Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET

Abstract

Background and objective: Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications.

Materials and methods: Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image.

Results: This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%±1.9% between our segmentation and manual segmentation.

Conclusions: The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.

Keywords: Attenuation correction; Combined MR/PET; Image segmentation; Multimodality Imaging; Radon transform; multiscale bilateral filter.

Figure 1

The schematic diagram of Radon transformation. The integration is along the line of response (LOR). Access the article online to view this figure in colour.

Figure 2

A T1-weighted MR image (A) and its corresponding sinogram (B).

Figure 3

Brain ellipse model and the calculation scheme in the Radon domain. Access the article online to view this figure in colour.

Figure 4

Comparison of profiles. (A) The profiles between the original and the noised MR images. (B) The profiles of the corresponding sinogram. Access the article online to view this figure in colour.

Figure 5

Kernels of the two filters.

Figure 6

Illustration of the multiscale processing steps. From top to bottom, the scale increases from 1 to i. Based on the sinogram images after multiscale bilateral decomposition, the algorithm processes the images step by step from step (1) to step (4). Access the article online to view this figure in colour.

Figure 7

Segmentation results of brain phantoms at different noise levels. From left to right, simulated brain image, sinogram data, segmented signogram, reconstructed skull, and different images between the segmented skull and the ground truth. From top to bottom, image without noise, that with 40% noise, and that with 80% noise.

Figure 8

Regional cerebral metabolism estimates are shown for the 17 volumes of interest (VOIs). Means and SDs are calculated across the 10 patients studied. TX, transmission.