Age-specific CT and MRI templates for spatial normalization

Abstract

Spatial normalization reshapes an individual's brain to match the shape and size of a template image. This is a crucial step required for group-level statistical analyses. The most popular standard templates are derived from MRI scans of young adults. We introduce specialized templates that allow normalization algorithms to be applied to stroke-aged populations. First, we developed a CT template: while this is the dominant modality for many clinical situations, there are no modern CT templates and popular algorithms fail to successfully normalize CT scans. Importantly, our template was based on healthy individuals with ages similar to what is commonly seen in stroke (mean 65 years old). This template allows studies where only CT scans are available. Second, we derived a MRI template that approximately matches the shape of our CT template as well as processing steps that aid the normalization of scans from older individuals (including lesion masking and the ability to generate high quality cortical renderings despite brain injury). The benefit of this strategy is that the resulting templates can be used in studies where mixed modalities are present. We have integrated these templates and processing algorithms into a simple SPM toolbox (http://www.mricro.com/clinical-toolbox/spm8-scripts).

Introduction

Spatial normalization refers to an automated process for warping the orientation, size and shape of an individual's brain scan to match a standard stereotaxic space. This process is useful for conducting group statistics (required for making inferences regarding a population), where tests can be applied to a group of images registered in uniform space. This application is common for functional neuroimaging studies (e.g. functional magnetic resonance imaging; fMRI) and lesion mapping studies (e.g. voxelwise lesion symptom mapping [VLSM, Bates et al., 2003, Rorden et al., 2007]). Contemporary spatial normalization algorithms are guided by template images derived from a population of neurologically healthy individuals, such that the normalization warps each individual's brain to approximately match the shape and size of the template image. The majority of these templates are derived from healthy young adults, though specialized templates have been created for pediatric studies (Wilke et al., 2008) and healthy aging (Grabner et al., 2006, Lemaître et al., 2005). Our aim was to develop templates designed for commonclinical imaging applications, i.e. for senior-adult brain injury applications, where the current templates may not be appropriate. Specifically, we developed a computerized axial tomography [CT] template that allows robust normalization for this modality, based on a population with ages comparable to typical stroke patients. CT is the dominant modality for many clinical situations yet there are no modern CT templates and popular algorithms fail to successfully normalize CT scans. Second, we created a Magnetic Resonance Imaging (MRI) template of aging adults designed to match the size and shape of our CT template. These templates allow popular neuroimaging normalization algorithms to be applied to a wider range of clinical populations.

CT remains the modality of choice for many clinical studies, with advantages typically including speed, cost, and reduced exclusion criteria relative to MRI. For example, Karnath et al. (2004) noted that CT was the preferred modality for 72 out of 140 stroke patients at admission in their university center of neurology. Likewise, it is the only modality suitable for many clinical and research situations, for example observing the consequences of improvised explosive devices (IEDs) or other penetrating head wounds which often leave metal fragments (e.g. Koenigs et al., 2008). It would be a dramatic loss of information and of representativeness if such cases would simply be neglected in anatomical studies, leaving researchers to only include those individuals who (for various clinical reasons) get an MRI brain scan during their hospital stay. Surprisingly, we know of no modern CT templates for normalization. SPM versions since the 1990's have included the template ‘transm’ that is based on low-resolution transmission images from 11 individuals scanned with a Siemens Ecat Exact HR+, a system that was designed for Positron Emission Tomography rather than CT, and presumably the effective resolution was poor relative to modern dedicated CT scanners as the resulting template does not reveal major features such as the ventricles (Fig. 1, left column). Similar to virtually all of the popular templates, this transmission template is normalized to match the MNI templates (from the Montreal Neurological Institute, Collins et al., 1994) allowing inferences to be made across studies that use different modalities. Solomon et al. (2007) described a sophisticated method to normalize CT scans to MNI space using the professional ABLe software. In this method, scans are thresholded to hide bone (bright values such as bone and the choroid plexus are set to dark intensities), the image is scalp-stripped using BET (Smith, 2002), and the resulting images are normalized using AIR (Woods et al., 1998). Despite this advanced processing, this method relies on an unsmoothed scalp-stripped template from a single young adult male acquired in the 1990s, and the quality of this image is inferior to modern CT images (Fig. 1, middle column). Developing a modern open-source CT template thus could aid analysis of datasets where MRI scans are not available or not appropriate, for example the Vietnam Head Injury Study (VHIS) registry which includes 199 individuals (Koenigs et al., 2010).

We suspect there are two major reasons why no modern CT templates exist. First, while CT scans have great clinical utility, they do expose participants to a dose of ionizing radiation, making it difficult to justify exposure for healthy volunteers. This is important, as template images should be generated from neurologically healthy individuals, as inclusion of participants with brain injury would lead to templates that are influenced by the deformations of shape and appearance resulting from the injury. We solved this by collecting data from individuals where a CT scan was ordered but where subsequent inspection was able to rule out major structural brain abnormalities. Second, image intensity in CT scans is calibrated (using Hounsfield units), but these units provide very little dynamic range for the CSF and soft tissue, components that are important for accurate brain alignment. Specifically, for a calibrated CT scan, approximate intensities of air are − 1000, cerebral spinal fluid (CSF) 0, white matter 25, gray matter 35, blood 60, blood with gadolinium (Gd) contrast ~ 150, and bone 1000. Whereas MRI scans are not calibrated like CT scans, a much larger proportion of the full-contrast range is assigned to different intracranial tissue types. Therefore, normalization using popular cost-functions applied to a raw CT scan will be primarily driven by the contrast of air and bone, and features such as ventricles will not align well (an artifact that is obvious from first principles, but which we provide evidence for later). This problem is obvious for cost functions such as least-squares (as air and bone have the most relative contrast), but can also be derived from first principles for other cost functions such as histogram-based mutual information (where soft tissues have relatively equivalent similarity metrics). However, as commonly observed by radiologists, while the magnitude of the difference between water and parenchyma in Hounsfield units is numerically small (relative to air and bone), there is robust difference in terms of signal to noise (so radiologists can choose brightness and contrast values to see this definition). To solve this, we developed an invertible intensity transform that allows us to use the popular cost functions during normalization while emphasizing the tissue contrast between CSF and parenchyma.

Our second aim was to create a MRI template that would match the approximate size and shape of our CT template. While participants in studies of adult stroke have a mean age in their 60s (e.g. Karnath et al., 2004, Karnath et al., 2011), most lesion mapping studies normalize data to the same MNI152 template that is distributed with popular packages such as SPM and FSL (Collins et al., 1994). According to the SPM source code, this template is based on 152 individuals (86 male) with a mean age of 25 (median = 24, stdev = 4.9). Therefore, the MNI152 template shows less age-related atrophy (wide sulci, large ventricles) than the average adult stroke patient. On one hand, using the MNI152 template is very useful, as it does provide a close correspondence between stroke studies and the majority of neuroimaging studies that are conducted on young individuals. On the other hand, the nonlinear functions employed creating an age-specific template for stroke patients will provide a more representative image of the actual extent and location of injury. In any case, as our CT template was drawn from a clinical aging adult population, the CT template necessarily had larger ventricles than previous templates based on young adults. Therefore, our aim was to create a MRI template that matched our CT template. The benefit of this strategy is that the resulting templates can be used in studies where mixed modalities are present (though it should be noted that the precise shape of the templates will vary slightly and that the extent of apparent injury varies across modality, requiring caution for mixed modality studies).

For our MRI template, we decided to use SPM's unified normalization–segmentation routines (Ashburner and Friston, 2005) that have proved very robust for normalizing scans from individuals with brain lesions (Andersen et al., 2010, Crinion et al., 2007). This method works by having a priori maps of gray matter, white matter and CSF based on a population of healthy adults. This normalization creates a virtuous cycle, where improved normalization leads to improved segmentation which in turn improves the normalization.

Unfortunately, regardless of the template used, automated normalization algorithms that work well for scans from healthy individuals can perform poorly when provided with images from individuals with brain injury. For example, the non-linear components often employed in normalization can act to shrink the size of a brain lesion (Andersen et al., 2010, Brett et al., 2001), and distort the local healthy tissue (as the abnormal tissue does not match the corresponding location on the template which was derived from healthy individuals). Brett et al. (2001) introduced the concept of lesion cost function masking, where regions identified as unusual do not contribute to the normalization transforms. However, this technique has historically been somewhat laborious, requiring the dilation and thresholding of the mask, and the reslicing and binarization of the lesion map to standard space. Here we create a SPM toolbox that incorporates all these steps for both our CT and MRI templates, easing the use of this method.

In summary, our objective was to develop CT and MRI templates and algorithms that can aid normalizing data from older (stroke-aged) individuals. We also conducted a careful validation to demonstrate that these methods can work reliably. Specifically, our CT normalization should outperform the usage of the decades old SPM transmission scan template as well as the proprietary method included with ABLe. Likewise, our validation was designed to establish that our MRI template is competitive with previously described techniques that have proved robust for normalizing T1-wegithed images from individuals with brain injury (Andersen et al., 2010, Crinion et al., 2007).