Recently, the Statistical Image Reconstruction (SIR) and compressed sensing (CS) framework has shown promise
in the x-ray computed tomography (CT) community. In this paper, we propose to establish an equivalence
between the unconstrained optimization problem and a constrained optimization with explicit data consistency
term. The immediate consequence of the equivalence is to enable one to use the well-developed optimization
method to solve the constrained optimization problem to refine the solution of the corresponding unconstrained
optimization problem. As an application of this equivalence, the method was used to develop a convergent and
numerically efficient implementation for the prior image constrained compressed sensing (PICCS).
The increase in the use of CT scanning in the clinical setting is raising concerns from the medical community. In order to reduce the dose of ionizing radiation imparted to patients during CT scans, statistical image recon struction was proposed. This family of algorithm aims at improving image noise characteristics by modeling the stochastic x-ray detection process in the reconstruction algorithm. It was shown however that statistical recon struction may lead to an anisotropic spatial resolution. In this abstract, we study this tradeoff in the context of a statistical formulation of the dose reduction using prior image constrained compressed sensing framework (DR-PICCS). Two numerically-simulated phantoms and a dataset acquired in vivo were used for this evaluation. It is demonstrated that the inclusion of a statistical model in DR-PICCS may whiten the NPS and uniformize the noise spatial distribution in the image. However, the images may suffer from an anisotropic spatial resolution while the images reconstructed using DR-PICCS without statistical model have more isotropic spatial resolution. Due to the flexibility offered in PICCS, a specially-designed prior image processing method has been used in statistical DR-PICCS to palliate for the anisotropy in spatial resolution.
KEYWORDS: In vivo imaging, Image restoration, Computed tomography, Image filtering, Reconstruction algorithms, Tissues, Blood, Signal attenuation, Phase modulation, Analytical research
Myocardial perfusion scans are an important tool in the assessment of myocardial viability following an infarction.
Cardiac perfusion analysis using CT datasets is limited by the presence of so-called partial scan artifacts. These
artifacts are due to variations in beam hardening and scatter between different short-scan angular ranges. In this
research, another angular range dependent effect is investigated: non-uniform noise spatial distribution. Images
reconstructed using filtered backprojection (FBP) are subject to this effect. Statistical image reconstruction
(SIR) is proposed as a potential solution. A numerical phantom with added Poisson noise was simulated and
two swines were scanned in vivo to study the effect of FBP and SIR on the spatial uniformity of the noise
distribution. It was demonstrated that images reconstructed using FBP often show variations in noise on the
order of 50% between different time frames. This variation is mitigated to about 10% using SIR. The noise level
is also reduced by a factor of 2 in SIR images. Finally, it is demonstrated that the measurement of quantitative
perfusion metrics are generally more accurate when SIR is used instead of FBP.
The appeal of compressed sensing (CS) in the context of medical imaging is undeniable. In MRI, it could
enable shorter acquisition times while in CT, it has the potential to reduce the ionizing radiation dose imparted
to patients. However, images reconstructed using a CS-based approach often show an unusual texture and a
potential loss in spatial resolution. The prior image constrained compressed sensing (PICCS) algorithm has been
shown to enable accurate image reconstruction at lower levels of sampling. This study systematically evaluates
an implementation of PICCS applied to myocardial perfusion imaging with respect to two parameters of its
objective function. The prior image parameter α was shown here to yield an optimal image quality in the range
0.4 to 0.5. A quantitative evaluation in terms of temporal resolution, spatial resolution, noise level, noise texture,
and reconstruction accuracy was performed.
Helical computed tomography revolutionized the field of x-ray computed tomography two decades ago. The simultaneous translation of an image object with a standard computed tomography acquisition allows for fast volumetric scan for long image objects. X-ray phase sensitive imaging methods have been studied over the past few decades to provide new contrast mechanisms for imaging an object. A Talbot-Lau grating interferometer based differential phase contrast imaging method has recently demonstrated its potential for implementation in clinical and industrial applications. In this work, the principles of helical computed tomography are extended to differential phase contrast imaging to produce volumetric reconstructions based on fan-beam data. The method demonstrates the potential for helical differential phase contrast CT to scan long objects with relatively small detector coverage in the axial direction.
A technique for dose reduction using prior image constrained compressed sensing (DR-PICCS) in computed
tomography (CT) is proposed in this work. In DR-PICCS, a standard FBP reconstructed image is forward
projected to get a fully sampled projection data set. Meanwhile, it is low-pass filtered and used as the prior
image in the PICCS reconstruction framework. Next, the prior image and the forward projection data are
used together by the PICCS algorithm to obtain a low noise DR-PICCS reconstruction, which maintains the
spatial resolution of the original FBP images. The spatial resolution of DR-PICCS was studied using a Catphan
phantom by MTF measurement. The noise reduction factor, CT number change and noise texture were studied
using human subject data consisting of 20 CT colonography exams performed under an IRB-approved protocol.
In each human subject study, six ROIs (two soft tissue, two colonic air columns, and two subcutaneous fat)
were selected for the CT number and noise measurements study. Skewness and kurtosis were used as figures of
merit to indicate the noise texture. A Bland-Altman analysis was performed to study the accuracy of the CT
number. The results showed that, compared with FBP reconstructions, the MTF curve shows very little change
in DR-PICCS reconstructions, spatial resolution loss is less than 0.1 lp/cm, and the noise standard deviation
can be reduced by a factor of 3 with DR-PICCS. The CT numbers in FBP and DR-PICCS reconstructions agree
well, which indicates that DR-PICCS does not change CT numbers. The noise textures indicators measured
from DR-PICCS images are in a similar range as FBP images.
Recently, iterative image reconstruction algorithms have been extensively studied in x-ray CT in order to produce
images with lower noise variance and high spatial resolution. However, the images thus reconstructed often
have unnatural image noise textures, the potential impact of which on diagnostic accuracy is still unknown.
This is particularly pronounced in total-variation-minimization-based image reconstruction, where the noise
background often manifests itself as patchy artifacts. In this paper, a quantitative noise texture evaluation
metric is introduced to evaluate the deviation of the noise histogram from that of images reconstructed using
filtered backprojection. The proposed texture similarity metric is tested using TV-based compressive sampling
algorithm (CSTV). It was demonstrated that the metric is sensitive to changes in the noise histogram independent
of changes in noise level. The results demonstrate the existence tradeoff between the texture similarity metric
and the noise level for the CSTV algorithm, which suggests a potential optimal amount of regularization. The
same noise texture quantification method can also be utilized to evaluate the performance of other iterative
image reconstruction algorithms.
KEYWORDS: Reconstruction algorithms, Sensors, Computed tomography, Image restoration, Signal attenuation, Scanners, In vivo imaging, X-rays, Medical imaging, Data acquisition
C-arm CT is used in neurovascular interventions where a large flat panel detector is used to acquire cone-beam projection data. In this case, data truncation problems due to the limited detector size are mild. When the cone beam CT method is applied to cardiac interventions severe data truncation artifacts reduce the clinical
utility of the reconstructions. However, accurate reconstruction is still possible given a priori knowledge of the reconstruction values within a small region inside the FOV. Several groups have studied the case of the interior problem where data is truncated from all views. In this paper, we applied these new mathematical discoveries
to C-arm cardiac cone-beam CT to demonstrate that accurate image reconstruction may be achieved for cardiac
interventions. The method is applied to iteratively reconstruct the image volume such that it satisfies several
physical conditions. In this work, the algorithm is applied to data from in-vivo cardiac canine studies collected
using a clinical C-arm system. It is demonstrated that the algorithm converges well to the reconstruction values
of non-truncated data reconstructed using the FDK algorithm. Furthermore, proper convergence is achieved
by using only an estimate of the average value within a subregion as a priori information (i.e. the exact value
at each pixel in the a priori region need not be known). Two methods for obtaining a priori information are
compared.
Differential phase contrast computed tomography (DPC-CT) is a novel X-ray imaging method that uses the
wave properties of imaging photons as the contrast mechanism. It has been demonstrated that differential phase
contrast images can be obtained using either synchrotron radiation or a conventional X-ray tube and a Talbot-
Lau-type interferometer. These data acquisition systems offer only a limited field of view and thus, are prone
to data truncation. In this work, we demonstrated that a small region of interest (ROI) of a large object can be
accurately and stably reconstructed using fully truncated projection datasets provided that a priori information
on electron density is known inside the ROI. The method reconstructs an image iteratively to satisfy a group
of physical conditions using a projection onto convex set (POCS) algorithm. This POCS algorithm is validated
using numerical simulations.
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.