Computational Diffusion MRI and Brain Connectivity : MICCAI Workshops, Nagoya, Japan, September 22nd, 2013. MICCAI Workshop on Computation Diffusion MRI (5th : 2013 : Nagoya-shi, Japan)最新文献

{"title":"Estimating Uncertainty in White Matter Tractography Using Wild Non-local Bootstrap.","authors":"Pew-Thian Yap, Hongyu An, Yasheng Chen, Dinggang Shen","doi":"10.1007/978-3-319-02475-2_13","DOIUrl":"10.1007/978-3-319-02475-2_13","url":null,"abstract":"<p><p>Statistics derived from diffusion MRI data, especially those related to tractography, are often highly non-linear and non-Gaussian with unknown complex distributions. In estimating the sampling distributions of these statistics, many existing techniques are limited by their reliance on models that assume normality and that are yet to be verified in complex situations where various noise sources, such as physiologic variation, scanner instability, and imaging noise, might be simultaneously present. In complex conditions as such, a viable solution is the bootstrap, which due to its distribution-independent nature is an appealing tool for the estimation of the variability of almost any statistic, without relying on complicated theoretical calculations, but purely on computer simulation. In this paper, we will examine whether a new bootstrap scheme, called the <i>wild non-local bootstrap</i> (W-NLB), is effective in estimating the uncertainty in tractography data. In contrast to the residual or wild bootstrap, which relies on a predetermined data model, or the repetition bootstrap, which requires repeated signal measurements, W-NLB does not assume a predetermined form of data structure and obviates the need for time-consuming multiple acquisitions. W-NLB hinges on the observation that local imaging information recurs in the image. This self-similarity implies that imaging information coming from spatially distant (non-local) regions can be exploited for more effective estimation of statistics of interest. <i>In silico</i> evaluations indicate that W-NLB produces distribution estimates that are in closer agreement to those generated using Monte Carlo simulations, compared with the conventional residual bootstrap. Evaluations using in vivo data show that W-NLB produces results that are in agreement with our knowledge on the white matter connection architecture.</p>","PeriodicalId":92492,"journal":{"name":"Computational Diffusion MRI and Brain Connectivity : MICCAI Workshops, Nagoya, Japan, September 22nd, 2013. MICCAI Workshop on Computation Diffusion MRI (5th : 2013 : Nagoya-shi, Japan)","volume":"2014 ","pages":"139-148"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8302449/pdf/nihms-1724405.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39221198","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}

{"title":"Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI.","authors":"Bryce Wilkins,&nbsp;Namgyun Lee,&nbsp;Vidya Rajagopalan,&nbsp;Meng Law,&nbsp;Natasha Leporé","doi":"10.1007/978-3-319-02475-2_2","DOIUrl":"https://doi.org/10.1007/978-3-319-02475-2_2","url":null,"abstract":"<p><p>In this paper we investigate the effect of single-shell q-space diffusion sampling strategies and applicable multiple-fiber analysis methods on fiber orientation estimation in Diffusion MRI. Specifically, we develop a simulation based on an in-vivo data set and compare a two-compartment \"ball-and-stick\" model, a constrained spherical deconvolution approach, a generalized Fourier transform approach, and three related methods based on transforms of Fourier data on the sphere. We evaluate each method for <i>N</i> = 20, 30, 40, 60, 90 and 120 angular diffusion-weighted samples, at SNR = 18 and diffusion-weighting <i>b</i> = 1000s/mm², common to clinical studies. Our results quantitatively show the methods' are most distinguished from each other by their fiber detection ability. Overall, the \"ball-and-stick\" model and spherical deconvolution approach were found to perform best, yielding the least orientation error, and greatest detection rate of fibers.</p>","PeriodicalId":92492,"journal":{"name":"Computational Diffusion MRI and Brain Connectivity : MICCAI Workshops, Nagoya, Japan, September 22nd, 2013. MICCAI Workshop on Computation Diffusion MRI (5th : 2013 : Nagoya-shi, Japan)","volume":"2013 ","pages":"13-24"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-02475-2_2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36788765","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}