Poster Presentations: P1P262

selectively binds to PHF-tau in AD brains, but does not bind to tau lesions in

non-AD tauopathies, nor to a -synuclein in PD brains.

P1-295 A DIFFERENTIAL PATTERN OF HIPPOCAMPAL

ATROPHY IN ALZHEIMER’S DISEASE WITH

COEXISTENT SMALLVESSEL DISEASE: A

MULTIVARIATE SHAPE-BASED ANALYSIS

Sean Nestor1, Sandra Black2, 1University of Toronto, Toronto, Ontario,

Canada; 2Sunnybrook Research Institute, Toronto, Ontario, Canada.

Contact e-mail: sean.nestor@mail.utoronto.ca

Background:Hippocampal volume is a sensitive marker of Alzheimer’s dis-

ease (AD) progression. Previous studies in AD have demonstrated heteroge-

neous atrophy profiles among hippocampal subregions. Cerebral small vessel

disease (SVD) often coexists with AD, and may differentially affect atrophy

within sub-regions of the hippocampus compared to AD alone. Shape-based

studies have traditionally used univariate methods, which independently test

significance at each element of a surface/structure (vertex/voxel). Conversely,

multivariate models such as Partial Least Squares (PLS) indentify signifi-

cantly distributed patterns of atrophy across all structural elements. Thus,

we examined with PLS,whether a differential pattern of hippocampal atrophy

existed between AD, AD+SVD and normal controls (NC).Methods: Cross-

sectional data were acquired from the Sunnybrook Dementia Study: AD

(n¼144), AD+SVD (n¼47) and NC (n¼95). All subjects had 1.5 Tesla

T1-SPGR MRIs (Matrix¼256x192; TE/TR¼35ms/5ms; flip-angle¼35�, in-plane resolution¼0.85930.859x1.2-1.4mm). Participants with SVD had

evidence of white matter hyperintensities on PD/T2-MRI and/or evidence

of subcortical lacunar infarcts. A novel surface-based technique was devel-

oped to measure hippocampal shape differences. Briefly, an average template

was generated and used to align all subjects to a common space. Next, the Ad-

vanced Normalization Tools SyN algorithm was used to nonlinearly register

all subjects to the template. A mesh was generated over the template hippo-

campi, and the inverse warp vector was indexed and multiplied by the normal

vector at each mesh vertex; this provided a vertex-wise scalar value of shape

difference in relation to the template surface across all subjects (Matlab-Math-

Works). PLS software (http://www.rotman-baycrest.on.ca/index.php?

section¼84) was adapted for surface-based parametric data. Mean-centred

PLS with bootstrapping(x1000) and permutation testing(x1000) was applied

to detect significant patterns of atrophy between groups. Analyses were cor-

rected for sex and age.Results: PLS revealed a significantly different pattern

of atrophy in NC versus AD and AD+SVD involving the anteriolateral CA1,

medial subiculum and posteriorlateral hippocampus (p<0.05). A trend was

realized for less atrophy in the left anterior-medial hippocampal region for

AD+SVD versus AD, with more involvement of the left lateral subiculum

in AD and left anteriolateral region in AD+SVD (p ¼0.06). Conclusions:

These data suggest that a differential pattern of hippocampal atrophy may ex-

ist in AD+SVD versus AD and may provide insight into how these coexistent

pathologies interact in-vivo.

P1-296 CORRECTION OF SCANNER DIFFERENCES IN

MULTICENTER J-ADNI AND U.S.-ADNI PET

STUDIES

Figure 1. Example segmentation results of our methods (SC, RC and RR)

compared to LSTopt (optimal baseline): Each row corresponds to one subject.

First column are the expert indications overlayed onto bias corrected T2-

MR, second to last columns are the LSTopt outputs followed by CPEs of

SC, HC and RR respectively, overlayed on the corresponding bias corrected

T2-MR. The color map of overlay range from blue (0) to red (1).

Ken Fujiwara1, Takashi Kato1, Kengo Ito1, Michio Senda1, Kenji Ishii1,

Kazunari Ishii2, Takeshi Iwatsubo3, Japanese Alzheimer’s Disease

Neuroimaging Initiative4, 1J-ADNI PETcore, Obu, Japan; 2J-ADNI

PETcore, Osakasayama, Japan; 3The University of Tokyo, Tokyo, Japan;4J-ADNI, Tokyo, Japan. Contact e-mail: fujiwara@micron-kobe.com

Background: It has been reported that systematic differences of cerebral

FDG-PET image among PET cameras can be obstacles to statistical analy-

ses of PET image. The purpose of this study was to evaluate and correct the

difference between scanners with brain phantom scans. Methods: FDG-

PET images were obtained in 95 normal subjects (NL) with major seven

models of PET scanner at the base line in J-ADNI study. The images of other

scanner models were excluded because of small number of scans. The im-

ages were classified into seven groups as to the scanner model. The PET im-

ages of Hoffman phantom were smoothed with Gaussian filter of 12mm in

FWHM and spatially normalized to PET template of which cerebellum was

masked in order to avoid mis-registration. The normalized PET images were

averaged in each scanner model and in overall. These spatial processes were

done using SPM8.Seven correction filter images were calculated as ratio of

the averaged overall scanner models to averaged each scanner model on

pixel by pixel basis. Difference-corrected images were created by multiply-

ing each image with the correction filter image of its own scanner model.

T-test group comparisons were made between scanner models with and

without the difference-correction on pixel basis. The images of 78 subjects

by six scanner models in US-ADNI were processed and analyzed using the

same methods as above.Results: The degree of difference between scanner

models became smaller after the difference correction. The differences of

PET images between J-ADNI and US-ADNI were reduced with the correc-

tion method. Conclusions: Systematic image difference owing to scanner

models can bemade to be smaller by the correction using the Hoffman phan-

tom data. T his correction method may be effective to obtain more accurate

statistical results in multi-center studies.

P1-297 EXTRACTING WHITE MATTER

HYPERINTENSITIES IN ALZHEIMER’S DISEASE

RISK AND AGING STUDIES USING SUPERVISED

SEGMENTATION METHODS

Vamsi Ithapu1, Vikas Singh2, Benjamin Austin1, Chris Hinrichs3,

Cynthia Carlsson4, Barbara Bendlin1, Sterling Johnson4, 1University of

Wisconsin-Madison, Madison, Wisconsin, United States; 2University of

Wisconsin-Madison, Madison, Wisconsin, United States; 3Univesity of

Wisconsin-Madison, Madison, Wisconsin, United States; 4VA GRECC,

Madison, Wisconsin, United States. Contact e-mail: ithapu@wisc.edu

Background: White matter hyperintensities (WMH) observed in T2-

weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Reso-

nance Images (MRI) may reflect comorbid neural injury or cerebral vascular

Table 1

Segmentation performance measures of LSTopt (optimal baseline) compared

to our methods, SC, RC and RR: TP - True positives, FP - False positives, TN

- Trne negatives, FN - False negatives. F-measure (or Dice Coe_cient) is the

maximumof the ratio of 2TP to 2TP+FP+FN. Average Pression (AP) (which

is the same as area under Precision Recall curve) and Break Even Point

(BEP) summarize the e_ectivity of each method in minimizing both FP and

FN simultaneously. F0:5 and F2 penalize FP over FN and FN over FP

respectively. Note that the second column represents the underlying learning

model of each of the above methods.

Method Model F AP BEP F0:5 F2

LSTopt MRF 0:406 0:310 0:394 0:402 0:474

SC SVM 0:520 0:525 0:504 0:518 0:586

RC RF 0:642 0:749 0:632 0:647 0:708

RR RF 0:649 0:762 0:638 0:645 0:723

Poster Presentations: P1 P263

disease burden. Accurate segmentation and quantification of these WMH is

of substantial interest in aging, and age related neurological disorders such

as Alzheimer’s Disease (AD). Several existing automated lesion detection

algorithms, that use unsupervised learning models, result in unreliable de-

tections. This is especially apparent in older population where WMH are

small, diffuse and irregular in shape, and sufficiently heterogeneous within

and across subjects.Methods:We pose the detection of hyperintensities as

a supervised inference problem and adapt two learning models, specifically,

Support Vector Machines (SVM) and Random Forests (RF). Structural in-

formation that characterizes a hyperintensity is incorporated through texture

features engineered using texton based filter banks, and training is employed

to supervise the models. Interpreting the segmentation as both regression

and classification, we provide a suite of effective segmentation methods.

The detections are further quantified as a scalar accumulation parameter, re-

ferred to as Effective WMH Volume (EV). Extensive evaluations are con-

Table 2

Regression analysis of the hyperintensity accumulation (EV ) scores from

two of our methods (RC and RR) with six vascular factors, CL - Cholesterol,

HDL - Highdensity Lipoprotein, SBP - Systolic Blood Pressure, DBP -

Diastolic Blood Pressure, BMI - Body Mass Index and CV Risk - 10–year

cardiovascular risk: EV –RC (EV – RR resp.) denotes the EV s calculated

using RC (RR resp.) method. Significant p–values (and corresponding t–

values) at 95% confidence level and higher are bold-faced.

Variables Statistics

Dependent Explanatory t–value p–value

EV –RC

CL 1:582 0:117

HDL �2:128 < 0:05

SBP 4:174 < 0:001

DBP 3:977 < 0:001

BMI 1:096 0:276

EV –RC

CV Risk 2:922 < 0:01

Variables Statistics

Dependent Explanatory t–value p–value

EV –RR

CL 1:581 0:117

HDL �2:094 < 0:05

SBP 4:166 < 0:001

DBP 3:991 < 0:001

BMI 1:067 0:289

EV –RR

CV Risk 2:932 < 0:01

ducted on 140 subjects (male: 55, female: 85). This included 107

healthy (median age: 61.16), 21 declining (median age: 60.14) and 12

mild cognitively impaired (MCI) (median age: 65.16) subjects, who

vary in cardiovascular and AD risk factors. A multivariate regression anal-

ysis is done to evaluate the association of this hyperintensity accumulation

to vascular risk factors. Results: Segmentation results, and related perfor-

mance measures (like Precision-Recall curves, Dice Coefficients etc.)

show that our methods outperform an existing state-of-the-art unsuper-

vised lesion detection algorithm. Secondary statistical analysis indicate

the association of hyperintensity accumulation to vascular risk factors,

in particular, systolic and diastolic blood pressure and Framingham risk

score. Conclusions: Our evaluations highlighted the importance of user

supervision in the form of expert indications, for accurate and reliable seg-

mentation of hyperintensities in older population. We described a new

summary measure of hyperintensity accumulation, and validated its effi-

cacy using cardiovascular risk factors. These experiments are accompa-

nied with an open source library (interfaced with existing neuroimaging

tools) that can be adapted for segmentation problems in other neuroimag-

ing studies.

P1-298 CORTICAL SURFACE PROJECTION OF PET

IMAGES FROM 11C- AND 18F-LABELED

RADIOTRACERS WITHOUT MAGNETIC

RESONANCE IMAGING

Vincent Dor�e1, Pierrick Bourgeat2, Luping Zhou3, Jurgen Fripp4,

Ralph Martins5, Lance Macaulay6, Colin Masters7, David Ames8,

Belinda Brown9, Christopher Rowe10, Olivier Salvado11,

Victor Villemagne12, 1CSIRO, Melbourne, Australia; 2CSIRO, Herston,

Australia; 3CSIRO, Herston, Australia; 4AeHRC, Herston, Australia; 5Edith

Cowan University, Perth, Australia; 6CSIRO, Parkville, Australia;7University of Melbourne, Melbourne, Australia; 8National Ageing

Research Institute Inc. (NARI), Parkville, Australia; 9Edith Cowan

University, Perth, Australia; 10Austin Hospital, Melbourne, Australia;11CSIRO, Melbourne, Australia; 12Austin Health, Melbourne, Australia.

Contact e-mail: vincent.dore@csiro.au

Background: Clinical PET imaging in Alzheimer’s disease relies in the

visualisation of Ab deposition and glucose metabolism in the cortical

gray matter. Due to the limited structural information in PET images,

automatic tissue-specific assessment is usually performed with the aid

of MR images, which may not always be available. We evaluated a novel

MR-less method to locally estimate and project the cortical tracer reten-

tion on a common surface template. Methods: Several subjects were

scanned with different radiotracers: 18 F-Flutemetamol, 18 F-Florbeta-

pir, 18 F-Florbetaben, 11 C-PIB, 18 F-NAV4694 and 18 F-FDG. First,

each individual PET image was normalised in the MNI space and

SUVR scaled with a common cortical cerebellum mask. Radiotracer re-

tention was then estimated within several GM prior atlases. On scans

acquired on a PET/CT scanner, CT was used to estimate the GM priors.

Atlas selection and Bayesian fusion were then used for generating esti-

mated surface values, reflecting the pattern of either high (A ligands) or

low (FDG) tracer retention. Surface projections and native transaxial,

sagittal and coronal PET images were visually graded by clinicians

blinded to clinical diagnosis. Images were read separately and graded

as normal, possible AD or probable AD. For sensitivity and specificity

calculations, "possible" and "probable AD" were combined. Results: In

the visual readouts, surface projection images provided higher inter-rater

reliability and much greater reader confidence than native PET images. Vi-

sual assessment of surface projections were both very sensitive and spe-

cific for AD and performed better than visual readouts of native PET

images. Conclusions: The proposed method demonstrated accurate esti-

mations of radiotracer retention in the cortex for various 11 C and 18 F la-

beled radiotracers. Our approach provides a practical and efficient clinical

inspection tool for PET.