Abstract
Magnetic resonance imaging of hippocampal internal architecture (HIA) at 3T is challenging. HIA is defined by layers of gray and white matter that are less than 1 mm thick in the coronal plane. To visualize HIA, conventional MRI approaches have relied on sequences with high in-plane resolution (≤0.5 mm) but comparatively thick slices (2–5 mm). However, thicker slices are prone to volume averaging effects that result in loss of HIA clarity and blurring of the borders of the hippocampal subfields in up to 61% of slices as has been reported. In this work we describe an approach to hippocampal imaging that provides consistently high HIA clarity using a commonly available sequence and post-processing techniques that is flexible and may be applicable to any MRI platform. We refer to this approach as High Resolution Multiple Image Co-registration and Averaging (HR-MICRA). This approach uses a variable flip angle turbo spin echo sequence to repeatedly acquire a whole brain T2w image volume with high resolution in three dimensions in a relatively short amount of time, and then co-register the volumes to correct for movement and average the repeated scans to improve SNR. We compared the averages of 4, 9, and 16 individual scans in 20 healthy controls using a published HIA clarity rating scale. In the body of the hippocampus, the proportion of slices with good or excellent HIA clarity was 90%, 83%, and 67% for the 16x, 9x, and 4x HR-MICRA images, respectively. Using the 4x HR-MICRA images as a baseline, the 9x HR-MICRA images were 2.6 times and 16x HR-MICRA images were 3.2 times more likely to have high HIA ratings (p < 0.001) across all hippocampal segments (head, body, and tail). The thin slices of the HR-MICRA images allow reformatting in any plane with clear visualization of hippocampal dentation in the sagittal plane. Clear and consistent visualization of HIA will allow application of this technique to future hippocampal structure research, as well as more precise manual or automated segmentation.
Highlights
The hippocampus is one of the most studied subcortical structures in the brain
Since signal-to-noise ratio (SNR) increases with the square root of the number of averages, we modeled the relationship between number of averages and improvement in hippocampal internal architecture (HIA) clarity using the square root of the number of averages instead of as a direct linear relationship between the raw number of averages
Examples of HIA clarity in representative slices of each hippocampal segment are shown in Figure 4 depicting their markedly different cross sectional appearances
Summary
The hippocampus is one of the most studied subcortical structures in the brain. It has been linked to the pathobiology of epilepsy (Sloviter, 1987; De Lanerolle et al, 1989; Wieser, 2004), Alzheimer’s disease (Jack et al, 1992), schizophrenia (Lahti et al, 2006; Kraguljac et al, 2013, 2016), PTSD (Smith, 2005; Shin et al, 2006; Wang et al, 2010), and TBI (Ariza et al, 2006). Hippocampal imaging research focused largely on volumetric measurements and surface morphometry, but in recent years there has been increasing interest in studying specific hippocampal subfields (Mueller et al, 2007; Van Leemput et al, 2009; Yushkevich et al, 2010, 2015a; Pluta et al, 2012; Wisse et al, 2012, 2016). Precise subfield segmentation requires direct visualization of the hippocampal internal architecture, defined by apposing layers of gray and white matter that create the characteristic spiral appearance of Ammon’s horn in coronal section. Manual subfield segmentation in some slices must rely on inferring the boundaries of Ammon’s horn based on “fuzzy” image features or expected boundary location as opposed to direct, clear visualization of the SRLM in each slice. The resulting automated segmentation may reflect the template to a greater or lesser degree than the target image
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