Influence of Mild White Matter Lesions on Voxel-based Morphometry

Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images

In automated analyses of brain morphometry, skull stripping or brain extraction is a critical first step because it provides accurate spatial registration and signal-intensity normalization. Therefore, it is imperative to develop an ideal skull-stripping method in the field of brain image analysis. Previous reports have shown that convolutional neural network (CNN) method is better at skull stripping than non-CNN methods. We aimed to evaluate the accuracy of skull stripping in a single-contrast CNN model using eight-contrast magnetic resonance (MR) images. A total of 12 healthy participants and 12 patients with a clinical diagnosis of unilateral Sturge-Weber syndrome were included in our study. A 3-T MR imaging system and QRAPMASTER were used for data acquisition. We obtained eight-contrast images produced by post-processing T1, T2, and proton density (PD) maps. To evaluate the accuracy of skull stripping in our CNN method, gold-standard intracranial volume (ICVG) masks were used to train the CNN model. The ICVG masks were defined by experts using manual tracing. The accuracy of the intracranial volume obtained from the single-contrast CNN model (ICVE) was evaluated using the Dice similarity coefficient [= 2(ICVE ⋂ ICVG)/(ICVE + ICVG)]. Our study showed significantly higher accuracy in the PD-weighted image (WI), phase-sensitive inversion recovery (PSIR), and PD-short tau inversion recovery (STIR) compared to the other three contrast images (T1-WI, T2-fluid-attenuated inversion recovery [FLAIR], and T1-FLAIR). In conclusion, PD-WI, PSIR, and PD-STIR should be used instead of T1-WI for skull stripping in the CNN models.

Degeneration of the Sensorimotor Tract in Degenerative Cervical Myelopathy and Compensatory Structural Changes in the Brain

Degenerative cervical myelopathy is a progressive neurodegenerative disease, that has become increasingly prevalent in the aging population worldwide. The current study determined the factors affecting degeneration in the sensorimotor tract with degenerative cervical myelopathy and its relationship with brain structure. We divided patients into hyperintensity (HS) and non-hyperintensity (nHS) groups and measured the fractional anisotropy and apparent diffusion coefficients of the lateral corticospinal tract (CST), fasciculus gracilis and fasciculus cuneatus (FGC). Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) techniques were used to estimate brain structure changes. Correlation of the modified Japanese Orthopaedic Association (mJOA) score, light touch, pinprick, motor score, and fractional anisotropy (FA) ratios of the CST at different levels were analyzed. Compared to healthy controls, the FA ratios of CST in the HS and nHS groups were decreased at all levels, and the apparent diffusion coefficient (ADC) ratio was increased only at C4/5 levels in the HS group. The FA ratio of FGC was decreased at the C3/4 and C4/5 levels in the HS group and only decreased at the C4/5 level in the nHS group. The ADC ratio was decreased only at the C4/5 level in the HS group. VBM analysis revealed that the volume of the precentral gyrus, postcentral gyrus, and paracentral lobule increased in patients compared to controls. TBSS analysis found no statistical significance between the sensory and motor tracts in white matter. The volume of clusters in HS and nHS groups negatively correlated with the C1/2 FA ratio of the CST. The results showed that the degeneration distance of the CST was longer than the FGC, and the degeneration distance was related to the degree of compression and spinal cord damage. Structural compensation and the neurotrophin family may lead to enlargement of the brain.

Clinical Application of MPRAGE Wave Controlled Aliasing in Parallel Imaging (Wave-CAIPI): A Comparative Study with MPRAGE GRAPPA

Purpose: To compare reliability and elucidate clinical application of magnetization-prepared rapid gradient-echo (MPRAGE) with 9-fold acceleration by using wave-controlled aliasing in parallel imaging (Wave-CAIPI 3 × 3) in comparison to conventional MPRAGE accelerated by using generalized autocalibrating partially parallel acquisition (GRAPPA) 2 × 1.

Methods: A total of 26 healthy volunteers and 33 patients were included in this study. Subjects were scanned with two MPRAGEs, GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 acquired in 5 min 21 s and 1 min 42 s, respectively, on a 3T MR scanner. Healthy volunteers underwent additional two MPRAGEs (CAIPI 3 × 3 and GRAPPA 3 × 3). The image quality of the four MPRAGEs was visually evaluated with a 5-point scale in healthy volunteers, and the SNR of four MPRAGEs was also calculated by measuring the phantom 10 times with each MPRAGE. Based on the results of the visual evaluation, voxel-based morphometry (VBM) analyses, including subfield analysis, were performed only for GRAPPA 2 × 1 and Wave-CAIPI 3 × 3. Correlation of segmentation results between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 was assessed.

Results: In visual evaluations, scores for MPRAGE GRAPPA 2 × 1 (mean rank: 4.00) were significantly better than those for Wave-CAIPI 3 × 3 (mean rank: 3.00), CAIPI 3 × 3 (mean rank: 1.83), and GRAPPA 3 × 3 (mean rank: 1.17), and scores for Wave-CAIPI 3×3 were significantly better than those for CAIPI 3 × 3 and GRAPPA 3 × 3. Image noise was evident at the center for additional MPRAGE CAIPI 3 × 3 and GRAPPA 3 × 3. The correlation of segmentation results between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3 was higher than 0.85 in all VOIs except globus pallidus. Subfield analysis of hippocampus also showed a high correlation between GRAPPA 2 × 1 and Wave-CAIPI 3 × 3.

Conclusion: MPRAGE Wave-CAIPI 3 × 3 shows relatively better contrast, despite of its short scan time of 1 min 42 s. The volumes derived from automated segmentation of MPRAGE Wave-CAIPI are considered to be reliable measures.