• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.157-168
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• 2020

Advancements in segmentation methodology has made automatic segmentation of brain structures using structural images accurate and consistent. One method of automatic segmentation, which involves registering atlas information from template space to subject space, requires a high quality atlas with accurate boundaries for consistent segmentation. The Allen Mouse Brain Atlas, which has been widely accepted as a high quality reference of the mouse brain, has been used in various segmentations and can provide accurate coordinates and boundaries of mouse brain structures for tractography. Through probabilistic tractography, diffusion tensor images can be used to map comprehensive neuronal network of white matter pathways of the brain. Comparisons between neural networks of mouse and human brains showed that various clinical tests on mouse models were able to simulate disease pathology of human brains, increasing the importance of clinical mouse brain studies. However, differences between brain size of human and mouse brain has made it difficult to achieve the necessary image quality for analysis and the conditions for sufficient image quality such as a long scan time makes using live samples unrealistic. In order to secure a mouse brain image with a sufficient scan time, an Ex-vivo experiment of a mouse brain was conducted for this study. Using FSL, a tool for analyzing tensor images, we proposed a semi-automated segmentation and tractography analysis pipeline of the mouse brain and applied it to various mouse models. Also, in order to determine the useful signal-to-noise ratio of the diffusion tensor image acquired for the tractography analysis, images with various excitation numbers were compared.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.164-172
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• 2001

본 논문에서는 시각화와 공간적, 속성 혼합 쿼리를 수행할 수 있는 관계형 데이터베이스를 설계하고 구현하였다. 쿼리에 사용되는 데이터형은 슬라이스, MPR, 볼륨 렌더링으로 시각화할 수 있으며, 쿼리는 아탈라스를 이용하는 경우와 그렇지 않는 경우를모두 고려하였다. 영상 데이터는 공간충전 곡선으로 공간적으로 클러스트링한 후 무손실 압축하여 데이터베이스에 저장된다. 본 논문은 저장 데이터의 양을 줄이기 위하여 관심영역의 크기에 따라 창의 크기가 변하는 적응적 Hibert 곡선을 제안하였으며, 실험에서 Hibert 곡선의 적용한 데이터보다 약 1.15배 높은 압축율을 보였다. 또한 아틀라스에 대한 뇌종양의 공간적 쿼리 결과를 통하여 본 의료 영상 데이터베이스의 유용성을 보였다.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.507-518
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• 2021

Magnetic resonance imaging (MRI) is a key technology that has been seeing increasing use in studying the structural and functional innerworkings of the brain. Analyzing the variability of brain connectome through tractography analysis has been used to increase our understanding of disease pathology in humans. However, there lacks standardization of analysis methods for small animals such as mice, and lacks scientific consensus in regard to accurate preprocessing strategies and atlas-based neuroinformatics for images. In addition, it is difficult to acquire high resolution images for mice due to how significantly smaller a mouse brain is compared to that of humans. In this study, we present an Allen Mouse Brain Atlas-based image data analysis pipeline for structural connectivity analysis involving structural region segmentation using mouse brain structural images and diffusion tensor images. Each analysis method enabled the analysis of mouse brain image data using reliable software that has already been verified with human and mouse image data. In addition, the pipeline presented in this study is optimized for users to efficiently process data by organizing functions necessary for mouse tractography among complex analysis processes and various functions.

• Korean Journal of Biological Psychiatry
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• v.21 no.3
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• pp.81-86
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• 2014

Objectives Total intracranial volume (TIV) is a major nuisance of neuroimaging research for interindividual differences of brain structure and function. Authors intended to prove the reliability of the atlas scaling factor (ASF) method for TIV estimation in FreeSurfer by comparing it with the results of manual tracing as reference method. Methods The TIVs of 26 normal children and 26 children with attention-deficit hyperactivity disorder (ADHD) were obtained by using FreeSurfer reconstruction and manual tracing with T1-weighted images. Manual tracing performed in every 10th slice of MRI dataset from midline of sagittal plane by one researcher who was blinded from clinical data. Another reseacher performed manual tracing independently for randomly selected 20 dataset to verify interrater reliability. Results The interrater reliability was excellent (intraclass coefficient = 0.91, p < 7.1e-07). There were no significant differences of age and gender distribution between normal and ADHD groups. No significant differences were found between TIVs from ASF method and manual tracing. Strong correlation between TIVs from 2 different methods were shown (r = 0.90, p < 2.2e-16). Conclusions The ASF method for TIV estimation by using FreeSurfer showed good agreement with the reference method. We can use the TIV from ASF method for correction in analysis of structural and functional neuroimaging studies with not only elderly subjects but also children, even with ADHD.