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+/-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.
Artifacts ; Brain ; Humans ; Male

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+/-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.
Artifacts ; Brain ; Humans ; Male

Purpose: The aim of the present study was to investigate the potential utility of the magnetic susceptibility-based assessment of cerebral oxygen metabolism with a dataset from a memory clinic. Materials and Methods: We collected and processed three-dimensional gradient-recalled echo phase data of 290 participants. To analyze oxygen metabolism, quantitative susceptibility mapping was performed for two major veins (superior sagittal sinus and straight sinus), and the venous oxygen saturation was estimated. Results: The estimated venous oxygen saturations of the two major veins were positively correlated with the clinical and volumetric measurements. They did not differ with the clinical diagnosis or clinical dementia rating. The values of the superior sagittal sinus were associated with the presence of the apolipoprotein E type 4 allele, when considering age, sex, and their interactions. Conclusion The results demonstrate that quantitative susceptibility mapping of clinically available three-dimensional susceptibility-weighted imaging sequences in a large-scale clinical dataset can estimate cerebral oxygen metabolism.

PURPOSE: The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and T2* quantification in the liver. MATERIALS AND METHODS: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of T2* and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a B0 field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and T2* from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. RESULTS: After correction, in the phantom experiments, the estimated T2* and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 microT/m with increased homogeneity in T2* and fat fractions obtained. CONCLUSION: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.
Compensation and Redress ; Iron ; Liver ; Magnetics ; Magnets

Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer’s disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

Purpose: This study aims to enhance the performance of deep learning models for segmenting thin anatomical structures in medical images by introducing a label-preserving data-augmentation strategy. Materials and Methods: We developed a data-augmentation technique that applies geometric transformations and their inverses sequentially to input images while preserving the corresponding labels. This method was evaluated on inner ear magnetic resonance images for the automatic segmentation of semicircular canals characterized by thin and circular structures. The dataset included both internal and external samples. For the internal dataset, 70 subjects were used for model training and eight subjects for internal validation. Images were acquired using a 3 tesla magnetic resonance imaging scanner with a three-dimensional high-resolution T2 sequence, and ground-truth segmentations were manually annotated by an experienced radiologist. For external validation, four subjects from a public dataset (Vestibular-Schwannoma-SEG dataset, part of The Cancer Imaging Archive) with high-resolution T2 images for inner ear analysis were used. We performed quantitative evaluations using metrics such as Dice, intersection over union (IoU), 95% Hausdorff distance (HD), and average surface distance (ASD). A qualitative visual assessment was also performed. Results: The proposed model exhibited improved performance in semicircular canal segmentation in both quantitative and qualitative evaluations. Metrics such as Dice, IoU, 95% HD, and ASD indicated better performance than conventional methods. Conclusion The proposed label-preserving data augmentation method improves the segmentation of thin anatomical structures in medical images and offers a robust and efficient solution for enhancing deep learning models in medical imaging.

PURPOSE: To compare different MR sequences for quantification of gadolinium concentration. MATERIALS AND METHODS: Gadolinium contrast agents were diluted into 36 different concentrations. They were scanned using gradient echo (GRE) and ultrashort echo time (UTE) and R1, R2* and phase values were estimated from collected data. For analysis, ROI masks were made for each concentration and then ROI value was measured by mean and standard deviation from the estimated quantitative maps. Correlation analysis was performed and correlation coefficient was calculated. RESULTS: Using GRE sequence, R1 showed a strong linear correlation at concentrations of 10 mM or less, and R2* showed a strong linear correlation between 10 to 100 mM. The phase of GRE generally exhibited a negative linear relationship for concentrations of 100 mM or less. In the case of UTE, the phase had a strong negative linear relationship at concentrations 100 mM or above. CONCLUSION: R1, which was calculated by conventional GRE, showed a high performance of quantification for lower concentrations, with a correlation coefficient of 0.966 (10 mM or less). R2* showed stronger potential for higher concentrations with a correlation coefficient of 0.984 (10 to 100 mM), and UTE phase showed potential for even higher concentrations with a correlation coefficient of 0.992 (100 mM or above).
Contrast Media ; Gadolinium* ; Masks