Tissue segmentation, which partitions brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), is a crucial step for subsequent volumetric and cortical surface analysis. However, effective segmentation of neonatal brain images still remains a great challenge in many emerging neonatal studies, which have the potential of revealing interesting brain developmental patterns and also neurodevelopmental disorders. In particular, two major factors confound neonatal tissue segmentation: (1) the inability of current imaging techniques to acquire neonatal brain images with sufficiently high resolution and signal-to-noise ratio (SNR) for tissue segmentation, and (2) the lack of prior knowledge for a more informed and guided segmentation. In this article, we address both of these issues. First, a dedicated neonatal phased array coil is devised to improve the SNR as well as spatial resolution. Second, a hybrid atlas, combining both the enhanced subject-specific cortical structural characteristics and also population probability maps, is constructed to improve the accuracy of neonatal brain tissue segmentation.
Normally, the brain volume of a neonate is about one half of an adult [Knickmeyer et al., 2008
]. As a result, spatial resolution of neonatal brain images is substantially limited, particularly when data acquisition time is a constraint. Moreover, rapid dynamic WM changes due to the ongoing myelination process in the neonatal brain [Connors et al., 2008
] further complicates the differentiation between GM and WM. In particular, GM and WM tissue contrast in neonates is manifested in an inverted fashion when compared with that of adults. All these factors confound the acquisition of good quality neonatal brain MR images, the effectiveness of brain tissue segmentation, and hence the accuracy of subsequent image analysis. To improve MR image quality without lengthening data acquisition time, a dedicated phased array neonatal head coil is devised. In a conventional MR imaging session, a volume coil (independent of age) covering the whole brain is utilized. However, it is a known fact that a surface coil with a smaller diameter and thus a smaller sensitivity region can achieve higher SNR in comparison with a volume coil [Roemer et al., 1990
]. It is hence possible that multiple smaller coils can be arranged in such a way that a larger region of interest (ROI) can be covered. In doing so, the resulting phased array coil capitalizes the advantages of surface coils in improving SNR and at the same time covers a larger ROI. In addition, coupled with parallel imaging technologies, it yields images with improved quality compared with images acquired using conventional techniques, which essentially translates to a better head start for tissue segmentation. But it should be pointed out that, despite the improved image quality, a dedicated algorithm for neonatal brain segmentation is still crucial.
Numerous brain image segmentation methods have been proposed, but they are mainly developed for adult brains [Pham et al., 2000
]. For the case of neonatal brains, existing methods include clustering [Anbeek et al., 2008
] and population-atlas-based segmentation approaches [Prastawa et al., 2005
; Warfield et al., 2000
; Weisenfeld and Warfield, 2009
; Xue et al., 2007
]. Affected by low image quality and also large intensity variability between non-myelinated and myelinated WM, clustering-based methods relying solely on image intensities can be very limited in terms of segmentation performance. In light of this, atlas-based methods employ population atlases as spatial priors for segmentation guidance. The existing atlases are usually built by averaging a group of spatially normalized segmented images, which we refer as population atlas. This approach is straightforward and easy to implement but has several inherent drawbacks. First, the atlases are in general blurry, especially in the cortical region. This is an inevitable consequence of averaging a group of images with varying anatomical structures, and hence the atlases fall short in providing sufficient prior information especially when fine tissue structures are concerned. Second, a recent study points out that an atlas built from images with anatomy similar to the to-be-segmented image can achieve better segmentation performance than atlases built from randomly selected images [Aljabar et al., 2009
]. Hence, appropriately weighting population images based on their similarity to the query image in building an atlas is a more appropriate approach than equal weighting. Third, the atlases are often aligned to the subjects utilizing affine or low degrees-of-freedom nonlinear transformation, which cannot guarantee topological correspondences and thus jeopardizes segmentation accuracy. In summary, a good atlas for guiding segmentation should have the following two properties: (1) contains a wide range of coarse-to-fine structural information to maximize guidance capacity and (2) capable of achieving sufficient homology with respect to the subject in order to minimize guidance error. To meet these requirements, we construct a hybrid atlas, by incorporating unique subject-specific cortical information from the to-be-segmented image in addition to a population atlas, with the goal of capturing sufficient coarse and fine brain structural information in neonatal images.
To construct such a hybrid atlas, a two-phase strategy is proposed. First, the subject-specific cortical GM folding patterns are extracted. Cortical folding patterns are complex and are posed as a difficult segmentation problem considering the fact that population atlas often appears blurry and provides inadequate cortical tissue prior information. It can be observed from the acquired neonatal images (shown in ) that the fine structures of the cortical folding are well delineated and hence should be properly leveraged as a cortical prior. To this end, we modify a vessel-tracking method [Frangi et al., 1998
] to enhance the cortical patterns, which will serve as subject-specific cortical GM characteristics. Second, a population atlas is constructed to provide global tissue spatial prior information and to mitigate bias which is prone to happen when the prior is derived from only a particular image. Combining the results from these two phases, a hybrid atlas can be constructed to take advantage of both pieces of information and to provide higher guidance power for segmentation of major and minor brain structures.
Figure 7 Cortical GM confidence map extraction. Original T1 image (a), an enlarged region (b), GM confidence map of the enlarged region (c), whole GM confidence map (d), and final cortical GM confidence map with subcortical regions removed by the topological template (more ...)
The effectiveness of our framework is validated using visual inspection and also quantitative comparison with manual segmentations, as well as two population-atlas-based methods. Experimental results indicate that by enhancing image acquisition and improving atlas building, neonatal segmentation accuracy is improved. The rest of the article is organized as follows. The phased array coil imaging technique is first introduced, and then the proposed tissue segmentation scheme is detailed. Experimental results are provided, followed by the conclusion of this article.
Imaging with Phased Array Coil
In MR imaging, there is a trade-off between SNR and image resolution [Mark et al., 1999
]. It can be described as:
, and Δz
are the voxel sizes, Nacq
is the number of acquisitions, BWread
is the readout bandwidth dependence, and Nx
, and Nz
are the number of k
-space samples. The equation indicates that, if one increases the image resolution (i.e., decreasing the voxel sizes Δx
, and Δz
), the SNR decreases accordingly. If the SNR-resolution trade-off is improved, the acquisition time needs to be increased. A parallel imaging technique is introduced later to leverage this trade-off to enhance both the SNR and image resolution without lengthening the acquisition time, which is critical especially for neonates with no sedation during the scan.
A phase array coil consisting of multiple small coils can be employed to improve SNR. A volume coil provides uniform coverage of a large ROI with the cost of a lower SNR. On the other hand, a small surface coil covers a small anatomical region with much higher signal sensitivity, leading to higher SNR compared with a volume coil. Multiple mutually decoupled surface coils are usually arranged in a way to provide a full coverage of a large ROI. Signal acquired by these coils can be combined to yield better image quality with higher SNR and within a relatively short acquisition time, as demonstrated in previous studies [Roemer et al., 1990
; Wald et al., 1995
]. The overall shape of the neonatal phased array coil used in this study was designed according to the average brain shape estimated from 60 normal pediatric subjects.
After acquiring a series of images by a number of coil elements (such as the eight coil elements in ), the question of how to combine these images needs to be addressed. Because of the limited reachable volume of each surface coil, voxels close to the coil yield better SNR and better tissue contrast, compared with those farther away. Sum of square metric is widely used to directly combine these coil images [Roemer et al., 1990
]. A better approach is by utilizing a coil profile, which can be obtained concurrently in the same scanning session by taking a low-resolution scan of the whole brain, as a reference image to generate a sensitivity map for each coil. An optimal weighting scheme can then be devised to take full advantage of each image in generating a final single combined image.
T1 MR images obtained from the eight phased array coil elements. Each image shows different sensitivity region, which corresponds to the individual location of each coil.
In this study, we propose a segmentation-oriented multi-channel image combination strategy. This is achieved by controlling the proportion of sensitivity map used in combination, with the goal of constructing a high GM-WM contrast image for facilitating the subsequent tissue segmentation. Specifically, the phased array coil consists of 8 receiving channels (see ). Their acquired images, denoted as Ci
= 1, …, 8 are accompanied by their respective sensitivity profiles, denoted as Pi
= 1, …, 8, respectively. Although the resolution of the coil profile images is low, they still contain too much structural information to be used as sensitivity maps. To estimate a sensitivity map from the respective coil profile Pi
, a low-pass filter [Lin et al., 2003
] is employed (). For simplicity, the same notation Pi
is used to also denote the generated sensitivity map for each coil. On the basis of sensitivity maps, a high-quality image I
can be reconstructed from the acquired coil images:
is the number of coils, which is 8 in this study. As we can see from this equation, the image Ci
is first corrected via inverse weighting by Pi
, which is effectively an intensity equalization process, and is then further weighted by the sensitivity map with exponent raised to q
. The parameter q
reflects the weight assigned to each of the sensitivity maps in the reconstruction. For example, when q
is 0, the 8 intensity-corrected images are combined in a spatially uniform manner. When q
is 1, we have the special case of directly combining coil images with sum-of-squares metric without the help of sensitivity maps as
, which is widely used in the previous studies [Roemer et al., 1990
]. As q
decreases (or increases), voxels farther away from the coils will be increasingly emphasized (or suppressed). Determining a suitable q
value will give us a balanced image with good whole brain tissue contrast. Sample results with q
= 0,0.3,1,2, are shown in . With the help of a manually segmented image, as shown in , the intensity distributions of GM and WM can be computed, as shown in . These distributions are indicative of how well GM and WM can be separated. Generally, the farther the GM and WM curves are, the easier the segmentation will be. We employ the symmetric Kullback–Leibler (KL) divergence to measure the difference between the GM and WM distributions. With q
values ranging from −1 to 2 with a 0.1 interval, the KL divergence curve for global region is shown in . The peaks of the KL divergence curve falls within the q
interval of 0–0.5. To verify this finding, we selected some small regions such as regions A, B, and C in , and found that their respective peaks of the parameter q
are actually in the same range. We chose a moderate value in this range as q
= 0.3 in this study. In this way, we can better combine the multichannel images than the traditional sum-of-square technique.
Illustrations of (a) coil image, (b) coil profile image, and (c) estimated sensitivity map. Note that, to obtain (c), non-brain tissues are first removed to better concentrate on the brain parenchyma.
Figure 3 Image reconstruction results with different q values. (a–d) show the results when q is set as 0, 0.3, 1, 2, respectively. (f–i) are the GM and WM intensity distributions corresponding to (a–d), obtained with the help of a manually (more ...)
Symmetric KL divergence between the GM and WM histograms for different q values in the global region, regions A, B, and C, respectively.
Quality Comparison Between Images Acquired Using Volume Coil and Phased Array Coil
To better demonstrate the image quality improvement brought forth by the phased array coil compared with images acquired from a volume coil used in our previous neonatal studies, we collect images using both coils using an MPRAGE sequence with a 3T Siemens scanner. A summary of the MR imaging parameters are provided in . It can be seen that, the image resolutions of both T1 and T2 are higher using the phased array coil than that of the volume coil, without significantly lengthening the acquisition time.
T1 and T2 imaging parameters for volume coil and phased array coil
The image quality can also be investigated by calculating the SNR. For this purpose, the SNR is defined as averaged intensity ratio of the given regions for a certain tissue and the background [Mark et al., 1999
is the mean intensity of the given regions. Notice that the SNR is spatially dependent for the phased array coil and is more uniform for a volume coil. We manually delineate four different regions of interest for the GM, WM, and background to obtain the mean SNR values (see ).
Left: Regions of interest on the GM, WM, and background (BG). Right: SNR values for GM and WM of 10 images taken with the conventional volume coil, and 10 images with the surface coils.
The resulting SNR values of 10 images taken using the phased array coil and another 10 images using volume coil (with all subjects randomly selected from a large dataset) are shown in the right of . The SNR values are higher by using phased array coil than that of volume coil for both GM and WM. In summary, by introducing a phased array coil technique for neonatal MRI acquisition and proposing a new multichannel image combination strategy, high-resolution images can be acquired with high SNR at a sufficiently short acquisition time, which provides a good head start for the problem of neonatal brain MR segmentation.