Investigating poisson noise filtering in Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is a potential\ud candidate to substitute digital mammography in breast cancer\ud screening. In DBT, projection images are acquired with low\ud levels of radiation, which significantly increases image noise. In\ud this work, we evaluate the effect of a denoising filter, designed for\ud digital mammography, on the reduction of quantum noise in\ud DBT images. This filter is based on an adaptive Wiener filter and\ud the Anscombe transformation, to reduce Poisson noise without\ud significantly affecting image sharpness. Denoising was applied to\ud a set of synthetic DBT images generated using a 3D\ud anthropomorphic software breast phantom. Images without noise\ud was also created to provide ground-truth information. In order to\ud evaluate the denoising performance in different steps of the DBT\ud imaging, filtering was applied separately to the projections\ud (before reconstruction) and to the tomographic slices (after\ud reconstruction). The performance of the filter was evaluated\ud considering qualitative and quantitative analysis of the images\ud before and after denoising.FAPESPCNP

Effect of denoising on the quality of reconstructed images in digital breast tomosynthesis

Individual projection images in Digital Breast Tomosynthesis (DBT) must be acquired with low levels of radiation,\ud which significantly increases image noise. This work investigates the influence of a denoising algorithm and the\ud Anscombe transformation on the reduction of quantum noise in DBT images. The Anscombe transformation is a\ud variance-stabilizing transformation that converts the signal-dependent quantum noise to an approximately signalindependent\ud Gaussian additive noise. Thus, this transformation allows for the use of conventional denoising algorithms,\ud designed for additive Gaussian noise, on the reduction of quantum noise, by working on the image in the Anscombe\ud domain. In this work, denoising was performed by an adaptive Wiener filter, previously developed for 2D\ud mammography, which was applied to a set of synthetic DBT images generated using a 3D anthropomorphic software\ud breast phantom. Ideal images without noise were also generated in order to provide a ground-truth reference. Denoising\ud was applied separately to DBT projections and to the reconstructed slices. The relative improvement in image quality\ud was assessed using objective image quality metrics, such as peak signal-to-noise ratio (PSNR) and mean structural\ud similarity index (SSIM). Results suggest that denoising works better for tomosynthesis when using the Anscombe\ud transformation and when denoising was applied to each projection image before reconstruction; in this case, an average\ud increase of 9.1 dB in PSNR and 58.3% in SSIM measurements was observed. No significant improvement was observed\ud by using the Anscombe transformation when denoising was applied to reconstructed images, suggesting that the\ud reconstruction algorithm modifies the noise properties of the DBT images.FAPESPCNP

Real-Time Quantum Noise Suppression In Very Low-Dose Fluoroscopy

Fluoroscopy provides real-time X-ray screening of patient's organs and of various radiopaque objects, which make it an invaluable tool for many interventional procedures. For this reason, the number of fluoroscopy screenings has experienced a consistent growth in the last decades. However, this trend has raised many concerns about the increase in X-ray exposure, as even low-dose procedures turned out to be not as safe as they were considered, thus demanding a rigorous monitoring of the X-ray dose delivered to the patients and to the exposed medical staff. In this context, the use of very low-dose protocols would be extremely beneficial. Nonetheless, this would result in very noisy images, which need to be suitably denoised in real-time to support interventional procedures. Simple smoothing filters tend to produce blurring effects that undermines the visibility of object boundaries, which is essential for the human eye to understand the imaged scene. Therefore, some denoising strategies embed noise statistics-based criteria to improve their denoising performances. This dissertation focuses on the Noise Variance Conditioned Average (NVCA) algorithm, which takes advantage of the a priori knowledge of quantum noise statistics to perform noise reduction while preserving the edges and has already outperformed many state-of-the-art methods in the denoising of images corrupted by quantum noise, while also being suitable for real-time hardware implementation. Different issues are addressed that currently limit the actual use of very low-dose protocols in clinical practice, e.g. the evaluation of actual performances of denoising algorithms in very low-dose conditions, the optimization of tuning parameters to obtain the best denoising performances, the design of an index to properly measure the quality of X-ray images, and the assessment of an a priori noise characterization approach to account for time-varying noise statistics due to changes of X-ray tube settings. An improved NVCA algorithm is also presented, along with its real-time hardware implementation on a Field Programmable Gate Array (FPGA). The novel algorithm provides more efficient noise reduction performances also for low-contrast moving objects, thus relaxing the trade-off between noise reduction and edge preservation, while providing a further reduction of hardware complexity, which allows for low usage of logic resources also on small FPGA platforms. The results presented in this dissertation provide the means for future studies aimed at embedding the NVCA algorithm in commercial fluoroscopic devices to accomplish real-time denoising of very low-dose X-ray images, which would foster their actual use in clinical practice