Breast imaging is an important area of research with many new techniques being investigated to further reduce the morbidity and mortality of breast cancer through early detection. Computerized phantoms can provide an essential tool to quantitatively compare new imaging systems and techniques. Current phantoms, however, lack sufficient realism in depicting the complex 3D anatomy of the breast. In this work, we created one-hundred realistic and detailed 3D computational breast phantoms based on high-resolution CT datasets from normal patients. We also developed a finiteelement application to simulate different compression states of the breast, making the phantoms applicable to multimodality imaging research. The breast phantoms and tools developed in this work were packaged into user-friendly software applications to distribute for breast imaging research.
Correlated-polarity noise reduction (CPNR) is a novel noise reduction technique that uses a statistical approach to reducing noise while maintaining excellent spatial resolution and a traditional noise appearance. It was demonstrated in application to CT imaging for the first time at SPIE 2013 and showed qualitatively excellent image quality at half of normal CT dose. In this current work, we measure quantitatively the spatial resolution and noise properties of CPNR in CT imaging. To measure the spatial resolution, we developed a metrology approach that is suitable for nonlinear algorithms such as CPNR. We introduce the formalism of Signal Modification Factor, SMF(u,v), which is the ratio in frequency space of the CPNR-processed image divided by the noise-free image, averaged over an ensemble of ROIs in a given anatomical context. SMF is a nonlinear analog to the MTF. We used XCAT computer-generated anthropomorphic phantom images followed by projection space processing with CPNR. The SMF revealed virtually no effect from CPNR on spatial resolution of the images (<7% degradation at all frequencies). Corresponding contextdependent NPS measurements generated with CPNR at half-dose were about equal to the NPS of full-dose images without CPNR. This result demonstrates for the first time the quantitative determination of a two-fold reduction in dose with CPNR with less than 7% reduction in spatial resolution. We conclude that CPNR shows strong promise as a method for reduction of noise (and hence, dose) in CT. CPNR may also be used in combination with iterative reconstruction techniques for yet further dose reduction, pending further investigation.
Assessment of the resolution properties of nonlinear imaging systems is a useful but challenging task. While the
modulation transfer function (MTF) fully describes contrast resolution as a function of spatial frequency for linear
systems, an equivalent metric does not exist for systems with significant nonlinearity. Therefore, this preliminary
investigation attempts to classify and quantify the amount of scaling and distortion imposed on a given image signal as
the result of a nonlinear process (nonlinear image processing algorithm).
As a proof-of-concept, a median filter is assessed in terms of its principle frequency response (PFR) and distortion
response (DR) functions. These metrics are derived in frequency space using a sinusoidal basis function, and it is shown
that, for a narrow-band sinusoidal input signal, the scaling and distortion properties of the nonlinear filter are described
exactly by PFR and DR, respectively. The use of matched sinusoidal basis and input functions accurately reveals the
frequency response to long linear structures of different scale. However, when more complex (multi-band) input signals
are considered, PFR and DR fail to adequately characterize the frequency response due to nonlinear interaction effects
between different frequency components during processing.
Overall, the results reveal the context-dependent nature of nonlinear image processing algorithm performance, and they
emphasize the importance of the basis function choice in algorithm assessment. In the future, more complex forms of
nonlinear systems analysis may be necessary to fully characterize the frequency response properties of nonlinear
algorithms in a context-dependent manner.
Correlated-polarity noise reduction (CPNR) is a novel noise reduction technique that uses a statistical approach to reduce
noise while maintaining excellent resolution and a “normal” noise appearance. It is applicable to any type of medical
imaging, and we introduced it at SPIE 2011 for reducing dose three-fold in radiography while maintaining excellent
image quality. In this current work, we demonstrate for the first time its use in reducing the noise in CT images as a
means of reducing the dose in CT. Simulated chest CT images were generated using the XCAT phantom and Poisson
noise was added to simulate a conventional full-dose CT image and a half-dose CT image. CPNR was applied to the
half-dose images in projection image space, and then the images were reconstructed using filtered backprojection with a
Feldkamp methodology. The resulting CPNR processed half-dose images showed essentially equivalent relative
standard deviation in the central heart region to the full-dose images, and about 0.7 times that in half-dose images that
were not processed with CPNR. This noise reduction was consistent with a two-fold reduction in dose that is possible
with CPNR in CT. The CPNR images demonstrated virtually identical sharpness of vessels and no apparent artifacts.
We conclude that CPNR shows strong promise as a new noise reduction method for dose reduction in CT. CPNR could
also be used in combination with model-based iterative reconstruction techniques for yet further dose reduction.
This paper describes a recently developed post-acquisition motion correction strategy for application to lower-cost
computed tomography (LCCT) for under-resourced regions of the world. Increased awareness regarding global health
and its challenges has encouraged the development of more affordable healthcare options for underserved people
worldwide. In regions such as sub-Saharan Africa, intermediate level medical facilities may serve millions with
inadequate or antiquated equipment due to financial limitations. In response, the authors have proposed a LCCT design
which utilizes a standard chest x-ray examination room with a digital flat panel detector (FPD). The patient rotates on a
motorized stage between the fixed cone-beam source and FPD, and images are reconstructed using a Feldkamp
algorithm for cone-beam scanning.
One of the most important proofs-of-concept in determining the feasibility of this system is the successful correction of
undesirable motion. A 3D motion correction algorithm was developed in order to correct for potential patient motion,
stage instabilities and detector misalignments which can all lead to motion artifacts in reconstructed images. Motion will
be monitored by the radiographic position of fiducial markers to correct for rigid body motion in three dimensions.
Based on simulation studies, projection images corrupted by motion were re-registered with average errors of 0.080 mm,
0.32 mm and 0.050 mm in the horizontal, vertical and depth dimensions, respectively. The overall absence of motion
artifacts in motion-corrected reconstructions indicates that reasonable amounts of motion may be corrected using this
novel technique without significant loss of image quality.
Reduction of image noise is an important goal in producing the highest quality medical images. A very important
benefit of reducing image noise is the ability to reduce patient exposure while maintaining adequate image quality.
Various methods have been described in the literature for reducing image noise by means of image processing, both
deterministic and statistical. Deterministic methods tend to degrade image resolution or lead to artifacts or non-uniform
noise texture that does not look "natural" to the observer. Statistical methods, including Bayesian estimation, have been
successfully applied to image processing, but may require more time-consuming steps of computing priors.
The approach described in this paper uses a new statistical method we have developed in our laboratory to reduce image
noise. This approach, Correlated-Polarity Noise Reduction (CPNR), makes an estimate of the polarity of noise at a
given pixel, and then subtracts a random value from a normal distribution having a sign that matches the estimated
polarity of the noise in the pixel. For example, if the noise is estimated to be positive in a given pixel, then a random
number that is also positive will be subtracted from that pixel.
The CPNR method reduces the noise in an image by about 20% per iteration, with little negative impact on image
resolution, few artifacts, and final image noise characteristics that appears "normal." Examples of the feasibility of this
approach are presented in application to radiography and CT, but it also has potential utility in tomosynthesis and
fluoroscopy.
This paper describes an initial investigation into means for producing lower-cost CT scanners for resource limited
regions of the world. In regions such as sub-Saharan Africa, intermediate level medical facilities serving millions have
no CT machines, and lack the imaging resources necessary to determine whether certain patients would benefit from
being transferred to a hospital in a larger city for further diagnostic workup or treatment. Low-cost CT scanners would
potentially be of immense help to the healthcare system in such regions. Such scanners would not produce state-of-theart
image quality, but rather would be intended primarily for triaging purposes to determine the patients who would
benefit from transfer to larger hospitals. The lower-cost scanner investigated here consists of a fixed digital radiography
system and a rotating patient stage. This paper describes initial experiments to determine if such a configuration is
feasible. Experiments were conducted using (1) x-ray image acquisition, a physical anthropomorphic chest phantom, and
a flat-panel detector system, and (2) a computer-simulated XCAT chest phantom. Both the physical phantom and
simulated phantom produced excellent image quality reconstructions when the phantom was perfectly aligned during
acquisition, but artifacts were noted when the phantom was displaced to simulate patient motion. An algorithm was
developed to correct for motion of the phantom and demonstrated success in correcting for 5-mm motion during 360-degree acquisition of images. These experiments demonstrated feasibility for this approach, but additional work is required to determine the exact limitations produced by patient motion.
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