KEYWORDS: Image restoration, Signal detection, Signal to noise ratio, Mammography, Image quality, Quantum noise, Digital mammography, Molybdenum, Databases, Data modeling
In this study, we assess the impact of an image restoration pipeline, designed for digital mammography, on the detectability of microcalcifications of different sizes across varied radiation exposures. The restoration pipeline first removes the noise of the image considering a Poisson-Gaussian noise model that incorporates quantum and electronic noise. Then, it appropriately merges the noisy and denoised images to achieve a signal-to-noise ratio (SNR) comparable to an image obtained at a higher radiation dose. We created a database of mammographic images acquired at radiation doses between 50% and 200% of the automatic exposure control (AEC) using a physical anthropomorphic breast phantom. Clustered microcalcifications with diameters ranging from 190𝜇m to 390𝜇m were artificially inserted into the phantom images in regions with increased density. The Channelized Hotelling Observer (CHO) was employed as the model observer (MO) to evaluate the detectability of microcalcifications. A pilot study was conducted to adjust the percentage of correct detection to approximately 75% for microcalcifications with a diameter of 270𝜇m at the AEC dose. We applied the restoration pipeline to the image dataset and calculated the percentage of correctly detected signals (PC) using the MO in a four-alternative forced choice (4-AFC) study. The results indicated a PC enhancement of up to 10% when applying restoration to simulate acquisitions with twice the AEC dose. Additionally, for images acquired with radiation doses below the AEC, our results demonstrated a potential dose reduction of up to 22.4% without compromising microcalcification detectability. The detection of microcalcifications with a diameter of 390𝜇m remained unaffected by variations in radiation dose.
Several clinical image databases are currently available to support scientific research in the medical field. These images are generally used to validate studies based on measuring the sensitivity and specificity of a particular clinical task. In the case of digital mammography, the radiation dose directly influences the quality of the image and consequently the performance of radiologists. Therefore, it is important to conduct studies to find a balance between image quality and radiation dose. Image processing methods are typically employed to optimize this relationship. For the evaluation of these methods, it is crucial to have a mammographic image database with specific characteristics, currently unavailable for scientific use. For example, this image database should contain sets of images from the same patient acquired at different radiation doses with breast lesions in known locations. This is achievable using computational methods for noise and microcalcification insertion into pre-acquired clinical images. In this context, the present work aims to present a cloud-based application for on-demand generation of a clinical mammographic image database with different radiation doses and breast lesions. From a set of pre-acquired clinical digital mammograms, it is possible to create N databases with different characteristics. This technique can also be considered as data augmentation.
In digital mammography, the physics of the acquisition system and post-processing algorithms can cause image noise to be spatially correlated. Noise correlation is characterized by non-constant noise power spectral density and can negatively affect image quality. Although the literature explores ways to quantify the frequency dependence of noise in digital mammography, there is still a lack of studies that explore the effect of this phenomenon on clinical tasks. Thus, the aim of this work is to evaluate the impact of noise correlation on the quality of digital mammography and the detectability of lesions using a virtual clinical trial (VCT) tool. Considering the radiographic factors of a standard full-dose acquisition, VCT was used to generate two sets of images: one containing mammograms corrupted with correlated noise and the other with uncorrelated (white) noise. Clusters of five to seven microcalcifications of different sizes and shapes were computationally inserted into the images at regions of dense tissue. We then designed a human observer study to investigate performance on a clinical task of locating microcalcifications on digital mammography from both image sets. In addition, nine objective image quality metrics were calculated on mammograms. The results obtained with four medical physicists showed that the average performance in localization was 72% for images with correlated noise and 95% with uncorrelated noise. Thus, our results suggest that correlated noise promotes a greater reduction in the conspicuity of subtle microcalcifications than uncorrelated noise. Furthermore, only four of the nine objective quality metrics calculated in this work were consistent with the results of the human observer study, highlighting the importance of using appropriate metrics to assess the quality of corrupted images with correlated noise. The source code for our framework is publicly available at https://github.com/LAVI-USP/SPIE2023-ImageQuality.
Many works have investigated methods to assess the quality of mammography images using objective image quality metrics. However, few studies have evaluated the ability of these metrics to predict the performance of human observers on specific tasks related to mammographic examination that are highly dependent on image quality. The propose of this work is to evaluate the quality of digital mammography acquired at a range of radiation doses through a set of objective metrics and to compare the results with the performance of human observers in the task of locating microcalcification clusters in these images. A dataset of 100 synthetic mammograms was simulated using a virtual clinical trials software. Microcalcification clusters of different sizes and contrasts were computationally inserted into the images. Acquisitions with five different radiation doses were simulated using a noise injection method proposed in a previous work. Four medical physicists with experience in analysis of mammographic images participated in the microcalcification cluster localization tests. The quality of digital mammography images was assessed considering nine well-known objective metrics. The metrics were calculated on both the raw data (DICOM ‘for processing’ tag) and the processed images (DICOM ‘for presentation’ tag). Finally, the association between readers performance and image quality index was conducted by calculating the percentage variation of all metrics as a function of radiation dose, taking the standard dose as a reference. Although the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are the most used in the literature, our results showed that Quality Index based on Local Variance (QILV) is the objective metric that best describes the behavior of human visual perception with the variation of radiation dose in digital mammography.
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