Digital breast tomosynthesis (DBT) provides pseudo-3D images by acquiring limited angle projections, thus alleviating an inherent limitation of tissue superposition in digital mammography (DM). DBT performance, however, may have limitations in terms of recovery of low-contrast structures and accuracy of material decomposition due to scatter radiation. Employing an anti-scatter grid in DBT can mitigate scatter radiation; however, this would lead to the loss of primary radiation. To compensate for the loss, an increased radiation dose is necessary. Additionally, it requires extra manufacturing costs and adds to the system’s complexity. In this work, we propose a deep-learning approach inspired by asymmetric scatter kernel superposition to estimate scatter in DBT. Unlike conventional kernel-based methods which estimate the scatter field based on the value of an individual pixel, the proposed method generates the scatter amplitude and width maps through a network. Additionally, the asymmetric factor map is also estimated from the network to accommodate local variations in conjunction with the object thickness and shape variation. Experiments demonstrate the superiority of the proposed approach. We believe the clinical impact of the proposed method is high since it can negate the additional radiation dose and the system complexity associated with integrating an anti-scatter grid in the DBT system.
KEYWORDS: Computed tomography, Medical imaging, Denoising, Image sharpness, Education and training, Image quality, Tunable filters, Image restoration, Image filtering, Signal to noise ratio
Self-supervised learning for CT image denoising is a promising technique because it does not require clean target data that are usually unavailable in the clinic. Noise2void (N2V) is one of the famous methods to denoise the image without paired target data and it has been used to denoise optical images and also medical images such as MRI, and CT. However, the performance of the N2V is still limited due to the restricted receptive field of the network and it decreases the prediction performance for CT images that have complex image context and non-uniform Poisson random noise. Thus, we proposed enhanced N2V that utilizes penalty-driven network optimization to further denoise the images while preserving the important details. We used the total variation term to further denoise the image and also the laplacian pyramids term to preserve the important edges of the image. The degree of the influence of each penalty term is controlled by the hyperparameter value and they are optimized to achieve the best image quality in terms of noise level and structure sharpness. For the experiment, the real dental CBCT projection data were used to train the network in the projection domain. After the network training, the test results were reconstructed and compared at each different dose level. Meanwhile, PSNR, SNR, and a line profile were also evaluated to quantitatively compare the original FDK images, and proposed method. In conclusion, the proposed method achieved further denoises the image than N2V even preserving the details. By penalty-driven optimization, the network was able to learn the spectral features of the image while still the receptive field is limited to avoid identity mapping. We hope that our method would increase the practical utility of network-based CT images denoising that usually the target data are unavailable.
KEYWORDS: Sensors, Computed tomography, Monte Carlo methods, Image filtering, Data modeling, Data acquisition, Scattering, Quantitative analysis, Optical simulations, Nonlinear filtering
In a cone-beam CT system, the use of bowtie-filter may induce artifacts in the reconstructed images. Through a Monte-Carlo simulation study, we confirm that the bowtie filter causes spatially biased beam energy difference thereby creating beam-hardening artifacts. We also note that cupping artifacts in conjunction with the object scatter and additional beam-hardening may manifest. In this study, we propose a dual-domain network for reducing the bowtie-filter induced artifacts by addressing the origin of artifacts. In the projection domain, the network compensates for the filter induced beam-hardening effects. In the image domain, the network reduces the cupping artifacts that generally appear in cone-beam CT images. Also, transfer learning scheme was adopted in the projection domain network to reduce the total training costs and to increase utility in the practical cases while maintaining the robustness of the dual-domain network. Thus, the pre-trained projection domain network using simple elliptical cylinder phantoms was utilized. As a result, the proposed network shows denoised and enhanced soft-tissue contrast images with much reduced image artifacts. For comparison, a single image domain U-net was also implemented as an ablation study. The proposed dual-domain network outperforms, in terms of soft-tissue contrast and residual artifacts, a single domain network that does not physically consider the cause of artifacts.
This work addresses equalization and thickness estimation of breast periphery in digital breast tomosynthesis (DBT). Breast compression in DBT would lead to a relatively uniform thickness at inner breast but not at the periphery. Proper peripheral enhancement or thickness correction is needed for diagnostic convenience and for accurate volumetric breast density estimation. Such correction methods have been developed albeit with several shortcomings. We present a thickness correction method based on a supervised learning scheme with a convolutional neural network (CNN), which is one of the widely-used deep learning structures, to improve the pixel value of the peripheral region. The network was successfully trained and showed a robust and satisfactory performance in our numerical phantom study.
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.