Imaging, 20(1):45-57, 2001.

SIENA Report

siena 201502.nii.gz 201604.nii.gz -B -f 0.32 -S -R

BET brain extraction results

201502

morphometrie-1

201604

morphometrie-2

FLIRT A-to-B registration results

.

morphometrie-3

Field-of-view and standard space masking

Red shows the common field-of-view of the two timepoint images and the standard-space-based field-of-view masking (if this was run). Blue shows the brain masks, including standard-space-based brain masking (if this was run). Green shows the intersection of the two.

201502

morphometrie-4

201604

morphometrie-5

FAST tissue segmentation

These images show the tissue segmentation used to find the brain/non-brain boundary. The exact segmentation of grey matter vs. white matter is not important.

201502

morphometrie-6

201604

morphometrie-7

Final brain edge movement image
atrophy 0 „growth“

201503

morphometrie-8

Estimated PBVC: -.5265720000

.

SIENA Methods

Two-timepoint percentage brain volume change was estimated with SIENA [Smith 2001, Smith 2002], part of FSL [Smith 2004]. SIENA starts by extracting brain and skull images from the two-timepoint whole-head input data [Smith 2002b]. The two brain images are then aligned to each other [Jenkinson 2001, Jenkinson 2002] (using the skull images to constrain the registration scaling); both brain images are resampled into the space halfway between the two. Next, tissue-type segmentation is carried out [Zhang 2001] in order to find brain/non-brain edge points, and then perpendicular edge displacement (between the two timepoints) is estimated at these edge points. Finally, the mean edge displacement is converted into a (global) estimate of percentage brain volume change between the two timepoints.

[Smith 2001] S.M. Smith, N. De Stefano, M. Jenkinson, and P.M. Matthews. Normalised accurate measurement of longitudinal brain change. Journal of Computer Assisted Tomography, 25(3):466-475, May/June 2001.

[Smith 2002] S.M. Smith, Y. Zhang, M. Jenkinson, J. Chen, P.M. Matthews, A. Federico, and N. De Stefano. Accurate, robust and automated longitudinal and cross-sectional brain change analysis. NeuroImage, 17(1):479-489, 2002.

[Smith 2004] S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004.

[Smith 2002b] S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, November 2002.

[Jenkinson 2001] M. Jenkinson and S.M. Smith. A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2):143-156, June 2001.

[Jenkinson 2002] M. Jenkinson, P.R. Bannister, J.M. Brady, and S.M. Smith. Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2):825-841, 2002.

[Zhang 2001] Y. Zhang, M. Brady, and S. Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans. on Medical Imaging, 20(1):45-57, 2001.

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