| Summary: | 碩士 === 國立臺灣大學 === 醫學工程學研究所 === 105 === Introduction:
White matter hyperintensities (WMHs) refer to white matter (WM) areas with increased signal intensity, appearing on T2-weighted and fluid-attenuated-inversion-recovery (FLAIR) MR images, caused by age-associated tissue decomposition in WM. Given morphological variability, scattered spatial distribution and similarity with gray matter (GM) intensity, WMHs pose great challenges in quantifying lesion volume and segmenting WM regions on T1-weighted images. Here, we proposed an automatic algorithm to identify WMHs based on microstructural indices derived from mean apparent propagator (MAP) MRI. The results of WM segmentation were compared before and after WMH localization and correction to verify the efficacy of this approach.
Methods:
Subjects: 13 patients with mild cognitive impairment were recruited in this research. Eight (age: 77.6 ± 4.9 years, 5 males and 3 females) were used as the training group. Five (age: 78.8 ± 9.0 years, 3 males and 2 females) were used as the testing group.
Imaging: MRI scans were performed on a 3T MRI system (TIM Trio, Siemens, Erlangen) with a 32-channel phased array coil. T1-weighted imaging utilized a 3D magnetization-prepared rapid gradient echo pulse sequence: TR/TE = 2000/3 ms, flip angle = 9o, FOV = 256 × 192 × 208 mm^3, matrix size = 256 × 192 × 208, and spatial resolution = 1 × 1 × 1 mm^3. Diffusion spectrum imaging (DSI) used a twice-refocused balanced echo diffusion echo planar imaging sequence, TR/TE = 9600/130 ms, FOV = 200 × 200 mm^2, matrix size = 80 × 80, 56 slices, slice thickness = 2.5 mm. A total of 102 diffusion encoding gradients with the maximum diffusion sensitivity bmax = 4000 s/mm^2 were applied on the grid points in a half sphere of the 3D q-space with |q| ≤ 3.6 units.
Analysis: According to Özaeslan’s approach, 11 MAP-MRI indices were estimated from diffusion datasets. Multiple regions of interest (ROIs) were selected in WM, GM, cerebrospinal fluid (CSF) and WMHs in training data. Values of each index in the ROIs were averaged and rescaled to characterize the index profiles of 4 different tissues. By classifying the voxels in testing group according to the index profiles generated by training group, WMHs were localized on diffusion-weighted MRI. Coordinates of voxels considered as WMHs were transformed from diffusion-weighted image space to T1-weighted image space via the transformation matrix between the two images. The voxels transformed to T1-weighted images were used as seeds for 3D region-growing to identify actual locations of WMHs on T1-weighted images. The WMH volume selected automatically by the algorithm was further compared with manual WMH mask using four similarity measures. Voxels identified as WMHs were given a proper value of signal intensity according to normal distribution of WM intensity on T1-weighted images. Tissue segmentation was performed using SPM12 and the segmentation results were compared before and after the images were processed with the correction algorithm.
Results:
The algorithm successfully localized WHMs on T1-weighted images. The similarity index between manual mask and automatic mask was higher than 0.7 in all the subjects. Furthermore, voxels of WMHs could be properly segmented in the tissue probability map of WM after correction.
Discussion and Conclusion:
This is the first study to automatically localize WMHs using diffusion parameters and correct their intensities to reduce segmentation errors. Our automatic approach combines several diffusion indices to characterize the microstructural alterations in WMHs, and has demonstrated the capability of localizing lesions and correcting segmentation errors. Future work will focus on using machine learning approach in order to improve the accuracy on WMH detection.
|
|---|
