2,640 research outputs found
Modeling and inference of multisubject fMRI data
Functional magnetic resonance imaging (fMRI) is a
rapidly growing technique for studying the brain in
action. Since its creation [1], [2], cognitive scientists
have been using fMRI to understand how we remember,
manipulate, and act on information in our environment.
Working with magnetic resonance physicists, statisticians, and
engineers, these scientists are pushing the frontiers of knowledge
of how the human brain works.
The design and analysis of single-subject fMRI studies
has been well described. For example, [3], chapters 10
and 11 of [4], and chapters 11 and 14 of [5] all give accessible
overviews of fMRI methods for one subject. In contrast,
while the appropriate manner to analyze a group of
subjects has been the topic of several recent papers, we do
not feel it has been covered well in introductory texts and
review papers. Therefore, in this article, we bring together
old and new work on so-called group modeling of fMRI
data using a consistent notation to make the methods more
accessible and comparable
Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions
Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an
emerging computed biomedical imaging modality that exploits optical contrast
and ultrasonic detection principles. Iterative image reconstruction algorithms
that are based on discrete imaging models are actively being developed for OAT
due to their ability to improve image quality by incorporating accurate models
of the imaging physics, instrument response, and measurement noise. In this
work, we investigate the use of discrete imaging models based on Kaiser-Bessel
window functions for iterative image reconstruction in OAT. A closed-form
expression for the pressure produced by a Kaiser-Bessel function is calculated,
which facilitates accurate computation of the system matrix.
Computer-simulation and experimental studies are employed to demonstrate the
potential advantages of Kaiser-Bessel function-based iterative image
reconstruction in OAT
A Multivariate Approach to Functional Neuro Modeling
This Ph.D. thesis, A Multivariate Approach to Functional Neuro Modeling, deals with the analysis and modeling of data from functional neuro imaging experiments. A multivariate dataset description is provided which facilitates efficient representation of typical datasets and, more importantly, provides the basis for a generalization theoretical framework relating model performance to model complexity and dataset size. Briefly summarized the major topics discussed in the thesis include: ffl An introduction of the representation of functional datasets by pairs of neuronal activity patterns and overall conditions governing the functional experiment, via associated micro- and macroscopic variables. The description facilitates an efficient microscopic re-representation, as well as a handle on the link between brain and behavior; the latter is obtained by hypothesizing variations in the micro- and macroscopic variables to be manifestations of an underlying system. ffl A review of two micros..
Smoothing dynamic positron emission tomography time courses using functional principal components
A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows Subsequent analysis by methods Such as Spectral Analysis to be substantially improved in terms of their mean squared error
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Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.
Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival
Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
Iterative image reconstruction algorithms for optoacoustic tomography (OAT),
also known as photoacoustic tomography, have the ability to improve image
quality over analytic algorithms due to their ability to incorporate accurate
models of the imaging physics, instrument response, and measurement noise.
However, to date, there have been few reported attempts to employ advanced
iterative image reconstruction algorithms for improving image quality in
three-dimensional (3D) OAT. In this work, we implement and investigate two
iterative image reconstruction methods for use with a 3D OAT small animal
imager: namely, a penalized least-squares (PLS) method employing a quadratic
smoothness penalty and a PLS method employing a total variation norm penalty.
The reconstruction algorithms employ accurate models of the ultrasonic
transducer impulse responses. Experimental data sets are employed to compare
the performances of the iterative reconstruction algorithms to that of a 3D
filtered backprojection (FBP) algorithm. By use of quantitative measures of
image quality, we demonstrate that the iterative reconstruction algorithms can
mitigate image artifacts and preserve spatial resolution more effectively than
FBP algorithms. These features suggest that the use of advanced image
reconstruction algorithms can improve the effectiveness of 3D OAT while
reducing the amount of data required for biomedical applications
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