X-ray fluorescence computed tomography (XFCT) is a synchrotron-based imaging modality employed for mapping the distribution of elements within slices or volumes of intact specimens. A pencil beam of external radiation is used to stimulate emission of characteristic X-rays from within a sample, which is scanned and rotated through the pencil beam in a first-generation tomographic geometry. It has long been believed that for each slice, the acquired measurement lines must
span the entire object at every projection view over 180 degrees to avoid reconstructing images with so-called truncation artifacts. However, recent developments in tomographic reconstruction theory have overturned those long-held beliefs about minimum-data requirements and shown that it is possible to obtain exact reconstruction of ROIs from truncated projections. In this work, we show how to exploit these developments to allow for region of interest imaging in XFCT.
In classical tomosynthesis, the x-ray source generally is moved along a curve segment, such as a circular trajectory, within a plane that is perpendicular to the detector plane. Studies suggest that when the angular coverage and number of projection views are limited, it can be difficult to reconstruct accurate images within planes perpendicular to the detector plane in classical tomosynthesis. In this work, we investigate imaging strategies in tomosynthesis using trajectories that are not confined within a plane perpendicular to the detector plane. We expect that such trajectories can increase data information and thus lead reconstructed images with improved quality. Numerical studies were conducted for evaluating the image-reconstruction quality in classical tomosynthesis and tomosynthesis with trajectories that are not confined within a plane perpendicular to the detector plane. The results of the studies indicated that, with the same number of views, (or equivalenntly, the same amount of image radiation), data acquired in tomosynthesis with the trajectories that are not confined within a plane perpendicluar to the detector plane generally contain more information than that acquired with classical tomosynthesis and can thus yield images with improved quality.
Chord-based algorithms can eliminate cone-beam artifacts in images reconstructed from a clinical computed
tomography (CT) scanner. The feasibility of using chord-based reconstruction algorithms was evaluated with
three clinical CT projection data sets.
The first projection data set was acquired using a clinical
64-channel CT scanner (Philips Brilliance 64) that
consisted of an axial scan from a quality assurance phantom. Images were reconstructed using (1) a full-scan
FDK algorithm, (2) a short-scan FDK algorithm, and (3) the
chord-based backprojection filtration algorithm
(BPF) using full-scan data. The BPF algorithm was capable of reproducing the morphology of the phantom
quite well, but exhibited significantly less noise than the two FDK reconstructions as well as the reconstruction
obtained from the clinical scanner.
The second and third data sets were obtained from scans of a head phantom and a patient's thorax. For both
of these data sets, the BPF reconstructions were comparable to the short-scan FDK reconstructions in terms of
image quality, although sharper features were indistinct in the BPF reconstructions.
This research demonstrates the feasibility of chord-based algorithms for reconstructing images from clinical
CT projection data sets and provides a framework for implementing and testing algorithmic innovations.
X-ray differential phase-contrast tomography (DPCT) is a method for reconstructing the spatial distribution of
the X-ray refractive index within an object from knowledge of differential projection data. Assuming geometrical
optics wave propagation, these data describe the angles by which the probing optical beams are deflected by
the object due to refraction. Phase-sensitive X-ray imaging methods such as diffraction enhanced imaging can
measure the required beam-deflection data, and are being actively developed for medical imaging applications.
In this work, we investigate and demonstrate the applicability of algorithms recently developed for conventional
tomography for obtaining region-of-interest images in DPCT from knowledge of truncated differential projection
data. A preliminary numerical study is conducted to validate and demonstrate the proposed reconstruction
Some of the recently developed image reconstruction algorithms for cone-beam computed tomography (CBCT)
involve the computation of the finite Hilbert transform. We have previously studied noise property of the finite
Hilbert transform and observed that it can be used for potentially improving the image noise property within a region
of interest (ROI) in IGRT. Imaging radiation dose is one of the critical issues in IGRT, and in addition to existing dose-reduction
schemes by use of ROI imaging, it is possible to achieve further patient dose reduction through modulating
beam intensity so that a sub-ROI in the ROI be exposed by high flux of x-ray photons and the rest of the ROI be
exposed by low flux of them. In this work, we investigate the technique for obtaining sub-ROI images, which is
supposed to include the target under treatment, with high contrast-to-noise ratio (CNR) and the images within the rest
of the ROI with low CNR. Numerical studies have been conducted as a preliminary in this work.
The back-projection filtration (BPF)algorithm is capable of reconstructing
ROI images from truncated data acquired with
a wide class of general trajectories. However, it has been observed
that, similar to other algorithms for convergent beam geometries,
the BPF algorithm involves a spatially varying
weighting factor in the backprojection step.
This weighting factor can not only increase the computation
load, but also amplify the noise in reconstructed images
The weighting factor can be eliminated
by appropriately rebinning the measured cone-beam
data into fan-parallel-beam data. Such an appropriate data rebinning
not only removes the weighting factor, but also retain other favorable
properties of the BPF algorithm. In this work, we conduct a preliminary
study of the rebinned BPF algorithm and its noise property. Specifically,
we consider an application in which the detector and source can move in
several directions for achieving ROI data acquisition. The combined
motion of the detector and source generally forms a complex trajectory.
We investigate in this work image reconstruction within an ROI from data
acquired in this kind of applications.
Current dedicated, cone-beam breast CT scanners generally use a circular
scanning configuration largely because it is relatively easy to implement
mechanically. It is also well-known, however, that a circular scanning
configuration produces insufficient cone-beam data for reconstrucing
accurate 3D breast images. Approximate algorithms, such as FDK has
been widely applied to reconstruct images from circular cone-beam
data. In the FDK reconstruction, it is possible to observe artifacts such as
intensity decay for locations that are not within the plane containing
the circular source trajectory. Such artifacts may potentially lead
to false positive and/or false negative diagnosis of breast cancer.
Non-circular imaging configurations may provide data sufficient for accurate image reconstruction.
In this work, we implement, investigate innovative, non-circular scanning
configurations such as helical and saddle configurations for data
acquisition on a dedicated, cone-beam breast CT scanner, and develop
novel algorithms to reconstruct accurate 3D images from these data.
A dedicated, cone-beam breast CT scanner capable of performing non-circular
scanning configurations was used in this research. We have investigated
different scanning configurations, including helical and saddle configurations.
A Defrise disk phantom and a dead mouse were scanned by use of these
configurations. For each configuration, cone-beam data were acquired
at 501 views over each turn. We have reconstructed images using our
BPF algorithm from data acquired with the helical scanning
In this work, we introduced an algorithm for image reconstruction in helical cone-beam CT based upon the backprojection-filtration (BPF) algorithm. This algorithm is a backprojection-filtration-type algorithm that reconstructs images from rebinned data. It retains the properties of the original BPF algorithm in that it requires minimum data and can reconstruct ROI images from truncated data. More importantly, due to the elimination of the spatially-variant weighting factor in the backprojection, it may improve the noise properties in reconstructed images. We have performed computer-simulation studies to investigate the ROI-image reconstruction and noise properties of this algorithm, and the quantitative results verify and demonstrate the proposed algorithm.
Usage of the backprojection filtration (BPF) algorithm for reconstructing images from motion-contaminated fan-beam data may result in motion-induced streak artifacts, which appear in the direction of the chords on which images are reconstructed. These streak artifacts, which are most pronounced along chords tangent to the edges of the moving object, may be suppressed by use of the weighted BPF (WBPF) algorithm, which can exploit the inherent redundancies in fan-beam data. More specifically, reconstructions using full-scan and short-scan data can allow for substantial suppression of these streaks, whereas those using reduced-scan data can allow for partial suppression. Since multiple different reconstructions of the same chord can be obtained by varying the amount of redundant data used, we have laid the groundwork for a possible method to characterize the amount of motion encoded within the data used for reconstructing an image on a particular chord. Furthermore, since motion artifacts in WBPF reconstructions using full-scan and short-scan data appear similar to those in corresponding fan-beam filtered backprojection (FFBP) reconstructions for the cases performed in this study, the BPF and WBPF algorithms potentially may be used to arrive at a more fundamental characterization of how motion artifacts appear in FFBP reconstructions.
A formula was recently described by Clackdoyle et. al. for image reconstruction within a region of interest (ROI) from knowledge of its truncated 2D Radon transform. In this work, we present an alternative, simple derivation of the formula by using the well-known relationship between the parallel-beam and fan-beam geometries. Based upon our derivation, the role of parameter t in the formula in ROI-image reconstruction can be clearly identified. We show that the parameter t determines the size of a reconstructible ROI from parallel-beam data containing truncations. Numerical studies were performed to by use of the formula with different t. We show that the formula yields ROI images with smaller sizes and lower quality than does our backprojection filtration algorithm.
In this work, we investigate exact image reconstruction
within a 3D region of interest from data acquired with
a circle-arc trajectory. In particular, the data
may contain both longitudinal and transverse truncations.
This work may find applications
in lung or heart imaging using a C-arm scanner.
When the arc portion of the trajectory is posterior
or anterior to the patient, exact images within the
lung or heart region can be reconstructed from truncated
In many applications of circular cone-beam CT, it is not uncommon that the size of the field of view (FOV) is smaller than that of the imaging object, thus leading to transverse truncation in projection data. Exact reconstruction in any region is not possible from such truncated data using conventional algorithms. Recently, an exact algorithm for image reconstruction on PI-line segments in helical cone-beam CT has been proposed. This algorithm, which we refer to as the backprojection-filtration (BPF) algorithm, can naturally address the problem of exact region of interest (ROI) reconstruction from such truncated data. In this work, we modified this algorithm to reconstructing images in circular cone-beam scan. The unique property of this modified algorithm is that it can reconstruct exact ROIs in midplane and approximate ROIs in other planes from transversely truncated data. We have performed computer-simulation studies to validate the theoretical assertions. Preliminary results demonstrate that the proposed algorithm provides a solution to the truncation problems caused by limited FOV size.
Recently, a 3D filtered-backprojection (FBP)-based algorithm for image reconstruction on PI-line segments in a helical cone-beam CT scan has been developed (Zou and Pan, 2004). In the present work, we derive new reconstruction algorithms for circular cone-beam scans based upon this algorithm and a concept of virtual PI-line. We prove that, in the case of conventional full- and short-scan, the newly derived algorithms are mathematically identical to existing algorithms. More importantly, in the case of reduced-scans in which the scanning angle range is less than that in a short-scan, the new algorithms can yield exact region of interest (ROI) reconstruction in mid-plane and approximate ROI reconstruction in off-mid-planes. We have performed a preliminary numerical study that verifies our theoretical assertions.
Recent algorithm development for image reconstruction for cone-beam CT has tackled exact image reconstruction for very general scanning configurations. The heart of the new algorithms is the concept of reconstruction on the chordn of a general source trajectory. Volume ROI reconstruction becomes possible by concatenating the chords on which the image has been obtained. For some scanning trajectories there maybe points in the image space where the image can theoretically be obtained exactly, yet no chord intersects these points. This article provides a consistency condition, based on the ideas of John's equation, that may be used to rebin cone-beam data so that all points satisfying Tuy's condition are reconstructible
by a chord algorithm.
In fan-beam computed tomography (CT), one may be interested in image reconstruction in a region of interest (ROI) from truncated data acquired over an angular range less than half-scan data. We developed recently a backprojection filtration (BPF) algorithm to reconstruct an ROI image from reduced scan data containing data truncations. In a reduced scan, the truncated data may still contain redundancy. In this work, we describe a new algorithm that can exploit data redundancy in truncated data for potentially suppressing the aliasing and noise artifacts in reconstructed images.
We have performed numerical studies to demonstrate the BPF algorithm.