X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.

OBJECTIVE: Digital breast tomosynthesis (DBT) has significantly improved the diagnosis of breast cancer due to its high sensitivity and specificity in detecting breast lesions compared to two-dimensional mammography. However, one of the primary challenges in DBT is the image blur resulting from x-ray source motion, particularly in DBT systems with a source in continuous-motion mode. This motion-induced blur can degrade the spatial resolution of DBT images, potentially affecting the visibility of subtle lesions such as microcalcifications. APPROACH: We addressed this issue by deriving an analytical in-plane source blur kernel for DBT images based on imaging geometry and proposing a post-processing image deblurring method with a generative diffusion model as an image prior. MAIN RESULTS: We showed that the source blur could be approximated by a shift-invariant kernel over the DBT slice at a given height above the detector, and we validated the accuracy of our blur kernel modeling through simulation. We also demonstrated the ability of the diffusion model to generate realistic DBT images. The proposed deblurring method successfully enhanced spatial resolution when applied to DBT images reconstructed with detector blur and correlated noise modeling. SIGNIFICANCE: Our study demonstrated the advantages of modeling the imaging system components such as source motion blur for improving DBT image quality.

Target sign in COVID-19, radiological interpretation and diagnostic contribution of digital thoracic tomosynthesis.

INTRODUCTION: Our objectives are: To describe the radiological semiology, clinical-analytical features and prognosis related to the target sign (TS) in COVID-19. To determine whether digital thoracic tomosynthesis (DTT) improves the diagnostic ability of radiography. MATERIAL AND METHODS: Retrospective, descriptive, single-centre, case series study, accepted by our ethical committee. Radiological, clinical, analytical and follow-up characteristics of patients with COVID-19 and TS on radiography and DTT between November 2020 and January 2021 were analysed. RESULTS: Eleven TS were collected in 7 patients, median age 35 years, 57% male. All TS presented with a central nodule and a peripheral ring, and in at least 82%, the lung in between was of normal density. All TS were located in peripheral, basal regions and 91% in posterior regions. TS were multiple in 43%. Contiguous TS shared the peripheral ring. Other findings related to pneumonia were associated in 86% of patients. DTT detected 82% more TS than radiography. Only one patient underwent a CT angiography of the pulmonary arteries, positive for acute pulmonary thromboembolism. Seventy-one per cent presented with pleuritic pain. No distinctive laboratory findings or prognostic worsening were detected. CONCLUSIONS: TS in COVID-19 predominates in peripheral and declining regions and can be multiple. Pulmonary thromboembolism was detected in one case. It occurs in young people, frequently with pleuritic pain and does not worsen the prognosis. DTT detects more than 80 % of TS than radiography.

Review of Breast Imaging in Transgender and Gender-Diverse Patients: Gender-Affirming Care, Histopathologic Findings, Breast Cancer Risk, and Screening Recommendations.

Gender diversity, especially pertaining to transgender and gender-diverse (TGD) populations, is often stigmatized. A small but not insignificant number of adults in the United States identify as TGD, including transgender, nonbinary, and other gender identities than cisgender. Accessing health care remains a significant challenge for TGD individuals because many health care systems adhere to a gender binary model and many TGD individuals experience negative interactions when interfacing with health care. There is also a scarcity of literature addressing their unique health care needs, limiting our current understanding of breast cancer risks and screening recommendations for TGD patients. This article reviews important considerations when providing care to TGD patients. It covers background information on gender identity and sexuality, explores gender-affirming care, discusses histopathologic findings of breast biopsy specimens, examines breast cancer risks, and presents current breast cancer screening recommendations for TGD patients. Education on TGD breast cancer risks and screening and creating a standardized screening protocol for TGD patients who may receive gender-affirming care through hormonal and surgical therapies could help improve their health care equity and access.

Tomosynthesis of the Appendicular Skeleton on a Twin Robotic X-ray System: A Cadaveric Fracture Study.

RATIONALE AND OBJECTIVES: Aiming to offset image quality limitations in radiographs due to superimposition, this study investigates the diagnostic potential of appendicular skeleton tomosynthesis. MATERIALS AND METHODS: Eight cadaveric extremities (four hands and feet) were examined employing the prototypical tomosynthesis mode of a twin robotic X-ray scanner. 12 protocols with varying sweep angles (10, 20 vs. 40°), frame rates (13 vs. 26 fps), and tube voltages (60 vs. 80 kV) were compared to radiographs. Four radiologists separately evaluated cortical and trabecular bone visualization and fracture patterns. Interreader reliability was assessed based on the intraclass correlation coefficient (ICC). RESULTS: Radiation dose in radiography was 0.59 ± 0.20 dGy * cm2 versus 0.11 ± 0.00 to 2.46 ± 0.17 dGy * cm2 for tomosynthesis. Cortical bone display was inferior for radiographs compared to 40° and 20° tomosynthesis. Best results were ascertained for the 80 kV/40°/26 fps protocol. Trabecular bone depiction was also superior in tomosynthesis (p ≤ 0.009) and best with the 80 kV/10°/26 fps setting. Interreader reliability was moderate for cortical bone display (ICC 0.521, 95% confidence interval 0.356-0.641) and good for trabecular bone (0.759, 0.697-0.810). Diagnostic accuracy for articular involvement and multifragment situations was higher in tomosynthesis (93.8-100%/92.2-100%) vs. radiography (85.9%/82.8%.). Diagnostic confidence was also better in tomosynthesis (p ≤ 0.003). CONCLUSION: Compared to radiography, tomosynthesis allows for superior assessability of cortical and trabecular bone and fracture morphology, especially at high framerates. Operating on a multipurpose X-ray system, tomosynthesis of the appendicular skeleton can be performed without additional scanner hardware.

Use of the lateral arm in tomosynthesis-guided SCOUT Reflector placement procedures.

Preoperative localization procedures are important for guiding surgical excision of non-palpable findings in the breast. The SCOUT Reflector (Merit Medical, South Jordan UT, USA) is a non-wire-based localization device that has been incorporated into clinical use with great success. SCOUT Reflectors can be placed using several imaging modalities, including tomosynthesis. One drawback to SCOUT Reflector placement under tomosynthesis guidance is an inability to directly visualize the introducer needle tip, a factor that limits precision. In this brief communication, we describe the use of a lateral arm attachment for tomosynthesis guided SCOUT Reflector placement. Precise SCOUT Reflector placement can be achieved using the lateral arm due to the ability to clearly visualize the introducer needle and the SCOUT Reflector within the introducer needle bore prior to deployment.

Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study.

OBJECTIVES: This retrospective single-center analysis aimed to evaluate whether artificial intelligence can detect type 2 diabetes mellitus by evaluating the pectoral muscle on digital breast tomosynthesis (DBT). MATERIAL METHOD: An analysis of 11,594 DBT images of 287 consecutive female patients (mean age 60, range 40-77 years) was conducted using convolutional neural networks (EfficientNetB5). The inclusion criterion was left-sided screening images with unsuspicious interpretation who also had a current glycosylated hemoglobin A1c (HBA1c) % value. The exclusion criteria were inadequate imaging, history of breast cancer, and/or diabetes mellitus. HbA1c values between 5.6 and 6.4% were categorized as prediabetic, and those with values ≥ 6.5% were categorized as diabetic. A recorded HbA1c ≤ 5.5% served as the control group. Each group was divided into 3 subgroups according to age. Images were subjected to pattern analysis parameters then cropped and resized in a format to contain only pectoral muscle. The dataset was split into 85% for training and 15% for testing the model's performance. The accuracy rate and F1-score were selected as performance indicators. RESULTS: The training process was concluded in the 15th epoch, each comprising 1000 steps, with an accuracy rate of 92% and a loss of only 0.22. The average specificity and sensitivity for all 3 groups were 95%. The F1-score was 0.95. AUC-ROC was 0.995. PPV was 94%, and NPV was 98%. CONCLUSION: Our study presented a pioneering approach, applying deep learning for the detection of diabetes mellitus status in women using pectoral muscle images and was found to function with an accuracy rate of 92%. CRITICAL RELEVANCE STATEMENT: AI can differentiate pathological changes within pectoral muscle tissue by assessing radiological images and maybe a potential diagnostic tool for detecting diabetes mellitus and other diseases that affect muscle tissues. KEY POINTS: • AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. • This technique allows for early and non-invasive detection of diabetes mellitus by AI. • AI may have broad applications in detecting pathological changes within muscle tissue.

Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise.

BACKGROUND: Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise. TYPES OF STUDIES REVIEWED: The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies. RESULTS: The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4. CONCLUSIONS AND PRACTICAL IMPLICATIONS: The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.

Image Quality Enhancement for Digital Breast Tomosynthesis: High-Density Object Artifact Reduction.

Breast cancer has a high incidence and mortality rate among women, early diagnosis is essential as it gives insight regarding the most appropriate therapeutic strategy for each case. Among all imaging diagnostic methods, digital breast tomosynthesis (DBT) is effective for early breast cancer detection. In DBT images, high-density object artifacts are generated when imaging objects with high X-ray absorptivity, which include metal artifacts, ripple artifacts, and deformation artifacts. In this study, we analyze the causes of these artifacts and propose a set of high-density object reconstruction methods based on iterative algorithms. Our method includes a reprojection-based high-density object projection data segmentation algorithm and an iterative reconstruction algorithm based on volume expansion. The experiments on simulation data and the human breast data with artificial surgical needles prove the effectiveness of our method. By using our algorithm, the problem of distorting the shape, size, and position of high-density objects during DBT reconstruction is raised, the influence of these artifacts is reduced, and the quality of the DBT reconstructed image is improved. We hope that our algorithm might contribute to promoting the usage of DBT.

An Image-Rich Educational Review of Breast Pain.

Breast pain is extremely common, occurring in 70% to 80% of women. Most cases of breast pain are from physiologic or benign causes, and patients should be reassured and offered treatment strategies to alleviate symptoms, often without diagnostic imaging. A complete clinical history and physical examination is key for distinguishing intrinsic breast pain from extramammary pain. Breast pain without other suspicious symptoms and with a negative history and physical examination result is rarely associated with malignancy, although it is a common reason for women to undergo diagnostic imaging. When breast imaging is indicated, guidelines according to the American College of Radiology Appropriateness Criteria should be followed as to whether mammography, US, or both are recommended. This review article summarizes the initial clinical evaluation of breast pain and evidence-based guidelines for imaging. Additionally, the article reviews cyclical and noncyclical breast pain and provides an image-rich discussion of the imaging presentation and management of benign and malignant breast pain etiologies.

AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net.

Purpose: The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency. Approach: The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance. Results: The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively. Conclusions: The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.

[Structured screening for sporadic breast cancer]. / Strukturierte Früherkennung des sporadischen Mammakarzinoms.

BACKGROUND: The aim of secondary prevention of breast cancer is to detect the disease at the earliest curable stage and thus to reduce breast cancer-specific mortality. To this end, the nationwide population-based mammography screening program (MSP) was set up in Germany in 2005 in addition to an interdisciplinary prevention project for high-risk groups. OBJECTIVE: Overview of the current state of the MSP, the upcoming age expansion, and potential further developments. MATERIAL AND METHODS: Narrative review article with topic-guided literature and data search. RESULTS: Approximately 50% of the 70,500 new cases of breast cancer that occur each year are related to the age group of the MSP. 10 years after introduction of the MSP, the incidence of advanced breast cancer stages and breast cancer-related mortality of the screening target group have steadily decreased by about one quarter, while no relevant trends were seen in the neighboring age groups at the population level. CONCLUSION: The MSP has effectively contributed to a reduction of breast cancer mortality. With the expansion of the age groups to 45-75 years, more women have access to structured, quality assured screening. With the use of advanced stratifications and diagnostics as well as artificial intelligence, the MSP could be further optimized.

The risk-based breast screening (RIBBS) study protocol: a personalized screening model for young women.

The optimal mammography screening strategy for women aged 45-49 years is a matter of debate. We present the RIBBS study protocol, a quasi-experimental, prospective, population-based study comparing a risk- and breast density-stratified screening model (interventional cohort) with annual digital mammography (DM) screening (observational control cohort) in a real-world setting. The interventional cohort consists of 10,269 women aged 45 years enrolled between 2020 and 2021 from two provinces of the Veneto Region (northen Italy). At baseline, participants underwent two-view digital breast tomosynthesis (DBT) and completed the Tyrer-Cuzick risk prediction model. Volumetric breast density (VBD) was calculated from DBT and the lifetime risk (LTR) was estimated by including VBD among the risk factors. Based on VBD and LTR, women were classified into five subgroups with specific screening protocols for subsequent screening rounds: (1) LTR ≤ 17% and nondense breast: biennial DBT; (2) LTR ≤ 17% and dense breast: biennial DBT and ultrasound; (3) LTR 17-30% or LTR > 30% without family history of BC, and nondense breast: annual DBT; (4) LTR 17-30% or > 30% without family history of BC, and dense breast: annual DBT and ultrasound; and (5) LTR > 30% and family history of BC: annual DBT and breast MRI. The interventional cohort is still ongoing. An observational, nonequivalent control cohort of 43,000 women aged 45 years participating in an annual DM screening programme was recruited in three provinces of the neighbouring Emilia-Romagna Region. Cumulative incidence rates of advanced BC at three, five, and ten years between the two cohorts will be compared, adjusting for the incidence difference at baseline.Trial registration This study is registered on Clinicaltrials.gov (NCT05675085).

Typical values of z-resolution for different Digital Breast Tomosynthesis systems evaluated in a multicenter study.

PURPOSE: The aim of the present study, conducted by a working group of the Italian Association of Medical Physics (AIFM), was to define typical z-resolution values for different digital breast tomosynthesis (DBT) models to be used as a reference for quality control (QC). Currently, there are no typical values published in internationally agreed QC protocols. METHODS: To characterize the z-resolution of the DBT models, the full width at half maximum (FWHM) of the artifact spread function (ASF), a technical parameter that quantifies the signal intensity of a detail along reconstructed planes, was analyzed. Five different commercial phantoms, CIRS Model 011, CIRS Model 015, Modular DBT phantom, Pixmam 3-D, and Tomophan, were evaluated on reconstructed DBT images and 82 DBT systems (6 vendors, 9 models) in use at 39 centers in Italy were involved. RESULTS: The ASF was found to be dependent on the detail size, the DBT angular acquisition range, the reconstruction algorithm and applied image processing. In particular, a progressively greater signal spread was observed as the detail size increased and the acquisition angle decreased. However, a clear correlation between signal spread and angular range width was not observed due to the different signal reconstruction and image processing strategies implemented in the algorithms developed by the vendors studied. CONCLUSIONS: The analysis led to the identification of typical z-resolution values for different DBT model-phantom configurations that could be used as a reference during a QC program.

Line-based iterative geometric calibration method for a tomosynthesis system.

BACKGROUND: A next generation tomosynthesis (NGT) system, capable of two-dimensional source motion, detector motion in the perpendicular direction, and magnification tomosynthesis, was constructed to investigate different acquisition geometries. Existing position-based geometric calibration methods proved ineffective when applied to the NGT geometries. PURPOSE: A line-based iterative calibration method is developed to perform accurate geometric calibration for the NGT system. METHODS: The proposed method calculates the system geometry through virtual line segments created by pairs of fiducials within a calibration phantom, by minimizing the error between the line equations computed from the true and estimated fiducial projection pairs. It further attempts to correct the 3D fiducial locations based on the initial geometric calibration. The method's performance was assessed via simulation and experimental setups with four distinct NGT geometries: X, T, XZ, and TZ. The X geometry resembles a conventional DBT acquisition along the chest wall. The T geometry forms a "T"-shaped source path in mediolateral (ML) and posteroanterior (PA) directions. A descending detector motion is added to both X and T geometries to form the XZ and TZ geometries, respectively. Simulation studies were conducted to assess the robustness of the method to geometric perturbations and inaccuracies in fiducial locations. Experimental studies were performed to assess the impact of phantom magnification and the performance of the proposed method for various geometries, compared to the traditional position-based method. Star patterns were evaluated for both qualitative and quantitative analyses; the Fourier spectral distortions (FSDs) graphs and the contrast transfer function (CTF) were extracted. The limit of spatial resolution (LSR) was measured at 5% modulation of the CTF. RESULTS: The proposed method presented is highly robust to geometric perturbation and fiducial inaccuracies. After the line-based iterative method, the mean distance between the true and estimated fiducial projections was [X, T, XZ, TZ]: [0.01, 0.01, 0.02, 0.01] mm. The impact of phantom magnification was observed; a contact-mode acquisition of a calibration phantom successfully provided an accurate geometry for 1.85× magnification images of a star pattern, with the X geometry. The FSD graphs for the contact-mode T geometry acquisition presented evidence of super-resolution, with the LSR of [0°-quadrant: 8.57, 90°-quadrant: 8.47] lp/mm. Finally, a contact-mode XZ geometry acquisition and a 1.50× magnification TZ geometry acquisition were reconstructed with three calibration methods-position-based, line-based, and iterative line-based. As more advanced methods are applied, the CTF becomes more isotropic, the FSD graphs demonstrate less spectral leakage as super-resolution is achieved, and the degree of blurring artifacts reduces significantly. CONCLUSIONS: This study introduces a robust calibration method tailored to the unique requirements of advanced tomosynthesis systems. By employing virtual line segments and iterative techniques, we ensure accurate geometric calibration while mitigating the limitations posed by the complex acquisition geometries of the NGT system. Our method's ability to handle various NGT configurations and its tolerance to fiducial misalignment make it a superior choice compared to traditional calibration techniques.

Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists.

Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.

Breast Radiologists' Perceptions on the Detection and Management of Invasive Lobular Carcinoma: Most Agree Imaging Beyond Mammography Is Warranted.

OBJECTIVE: To determine breast radiologists' confidence in detecting invasive lobular carcinoma (ILC) on mammography and the perceived need for additional imaging in screening and preoperative settings. METHODS: A 16-item anonymized survey was developed, and IRB exemption obtained, by the Society of Breast Imaging (SBI) Patient Care and Delivery Committee and the Lobular Breast Cancer Alliance. The survey was emailed to 2946 radiologist SBI members on February 15, 2023. The survey recorded demographics, perceived modality-specific sensitivity for ILC to the nearest decile, and opinions on diagnosing ILC in screening and staging imaging. Five-point Likert scales were used (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). RESULTS: Response rate was 12.4% (366/2946). Perceived median (interquartile range) modality-specific sensitivities for ILC were MRI 90% (80-90), contrast-enhanced mammography 80% (70-90), molecular breast imaging 80% (60-90), digital breast tomosynthesis 70% (60-80), US 60% (50-80), and 2D mammography 50% (30-60). Only 25% (85/340) respondents were confident in detecting ILC on screening mammography in dense breasts, while 67% (229/343) were confident if breasts were nondense. Most agreed that supplemental screening is needed to detect ILC in women with dense breasts (272/344, 79%) or a personal history of ILC (248/341, 73%), with 34% (118/334) indicating that supplemental screening would also benefit women with nondense breasts. Most agreed that additional imaging is needed to evaluate extent of disease in women with newly diagnosed ILC, regardless of breast density (dense 320/329, 97%; nondense 263/329, 80%). CONCLUSION: Most breast radiologists felt that additional imaging beyond mammography is needed to more confidently screen for and stage ILC.

Women's Experiences with Digital Breast Tomosynthesis and Targeted Breast Ultrasound for Focal Breast Complaints: A Survey Study.

Background: Owing to its high sensitivity, as concluded in the Breast UltraSound Trial (BUST), targeted ultrasound (US) now seems a promising accurate stand-alone modality for diagnostic evaluation of breast complaints. This approach implies omission of bilateral digital breast tomosynthesis (DBT) in women with clearly benign US findings. Within BUST, radiologists started with US followed by DBT. This side-study investigates women's experiences with DBT, their main motivation to undergo diagnostic imaging, and their view on US as a stand-alone modality. Methods: A subset of BUST participants completed a questionnaire on their DBT experiences, reason for undergoing diagnostic assessment, and view on US-only diagnostics. Responses were analyzed with descriptive statistics and logistic regression analyses. Results: In total, 778 of 838 women (response rate 92.8%) were included (M = 47, SD = 11.16). Of them, 16.8% reported no burden of DBT, 33.5% slight burden, 31.0% moderate, and 12.7% severe burden. Furthermore, 13% reported no pain, 35.3% slight pain, 33.2% moderate, and 11.3% severe pain. Moreover, 88.3% indicated that the most important reason for breast assessment was explanation of their complaint and to rule out breast cancer, whereas 3.2% wanted to "check" both breasts. And 82.4% reported satisfaction with US only in case of a nonmalignancy. Conclusions: Our study shows that most women in the diagnostic setting experience at least slight-to-moderate DBT-related burden and pain, and that explanation for their symptoms is their main interest. Also, the majority report satisfaction with US only in case of nonmalignant findings. However, exploration of women's perspectives outside this study is needed as our participants all underwent both examinations.

Effectiveness of hybrid digital breast tomosynthesis/digital mammography compared to digital mammography in women presenting for routine screening at Maroondah BreastScreen: Study protocol for a co-designed, non-randomised prospective trial.

BACKGROUND: Digital breast tomosynthesis (DBT) for breast cancer screening has been shown in international trials to increase cancer detection compared with mammography; however, results have varied across screening settings, and currently there is limited and conflicting evidence on interval cancer rates (a surrogate for screening effectiveness). Australian pilot data also indicated substantially longer screen-reading time for DBT posing a barrier for adoption. There is a critical need for evidence on DBT to inform its role in Australia, including evaluation of potentially more feasible models of implementation, and quantification of screening outcomes by breast density which has global relevance. METHODS: This study is a prospective trial embedded in population-based Australian screening services (Maroondah BreastScreen, Eastern Health, Victoria) comparing hybrid screening comprising DBT (mediolateral oblique view) and digital mammography (cranio-caudal view) with standard mammography screening in a concurrent group attending another screening site. All eligible women aged ≥40 years attending the Maroondah service for routine screening will be enrolled (unless they do not provide verbal consent and opt-out of hybrid screening; are unable to provide consent; or where a 'pushback' image on hybrid DBT cannot be obtained). Each arm will enrol 20,000 women. The primary outcomes are cancer detection rate (per 1000 screens) and recall rate (percentage). Secondary outcomes include 'opt-out' rate; cohort characteristics; cancer characteristics; assessment outcomes; screen-reading time; and interval cancer rate at 24-month follow-up. Automated volumetric breast density will be measured to allow stratification of outcomes by mammographic density. Stratification by age and screening round will also be undertaken. An interim analysis will be undertaken after the first 5000 screens in the intervention group. DISCUSSION: This is the first Australian prospective trial comparing hybrid DBT/mammography with standard mammography screening that is powered to show differences in cancer detection. Findings will inform future implementation of DBT in screening programs world-wide and provide evidence on whether DBT should be adopted in the broader BreastScreen program in Australia or in subgroups of screening participants. TRIAL REGISTRATION: The trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR, ACTRN12623001144606, https://www.anzctr.org.au/). Registration will be updated to reflect trial progress and protocol amendments.

Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions.

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting.

Specimen tomosynthesis provides no additional value to specimen ultrasound in ultrasound-visible malignant breast lesions.

BACKGROUND: The aim of this study was to evaluate the accuracy and added value of specimen tomosynthesis (ST) to specimen ultrasound (SUS) in margin assessment of excised breast specimens in breast-conserving therapy for non-palpable US-visible breast lesions. MATERIALS: Between January 2018 and August 2019, all consecutive patients diagnosed with non-palpable breast cancer visible by ultrasound (US), treated with breast-conserving surgery (BCS) and requiring radiological intraoperative breast specimen assessment, were included in this study. Excised breast specimens were examined with SUS by radiologists blinded to the ST results, and margins smaller than 10 mm were recorded. STs were evaluated retrospectively by experienced radiologists. RESULTS: A total of 120 specimens were included. SUS showed a statistically significant correlation with pathological margin measurements, while ST did not and provided no additional information. The odds ratios (ORs) for SUS to predict a positive margin was 3.429 (confidence interval (CI) = 0.548-21.432) using a 10-mm cut-off point and 14.182 (CI = 2.134-94.254) using a 5-mm cut-off point, while the OR for ST were 2.528 (CI = 0.400-15.994) and 3.188 (CI = 0.318-31.998), respectively. CONCLUSIONS: SUS was superior in evaluating intraoperative resection margins of US-visible breast resection specimens when compared to ST. Therefore, ST could be considered redundant in applicable situations.