Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: An illustration in ADNI 3 T MRI data

Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
NeuroImage (Impact Factor: 6.36). 12/2009; 50(2):434-45. DOI: 10.1016/j.neuroimage.2009.12.007
Source: PubMed

ABSTRACT Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i.e., an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation.

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Available from: Sandhitsu Das, Sep 27, 2015
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    • "We notice that all the images undergo only one interpolation, and are therefore consistently processed in order to not introduce biases on the intensities due to asymmetric resamplings (Yushkevich et al., 2010). "
    NeuroImage 07/2015; 115(July, 15):224-234. · 6.36 Impact Factor
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    • "In standard implementation of deformable registration the reference space is commonly chosen as the native space of one of the images (say, the first image), and the results are consequently influenced by the ordering of the input images, thereby breaking the symmetry of registration. The spurious dependence of the pointwise correspondences on the choice of the reference image has been shown to be related to a bias introduced into the estimation of Alzheimer's disease effects (Fox et al., 2011; Hua et al., 2011; Thompson and Holland, 2011; Yushkevich et al., 2010). In longitudinal studies in particular, favoring one time point over another may result in errors dominating the subtle changes one seeks to measure (Reuter et al., 2012). "
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    ABSTRACT: The choice of a reference image typically influences the results of deformable image registration, thereby making it asymmetric. This is a consequence of a spatially non-uniform weighting in the cost function integral that leads to general registration inaccuracy. The inhomogeneous integral measure - which is the local volume change in the transformation, thus varying through the course of the registration - causes image regions to contribute differently to the objective function. More importantly, the optimization algorithm is allowed to minimize the cost function by manipulating the volume change, instead of aligning the images. The approaches that restore symmetry to deformable registration successfully achieve inverse-consistency, but do not eliminate the regional bias that is the source of the error. In this work, we address the root of the problem: the non-uniformity of the cost function integral. We introduce a new quasi-volume-preserving constraint that allows for volume change only in areas with well-matching image intensities, and show that such a constraint puts a bound on the error arising from spatial non-uniformity. We demonstrate the advantages of adding the proposed constraint to standard (asymmetric and symmetrized) demons and diffeomorphic demons algorithms through experiments on synthetic images, and real X-ray and 2D/3D brain MRI data. Specifically, the results show that our approach leads to image alignment with more accurate matching of manually defined neuroanatomical structures, better tradeoff between image intensity matching and registration-induced distortion, improved native symmetry, and lower susceptibility to local optima. In summary, the inclusion of this space- and time-varying constraint leads to better image registration along every dimension that we have measured it. Copyright © 2014. Published by Elsevier Inc.
    NeuroImage 10/2014; 106. DOI:10.1016/j.neuroimage.2014.10.059 · 6.36 Impact Factor
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    • "As shown in [8] [13], asymmetric image registration can introduce significant bias into longitudinal image analyses. We thus choose a symmetric registration setup, 6 degree-of-freedom global rigid registration similar to [13], and transform images, I t , and priors, π t , of all time points, to a common intermediate space [8] "
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    ABSTRACT: We propose a consistent approach to automatically segmenting longitudinal magnetic resonance scans of pathological brains. Using symmetric intra-subject registration, we align corresponding scans. In an expectation-maximization framework we exploit the availability of probabilistic segmentation estimates to perform a symmetric intensity normalisation. We introduce a novel technique to perform symmetric differential bias correction for images in presence of pathologies. To achieve a consistent multi-time-point segmentation, we propose a patch-based coupling term using a spatially and temporally varying Markov random field. We demonstrate the superior consistency of our method by segmenting repeat scans into 134 regions. Furthermore, the approach has been applied to segment baseline and six month follow-up scans from 56 patients who have sustained traumatic brain injury (TBI). We find significant correlations between regional atrophy rates and clinical outcome: Patients with poor outcome showed a much higher thalamic atrophy rate (4.9 ± 3.4%) than patients with favourable outcome (0.6 ± 1.9%).
    IEEE International Symposium on Biomedical Imaging (ISBI) 2014; 04/2014
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