این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Iranian Journal of Medical Physics، جلد ۴، شماره Issue ۱,۲، صفحات ۱-۱۸
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Improving the Performance of ICA Algorithm for fMRI Simulated Data Analysis Using Temporal and Spatial Filters in the Preprocessing Phase
چکیده انگلیسی مقاله Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evaluated. In this study, besides the comparison of simple and noisy Independent Component Analysis (ICA) algorithms, the quantity effects of some spatial and temporal filtering have been evaluated on the functionality of ICA algorithms. Noisy ICA algorithms perform with a higher accuracy (up to 16%) in comparison to simple ICA for noisy fMRI data, although it is more time consuming than simple ICA. The accuracy of the results is improved by 8-10% using spatial and temporal filtering prior to simple ICA. Materials and Methods: Simple ICA and noisy ICA methods have been compared for analyzing simulated fMRI data sets. The impact of some temporal and spatial filters on the functionality of simple ICA algorithms has been evaluated. Implemented filters have been proposed in low and high pass group. Results: The sensitivity, specificity and temporal accuracy of simple ICA algorithms has been improved by using high pass filters. Although low pass filtering has some positive effects on the performance of simple ICA algorithms in the low SNR levels, in the high signal-noise Ratio (SNR) levels these low pass filters may cause a decrease in the sensitivity, specificity and temporal accuracy of simple ICA methods. Discussion and Conclusion: The results obtained from simple and noisy ICA algorithms for analyzing fMRI data having high SNR levels are approximately similar. Infomax algorithm uses Gradient based methods for estimating unmixing matrix has better sensitivity, specificity and temporal accuracy than Fast ICA for analyzing noisy ICA data. An alternative to the complicated and time consuming noisy ICA algorithms is to preprocess and denoise fMRI data prior to analyzing it by simple ICA algorithms.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله a برومند | a. boroomand
m.sc. in biomedical engineering, tehran university of medical sciences, tehran, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی تهران (Tehran university of medical sciences)

a احمدیان | a. ahmadian
associate professor, physics and biomedical engineering dept., tehran university of medical sciences, tehran, iran research center for science amp;amp; technology in medicine, imam khomeini hospital, tehran, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی تهران (Tehran university of medical sciences)

محمدعلی عقابیان | m a oghabian
associate professor, physics and biomedical engineering dept., tehran university of medical sciences, tehran, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی تهران (Tehran university of medical sciences)

نشانی اینترنتی http://ijmp.mums.ac.ir/article_7695.html
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده Original Paper
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات