• Smart Media Journal
• /
• v.5 no.1
• /
• pp.44-48
• /
• 2016

In a longitudinal neuroimaging study, the upgrades of a magnetic resonance imaging (MRI) scanner due to outdated hardwares and softwares make it difficult to maintain the same MRI conditions in the long-term research period. Particularly, high field MRI systems such 3T scanners become popular in recent years. However, it is still unclear whether an integrated analysis of 3T and 1.5T images is possible without consideration of the field strength. In this study, we evaluated the reproducibility of hippocampal volumes between brain images with different field strengths and slice orientations. 296 participants underwent both 3T and 1.5T MRI and both sagittal and axial scans for high resolution brain images, and their hippocampal volumes were measured using Freesurfer, a well-known software for neuroimaging analysis. Paired t-tests showed that the hippocampal volumes were significantly different between the image types. These results suggest that it is necessary to develop data analysis techniques for integrating diverse types of MRI images.

• Investigative Magnetic Resonance Imaging
• /
• v.17 no.4
• /
• pp.286-293
• /
• 2013

Purpose : The objective of this study was to analyze the brain volume according to the brain image of healthy adults in the 20s taken with different inversion time (TI). Materials and Methods: Brain images of healthy adults in the 20 s were acquired using magnetization prepared rapid acquisition gradient echo (MPRAGE) pulse sequence with 1.5 mm thickness of pieces and four inversion times (1100 ms, 1000 ms, 900 ms, 800 ms). The acquired brain images were analyzed to measure the volume of white matter (WM), gray matter (GM), intracranial volume (ICV). The statistical difference according to brain volume and gender was analyzed for each TI. Results: The brain volume calculated using Freesurfer was WM$486.52{\pm}48.64cm^3$ and GM=$646.83{\pm}57.12cm^3$ in mean when adjusted by mean ICV=$1278.94{\pm}154.92cm^3$. Men's brain volume(WM, GM, ICV) was larger than women's brain volume. In the intrarater reliability test, all of the intraclass correlation coefficients were high (0.992 for WM, 0.988 for GM, and 0.997 for ICV). In the repeated measures analysis of variance, GM and ICV did not show a significant difference at each TI (GM p=0.143, ICV p=0.052), but WM showed a significant (p=0.001). In the linear structure relation analysis, all of the Pearson correlation coefficients were high. Conclusion: WM, GM, and ICV indicated high reliability and solid linear structure relations, but WM showed significant differences at each TI. The brain volume of healthy adults in the 20s could be used in comparison with that of patients for reference purposes and to predict the structural change of brain. It would be needed to conduct additional studies to examine the contract, SNR, and lesion detection ability according to variable TI.

• Journal of the Korean Society of Radiology
• /
• v.8 no.5
• /
• pp.231-240
• /
• 2014

The purpose of this study were to analyze that the Seoul neuropsychological screening battery (SNSB) for the evaluating cognitive assessment of the Parkinson's disease patients with mild cognitive impairment (PD-MCI) and the changes of the cerebral ventricle volume in the brain magnetic resonance imaging (MRI), and we has been bring forward the guideline to determine the diagnostic criteria for the PD-MCI. To achieve this, we was diagnosed with Parkinson's disease patients (PD-MCI group: 34 patients; Parkinson's disease with normal cognition, PD-NC group: 34 patients) to perform the SNSB test for the attention, language, memory, visuospatial, and frontal/executive functions and the brain MRI. Additionally, to compared the change of the cerebral ventricle volume, we performed the brain MRI for the 32 normal control (NC) group. The volumetric analysis for a specific cerebral ventricle performed by using Freesurfer Ver. 5.1 (Massachusetts general Hospital, Boston MA, USA). As a results, compared to the PD-NC group, the PD-MCI group were statistically significant reduction in the ability to perform the memory and the visuospatial function (p<0.05). The volumetric changes for a specific cerebral ventricle were statistically significant variation in the left and right lateral ventricle, left and right inferior lateral ventricle, and 3rd ventricle. Although, in order to compared the objectification, the normalized percentage applied to the volumetric changes showed to extend the PD-MCI group than the PD-NC group. Specially, the left and right ventricle extension for the PD-MCI patients conspicuously had showed a quantitative linear relationship between the memory and the visuospatial function for the SNSB (r>0.5, p<0.05). Therefore, we were able to judge the diagnostic criteria of the PD-MCI through that can observe the volumetric variation of the specific cerebral ventricle by using Freesurfer in brain MRI, and to analyze the correlation between the SNSB.

• Investigative Magnetic Resonance Imaging
• /
• v.14 no.1
• /
• pp.56-63
• /
• 2010

Purpose : DIR image is relatively free from susceptibility artifacts therefore, DIR image can make it possible to reliably measure cortical thickness/volume. One drawback of the DIR acquisition is the long scan time to acquire the fully sampled 3D data set. To solve this problem, we applied a parallel imaging method (GRAPPA) and verify the reliability of using the volumetric study. Materials and methods : Six healthy volunteers (3 males and 3 females; age $25.33{\pm}2.25$ years) underwent MRI using the 3D DIR sequence at a 3.0T Siemens Tim Trio MRI scanner. GRAPPA simulation was performed from the fully sampled data set for reduction factor 2. Data reconstruction was performed using MATLAB R2009b. Freesurfer v.4.3.0 was used to evaluate the cortical thickness of the entire brain, and to extract white matter information from the DIR image, Analyze 9.0 was used. The global cortical thickness estimated from the reconstructed image was compared with reference image by using a T-test in SPSS. Results : Although reduced SNR and blurring are observed from the reconstructed image, in terms of segmentation the effect was not so significant. The volumetric result was validated that there were no significant differences in many cortical regions. Conclusion : This study was performed with DIR image for a volumetric MRI study. To solve the long scan time of 3D DIR imaging, we applied GRAPPA algorithm. According to the results, fast imaging can be done with reduction factor 2 with little loss of image quality at 3.0T.

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.233-242
• /
• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.