CHARLOTTE BJUREN

SUPERVISORS: PROF. ALEX HOUSTON PROF. PETER HANCOCK

The use of PCA-based Methods in the Design of Normal Spect

rCBF atlases.

Aim of PhD.

The aim of my PhD is to investigate the potential of a multivariate method for computer aided detection of abnormalities in SPECT imaging

We know that most brains are physiologically different; using a standardised template as found brain imaging software only corrects for anatomical differences.

To address this problem a procedure has been proposed for

producing normal atlases, that involves registering and normalising normal images and extracting the mean image plus an appropriate number of normal variants (Eigen images) using Principal Components Analysis

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Postgraduate Presentation

MRI ImagesMRI Images SPECT ImagesSPECT Images

Medical Image Format

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Postgraduate Presentation

Single Photon Emission Computed Tomography

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How Images are Processed.

Registration to template, (alignment of images) are done in Brass, or Multi-Modality.

Brass automatically fits images to an HMPAO templates, created from 35 individuals.

Displays Defect and Difference Images.Multi-Modality can load up to 3 studies from

different modalities. (Pet, CT, MRI & SPECT).A good visualisation software is Hybrid

Viewer.

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Postgraduate Presentation

EFFECT OF IMAGE REGISTRATION ON A SINGLE TRANSVERSE SLICE

IMAGE SLICE TEMPLATE SLICE

REGISTERED IMAGE SLICE

MEAN IMAGE SLICE

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Can’t tell individual from Mean Image!

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Postgraduate Presentation

MEAN IMAGE

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EIGENIMAGE 1

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Extraction of Eigen Images.

Eigen vectors are formed by performing PCA on a set of Normal Images using the voxel as a variable. Since these eigenvectors are in the form of images, they are called eigen images.

They represent the ordered normal variants within the normal dataset and will include mainly physiological variation within the normal dataset and statistical noise.

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Postgraduate Presentation

Optimisation of the number of Eigen images.

• The ration of variance contributed by an eigen image to the total variance in the normal dataset is given by its Eigen value.(eigenimages are ordered by their Eigen values)

• It maybe assumed that signal-to-noise ratio in a Eigen image will decrease as the eigenvalue decreases.

• An indication of how many Eigen images to include in the atlas is provided by a plot of Eigen values.(altough more sophisticated methods exist)

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PLOT OF EIGENVALUE (%) VERSUS NUMBER

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10

EIGENIMAGE NUMBER

EIG

EN

VA

LU

E (

%)

CUT-CUT-OFF AT OFF AT PP = 5 = 5

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SLICE OF NORMAL BRAIN

MEAN SLICE

SLICE OF EIGENIMAGE 1

First Year ProjectFirst Year Project

• The first year will involve developing a The first year will involve developing a statistical method for determining the statistical method for determining the optimal number of Eigen images.optimal number of Eigen images.

• The method will then be applied to HMPAO The method will then be applied to HMPAO brain SPECT-imaging.brain SPECT-imaging.

• We will compare various optimisation We will compare various optimisation methods of extracting Eigen images such as methods of extracting Eigen images such as an Eigenvalue Scree Plot vs Jacknifing.an Eigenvalue Scree Plot vs Jacknifing.

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Reference List

Houston, A. S. (1998). Combining cross-validation and jackknifing to assess the validity of a normal brain atlas. In E. H. M. S. Berry (Ed.), (pp. 53-56). University of Leeds.

Houston, A. S., Kemp, P. M., Griffiths, P. T., & MacLeod, M. A. (1994a). An estimation of noise levels in HMPAO RCBF SPECT images using simulation and phantom data; comparison with results obtained from repeated normal controls. Physics in Medicine and Biology, 39, 873-884.

Houston, A. S., Kemp, P. M., & MacLeod, M. A. (1995). How can we define normality in a medical image? In Yves Bizais (Ed.), Information Processing in Medical Imaging: International Conference, Ile De Berder, France, June 1995 14th (Computational Imaging and Vision) (pp. 351-352). Kluwer Academic Publisher.

Houston, A. S., Fleming, J. S., Ward, T., & Hoffmann, S. M. A. (2009). Optimization of the parameters of a method for computer-aided detection of perfusion deficiencies in brain images. Nuclear Medicine Communications, 30.

Charlotte’s Homepage: charlottebjuren.com

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