CT image reconstruction requires certain requirements for projection data. When the projection data is complete, analytical algorithms (such as filtered back-projection algorithm, FBP) or iterative algorithms (ART) can be used for reconstruction. However, in the actual process of obtaining projection, due to limitations such as the geometric position of machine scanning, the structure of the scanning object, and the reasonable and lowest possible radiation dose, the system is difficult to obtain complete projection data, resulting in sparse projection reconstruction or limited angle projection reconstruction problems. This paper proposes a deconvolution iterative algorithm based on direct backprojection for the limited angle reconstruction problem of G-arm CT, and uses the newly proposed reconstruction algorithm combined with L0-GIF to simulate the reconstruction of limited angle projection data. By changing the missing angle, the reconstruction effect under different missing angles was studied. The experimental results showed that our algorithm can achieve good reconstruction quality below a missing angle of 100.
In single photon emission computed tomography(SPECT), the non-stationary Poisson noise in the projection data is
one of the major degrading factors that jeopardize the quality of reconstructed images. In our previous researches for
low-dose CT reconstruction, based on the noise properties of the log-transformed projection data, a penalized
weighted least-squares (PWLS) cost function was constructed and the ideal projection data(i.e., line integral) was
then estimated by minimizing the PWLS cost function. The experimental results showed the method could effectively suppress the noise without noticeable sacrifice of the spatial resolution for both fan- and cone-beam
low-dose CT reconstruction. In this work, we tried to extend the PWLS projection restoration method to SPECT by redefining the weight term in PWLS cost function, because the weight is proportional to measured photon counts for transmission tomography (i.e., CT) while inversely proportional to measured photon counts for emission tomography (i.e., SPECT and PET). The iterative Gauss-Seidel algorithm was then used to minimize the cost function, and since
the weight term was updated in each iteration, we refer our implementation as penalized reweighted least-squares
(PRWLS) approach. The restorated projection data was then reconstructed by an analytical cone-beam SPECT reconstruction algorithm with compensation for non-uniform attenuation. Both high and low level Poisson noise was
simulated in the cone-beam SPECT projection data, and the reconstruction results showed feasibility and efficacy of
our proposed method on SPECT.
Single photon emission computed tomography (SPECT) is an important nuclear medicine imaging technique and has
been using in clinical diagnoses. The SPECT image can reflect not only organizational structure but also functional
activities of human body, therefore diseases can be found much earlier. In SPECT, the reconstruction is based on the
measurement of gamma photons emitted by the radiotracer. The number of gamma photons detected is proportional to
the dose of radiopharmaceutical, but the dose is limited because of patient safety. There is an upper limit in the number
of gamma photons that can be detected per unit time, so it takes a long time to acquire SPECT projection data.
Sometimes we just can obtain highly under-sampled projection data because of the limit of the scanning time or imaging
hardware. How to reconstruct an image using highly under-sampled projection data is an interesting problem. One
method is to minimize the total variation (TV) of the reconstructed image during the iterative reconstruction. In this work,
we developed an OSEM-TV SPECT reconstruction algorithm, which could reconstruct the image from highly
under-sampled projection data with non-uniform attenuation. Simulation results demonstrate that the OSEM-TV
algorithm performs well in SPECT reconstruction with non-uniform attenuation.
Single photon emission computed tomography (SPECT) provides functional information of the interested organs. Such information is very helpful in the early detection of diseases. However, the resolution of SPECT is low. The cone-beam
SPECT reconstruction can improve the photon density and spatial resolution of the reconstructed image. In practice,
because of the data sufficiency and scanning efficiency, helical cone-beam geometry is preferred. The iterative helical
cone-beam reconstruction is time-consuming; the analytical methods are considerably faster and more efficient in clinic. Due to the attenuation of gamma photons, the analytical SPECT reconstruction problem is more complicated. Attenuation should be compensated for to obtain quantitative results. In this paper, based on the Novikov's reconstruction formula and
our Ray-driven Technology, we present an analytical SPECT reconstruction algorithm for helical cone-beam geometry
using Ray-driven Technology. The simulation results demonstrate the accuracy and robustness of our method.
SPECT (single photon emission computed tomography) is a non-invasive, cost-effective means for assessment of
tissue/organ functions in nuclear medicine. For more accurate diagnosis, quantitative reconstruction of radiotracer
concentration at any location inside the body is desired. To achieve this goal, we have to address a number of factors that
significantly degrade the acquired projection data. The cone-beam SPECT system has higher resolution comparing with
parallel-beam and fan-beam SPECT, which is highly advantageous in small object detection. In this paper, we used four
analytical reconstruction schemes for cone-beam SPECT that allow simultaneous compensation for non-uniform
attenuation and distance-dependent resolution variation (DDRV), as well as accurate treatment of Poisson noise. The
simulation results show that the reconstruction scheme 1 and 4 both can obtain good reconstruction results.
Single photon emission computed tomography (SPECT) is a nuclear medicine imaging technique and widely used in the
clinical applications. SPECT image reflects not only organizational structure but also functional activities of human body,
such as blood-flow and metabolism condition, therefore diseases can be found much earlier. For many clinical
applications, cone-beam geometry is preferred, which can improve count density and spatial resolution, and quantitative
reconstruction of radiotracer distribution inside the body is desired. In this paper, we developed an efficient, analytical
solution to cone-beam SPECT reconstruction with simultaneous compensation for attenuation and distance-dependent
resolution variation (DDRV), as well as accurate treatment of Poisson noise. The simulation results show our
reconstruction framework is feasible.
In the conventional single photon emission computed tomography (SPECT), reconstruction algorithm requires full
projection data to reconstruct the images, which will be
time-consuming. While in clinic, doctors usually just care about
the region of interest (ROI), such as heart, not whole body, in this case, a local SPECT reconstruction algorithm is
needed to reconstruct the ROI by only using the projection data from the ROI. In SPECT, the non-stationary Possion
noise in the projection data (sinogram) is a major cause to compromise the quality of the reconstructed images. To
improve the reconstruction quality, we must remove the Possion noise in the sinogram before reconstruction. However,
the conventional space or frequency domain de-noising methods possibly remove the edge information, which is very
important for the accurate reconstruction, especially for the local SPECT reconstruction with non-uniform attenuation.
Wavelet transform, due to its excellent localization property, has rapidly become an indispensable image processing tool
for de-noising. In this paper, we tried to find out the properties of wavelet based de-noising methods for local SPECT
reconstruction with non-uniform attenuation. From the de-noising results, we can see that wavelet based de-noising
methods have good performance for local SPECT reconstruction.
SPECT is one of the nuclear medicine imaging techniques and widely used in the clinical applications. Different from
CT, SPECT achieved the functional image of the organ of interest, and the diseases can be found much earlier. Conebeam
SPECT reconstruction can improve the photon density and spatial resolution of the reconstructed image, but it is
time consuming. In clinic, doctors usually just care about the region of interest (ROI), such as heart, not whole body.
Local reconstruction can reduce the reconstruction time. In this paper, based on Novikov's analytical SPECT
reconstruction algorithm, we built a framework for local cone-beam SPECT reconstruction with non-uniform attenuation.
The simulation results show our reconstruction framework is feasible.
Ablation is a kind of successful treatment for cancer. The technique inserts a special needle into a tumor and produces
heat from Radiofrequency at the needle tip to ablate the tumor. Open configure MR system can take MR images almost
real time and now is applied in liver cancer treatments. During a surgery, surgeons select images in which liver tumors
are seen clearly, and use them to guide the surgery. However, in some cases with severe chirrhosis, the tumors can't be
visualized in the MR images. In such cases, the combination of preoperative CT images will be greatly helpful, if CT
images can be registered to the position of MR images accurately. It is a difficult work since the shape of the liver in the
MR image is different from that of CT images due to the influent of the surgery. In this paper, we use Bspline based
FFD nonrigid image registration to attack the problem. The method includes four steps. Firstly the MRI inhomogeneity
is corrected. Secondly, parametric active contour with the gradient vector flow is used to extract the liver as region of
interest (ROI) because the method is robust and can obtain satisfied results. Thirdly, affine registration is use to match
CT and MR images roughly. Finally, Bspline based FFD nonrigid registration is applied to obtain accuracy registration.
Experiments show the proposed method is robust and accuracy.
In the X-ray coronary digital subtraction angiography, there are serious motion artifacts and noises, and backgrounds
such as ribs, spine, cathers and etc, which are tube structures and like vessels. It's difficult to separate vessels from the
background automatically if they are close each other. In this paper, an automatic extraction of coronary vessels from X-ray
digital subtraction angiography is proposed. We used edge preserving smooth filter to reduce the noises in the images
and keep the vessel edge firstly. Then affine and B-spline based FFD nonrigid registration is applied to the images.
Compared with the segmentation method, the proposed method can remove background greatly and extract the coronary vessel very well.
SPECT (single photon emission computed tomography) is a tomography technique that can greatly show information about the metabolic activity in body and improve clinical diagnosis. In SPECT, because of photoelectric absorption and Compton scattering, the emitted gamma photons are attenuated inside the body before arriving at the detector. The goal of quantitative SPECT reconstruction is to obtain an accurate reconstructed image of the radioactivity distribution in the interested area of a human body, so the compensation for non-uniform attenuation is necessary in the quantitative SPECT reconstruction. In this paper, based on the explicit inversion formula for the attenuated Radon transform discovered by R. Novikov, we present a wavelet based SPECT reconstruction algorithm with non-uniform attenuation. We know that the wavelet transform has characteristics of multi-resolution analysis and localized analysis, and these characteristics can be applied to de-noising and localized reconstruction. Simulation results show that our wavelet based SPECT reconstruction algorithm is accurate.
KEYWORDS: Signal to noise ratio, X-ray computed tomography, Data modeling, X-rays, Sensors, Smoothing, Calibration, Linear filtering, Image filtering, Scanners
To treat the noise in low-dose x-ray CT projection data more accurately, analysis of the noise properties of the data and development of a corresponding efficient noise treatment method are two major problems to be addressed. In order to obtain an accurate and realistic model to describe the x-ray CT system, we acquired thousands of repeated measurements on different phantoms at several fixed scan angles by a GE high-speed multi-slice spiral CT scanner. The collected data were calibrated and log-transformed by the sophisticated system software, which converts the detected photon energy into sinogram data that satisfies the Radon transform. From the analysis of these experimental data, a nonlinear relation between mean and variance for each datum of the sinogram was obtained. In this paper, we integrated this nonlinear relation into a penalized likelihood statistical framework for a SNR (signal-to-noise ratio) adaptive smoothing of noise in the sinogram. After the proposed preprocessing, the sinograms were reconstructed with unapodized FBP (filtered backprojection) method. The resulted images were evaluated quantitatively, in terms of noise uniformity and noise-resolution tradeoff, with comparison to other noise smoothing methods such as Hanning filter and Butterworth filter at different cutoff frequencies. Significant improvement on noise and resolution tradeoff and noise property was demonstrated.
In the past decades, analytical (non-iterative) methods have been extensively investigated and developed for the reconstruction of three-dimensional (3D) single-photon emission computed tomography (SPECT). However, it becomes possible only recently when the exact analytic non-uniform attenuation reconstruction algorithm was derived. Based on the explicit inversion formula for the attenuated Radon transform discovered by Novikov (2000), we extended the previous researches of inverting the attenuated Radon transform of parallel-beam collimation geometry to fan-beam and variable focal-length fan-beam (VFF) collimators and proposed an efficient, analytical solution to 3D SPECT reconstruction with VFF collimators, which compensates simultaneously for non-uniform attenuation, scatter, and spatially-variant or distance-dependent resolution variation (DDRV), as well as suppression of signal-dependent non-stationary Poisson noise. In this procedure, to avoid the reconstructed images being corrupted by the presence of severe noise, we apply a Karhune-Loève (K-L) domain adaptive Wiener filter, which accurately treats the non-stationary Poisson noise. The scatter is then removed by our scatter estimation method, which is based on the energy spectrum and modified from the triple-energy-window acquisition protocol. For the correction of DDRV, a distance-dependent deconvolution is adapted to provide a solution that realistically characterizes the resolution kernel in a real SPECT system. Finally image is reconstructed using our VFF non-uniform attenuation inversion formula.
In this work, we have developed a new method for SPECT (single photon emission computed tomography) image reconstruction, which has shown the potential to provide higher resolution results than any other conventional methods using the same projection data. Unlike the conventional FBP- (filtered backprojection) and EM- (expectation maximization) type algorithms, we utilize as much system response information as we can during the reconstruction process. This information can be pre-measured during the calibration process and stored in the computer. By selecting different sampling schemes for the point response measurement, different system kernel matrices are obtained. Reconstruction utilizing these kernels generates a set of reconstructed images of the same source. Based on these reconstructed images and their corresponding sampling schemes, we are able to achieve a high resolution final image that best represents the object. Because a uniform attenuation, resolution variation and some other effects are included during the formation of the system kernel matrices, the reconstruction from the acquired projection data also compensates for all these effects correctly.
Based on Kunyansky's and our previous work, an efficient, analytical solution to the reconstruction problem of myocardial perfusion SPECT has been developed that allows simultaneous compensation for non-uniform attenuation, scatter, and system-dependent resolution variation, as well as suppression of signal-dependent Poisson noise. To avoid reconstructed images being corrupted by the presence of Poisson noise, a Karhunen-Loeve (K-L) domain adaptive Wiener filter is applied first to suppress the noise in the primary- and scatter-window measurements. The scatter contribution to the primary-energy-window measurements is then removed by our scatter estimation method, which is energy spectrum based, modified from the triple-energy-window acquisition protocol. The resolution variation is corrected by the depth-dependent deconvolution, which, being based on the central-ray approximation and the distance-frequency relation, deconvolves the scatter-free data with the accurate detector-response kernel in frequency domain. Finally, the deblurred projection data are analytically reconstructed with non-uniform attenuation by an algorithm based on Novikov's explicit inversion formula. The preliminary Monte Carlo simulation results using a realistic human thoracic phantom demonstrate that, for parallel-beam geometry, the proposed analytical reconstruction scheme is computationally comparable to filtered backprojection and quantitatively equivalent to iterative maximum a posteriori expectation-maximization reconstruction. Extension to other geometries is under progress.
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.