Quantitative magnetic resonance imaging of brain iron deposition: comparison between quantitative susceptibility mapping and transverse relaxation rate (R2*) mapping / 南方医科大学学报

<p><b>OBJECTIVE</b>To evaluate the accuracy and sensitivity of quantitative susceptibility mapping (QSM) and transverse relaxation rate (R2*) mapping in the measurement of brain iron deposition.</p><p><b>METHODS</b>Super paramagnetic iron oxide (SPIO) phantoms and mouse models of Parkinson's disease (PD) related to iron deposition in the substantia nigra (SN) underwent 7.0 T magnetic resonance (MR) scans (Bruker, 70/16) with a multi-echo 3D gradient echo sequence, and the acquired data were processed to obtain QSM and R2*. Linear regression analysis was performed for susceptibility and R2* in the SPIO phantoms containing 5 SPIO concentrations (30, 15, 7.5, 3.75 and 1.875 µg/mL) to evaluate the accuracy of QSM and R2* in quantitative iron analysis. The sensitivities of QSM and R2* mapping in quantitative detection of brain iron deposition were assessed using mouse models of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahy-dropyridine (MPTP) in comparison with the control mice.</p><p><b>RESULTS</b>In SPIO phantoms, QSM provided a higher accuracy than R2* mapping and their goodness-of-fit coefficients (R) were 0.98 and 0.89, respectively. In the mouse models of PD and control mice, the susceptibility of the SN was significantly higher in the PD models (5.19∓1.58 vs 2.98∓0.88, n=5; P<0.05), while the R2* values were similar between the two groups (20.22∓0.94 vs 19.74∓1.75; P=0.60).</p><p><b>CONCLUSION</b>QSM allows more accurate and sensitive detection of brain iron deposition than R2*, and the susceptibility derived by QSM can be a potentially useful biomarker for studying PD.</p>

Differential diagnosis of hepatocellular carcinoma and hepatic hemangiomas based on radiomic features of gadoxetate disodium-enhanced magnetic resonance imaging / 南方医科大学学报

<p><b>OBJECTIVE</b>To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE).</p><p><b>METHODS</b>Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions. The radiomic texture features of each lesion were extracted on the hepatobiliary phase images, and the performance of each feature was assessed in differentiation and classification of HCC and HHE. In multivariate analysis, the performance of 3 feature selection algorithms (namely minimum redundancy-maximum relevance, mRmR; neighborhood component analysis, NCA; and sequence forward selection, SFS) was compared. The optimal feature subset was determined according to the optimal feature selection algorithm and used for testing the 3 classifier algorithms (namely the support vector machine, RBF-SVM; linear discriminant analysis, LDA; and logistic regression). All the tests were repeated 5 times with 10-fold cross validation experiments.</p><p><b>RESULTS</b>More than 50% of the radiomic features exhibited strong distinguishing ability, among which gray level co-occurrence matrix feature S (3, -3) SumEntrp showed a good classification performance with an AUC of 0.72 (P<0.01), a sensitivity of 0.83 and a specificity of 0.57. For the multivariate analysis, 15 features were selected based on the SFS algorithm, which produced better results than the other two algorithms. Testing of these 15 selected features for their average cross-validation performance with RBF-SVM classifier yielded a test accuracy of 0.82∓0.09, an AUC of 0.86∓0.12, a sensitivity of 0.88∓0.11, and a specificity of 0.76∓0.18.</p><p><b>CONCLUSION</b>The radiomic features based on gadoxetate disodium-enhanced magnetic resonance images allow efficient differential diagnosis of HCC and HHE, and can potentially provide important assistance in clinical diagnosis of the two diseases.</p>

A two-point Dixon technique for water-fat separation using multiresolution and region-growing algorithm / 南方医科大学学报

<p><b>OBJECTIVE</b>An improved water-fat separation method based on region-growing was proposed for use in regions with low signal-noise ratio (SNR).</p><p><b>METHODS</b>Region-growing method was applied to 4 sub-images acquired by a down- sampling operation on the acquired phasor maps. The spatial smoothing constraint was exploited to calculate 4 error phasor maps to construct the final smooth error phasor map, which was used in two-point Dixon technique for water-fat separation.</p><p><b>RESULTS</b>The simulation experiment showed that the proposed method produced smaller errors, and for clinical images of the knees, abdomen and lower limbs, the proposed method achieved accurate water-fat separations.</p><p><b>CONCLUSION</b>The proposed method is more robust and reliable than the original global region-growing algorithm, and serves as a promising water-fat separation method for clinical applications.</p>

Adaptive regularized super-resolution reconstruction for magnetic resonance images / 南方医科大学学报

To increase the resolution and signal-to-noise ratio (SNR) of magnetic resonance (MR) images, an adaptively regularized super-resolution reconstruction algorithm was proposed and applied to acquire high resolution MR images from 4 subpixel-shifted low resolution images on the same anatomical slice. The new regularization parameter, which allowed the cost function of the new algorithm to be locally convex within the definition region, was introduced by the piori information to enhance detail restoration of the image with a high frequency. The experiment results proved that the proposed algorithm was superior to other counterparts in achieving the reconstruction of low-resolution MR images.

Research on algorithms based on Markov random models for diffusion tensor-magnetic resonance images / 南方医科大学学报

With the utilization of diffusion tensor information of image voxels, a novel MRF (Markov Random Field) segmentation algorithm was proposed for diffusion tensor MRI (DT-MRI) images benefitted from the introduction of Frobenius norm. The comparison of the segmentation effects between the proposed algorithm and K-means segmentation algorithm for DT-MRI image was made, which showed that the new algorithm could segment the DT-MRI images more accurately than the K-means algorithm. Moreover, with the same segmentation algorithm of MRF, better outcomes were achieved in DT-MRI than in conventional MRI (T2WI) image.

Adaptive super resolution algorithm for under-sampled images / 南方医科大学学报

A new algorithm of adaptive super-resolution (SR) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution (HR) image from the low-resolution (LR) image sequence, which takes into full account the inaccurate estimates of motion error, point spread function (PSF) and the additive Gaussian noise in the LR image sequence. We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size. This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence, which is verified by the experiment on optical images.