Analytic-based algorithms such as the FDK algorithm is used currently for image reconstruction from
data acquired with prototypes of dedicated breast CT scanners. In general, analytic-based algorithms require
data collected at a large number (~500) of views. In current
breast-CT scans, imaging dose delivered to the
patient is about the same as that used in a typical two-view mammography exam. This highly limited total
imaging dose, when distributed over a large number of views in breast CT, can result in low-SNR data. There
exists a renewed interest in developing optimization-based (i.e., iterative) algorithms for image reconstruction
from low-SNR data and/or from sparse-view data collected at a reduced number of views. Results of recent
studies on optimization-based algorithms from CT data suggest that the algorithms may reconstruct images
of quality higher than than analytic-based algorithms from low-SNR data and/or from sparse-view data. In
this work, we investigated image reconstruction from low-SNR
patient-breast-CT data collected at a large
number (~500), as well as at reduced numbers, of views. The result of the study appears to indicate that
optimization-based reconstructions can yield breast-CT images from low-SNR data comparable to, or better
than, the corresponding FDK reconstructions.
KEYWORDS: Computed tomography, Reconstruction algorithms, Image-guided intervention, Medical imaging, Physics, Current controlled current source, CT reconstruction, Data acquisition, Computer simulations
Cone-beam computed tomography (CBCT) has been increasingly used during surgical procedures for providing accurate three-dimensional anatomical information for intra-operative navigation and verification. High-quality CBCT images are in general obtained through reconstruction from projection data acquired at hundreds of view angles, which is associated with a non-negligible amount of radiation exposure to the patient. In this work, we have applied a novel image-reconstruction algorithm, the adaptive-steepest-descent-POCS (ASD-POCS) algorithm, to reconstruct CBCT images from projection data at a significantly reduced number of view angles. Preliminary results from experimental studies involving both simulated data and real data show that images of comparable quality to those presently available in clinical image-guidance systems can be obtained by use of the ASD-POCS algorithm from a fraction of the projection data that are currently used. The result implies potential value of the proposed reconstruction technique for low-dose intra-operative CBCT imaging applications.
Micro-CT enables convenient visualization and quantitative analysis of small animals and biological tissue samples. However, high-quality volume images in general require acquisition of cone-beam projection data from hundres of view angles. This prolonged imaging process limits system throughput and may cause potential radiation damage to the imaged objects. It is therefore desirable to have a technique which can generate volume images with satisfactory quality, but from a smaller amount of projection data. On the other hand, many objects subject to the micro-CT scans have sparse spatial distribution, and this sparcity could be exploited and incorporated as prior knowledge in innovative design of algorithms that are capable of reconstructuring images from few-view projection data. In this work we applied a new iterative algorithm based upon constrained total-variation minimization to reconstructing images from as few as five projections. Preliminary results suggest that the algorithm can yield potentially useful images from substantially less projection data than required by existing algorithms. This has practical implications of reducing scanning time and minimizing radiation damage to the imaged objects.
Interfraction motion of a treatment target such as the prostate in radiation therapy (RT) is, in part, responsible for
large planning target volume (PTV) margins and related side effects. Online adjustment of the treatment based on
timely cone-beam CT (CBCT) images can be particularly useful for patients with large interfraction motion. However,
radiation dose to the patient due to frequent CBCT poses a radiation safety concern. One unique feature of CBCT
for interfraction motion detection is the availability of a prior anatomical image most of which has not changed. We
propose an iterative algorithm, for image reconstruction from a very limited number of projections in CBCT, that is
based on total variation (TV) minimization subject to the constraints of data fidelity and positivity and that utilizes
anatomical image prior information. Numerical studies for a 2D fan-beam geometry suggests the proposed algorithm
can potentially contribute to lowering the radiation dose to the patient by allowing satisfactory image reconstruction
from a very limited number of projections.
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.
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.
KEYWORDS: Breast, Computed tomography, Reconstruction algorithms, Image restoration, CT reconstruction, Data acquisition, Medical imaging, Scanners, 3D image processing, Algorithm development
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
configuration.
There exists a strong need to reconstruct computed tomographic (CT) images with practically useful quality from
a small number of projections in image-guided radiation therapy: for lowering radiation dose delivered to the subject,
for shortening the imaging time, and for reducing the
imaging-configuration complexity. We have recently developed
an iterative image reconstruction algorithm based on total-variation (TV) minimization from incomplete projection
data in CT. In numerical studies with a variety of incomplete projection-data sets including truncated data, reduced
scan range, and sparse sampling, the developed algorithm seems to yield reasonable reconstruction, as compared
to some of the existing algorithms, such as algebraic reconstruction technique (ART) and expectation minimization
(EM). The TV-based algorithm begins in general with a uniform image as an initial guess, and goes through iteration
steps to minimize the image TV subject to satisfying the given incomplete projection data. In image-guided radiation
therapy (IGRT), a patient usually undergoes CT scanning for treatment planning, which can provide the reference
image for image guidance. Therefore, we propose a TV-based algorithm with a priori information in few-view CT
for IGRT, in an attempt to further reduce the number of projections needed for image reconstruction from what the
TV-based algorithm uses when no a priori information is included. In this work, we report the initial results of a
preliminary numerical study that we have conducted to demonstrate this approach.
We use a task-based study to objectively evaluate the effect of variable versus fixed focal length in determining
the position of a lesion in helical cone-beam computed tomography (HCBCT). This method will be used
to assess whether variable focal length CBCT scans provide a measurable improvement in estimating lesion
position relative to fixed focal length CBCT in diagnostic applications. In this simulation study a 1 cm diameter
spherical lesion is placed at four different positions within a three-dimensional Shepp-Logan head phantom. The
axial plane is taken to point along the z-axis, which is also the central axis of the helix. The lesion is placed at
the center of the Shepp-Logan phantom, at positions displaced ±5 cm in x, and at a position displaced 5 cm
in y. Four different scans of pitch length 10 cm are then performed using 128 views over 360° with a 100×300
pixel (20 cm×60 cm) detector. Two scans have a fixed focal length of 50 cm between the X-ray source and
the center of rotation (COR), varying only in the starting angle of the source (0° and 90°). We call this the
circular configuration. The other two scans have a variable focal length following the curvature of the head
phantom and ranging from 37.5 cm to 50 cm. We call this the elliptical configuration. The detector rotates
with the source but maintains a constant distance of 30 cm from the COR. A likelihood gridding technique
is used to assess bias and variance in the position estimates determined from each scan configuration. We
find that the biases are small relative to the variances, and have no apparent preferred direction. Of the 24
circular to elliptical comparisons made, we find that in 14 cases the elliptical scan has a smaller variance that
is statistically significant(p ≤ 0.05). By contrast, we find no statistically significant cases in which the circular
scan gives a smaller variance compared to the elliptical scan. We conclude that using a variable focal length
adapted to the contours of the head phantom provides more precise results, but caution that this is a limited
pilot study and many more factors will be accounted for in future work.
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.
We have recently developed a prototype cone-beam micro-CT system for studying new image reconstruction
algorithms experimentally as well as for small animal imaging. Dual energy methods have widely been investigated
and applied for eliminating beam hardening artifacts in the reconstructed images and for performing quantitative CT.
We have proposed earlier a pre-reconstruction-type dual energy method which has the advantage that the cone-beam
CT data from two measurements with different x-ray energy spectra are first processed to obtain quantities that are
approximately consistent with the x-ray transform, and then image reconstruction is performed. In this work, the
proposed method is applied to the cone-beam micro-CT data acquired by our prototype system. Only the thickness
maps of the object based on projection images are shown in this work as a preliminary.
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