Yoonho Nam

Anomalous magnetic properties and magnetic phase diagram of La1-xBaxMnO3

Abstract The phase diagram of colossal magnetoresistive compounds La 1− x Ba x MnO 3 (0⩽ x ⩽1) was obtained for the first time through magnetization ( 5 K ⩽T⩽360 K ,−7 T ⩽H⩽7 T ) and resistivity ( 100 K ⩽T⩽400 K ) measurements. Unexpectedly, La 1− x Ba x MnO 3 compounds were found to be ferromagnetic for all x with varying magnitude of saturation magnetization ( M S ) with theoretically maximum M S for 0.13 x M S for both x x >0.5. Near x =0.5, the ground state changes from single phase ferromagnetic metallic state to multiphase ferromagnetic insulator state. In the two reduced M S doping regimes of x x >0.5, the former showed an anomalous strong field history-dependent magnetization behavior, but the latter showed a rather conventional field history-dependent magnetization behavior. We discuss the possible causes of these differences and spin structures of La 1− x Ba x MnO 3 compounds.

Improved estimation of myelin water fraction using complex model fitting

In gradient echo (GRE) imaging, three compartment water modeling (myelin water, axonal water and extracellular water) in white matter has been demonstrated to show different frequency shifts that depend on the relative orientation of fibers and the B0 field. This finding suggests that in GRE-based myelin water imaging, a signal model may need to incorporate frequency offset terms and become a complex-valued model. In the current study, three different signal models and fitting approaches (a magnitude model fitted to magnitude data, a complex model fitted to magnitude data, and a complex model fitted to complex data) were investigated to address the reliability of each model in the estimation of the myelin water signal. For the complex model fitted to complex data, a new fitting approach that does not require background phase removal was proposed. When the three models were compared, the results from the new complex model fitting showed the most stable parameter estimation.

Initial study on in vivo conductivity mapping of breast cancer using MRI

To develop and apply a method to measure in vivo electrical conductivity values using magnetic resonance imaging (MRI) in subjects with breast cancer.

Computer-aided detection of metastatic brain tumors using magnetic resonance black-blood imaging

ObjectivesThe objective of this study was to develop a computer-aided detection system for automated brain metastases detection using magnetic resonance black-blood imaging and compare its applicability with conventional magnetization-prepared rapid gradient echo (MP-RAGE) imaging. Materials and MethodsTwenty-six patients with brain metastases were imaged with a contrast-enhanced, 3-dimensional, whole-brain magnetic resonance black-blood pulse sequence. Approval from the institutional review board and informed consent from the patients were obtained. Preprocessing steps included B1 inhomogeneity correction and brain extraction. The computer-aided detection system used 3-dimensional template matching, which measured normalized cross-correlation coefficient to generate possible metastases candidates. An artificial neural network was used for classification after various volume features were extracted. The same detection procedure was tested with contrast-enhanced MP-RAGE, which was also acquired from the same patients. ResultsThe performance of the proposed detection method was measured by the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. In the black-blood case, detection process displayed an AUROC of 0.9355, a sensitivity value of 81.1%, and a specificity value of 98.2%. Magnetization-prepared rapid gradient echo data showed an AUROC of 0.6508, a sensitivity value of 30.2%, and a specificity value of 99.97%. ConclusionsThe results demonstrate that accurate automated detection of metastatic brain tumors using contrast-enhanced black-blood imaging sequence is possible compared with using conventional contrast-enhanced MP-RAGE sequence.

Three dimension double inversion recovery gray matter imaging using compressed sensing

The double inversion recovery (DIR) imaging technique has various applications such as black blood magnetic resonance imaging and gray/white matter imaging. Recent clinical studies show the promise of DIR for high resolution three dimensional (3D) gray matter imaging. One drawback in this case however is the long data acquisition time needed to obtain the fully sampled 3D spatial frequency domain (k-space) data. In this paper, we propose a method to solve this problem using the compressed sensing (CS) algorithm with contourlet transform. The contourlet transform is an effective sparsifying transform especially for images with smooth contours. Therefore, we applied this algorithm to undersampled DIR images and compared with a CS algorithm using wavelet transform by evaluating the reconstruction performance of each algorithm for undersampled k-space data. The results show that the proposed CS algorithm achieves a more accurate reconstruction in terms of the mean structural similarity index and root mean square error than the CS algorithm using wavelet transform.

Physiological noise compensation in gradient-echo myelin water imaging.

In MRI, physiological noise which originates from cardiac and respiratory functions can induce substantial errors in detecting small signals in the brain. In this work, we explored the effects of the physiological noise and their compensation methods in gradient-echo myelin water imaging (GRE-MWI). To reduce the cardiac function induced inflow noise, flow saturation RF pulses were applied to the inferior portion of the head, saturating inflow blood signals. For the respiratory function induced B0 fluctuation compensation, a navigator echo was acquired, and respiration induced phase errors were corrected during reconstruction. After the compensations, the resulting myelin water images show substantially improved image quality and reproducibility. These improvements confirm the importance and usefulness of the physiological noise compensations in GRE-MWI.

Mechanisms of T2* anisotropy and gradient echo myelin water imaging

In MRI, structurally aligned molecular or micro‐organization (e.g. axonal fibers) can be a source of substantial signal variations that depend on the structural orientation and the applied magnetic field. This signal anisotropy gives us a unique opportunity to explore information that exists at a resolution several orders of magnitude smaller than that of typical MRI. In this review, one of the signal anisotropies, T2* anisotropy in white matter, and a related imaging method, gradient echo myelin water imaging (GRE‐MWI), are explored. The T2* anisotropy has been attributed to isotropic and anisotropic magnetic susceptibility of myelin and compartmentalized microstructure of white matter fibers (i.e. axonal, myelin, and extracellular space). The susceptibility and microstructure create magnetic frequency shifts that change with the relative orientation of the fiber and the main magnetic field, generating the T2* anisotropy. The resulting multi‐component magnitude decay and nonlinear phase evolution have been utilized for GRE‐MWI, assisting in resolving the signal fraction of the multiple compartments in white matter. The GRE‐MWI method has been further improved by signal compensation techniques including physiological noise compensation schemes. The T2* anisotropy and GRE‐MWI provide microstructural information on a voxel (e.g. fiber orientation and tissue composition), and may serve as sensitive biomarkers for microstructural changes in the brain. Copyright

Robust mapping of the myelin water fraction in the presence of noise: Synergic combination of anisotropic diffusion filter and spatially regularized nonnegative least squares algorithm

To improve the mapping of myelin water fraction (MWF) despite the presence of measurement noise, and to increase the visibility of fine structures in MWF maps.

Improvement of the SNR and resolution of susceptibility-weighted venography by model-based multi-echo denoising

The vein structures of the brain are important for understanding brain function and structure, especially when functional magnetic resonance imaging (fMRI) is utilized, as fMRI is based on changes in the blood-oxygen-level-dependent (BOLD) signal, which is directly related to veins. The aim of the present study was to develop an effective method to produce high signal-to-noise-ratio (SNR) and high-resolution multi-contrast susceptibility-weighted (SW) images of vein structures from 3T magnetic resonance (MR) scanners using multi-gradient-echo MR acquisition and a successive denoising process for both magnitude and phase data. Successive multi-echo MR images were acquired at multiple time points using a multigradient-recalled echo sequence at 3T, and noise in the magnitude and phase data was effectively suppressed using model-based denoising methods. A T(2)* relaxation model was used to denoise the magnitude data and a linear phase model was used to denoise the phase data. SW venography images were obtained from the denoised MR data and compared with conventional SW venography. To evaluate the performance of our denoising methods, we conducted numerical simulation studies and compared the mean-squared-error (MSE), SNR, and contrast-to-noise ratio (CNR) that we obtained using our procedure with those obtained using conventional denoising methods. In addition, images were inspected visually. Numerical simulations showed that our proposed model-based denoising methods were the most effective at suppressing noise. In vivo experiments also showed a substantial increase in the SNR of the phase mask obtained using the proposed denoising process (twice that of the conventional GRE-based phase mask). The T(2)* relaxation model method improved the SNR of the magnitude image (1.17-1.35 times that of the GRE-based magnitude image). Noise suppression of both magnitude and phase data using our proposed method resulted in an overall increase in the SNR and CNR in the final SW venography (1.1-1.5-fold and 1.96-fold higher SNR and CNR, respectively, than that of the GRE-based SW venography). We demonstrated that high SNR and high-resolution SW venograms can be obtained using multi-echo gradient-recalled acquisition and successive model-based denoising of both magnitude and phase data.

Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map‐weighted imaging (SMWI)

To enhance the visibility of nigrosome 1 in substantia nigra, which has recently been suggested as an imaging biomarker for Parkinsons disease (PD) at 3T magnetic resonance imaging (MRI).