Generating a Minimal Set of Templates for the Hippocampal Region in MR Neuroimages

OBJECTIVES We detail a procedure for generating a set of templates for the hippocampal region in magnetic resonance (MR) images, representative of the clinical conditions of the population under investigation. METHODS The first step is robust standardization of the intensity scale of brain MR images, belonging to patients with different degrees of neuropathology (Alzheimer’s disease). So similar tissues have similar intensities, even across images coming from different sources. After the automatic extraction of the hippocampal region from a large dataset of images, we address template generation, choosing by clusterization methods a small number of the extracted regions. RESULTS We assess that template generation is largely independent on the clusterization method and on the number and the clinical condition of the patients. The templates are chosen as the most representative images in a population. The estimation of the “minimum” number of templates for the hippocampal region can be proposed, using a metric based on the geometrical position of the extracted regions. CONCLUSIONS This study describes a simple and easily reproducible procedure to generate templates for the hippocampal region. It can be generalized and applied to other brain regions, which may be relevant for neuroimaging studies. Introduction Magnetic resonance imaging (MRI) has been used in numerous in vivo anatomical studies of the brain,1 especially for the hippocampus, and plays an important role in the diagnosis of temporal lobe epilepsy, or degenerative diseases such as Alzheimer’s dementia, and in the evaluation of their course.2 In MR images, a significant atrophy or a degeneration not only in the striatum, but also of other structures such as white matter (WM), cerebral cortex, amygdala, and hippocampus, contribute to identifying Huntington’s chorea or cognitive impairment.3,4 Anatomical reference images (templates) are then becoming of vital importance for result comparison, and to allow better identification of structures. Such templates are primarily intended to serve as anatomical references for spatial normalization usually required before studying human anatomical or functional variability. Atlases derived from those templates, built with different modalities and most often characterized by specific structure labeling,5,6 have been used with success in various computer-aided decision systems. They are usually built from a single acquisition of different control subjects reflecting the population targeted by the clinical study.7,8 Copyright ◦C 2012 by the American Society of Neuroimaging 1 Atlas-generation has to account for inter-subject variability of anatomy and function, in order to offer a powerful framework which facilitates comparison over time, between subjects, between groups of subjects and across sites.9 This the significant goal. With this aim, various probabilistic and statistical approaches have been developed to establish anatomical reference images (templates), representative of the population under investigation.10,11 Studying and quantifying local anatomical differences or changes in a population, in a sense of characterizing anatomical differences between subjects and templates, is a very challenging and difficult task. Especially the problem of identifying differences in relative positions of brain structures and detecting local differences requires complex models. Linear or nonlinear forms of spatial normalization are used to register images from larger cohorts into a common stereotactic space, enabling region by region comparisons.1,7-12,14-17 In this work, we describe a rather simple procedure for generating a set of templates for the hippocampal region. It represents a refinement and a generalization of the Calvini et al12 research. Against that background, many aspects are differently treated, especially regarding image standardization and the estimation of the “minimum” number of templates for the hippocampal region, and the effect of these different strategies are evaluated. The complete procedure foresees different steps. First, we build up a robust method for standardizing the intensity scale of brain MR images. This way similar tissues have similar intensities, even across images coming from different sources. Then we automatically extract the hippocampal region from a large dataset of MR images, with great accuracy. Subsequently, template generation is addressed, by choosing a small number of the extracted regions, using clusterization methods. Because morphological variability is captured by rigid transformations, a number of templates are necessary for group analysis. Thus, the number of images belonging to each considered template set, obtained in the clusterization step, are the means to account for variability. Finally, we propose an estimation of the “minimum” number of templates for the chosen brain area, using a metric based on the geometrical position of the extracted hippocampal regions. We also attempt a preliminary generalization test of the procedure, applying it to other brain areas, which may be relevant for future neuroimaging studies. The procedure was developed within the “Medical Application on a Grid Infrastructure Connection” (MAGIC-5) group, an Italian collaboration related to Istituto Nazionale di Fisica Nucleare (INFN), involving many academic and clinical institutions in the field of computer-aided detection (CAD) software systems for the analysis of medical images,11,13 such as MR or positron emission tomography (PET) images, as a support for the early diagnosis of neurological pathologies, especially the Alzheimer’s disease (AD). 12 Materials and Methods Materials Various datasets of brain T1-weighted MR images downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) website (http://www.loni.ucla.edu/ADNI/) were used to develop the procedure detailed in the next paragraphs. Each image in the ADNI dataset has undergone specific image preprocessing correction steps. These corrections include gradwarp, B1, and N3 corrections. Gradwarp is a system-specific correction of image geometry distortion due to gradient nonlinearity. B1 nonuniformity correction procedure employs the B1 calibration scans to correct the image intensity nonuniformity, that results when radiofrequency transmission is performed with a more uniform body coil, while reception is performed with a less uniform head coil. N3 is a histogram peak sharpening algorithm applied to all images, after gradwarp and after B1 corrections, for systems on which these two correction steps are performed. N3 processing reduces residual intensity nonuniformity. Each dataset is nonhomogeneous in terms of age, cardinality, and pathology of the subjects, with clinical conditions ranging from good health state (Normal) to probable dementia of AD type as well as with MCI. The minimental state examination (MMSE) test was used to estimate the severity of the cognitive impairment. In particular, for a study including 190 patients, we have 65 Normal (27 women and 38 men) with average MMSE score 29.1 ± 1.1 and average age 81.9 ± 6.4; 63 MCI (30 women and 33 men) with average MMSE score 26.2 ± 2.3 and average age 70.3 ± 8.5; and 62 AD (28 women and 34 men) with average MMSE score 24.5 ± 2.7 and average age 75.7 ± 5.1. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials. The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California — San Francisco. ADNI is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the United States and Canada. The initial goal of ADNI was to recruit 800 adults, ages 55 to 90, to participate in the research, approximately 200 cognitively normal older individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years and 200 people with early AD to be followed for 2 years. For up-to-date information, see