Lucas R. Borges

Method for simulating dose reduction in digital mammography using the Anscombe transformation.

Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. Results: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. Conclusions: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

Pipeline for effective denoising of digital mammography and digital breast tomosynthesis

Denoising can be used as a tool to enhance image quality and enforce low radiation doses in X-ray medical imaging. The effectiveness of denoising techniques relies on the validity of the underlying noise model. In full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT), calibration steps like the detector offset and flat-fielding can affect some assumptions made by most denoising techniques. Furthermore, quantum noise found in X-ray images is signal-dependent and can only be treated by specific filters. In this work we propose a pipeline for FFDM and DBT image denoising that considers the calibration steps and simplifies the modeling of the noise statistics through variance-stabilizing transformations (VST). The performance of a state-of-the-art denoising method was tested with and without the proposed pipeline. To evaluate the method, objective metrics such as the normalized root mean square error (N-RMSE), noise power spectrum, modulation transfer function (MTF) and the frequency signal-to-noise ratio (SNR) were analyzed. Preliminary tests show that the pipeline improves denoising. When the pipeline is not used, bright pixels of the denoised image are under-filtered and dark pixels are over-smoothed due to the assumption of a signal-independent Gaussian model. The pipeline improved denoising up to 20% in terms of spatial N-RMSE and up to 15% in terms of frequency SNR. Besides improving the denoising, the pipeline does not increase signal smoothing significantly, as shown by the MTF. Thus, the proposed pipeline can be used with state-of-the-art denoising techniques to improve the quality of DBT and FFDM images.

Method for inserting noise in digital mammography to simulate reduction in radiation dose

The quality of clinical x-ray images is closely related to the radiation dose used in the imaging study. The general principle for selecting the radiation is ALARA (“as low as reasonably achievable”). The practical optimization, however, remains challenging. It is well known that reducing the radiation dose increases the quantum noise, which could compromise the image quality. In order to conduct studies about dose reduction in mammography, it would be necessary to acquire repeated clinical images, from the same patient, with different dose levels. However, such practice would be unethical due to radiation related risks. One solution is to simulate the effects of dose reduction in clinical images. This work proposes a new method, based on the Anscombe transformation, which simulates dose reduction in digital mammography by inserting quantum noise into clinical mammograms acquired with the standard radiation dose. Thus, it is possible to simulate different levels of radiation doses without exposing the patient to new levels of radiation. Results showed that the achieved quality of simulated images generated with our method is the same as when using other methods found in the literature, with the novelty of using the Anscombe transformation for converting signal-independent Gaussian noise into signal-dependent quantum noise.

Application of neural networks to model the signal-dependent noise of a digital breast tomosynthesis unit

This work presents a practical method for estimating the spatially-varying gain of the signal-dependent portion of the noise from a digital breast tomosynthesis (DBT) system. A number of image processing algorithms require previous knowledge of the noise properties of a DBT unit. However, this information is not easily available and thus must be estimated. The estimation of such parameters requires a large number of calibration images, as it often changes with acquisition angle, spatial position and radiographic factors. This could represent a barrier in the algorithm’s deployment, mainly for clinical applications. Thus, we modeled the gain of the Poisson noise of a commercially available DBT unit as a function of the radiographic factors, acquisition angle, and pixel position. First, we measured the noise parameters of a clinical DBT unit by acquiring 36 sets of calibration images (raw projections) using uniform phantoms of different thicknesses, within a range of radiographic factors commonly used in clinical practice. With this information, we trained a multilayer perceptron artificial neural network (MLP-ANN) to predict the gain of the Poisson noise automatically as a function of the acquisition setup. Furthermore, we varied the number of calibration images in the learning step of the MLP-ANN to determine the minimum number of images necessary to obtain an accurate model. Results show that the MLP-ANN was able to yield the desired parameters with average error of less than 2%, using a learning dataset limited to only seven sets of calibration images. The accuracy of the model, along with its computational efficiency, makes this method an attractive tool for clinical image-based applications.

Restored low-dose digital breast tomosynthesis: a perception study

This work investigates the perception of noise from restored low-dose digital breast tomosynthesis (DBT) images. First, low-dose DBT projections were generated using a dose reduction simulation algorithm. A dataset of clinical images from the Hospital of the University of Pennsylvania was used for this purpose. Low-dose projections were then denoised with a denoising pipeline developed specifically for DBT images. Denoised and noisy projections were combined to generate images with signal-to-noise ratio comparable to the full-dose images. The quality of restored low-dose and full-dose projections were first compared in terms of an objective no-reference image quality metric previously validated for mammography. In the second analysis, regions of interest (ROIs) were selected from reconstructed full-dose and restored low-dose slices, and were displayed side-by-side on a high-resolution medical display. Five medical physics specialists were asked to choose the image containing less noise and less blur using a 2-AFC experiment. The objective metric shows that, after the proposed image restoration framework was applied, images with as little as 60% of the AEC dose yielded similar quality indices when compared to images acquired with the full-dose. In the 2-AFC experiments results showed that when the denoising framework was used, 30% reduction in dose was possible without any perceived difference in noise or blur. Note that this study evaluated the observers perception to noise and blur and does not claim that the dose of DBT examinations can be reduced with no harm to the detection of cancer. Future work is necessary to make any claims regarding detection, localization and characterization of lesions.

Metal artifact reduction using a patch-based reconstruction for digital breast tomosynthesis

Digital breast tomosynthesis (DBT) is rapidly emerging as the main clinical tool for breast cancer screening. Although several reconstruction methods for DBT are described by the literature, one common issue is the interplane artifacts caused by out-of-focus features. For breasts containing highly attenuating features, such as surgical clips and large calcifications, the artifacts are even more apparent and can limit the detection and characterization of lesions by the radiologist. In this work, we propose a novel method of combining backprojected data into tomographic slices using a patch-based approach, commonly used in denoising. Preliminary tests were performed on a geometry phantom and on an anthropomorphic phantom containing metal inserts. The reconstructed images were compared to a commercial reconstruction solution. Qualitative assessment of the reconstructed images provides evidence that the proposed method reduces artifacts while maintaining low noise levels. Objective assessment supports the visual findings. The artifact spread function shows that the proposed method is capable of suppressing artifacts generated by highly attenuating features. The signal difference to noise ratio shows that the noise levels of the proposed and commercial methods are comparable, even though the commercial method applies post-processing filtering steps, which were not implemented on the proposed method. Thus, the proposed method can produce tomosynthesis reconstructions with reduced artifacts and low noise levels.

Validation of no-reference image quality index for the assessment of digital mammographic images

To ensure optimal clinical performance of digital mammography, it is necessary to obtain images with high spatial resolution and low noise, keeping radiation exposure as low as possible. These requirements directly affect the interpretation of radiologists. The quality of a digital image should be assessed using objective measurements. In general, these methods measure the similarity between a degraded image and an ideal image without degradation (ground-truth), used as a reference. These methods are called Full-Reference Image Quality Assessment (FR-IQA). However, for digital mammography, an image without degradation is not available in clinical practice; thus, an objective method to assess the quality of mammograms must be performed without reference. The purpose of this study is to present a Normalized Anisotropic Quality Index (NAQI), based on the Rényi entropy in the pseudo-Wigner domain, to assess mammography images in terms of spatial resolution and noise without any reference. The method was validated using synthetic images acquired through an anthropomorphic breast software phantom, and the clinical exposures on anthropomorphic breast physical phantoms and patient’s mammograms. The results reported by this noreference index follow the same behavior as other well-established full-reference metrics, e.g., the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Reductions of 50% on the radiation dose in phantom images were translated as a decrease of 4dB on the PSNR, 25% on the SSIM and 33% on the NAQI, evidencing that the proposed metric is sensitive to the noise resulted from dose reduction. The clinical results showed that images reduced to 53% and 30% of the standard radiation dose reported reductions of 15% and 25% on the NAQI, respectively. Thus, this index may be used in clinical practice as an image quality indicator to improve the quality assurance programs in mammography; hence, the proposed method reduces the subjectivity inter-observers in the reporting of image quality assessment.

Simulation of Dose Reduction in Digital Breast Tomosynthesis

Clinical evaluation of dose reduction studies in x-ray breast imaging is problematic because it is difficult to justify imaging the same patient at a variety of radiation doses. One common alternative is to use simulation algorithms to manipulate a standard-dose exam to mimic reduced doses. Although there are several dose-reduction simulation methods for full-field digital mammography, the availability of similar methods for digital breast tomosynthesis DBT is limited. This work proposes a method for simulating dose reductions in DBT, based on the insertion of noise in a variance-stabilized domain. The proposed method has the advantage of performing signal-dependent noise injection without knowledge of the noiseless signal. We compared clinical low-dose DBT projections and reconstructed slices to simulated ones by means of power spectra, mean pixel values, and local standard deviations. The results of our simulations demonstrate low error <5i¾?% between real and simulated images.

Unbiased Injection of Signal-Dependent Noise in Variance-Stabilized Range

The design, optimization, and validation of many image-processing or image-based analysis systems often require testing of the system performance over a dataset of images corrupted by noise at different signal-to-noise ratio (SNR) regimes. A noise-free ground-truth image may not be available, and different SNRs are simulated by injecting extra noise into an already noisy image. However, noise in real-world systems is typically signal dependent, with variance determined by the noise-free image. Thus, the noise to be injected shall also depend on the unknown ground-truth image. To circumvent this issue, we consider the additive injection of noise in variance-stabilized range, where no previous knowledge of the ground-truth signal is necessary. Specifically, we design a special noise-injection operator that prevents the errors on expectation and variance that would otherwise arise when standard variance-stabilizing transformations are used for this task. Thus, the proposed operator is suitable for accurately injecting signal-dependent noise, even to images acquired at very low counts.

Use of Wavelet Multiresolution Analysis to Reduce Radiation Dose in Digital Mammography

This paper investigates the use of a wavelet multiresolution analysis to reduce noise in mammographic images acquired with low levels of radiation dose. We studied the use of a wavelet denoising technique to filter the quantum noise that is incorporated in mammographic images when the radiation dose is reduced. Results were obtained by denoising a set of mammographic images acquired with different levels of radiation exposure, using an anthropomorphic breast phantom. Parameters of the algorithm were adjusted to provide more efficient reduction of noise without blurring or insertion of artifacts. We used the Anscombe transformation before denoising to convert the Poisson signal-correlated noise into an approximately additive white Gaussian noise. Evaluation of denoising performance were conducted by comparing image quality indexes between mammograms acquired with normal radiation dose and those acquired at lower doses levels, after denoising by the proposed technique.