Poster Presentations: Saturday, July 23, 2016
temporal lobe (MTL) subregions and hippocampal subfields, eventually spreading to most of the cortex. Since neurodegeneration in AD closely follows NFT pathology, granular MRI-based measures of change in MTL subregions can be more sensitive biomarkers for treatment evaluation than conventional markers such as hippocampal volume, particularly in preclinical disease, when effects are subtle and largely contained to the MTL. Several large studies, including ADNI, collect T2-weighted MRI scans of the MTL that offer higher resolution and significantly better contrast for visualizing hippocampal and MTL subregion boundaries than conventional 1mm isotropic T1w MRI. We previously developed an automatic multi-atlas segmentation technique “ASHS” that extracts MTL subregion volume and thickness measures in 3T and 7T MRI scans, and showed that it is can reliably reproduce manual segmentation. Until now, however, ASHS required access to a high-performance computing cluster. Through extensive optimization, we have accelerated ASHS by more than one order of magnitude, making it possible to use on commodity computers without compromising reliability. Methods: ASHS uses deformable registration (Avants et al., 2008) to warp 20-30 expert-labeled example scans called atlases to a new subject’s T2w MRI scan, and combines the deformed segmentations using intelligent label fusion. Registration accounts for >95% of computational requirements of ASHS. We optimized the registration in ASHS using separable one-dimensional algorithms for computing the normalized cross-correlation metric of image similarity, as well as approximate deformation field regularization. We evaluate ASHS using
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ten-fold cross-validation on 29 3T expert-labeled scans. Results: Optimized ASHS runs 16 times faster than the published ASHS algorithm on the same hardware. Processing for one subject takes 24 minutes on an 8-core MacBook laptop. Segmentation accuracy is not statistically different from previously published results. Conclusions: Accelerated ASHS will enable a broader range of researchers to derive quantitative measures of MTL subregions in T2w-MRI scans, which are increasingly commonplace. Unlike the automatic hippocampal subfield measures from T1w-MRI (Iglesias et al., 2015), T2w-MRI measures derived by ASHS have been explicitly validated against manual segmentation and have high reliability. IC-P-175
HYBRID DIFFUSION IMAGING (HYDI) OF WHITE MATTER CHANGES IN OLDER ADULTS WITH SUBJECTIVE COGNITIVE DECLINE (SCD): ASSESSMENT OF ORIENTATION DISPERSION AND AXONAL DENSITY
Sourajit Mitra Mustafi1,2, Pratik K. Gandhi1,2, Shannon L. Risacher1,2, John D. West1,2, Eileen F. Tallman1,2, Darren P. O’Neill1,2, Martin R. Farlow1,2, Frederick W. Unverzagt1,2, Liana G. Apostolova1,2, Andy J. Saykin1,2, Yu-Chien Wu1,2, 1Indiana University School of Medicine, Indianapolis, IN, USA; 2Indiana Alzheimer Disease Center, Indianapolis, IN, USA. Contact e-mail:
[email protected] Background: Diffusion tensor imaging (DTI) has been widely used
to assess white matter (WM) “integrity” in AD but further improvements are needed before it can be considered a robust biomarker. In this study, Hybrid Diffusion Imaging (HYDI)1 was used to study
Figure 1. WM ROIs defined as intersection of mean FA skeleton with JHU white matter atlas. The four limbic system WM ROIs overlaid on mean FA map are shown here.
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Poster Presentations: Saturday, July 23, 2016
Figure 2. Bar figures of NODDI derived orientation dispersion index (ODI) for various limbic system ROIs in cognitive normal (yellow bars) and SCD (grey bars) subjects. Mean values and standard deviation across subjects are depicted. The arrow show statistically significant ROI. The p-value < 0.05 is considered significant.
changes in orientation dispersion and axonal density index of WM in subjective cognitive decline (SCD), which may represent an early prodromal AD stage. These advanced diffusion indices were derived via parametric diffusion modeling, neurite orientation dispersion and density imaging (NODDI)2, and may be more sensitive than DTI. Methods: Thirteen SCD (age 68+11y) and eleven cognitively normal (CN) subjects (age 70+6y) were included. SCD is defined by a Cognitive Change Index(CCI) > 20 but psychometric performance within normal limits. All subjects received HYDI in a Siemens Prisma scanner using a multi-band accelerated EPI diffusion sequence. The HYDI protocol consisted of 5 diffusion-weighted b-value shells, and was used for parametric diffusion model fitting (NODDI), non-parametric q-space analysis, and DTI. Maps of diffusion metrics were non-linearly transformed to standard MNI space using FSL FNIRT. Four WM ROIs were defined in standard MNI space by intersecting subjects’ mean WM skeleton with the WM atlas from John Hopkins University (JHU) ICBMDT-813 (Fig. 1). ANCOVA analysis was used to test significance of diffusion metrics between SCD and CN groups covarying for
Table 1 Mean and Standard deviation for all diffusion metrics and ROIs
Model
Diffusion indices
NODDI Orientation dispersion index
Abbreviations Unit
Intensity range
Subject type
Cingulum (Cingulate Measures gyrus)
pvalues
ODI
0-1
CN
Mean
0.108
0.145
0.209
SD Mean SD Mean
0.023 0.096 0.012 0.598
0.923
0.035 0.234 0.036 0.455
SD Mean SD Mean
0.038 0.601 0.035 0.897
0.631
0.028 0.485 0.045 0.764
SD Mean SD Mean
0.043 0.907 0.039 0.618
0.298
0.035 0.795 0.055 0.501
SD Mean SD Mean
0.043 0.635 0.033 1371
0.508
0.052 0.485 0.059 1282
AU
SCD Intracellular volume fraction
Vic
AU
0-1
CN
SCD Po q-space Probability of water molecule with minimal diffusion
AU
0-1
CN
SCD DTI
Fractional anisotropy
FA
AU
0-1
CN
SCD Axial diffusivity
AD
106 mm2/s
0-3000
CN
SCD Radial diffusivity
RD
106 mm2/s
0-3000
CN
SCD Mean diffusivity
MD
106 mm2/s
0-3000
CN
SCD
Significant p value < 0.05 is marked in bold.
SD Mean SD Mean
48 1382 45 439
SD Mean SD Mean
42 420 34 749
SD Mean SD
21 741 23
Cingulum pFornix (Hippocampus) values (Cres)
0.265
77 1234 65 534
0.388
36 530 50 783 18 765 26
pUncinate pvalues fasciculus values
0.086
0.164 0.028
0.169
0.137
0.069
0.014 0.177 0.022 0.529 0.200
0.023 0.180 0.026 0.483
0.160
0.142
0.040 0.549 0.035 0.809 0.387
0.038 0.463 0.042 0.811
0.163
0.436
0.051 0.829 0.060 0.614 0.300
0.056 0.784 0.051 0.573
0.102
0.113
0.046 0.604 0.040 1454 0.532
0.052 0.544 0.044 1303
0.334
0.880
52 1433 83 480
0.070
62 494 90 805 41 807 82
0.343
59 1278 68 466
0.149
0.664
48 490 39 745
0.487
30 753 27
Poster Presentations: Saturday, July 23, 2016
age (significance at p