PURPOSE: To investigate differences in microcalcification detection performance for different acquisition setups in digital breast tomosynthesis (DBT), a convex dose distribution and sparser number of projections compared to the standard set-up was evaluated via a virtual clinical trial (VCT). METHODS AND MATERIALS: Following the Institutional Review Board (IRB) approval and patient consent, mediolateral oblique (MLO) DBT views were acquired at twice the automatic exposure controlled (AEC) dose level; omitting the craniocaudal (CC) view limited the total examination dose. Microcalcification clusters were simulated into the DBT projections and noise was added to simulate lower dose levels. Three set-ups were evaluated: (1) 25 DBT projections acquired with a fixed dose/projection at the clinically used AEC dose level, (2) 25 DBT projections with dose/projection following a convex dose distribution along the scan arc, and (3) 13 DBT projections at higher dose with the total scan dose equal to the AEC dose level and preserving the angular range of 50° (sparse). For the convex set-up, dose/projection started at 0.035 mGy at the extremes and increased to 0.163 mGy for the central projection. A Siemens prototype algorithm was used for reconstruction. An alternative free-response receiver operating characteristic (AFROC) study was conducted with 6 readers to compare the microcalcification detection between the acquisition set-ups. Sixty cropped VOIs of 50x50x(breast thickness) mm3 per set-up were included, of which 50% contained a microcalcification cluster. In addition to localization of the cluster, the readers were asked to count the individual calcifications. The area under the AFROC curve was used to compare the different acquisition set-ups and a paired t-test was used to test significance. RESULTS: The AUCs for the standard, convex and sparse set-up were 0.97±0.01, 0.95±0.02 and 0.89±0.03, respectively, indicating no significant difference between standard and convex set-up (p=0.309), but a significant decrease in detectability was found for the sparse set-up (p=0.001). The number of detected calcifications per cluster was not significantly different between standard and convex set-ups (p=0.049), with 42%±9% and 40%±8%, respectively. The sparse set-up scored lower with a relative number of detected microcalcifications of 34%±11%, but this decrease was not significant (p=0.031). CONCLUSION: A convex dose distribution that increased dose along the scan arc towards the central projections did not increase detectability of microcalcifications in the DBT planes compared to the current AEC set-up. Conversely, a sparse set of projections acquired over the total scan arc decreased microcalcification detectability compared to the variable dose and current clinical set-up.
Digital breast tomosynthesis (DBT) is an important imaging modality for breast cancer screening. The morphology of breast masses and the shape of the microcalcifications are important factors to detect and determine the malignancy of breast cancer. Recently, convolutional neural networks (CNNs) have been used for denoising in medical imaging and have shown potential to improve the performance of radiologists. However, they can impose noise spatial correlation in the restoration process. Noise correlation can negatively impact radiologists’ performance, creating image signals that can resemble breast lesions. In this work, we propose a deep CNN that restores low-dose DBT projections by partially filtering out the noise, but imposes fidelity of the noise correlation between the original and restored images, avoiding artifacts that may resemble signs of breast cancer. The combination of a loss function that calculates the difference in the power spectra (PS) of the input and output images and another one that seeks image visual perception is proposed. We compared the performance of the proposed neural network with traditional denoising methods that do not consider the noise correlation in the restoration process and found superior results in terms of PS for our approach.
Aim: To assess the difference in radiomic feature values between pairs of mammographic images used for processing(FOR PROC) and for presentation(FOR PRES) as well as the ability to determine the BIRADS density classification from these radiomic features with different classification models. Methods: A dataset of FOR PROC and FOR PRES image pairs annotated with labels for the BI-RADS classification done by a radiologist is used in this study. The differences in radiomic feature values between the image types are evaluated with the intraclass correlation coefficient(ICC). Additionally, the discriminative power of radiomic feature values regarding the BI-RADS score is evaluated with Logistic Regression, Random Forest and a 5-layer deep Neural Network. The results of these models are evaluated with a 5-fold crossvalidation. Results: The reliability of radiomic feature is generally low between pairs of FOR PROC and FOR PRES images for all radiomic feature groups. Furthermore, the simple models used to determine the ability to assign the BI-RADS density classification based on the radiomic feature values reached insufficient accuracy to be considered adequate. Conclusion: The study revealed low reliability between both image types. Furthermore radiomic features alone seem to be insufficient to determine the BI-RADS classification using simple models.
The aim of the study is to assess the feasibility of a homemade phantom for image quality evaluation in Contrast Enhanced Spectral Mammography (CESM). The phantom was composed by a PMMA slab with holes of different diameters (10, 5 and 2.5 mm) and thicknesses (5, 4, 3 and 2 mm) filled with diluted iodine contrast medium, resulting in concentrations of 1.9, 1.5, 1.1 and 0.7 mg/cm2 (±0.2 mg/cm2 ), similar to the clinical concentrations. Furthermore, we added tissue-equivalent slabs with anatomical background and we simulated 3 different configurations equivalent to 32, 60 and 90 mm breast thicknesses. Image acquisitions were performed on a Hologic 3Dimensions mammography system using AEC clinical parameters. The acquisitions included a low energy exposure followed by an high energy one, and the resulting processed images were a subtraction of the 2 acquired images. For each configuration, the CNR on the low, high and subtracted images were calculated. The results showed that CNR values measured on the processed subtracted images were much higher respect to the CNR measured on the “for processing” low and high energy images. Furthermore, as expected, an increase in CNR for increasing iodine concentration was verified on the processed images, but not always on raw images that contained anatomical background. Preliminary results showed that the phantom is suitable for image quality evaluation in CESM but further studies with different acquisition parameters and on different mammography systems are necessary to assess the repeatability and the consistency of the measurements.
Image quality directly influences the accuracy of lesion detection and characterization in x-ray mammograms. Thus, it is crucial that acceptable image quality is maintained while using as little ionizing radiation as possible. In this scenario, denoising plays an important role in recovering image quality while keeping constant radiation dose. Although most ‘off-the-shelf’ denoising algorithms assume signal-independent and frequency-independent (white) Gaussian noise, in x-ray generation and detection this assumption is seldom valid. In this work we leverage a recently published variance-stabilizing transform and a frequency-dependent denoising algorithm to address signal-dependent and frequency-dependent denoising of x-ray mammograms subject to structural and correlated noise. To illustrate the application of the proposed pipeline, we restored synthetic mammograms generated by a virtual clinical trial platform. The results showed that the denoising pipeline was able to recover the quality of mammograms acquired at lower radiation levels to achieve similar image quality of full-dose acquisitions, in terms of the QILV, residual variance and power spectrum metrics. The bias2 metric indicates that even though the pipeline is able to achieve very similar noise levels to a full-dose acquisition, there is a penalty to the signal, which becomes biased due to blur and smearing as the dose level is reduced.
