Digital breast tomosynthesis (DBT) has been investigated as a promising alternative to conventional X-ray
mammography for breast cancer screening. By reconstructing 3D volumetric images from multiple 2D projections
measured over a limited angular range, it can offer depth-directional information and improve both sensitivity
and specificity of cancer detection in dense breasts. The diagnostic performance of DBT can be affected by
a number of imaging parameters. The angular range of scan orbit is one of the most crucial factors, since
it determines the depth-directional resolution. Recently, we proposed the wide angle tomosynthesis based on
voltage modulations of X-ray source. By using X-rays with large penetration power on exterior positions, it
can acquire high-SNR projections over a wide angular range. In this paper, we present comparative studies on
exposure conditions in DBT, including narrow and wide angle scan using an invariant tube voltage of X-ray
source, and wide angle scan with the voltage modulation technique. In addition, we compared the conventional
reconstruction methods with recently proposed IDIR algorithms. In preliminary studies, the wide-angle scheme
with proposed IDIR algorithm showed superior performances in detecting abnormal lesions over conventional
approaches.
KEYWORDS: Reconstruction algorithms, X-rays, Grazing incidence, Algorithms, Dual energy imaging, Signal attenuation, Tomography, X-ray imaging, Data acquisition, Systems modeling
In dual energy computed tomography (DECT), two sets of projection data are acquired using a couple of
independent X-ray spectra. Since the attenuation characteristic of a material without a K-edge in a typical
medical X-ray spectrum range is accurately described by the linear combination of two phenomena, which are
the photoelectric attenuation and the Compton scatter, the DECT is theoretically capable of separating one
material from another. However, the material decomposition (MD) is still a challenging problem in DECT, since
two sets of sinograms from distinct X-ray spectra are not spatially aligned in practices. To avoid this problem, the
MD is often achieved by a weighted summation of two reconstructed volumes that correspond to a couple of sets
of projection data, which the monochromatic approximation is generally used in the reconstruction procedure.
The accuracy of the MD, therefore, can be limited due to the erroneous ignorance of the energy dependency of the
acquisition model. In this paper, we propose a novel algorithm, named information theoretic discrepancy based
iterative reconstruction (IDIR) algorithm, for an accurate MD in dual energy X-ray systems. The generalized
information theoretic discrepancy (GID) measure is newly employed as the objective value. Using particular
features of the GID, a tractable objective function for the
material-selective reconstruction is derived, which
accounts the exact polychromatic model of transmission tomography. Since the spectral model of measured data
is explicitly considered, the accurate MD is possible even for misaligned projections. In numerical experiments,
the proposed method showed superior reconstruction performance over the conventional approach.
The X-ray tomosynthesis that measures several low dose projections over a limited angular range has been investigated
as an alternative method of X-ray mammography for breast cancer screening. An extension of the scan
coverage increases the vertical resolution by mitigating the interplane blurring. The implementation of a wide
angle tomosynthesis equipment, however, may not be straightforward, mainly due to the image deterioration
from the statistical noise in exterior projections. In this paper, we adopt the voltage modulation scheme to
enlarge the coverage of the tomosynthesis scan. The higher tube voltages are used for outer angles, which offers
the sufficient penetrating power for outlying frames in which the pathway of X-ray photons is elongated. To
reconstruct 3D information from voltage modulated projections, we propose a novel algorithm, named information
theoretic discrepancy based iterative reconstruction (IDIR) algorithm, which allows to account for the
polychromatic acquisition model. The generalized information theoretic discrepancy (GID) is newly employed as
the objective function. Using particular features of the GID, the cost function is derived in terms of imaginary
variables with energy dependency, which leads to a tractable optimization problem without using the monochromatic
approximation. In preliminary experiments using simulated and experimental equipment, the proposed
imaging architecture and IDIR algorithm showed superior performances over conventional approaches.
The X-ray mammography is the primary imaging modality for breast cancer screening. For the dense breast,
however, the mammogram is usually difficult to read due to tissue overlap problem caused by the superposition
of normal tissues. The digital breast tomosynthesis (DBT) that measures several low dose projections over a
limited angle range may be an alternative modality for breast imaging, since it allows the visualization of the
cross-sectional information of breast. The DBT, however, may suffer from the aliasing artifact and the severe noise
corruption. To overcome these problems, a total variation (TV) regularized statistical reconstruction algorithm
is presented. Inspired by the dual formulation of TV minimization in denoising and deblurring problems, we
derived a gradient-type algorithm based on statistical model of X-ray tomography. The objective function is
comprised of a data fidelity term derived from the statistical model and a TV regularization term. The gradient
of the objective function can be easily calculated using simple operations in terms of auxiliary variables. After
a descending step, the data fidelity term is renewed in each iteration. Since the proposed algorithm can be
implemented without sophisticated operations such as matrix inverse, it provides an efficient way to include the
TV regularization in the statistical reconstruction method, which results in a fast and robust estimation for low
dose projections over the limited angle range. Initial tests with an experimental DBT system confirmed our
finding.
Near-infrared spectroscopy (NIRS) can be employed to investigate brain activities associated with regional changes of the oxy- and deoxyhemoglobin concentration by measuring the absorption of near-infrared light through the intact skull. NIRS is regarded as a promising neuroimaging modality thanks to its excellent temporal resolution and flexibility for routine monitoring. Recently, the general linear model (GLM), which is a standard method for functional MRI (fMRI) analysis, has been employed for quantitative analysis of NIRS data. However, the GLM often fails in NIRS when there exists an unknown global trend due to breathing, cardiac, vasomotion, or other experimental errors. We propose a wavelet minimum description length (Wavelet-MDL) detrending algorithm to overcome this problem. Specifically, the wavelet transform is applied to decompose NIRS measurements into global trends, hemodynamic signals, and uncorrelated noise components at distinct scales. The minimum description length (MDL) principle plays an important role in preventing over- or underfitting and facilitates optimal model order selection for the global trend estimate. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches.
KEYWORDS: Near infrared spectroscopy, Data modeling, Brain, Wavelets, Functional magnetic resonance imaging, Positron emission tomography, Brain activation, Linear filtering, Neuroimaging, Magnetic resonance imaging
Near infrared spectroscopy (NIRS) is a relatively new non-invasive brain imaging method to measure brain
activities associated with regional changes of the oxy- and deoxy- hemoglobin concentration. Typically, functional
MRI or PET data are analyzed using the general linear model (GLM), in which measurements are modeled as a
linear combination of explanatory variables plus an error term. However, the GLM often fails in NIRS if there
exists an unknown global trend due to breathing, cardiac, vaso- motion and other experimental errors. In order
to overcome these problems, we propose a wavelet-MDL based detrending algorithm. Specifically, the wavelet
transform is applied to NIRS measurements to decompose them into global trends, signals and uncorrelated
noise components in distinct scales. In order to prevent the over-fitting the minimum length description (MDL)
principle is applied. Experimental results demonstrate that the new detrending algorithm outperforms the
conventional approaches.
KEYWORDS: Near infrared spectroscopy, Functional magnetic resonance imaging, Scanning probe microscopy, Statistical analysis, Brain mapping, Smoothing, Data modeling, Magnetic resonance imaging, Brain, 3D modeling
Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain
tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain
statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically
analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from
the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM
offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous
recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer
is estimated using Horn's algorithm. These powerful tools allows us the super-resolution localization of the brain
activation which is not possible using the conventional NIRS analysis tools.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.