Estimation of the absorbed dose in simultaneous digital breast tomosynthesis and mechanical imaging.

Purpose: Use of mechanical imaging (MI) as complementary to digital mammography (DM), or in simultaneous digital breast tomosynthesis (DBT) and MI - DBTMI, has demonstrated the potential to increase the specificity of breast cancer screening and reduce unnecessary biopsies compared with DM. The aim of this study is to investigate the increase in the radiation dose due to the presence of an MI sensor during simultaneous image acquisition when automatic exposure control is used. Approach: A radiation dose study was conducted on clinically available breast imaging systems with and without an MI sensor present. Our estimations were based on three approaches. In the first approach, exposure values were compared in paired clinical DBT and DBTMI acquisitions in 97 women. In the second approach polymethyl methacrylate (PMMA) phantoms of various thicknesses were used, and the average glandular dose (AGD) values were compared. Finally, a rectangular PMMA phantom with a 45 mm thickness was used, and the AGD values were estimated based on air kerma measurements with an electronic dosemeter. Results: The relative increase in exposure estimated from digital imaging and communications in medicine headers when using an MI sensor in clinical DBTMI was 11.9 % ± 10.4 . For the phantom measurements of various thicknesses of PMMA, the relative increases in the AGD for DM and DBT measurements were, on average, 10.7 % ± 3.1 and 11.4 % ± 3.0 , respectively. The relative increase in the AGD using the electronic dosemeter was 11.2 % ± < 0.001 in DM and 12.2 % ± < 0.001 in DBT. The average difference in dose between the methods was 11.5 % ± 3.3 . Conclusions: Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by an average of 11.5 % ± 3.3 . The increase in dose is within the acceptable values for mammography screening recommended by European guidelines.

Assessing Digital Breast Tomosynthesis Impact on Early Cancer Detection: Insights from Consecutive Screening.

Background Analysis of how digital breast tomosynthesis (DBT) screening affects consecutive screening performance is important to estimate its future value in screening. Purpose To evaluate whether DBT contributes to early detection of breast cancer by assessing cancer detection rates (CDRs), including the fraction of invasive cancers and cancer subtypes in consecutive routine digital mammography (DM). Materials and Methods The paired prospective Malmö Breast Tomosynthesis Screening Trial (MBTST) was performed between January 2010 and February 2015. Participating women underwent one-view DBT and two-view DM at one screening occasion. In this secondary analysis, women were followed up through their first (DM1) and second (DM2) consecutive two-view DM screening rounds after MBTST participation. Cancer diagnoses were identified by referencing records. A logistic regression model, adjusted for age, was used to calculate the odds of luminal A-like cancers with use of the MBTST as reference. Results There were 14 848 final participants in the MBTST (median age, 57 years [IQR, 49-65 years]). Of those, 12 876 women were screened in DM1 (median age, 58 years [IQR, 50-66 years]) and 10 883 were screened in DM2 (median age, 59 years [IQR, 51-67 years]). Compared with CDRs in the trial of 6.5 of 1000 women (95% CI: 5.2, 7.9) for DM and 8.7 of 1000 women (95% CI: 7.3, 10.3) for DBT, the CDR was lower in DM1 (4.6 of 1000 women [95% CI: 3.6, 5.9]) and DM2 (5.3 of 1000 women [95% CI: 4.1, 6.9]). The proportion of invasive cancers was 84.9% (118 of 139 cancers) in the MBTST; the corresponding numbers were 66% (39 of 59 cancers) for DM1 and 83% (50 of 60 cancers) for DM2. The odds of luminal A-like cancers were lower in DM1 at 0.28 (95% CI: 0.12, 0.66 [P = .004]) but not in DM2 at 0.80 (95% CI: 0.40, 1.58 [P = .52]) versus screening in the MBTST. Conclusion CDR and the fraction of invasive cancers were lower in DM1 and then increased in DM2 following the MBTST, indicating earlier cancer detection mainly due to increased detection of luminal A-like cancers in DBT screening. Clinical trials registration no. NCT01091545 © RSNA, 2024 See also the editorial by Hooley and Philpotts in this issue.

Simulation of breast lesions based upon fractal Perlin noise.

PURPOSE: Steadily increasing use of computational/virtual phantoms in medical physics has motivated expanding development of new simulation methods and data representations for modelling human anatomy. This has emphasized the need for increased realism, user control, and availability. In breast cancer research, virtual phantoms have gained an important role in evaluating and optimizing imaging systems. For this paper, we have developed an algorithm to model breast abnormalities based on fractal Perlin noise. We demonstrate and characterize the extension of this approach to simulate breast lesions of various sizes, shapes, and complexity. MATERIALS AND METHOD: Recently, we developed an algorithm for simulating the 3D arrangement of breast anatomy based on Perlin noise. In this paper, we have expanded the method to also model soft tissue breast lesions. We simulated lesions within the size range of clinically representative breast lesions (masses, 5-20 mm in size). Simulated lesions were blended into simulated breast tissue backgrounds and visualized as virtual digital mammography images. The lesions were evaluated by observers following the BI-RADS assessment criteria. RESULTS: Observers categorized the lesions as round, oval or irregular, with circumscribed, microlobulated, indistinct or obscured margins. The majority of the simulated lesions were considered by the observers to have a realism score of moderate to well. The simulation method provides almost real-time lesion generation (average time and standard deviation: 1.4 ± 1.0 s). CONCLUSION: We presented a novel algorithm for computer simulation of breast lesions using Perlin noise. The algorithm enables efficient simulation of lesions, with different sizes and appearances.

Finite element model of mechanical imaging of the breast.

Purpose: Malignant breast lesions can be distinguished from benign lesions by their mechanical properties. This has been utilized for mechanical imaging in which the stress distribution over the breast is measured. Mechanical imaging has shown the ability to identify benign or normal cases and to reduce the number of false positives from mammography screening. Our aim was to develop a model of mechanical imaging acquisition for simulation purposes. To that end, we simulated mammographic compression of a computer model of breast anatomy and lesions. Approach: The breast compression was modeled using the finite element method. Two finite element breast models of different sizes were used and solved using linear elastic material properties in open-source virtual clinical trial (VCT) software. A spherical lesion (15 mm in diameter) was inserted into the breasts, and both the location and stiffness of the lesion were varied extensively. The average stress over the breast and the average stress at the lesion location, as well as the relative mean pressure over lesion area (RMPA), were calculated. Results: The average stress varied 6.2-6.5 kPa over the breast surface and 7.8-11.4 kPa over the lesion, for different lesion locations and stiffnesses. These stresses correspond to an RMPA of 0.80 to 1.46. The average stress was 20% to 50% higher at the lesion location compared with the average stress over the entire breast surface. Conclusions: The average stress over the breast and the lesion location corresponded well to clinical measurements. The proposed model can be used in VCTs for evaluation and optimization of mechanical imaging screening strategies.

Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening.

Purpose: Image-based analysis of breast tumor growth rate may optimize breast cancer screening and diagnosis by suggesting optimal screening intervals and guide the clinical discussion regarding personalized screening based on tumor aggressiveness. Simulation-based virtual clinical trials (VCTs) can be used to evaluate and optimize medical imaging systems and design clinical trials. This study aimed to simulate tumor growth over multiple screening rounds. Approach: This study evaluates a preliminary method for simulating tumor growth. Clinical data on tumor volume doubling time (TVDT) was used to fit a probability distribution ("clinical fit") of TVDTs. Simulated tumors with TVDTs sampled from the clinical fit were inserted into 30 virtual breasts ("simulated cohort") and used to simulate mammograms. Based on the TVDT, two successive screening rounds were simulated for each virtual breast. TVDTs from clinical and simulated mammograms were compared. Tumor sizes in the simulated mammograms were measured by a radiologist in three repeated sessions to estimate TVDT. Results: The mean TVDT was 297 days (standard deviation, SD, 169 days) in the clinical fit and 322 days (SD, 217 days) in the simulated cohort. The mean estimated TVDT was 340 days (SD, 287 days). No significant difference was found between the estimated TVDTs from simulated mammograms and clinical TVDT values ( p > 0.5 ). No significant difference ( p > 0.05 ) was observed in the reproducibility of the tumor size measurements between the two screening rounds. Conclusions: The proposed method for tumor growth simulation has demonstrated close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.

VIRTUAL CLINICAL TRIALS IN MEDICAL IMAGING SYSTEM EVALUATION AND OPTIMISATION.

Virtual clinical trials (VCTs) can be used to evaluate and optimise medical imaging systems. VCTs are based on computer simulations of human anatomy, imaging modalities and image interpretation. OpenVCT is an open-source framework for conducting VCTs of medical imaging, with a particular focus on breast imaging. The aim of this paper was to evaluate the OpenVCT framework in two tasks involving digital breast tomosynthesis (DBT). First, VCTs were used to perform a detailed comparison of virtual and clinical reading studies for the detection of lesions in digital mammography and DBT. Then, the framework was expanded to include mechanical imaging (MI) and was used to optimise the novel combination of simultaneous DBT and MI. The first experiments showed close agreement between the clinical and the virtual study, confirming that VCTs can predict changes in performance of DBT accurately. Work in simultaneous DBT and MI system has demonstrated that the system can be optimised in terms of the DBT image quality. We are currently working to expand the OpenVCT software to simulate MI acquisition more accurately and to include models of tumour growth. Based on our experience to date, we envision a future in which VCTs have an important role in medical imaging, including support for more imaging modalities, use with rare diseases and a role in training and testing artificial intelligence (AI) systems.