Adaptively and spatially estimating the hemodynamic response functions in fMRI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6892 LNCS, Issue PART 2, 2011, Pages 269-276

  Wang, Jiaping ^a   Zhu, Hongtu ^a,b   Fan, Jianqing ^d   Giovanello, Kelly ^b,c   Lin, Weili ^b  

^a University of North Carolina at Chapel Hill   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c The University of North Carolina at Chapel Hill   (United States)

^d Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SMOOTHING; BRAIN REGIONS; DATA SETS; FINITE IMPULSE RESPONSE MODEL; FINITE SAMPLE PERFORMANCE; FREQUENCY DOMAINS; FUNCTIONAL MRI; HEMODYNAMIC RESPONSE FUNCTIONS; LOGIT MODELS; MULTISCALES; QUANTITATIVE INFORMATION; RELATIVE TIMING; SIMULATION STUDIES; TIME TO PEAK;

BRAIN; COMPUTER AIDED ANALYSIS; COMPUTER SIMULATION; DATA REDUCTION; IMPULSE RESPONSE; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; MEDICAL COMPUTING; MEDICAL IMAGING; SAMPLING;

ESTIMATION;

ALGORITHM; ARTICLE; BRAIN; BRAIN CIRCULATION; BRAIN MAPPING; COMPUTER SIMULATION; HEMODYNAMICS; HUMAN; IMAGE PROCESSING; METHODOLOGY; NORMAL DISTRIBUTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PHYSIOLOGY; REGRESSION ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; BRAIN; BRAIN MAPPING; CEREBROVASCULAR CIRCULATION; COMPUTER SIMULATION; HEMODYNAMICS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LEAST-SQUARES ANALYSIS; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; NORMAL DISTRIBUTION;

EID: 82255167035     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23629-7_33     Document Type: Conference Paper

References (11)

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