Computational diffusion MRI : MICCAI Workshop最新文献_第7页

Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering. 基于方向分布函数和谱k均值聚类的人类杏仁核子场分割。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2016-10-01 DOI: 10.1007/978-3-319-54130-3_10

Qiuting Wen, Brian D Stirling, Long Sha, Li Shen, Paul J Whalen, Yu-Chien Wu

{"title":"Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering.","authors":"Qiuting Wen, Brian D Stirling, Long Sha, Li Shen, Paul J Whalen, Yu-Chien Wu","doi":"10.1007/978-3-319-54130-3_10","DOIUrl":"https://doi.org/10.1007/978-3-319-54130-3_10","url":null,"abstract":"Amygdala plays an important role in fear and emotional learning, which are critical for human survival. Despite the functional relevance and unique circuitry of each human amygdaloid subnuclei, there has yet to be an efficient imaging method for identifying these regions in vivo. A data-driven approach without prior knowledge provides advantages of efficient and objective assessments. The present study uses high angular and high spatial resolution diffusion magnetic resonance imaging to generate orientation distribution function, which bears distinctive microstructural features. The features were extracted using spherical harmonic decomposition to assess microstructural similarity within amygdala subfields are identified via similarity matrices using spectral k-mean clustering. The approach was tested on 32 healthy volunteers and three distinct amygdala subfields were identified including medial, posterior-superior lateral, and anterior-inferior lateral.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"2016 ","pages":"123-132"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-54130-3_10","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9770201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo. 利用并行马尔可夫链蒙特卡罗加速全局轨迹成像。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2016-01-01 DOI: 10.1007/978-3-319-28588-7_11

Haiyong Wu, Geng Chen, Zhongxue Yang, Dinggang Shen, Pew-Thian Yap

{"title":"Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo.","authors":"Haiyong Wu, Geng Chen, Zhongxue Yang, Dinggang Shen, Pew-Thian Yap","doi":"10.1007/978-3-319-28588-7_11","DOIUrl":"https://doi.org/10.1007/978-3-319-28588-7_11","url":null,"abstract":"Global tractography estimates brain connectivity by determining the optimal configuration of signal-generating fiber segments that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. That is, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its both ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that updating of independent fiber segments can be done concurrently. The experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or improve tractography performance.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"2016 ","pages":"121-130"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-28588-7_11","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9829014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Subject Information Transfer. 利用学科间信息传递增强扩散MRI数据的角分辨率。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2016-01-01 DOI: 10.1007/978-3-319-28588-7_13

Geng Chen, Pei Zhang, Ke Li, Chong-Yaw Wee, Yafeng Wu, Dinggang Shen, Pew-Thian Yap

{"title":"Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Subject Information Transfer.","authors":"Geng Chen, Pei Zhang, Ke Li, Chong-Yaw Wee, Yafeng Wu, Dinggang Shen, Pew-Thian Yap","doi":"10.1007/978-3-319-28588-7_13","DOIUrl":"https://doi.org/10.1007/978-3-319-28588-7_13","url":null,"abstract":"Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired, but unfortunately, are not always practically available. In this paper, we propose to improve ODF estimation by using inter-subject correlation. Specifically, diffusion-weighted images acquired from different subjects, when transformed to the space of a target subject, can not only provide signal denoising with additional information, but also drastically increase the number of angular samples for better ODF estimation. This is largely because of the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"2016 ","pages":"145-157"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-28588-7_13","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9831594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Super-Resolution Reconstruction of Diffusion-Weighted Images using 4D Low-Rank and Total Variation 基于四维低秩和全变分的扩散加权图像超分辨率重建

Computational diffusion MRI : MICCAI Workshop Pub Date : 2015-10-01 DOI: 10.1007/978-3-319-28588-7_2

Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, Dinggang Shen

{"title":"Super-Resolution Reconstruction of Diffusion-Weighted Images using 4D Low-Rank and Total Variation","authors":"Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, Dinggang Shen","doi":"10.1007/978-3-319-28588-7_2","DOIUrl":"https://doi.org/10.1007/978-3-319-28588-7_2","url":null,"abstract":"Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post-processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input low-resolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high-resolution image with the help of two regularizations. The first regularization is 4-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"2015 ","pages":"15-25"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-28588-7_2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9777212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease. 大脑网络的代数连接显示分离模式导致阿尔茨海默病网络鲁棒性降低。

Computational diffusion MRI : MICCAI Workshop Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-11182-7_6

Madelaine Daianu, Neda Jahanshad, Talia M Nir, Cassandra D Leonardo, Clifford R Jack, Michael W Weiner, Matthew A Bernstein, Paul M Thompson

{"title":"Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease.","authors":"Madelaine Daianu, Neda Jahanshad, Talia M Nir, Cassandra D Leonardo, Clifford R Jack, Michael W Weiner, Matthew A Bernstein, Paul M Thompson","doi":"10.1007/978-3-319-11182-7_6","DOIUrl":"https://doi.org/10.1007/978-3-319-11182-7_6","url":null,"abstract":"Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative - 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD.","PeriodicalId":72661,"journal":{"name":"Computational diffusion MRI : MICCAI Workshop","volume":"2014 ","pages":"55-64"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-11182-7_6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9770197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24