Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence.

OBJECTIVES: Digital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow. METHODS: An AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading. RESULTS: By excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected-26% (25/95) more than DM screening (p < 0.001)-while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689). CONCLUSION: AI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach. KEY POINTS: • Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload. • Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader. • Retrospective study based on paired mammography and digital breast tomosynthesis screening data.

How does image quality affect radiologists' perceived ability for image interpretation and lesion detection in digital mammography?

OBJECTIVES: To study how radiologists' perceived ability to interpret digital mammography (DM) images is affected by decreases in image quality. METHODS: One view from 45 DM cases (including 30 cancers) was degraded to six levels each of two acquisition-related issues (lower spatial resolution and increased quantum noise) and three post-processing-related issues (lower and higher contrast and increased correlated noise) seen during clinical evaluation of DM systems. The images were shown to fifteen breast screening radiologists from five countries. Aware of lesion location, the radiologists selected the most-degraded mammogram (indexed from 1 (reference) to 7 (most degraded)) they still felt was acceptable for interpretation. The median selected index, per degradation type, was calculated separately for calcification and soft tissue (including normal) cases. Using the two-sided, non-parametric Mann-Whitney test, the median indices for each case and degradation type were compared. RESULTS: Radiologists were not tolerant to increases (medians: 1.5 (calcifications) and 2 (soft tissue)) or decreases (median: 2, for both types) in contrast, but were more tolerant to correlated noise (median: 3, for both types). Increases in quantum noise were tolerated more for calcifications than for soft tissue cases (medians: 3 vs. 4, p = 0.02). Spatial resolution losses were considered less acceptable for calcification detection than for soft tissue cases (medians: 3.5 vs. 5, p = 0.001). CONCLUSIONS: Perceived ability of radiologists for image interpretation in DM was affected not only by image acquisition-related issues but also by image post-processing issues, and some of those issues affected calcification cases more than soft tissue cases. KEY POINTS: • Lower spatial resolution and increased quantum noise affected the radiologists' perceived ability to interpret calcification cases more than soft tissue lesion or normal cases. • Post-acquisition image processing-related effects, not only image acquisition-related effects, also impact the perceived ability of radiologists to interpret images and detect lesions. • In addition to current practices, post-acquisition image processing-related effects need to also be considered during the testing and evaluation of digital mammography systems.

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study.

OBJECTIVES: This study aimed to evaluate the image quality of 7 iterative reconstruction (IR) algorithms in comparison to filtered back-projection (FBP) algorithm. METHODS: An anthropomorphic chest phantom was scanned on 4 computed tomography scanners and reconstructed with FBP and IR algorithms. Image quality of anatomical details-large/medium-sized pulmonary vessels, small pulmonary vessels, thoracic wall, and small and large lesions-was scored. Furthermore, general impression of noise, image contrast, and artifacts were evaluated. Visual grading regression was used to analyze the data. Standard deviations were measured, and the noise power spectrum was calculated. RESULTS: Iterative reconstruction algorithms showed significantly better results when compared with FBP for these criteria (regression coefficients/P values in parentheses): vessels (FIRST: -1.8/0.05, AIDR Enhanced: <-2.3/0.01, Veo: <-0.1/0.03, ADMIRE: <-2.1/0.04), lesions (FIRST: <-2.6/0.01, AIDR Enhanced: <-1.9/0.03, IMR1: <-2.7/0.01, Veo: <-2.4/0.02, ADMIRE: -2.3/0.02), image noise (FIRST: <-3.2/0.004, AIDR Enhanced: <-3.5/0.002, IMR1: <-6.1/0.001, iDose: <-2.3/0.02, Veo: <-3.4/0.002, ADMIRE: <-3.5/0.02), image contrast (FIRST: -2.3/0.01, AIDR Enhanced: -2.5/0.01, IMR1: -3.7/0.001, iDose: -2.1/0.02), and artifacts (FIRST: <-3.8/0.004, AIDR Enhanced: <-2.7/0.02, IMR1: <-2.6/0.02, iDose: -2.1/0.04, Veo: -2.6/0.02). The iDose algorithm was the only IR algorithm that maintained the noise frequencies. CONCLUSIONS: Iterative reconstruction algorithms performed differently on all evaluated criteria, showing the importance of careful implementation of algorithms for diagnostic purposes.

One-view breast tomosynthesis versus two-view mammography in the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study.

BACKGROUND: Digital breast tomosynthesis is an advancement of the mammographic technique, with the potential to increase detection of lesions during breast cancer screening. The main aim of the Malmö Breast Tomosynthesis Screening Trial (MBTST) was to investigate the accuracy of one-view digital breast tomosynthesis in population screening compared with standard two-view digital mammography. METHODS: In this prospective, population-based screening study, of women aged 40-74 years invited to attend national breast cancer screening at Skåne University Hospital, Malmö, Sweden, a random sample was asked to participate in the trial (every third woman who was invited to attend regular screening was invited to participate). Participants had to be able to speak English or Swedish and were excluded from the study if they were pregnant. Participants underwent screening with two-view digital mammography (ie, craniocaudal and mediolateral oblique views) followed by one-view digital breast tomosynthesis with reduced compression in the mediolateral oblique view (with a wide tomosynthesis angle of 50°) at one screening visit. Images were read with masked double reading and scoring by two separate reading groups, one for each method, made up of seven radiologists. Any cancer detected with a malignancy probability score of three or higher by any reader in either group was discussed in a consensus meeting of at least two readers, from which the decision of whether or not to recall the woman for further investigation was made. The primary outcome measures were sensitivity and specificity of breast cancer detection. Secondary outcome measures were screening performance measures of cancer detection, recall, and interval cancers (cancers clinically detected between screenings), and positive predictive value for screen recalls and negative predictive value of each method. Outcomes were analysed in the per-protocol population. Follow-up of the participants for at least 2 years allowed for identification of interval cancers. This trial is registered with ClinicalTrials.gov, number NCT01091545. FINDINGS: Between Jan 27, 2010, and Feb 13, 2015, of 21 691 women invited, 14 851 (68%) agreed to participate. Three women withdrew consent during follow-up and were excluded from the analyses. 139 breast cancers were detected in 137 (<1%) of 14 848 women. Sensitivity was higher for digital breast tomosynthesis than for digital mammography (81·1%, 95% CI 74·2-86·9, vs 60·4%, 52·3-68·0) and specificity was slightly lower for digital breast tomosynthesis than was for digital mammography (97·2%, 95% CI 97·0-97·5, vs 98·1%, 97·9-98·3). The proportion of cancers detected was significantly higher with digital breast tomosynthesis than with digital mammography (8·7 cancers per 1000 women screened, 95% CI 7·3-10·3 vs 6·5 cancers per 1000 screened, 5·2-7·9; p<0·0001). The proportion of women recalled after discussion was higher among cancers detected by digital breast tomosynthesis than for those detected by digital mammography after consensus (3·6%, 95% CI 3·3-3·9 vs 2·5%, 2·2-2·8; p<0·0001). The positive predictive value for screen recalls was 24·1% (95% CI 20·5-28·0) for digital breast tomosynthesis and 25·9% (21·6-30·7) for digital mammography, and the negative predictive value was 99·8% (99·7-99·9) and 99·6% (99·4-99·7), respectively. The proportion of women who developed interval cancers after trial screening was 1·48 cancers per 1000 women screened (95% CI 0·93-2·24). INTERPRETATION: Breast cancer screening by use of one-view digital breast tomosynthesis with a reduced compression force has higher sensitivity at a slightly lower specificity for breast cancer detection compared with two-view digital mammography and has the potential to reduce the radiation dose and screen-reading burden required by two-view digital breast tomosynthesis with two-view digital mammography. FUNDING: The Swedish Cancer Society, The Swedish Research Council, The Breast Cancer Foundation, The Swedish Medical Society, The Crafoord Foundation, The Gunnar Nilsson Cancer Foundation, The Skåne University Hospital Foundation, Governmental funding for clinical research, The South Swedish Health Care Region, The Malmö Hospital Cancer Foundation and The Cancer Foundation at the Department of Oncology, Skåne University Hospital.

Evaluation of an iterative model-based reconstruction of pediatric abdominal CT with regard to image quality and radiation dose.

Background In pediatric patients, computed tomography (CT) is important in the medical chain of diagnosing and monitoring various diseases. Because children are more radiosensitive than adults, they require minimal radiation exposure. One way to achieve this goal is to implement new technical solutions, like iterative reconstruction. Purpose To evaluate the potential of a new, iterative, model-based method for reconstructing (IMR) pediatric abdominal CT at a low radiation dose and determine whether it maintains or improves image quality, compared to the current reconstruction method. Material and Methods Forty pediatric patients underwent abdominal CT. Twenty patients were examined with the standard dose settings and 20 patients were examined with a 32% lower radiation dose. Images from the standard examination were reconstructed with a hybrid iterative reconstruction method (iDose4), and images from the low-dose examinations were reconstructed with both iDose4 and IMR. Image quality was evaluated subjectively by three observers, according to modified EU image quality criteria, and evaluated objectively based on the noise observed in liver images. Results Visual grading characteristics analyses showed no difference in image quality between the standard dose examination reconstructed with iDose4 and the low dose examination reconstructed with IMR. IMR showed lower image noise in the liver compared to iDose4 images. Inter- and intra-observer variance was low: the intraclass coefficient was 0.66 (95% confidence interval = 0.60-0.71) for the three observers. Conclusion IMR provided image quality equivalent or superior to the standard iDose4 method for evaluating pediatric abdominal CT, even with a 32% dose reduction.

Can mechanical imaging increase the specificity of mammography screening?

OBJECTIVES: This study aimed to investigate the effects of adding adjunct mechanical imaging to mammography breast screening. We hypothesized that mechanical imaging could detect increased local pressure caused by both malignant and benign breast lesions and that a pressure threshold for malignancy could be established. The impact of this on breast screening was investigated with regard to reductions in recall and biopsy rates. METHODS: 155 women recalled from breast screening were included in the study, which was approved by the regional ethical review board (dnr 2013/620). Mechanical imaging readings were acquired of the symptomatic breast. The relative mean pressure on the suspicious area (RMPA) was defined and a threshold for malignancy was established. RESULTS: Biopsy-proven invasive cancers had a median RMPA of 3.0 (interquartile range (IQR) = 3.7), significantly different from biopsy-proven benign at 1.3 (IQR = 1.0) and non-biopsied cases at 1.0 (IQR = 1.3) (P < 0.001). The lowest RMPA for invasive cancer was 1.4, with 23 biopsy-proven benign and 33 non-biopsied cases being below this limit. Had these women not been recalled, recall rates would have been reduced by 36% and biopsy rates by 32%. CONCLUSIONS: If implemented in a screening situation, this may substantially lower the number of false positives. KEY POINTS: • Mechanical imaging is used as an adjunct to mammography in breast screening. • A threshold pressure can be established for malignant breast cancer. • Recalls and biopsies can be substantially reduced.

Improvements to image quality using hybrid and model-based iterative reconstructions: a phantom study.

BACKGROUND: The number of computed tomography (CT) examinations is increasing and leading to an increase in total patient exposure. It is therefore important to optimize CT scan imaging conditions in order to reduce the radiation dose. The introduction of iterative reconstruction methods has enabled an improvement in image quality and a reduction in radiation dose. PURPOSE: To investigate how image quality depends on reconstruction method and to discuss patient dose reduction resulting from the use of hybrid and model-based iterative reconstruction. MATERIAL AND METHODS: An image quality phantom (Catphan® 600) and an anthropomorphic torso phantom were examined on a Philips Brilliance iCT. The image quality was evaluated in terms of CT numbers, noise, noise power spectra (NPS), contrast-to-noise ratio (CNR), low-contrast resolution, and spatial resolution for different scan parameters and dose levels. The images were reconstructed using filtered back projection (FBP) and different settings of hybrid (iDose4) and model-based (IMR) iterative reconstruction methods. RESULTS: iDose4 decreased the noise by 15-45% compared with FBP depending on the level of iDose4. The IMR reduced the noise even further, by 60-75% compared to FBP. The results are independent of dose. The NPS showed changes in the noise distribution for different reconstruction methods. The low-contrast resolution and CNR were improved with iDose4, and the improvement was even greater with IMR. CONCLUSION: There is great potential to reduce noise and thereby improve image quality by using hybrid or, in particular, model-based iterative reconstruction methods, or to lower radiation dose and maintain image quality.

Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmö Breast Tomosynthesis Screening Trial, a population-based study.

OBJECTIVE: To assess the performance of one-view digital breast tomosynthesis (DBT) in breast cancer screening. METHODS: The Malmö Breast Tomosynthesis Screening Trial is a prospective population-based one-arm study with a planned inclusion of 15000 participants; a random sample of women aged 40-74 years eligible for the screening programme. This is an explorative analysis of the first half of the study population (n = 7500). Participants underwent one-view DBT and two-view digital mammography (DM), with independent double reading and scoring. Primary outcome measures were detection rate, recall rate and positive predictive value (PPV). McNemar's test with 95 % confidence intervals was used. RESULTS: Breast cancer was found in sixty-eight women. Of these, 46 cases were detected by both modalities, 21 by DBT alone and one by DM alone. The detection rate for one-view DBT was 8.9/1000 screens (95 % CI 6.9 to 11.3) and 6.3/1000 screens (4.6 to 8.3) for two-view DM (p < 0.0001). The recall rate after arbitration was 3.8 % (3.3 to 4.2) for DBT and 2.6 % (2.3 to 3.0) for DM (p < 0.0001). The PPV was 24 % for both DBT and DM. CONCLUSION: Our results suggest that one-view DBT might be feasible as a stand-alone screening modality. KEY POINTS: One-view DBT as a stand-alone breast cancer screening modality has not been investigated. One-view DBT increased the cancer detection rate significantly. The recall rate increased significantly but was still low. Breast cancer screening with one-view DBT as a stand-alone modality seems feasible.

Image Quality in Oncologic Chest Computerized Tomography With Iterative Reconstruction: A Phantom Study.

OBJECTIVE: The purpose of this study was to validate iterative reconstruction technique in oncologic chest computed tomography (CT). METHODS: An anthropomorphic thorax phantom with 4 simulated tumors was scanned on a 64-slice CT scanner with 2 different iterative reconstruction techniques: one model based (MBIR) and one hybrid (ASiR). Dose levels of 14.9, 11.1, 6.7, and 0.6 mGy were used, and all images were reconstructed with filtered back projection (FBP) and both iterative reconstruction algorithms. Hounsfield units (HU) and absolute noise were measured in the tumors, lung, heart, diaphragm, and muscle. Contrast-to-noise ratios (CNRs) and signal-to-noise ratios (SNRs) were calculated. RESULTS: Model-based iterative reconstruction (MBIR) increased CNRs of the tumors (21.1-192.2) and SNRs in the lung (-49.0-165.6) and heart (3.1-8.5) at all dose levels compared with FBP (CNR, 1.1-23.0; SNR, -7.5-31.6 and 0.2-1.1) and with adaptive statistical iterative reconstruction (CNR, 1.2-33.2; SNR, -7.3-37.7 and 0.2-1.5). At the lowest dose level (0.6 mGy), MBIR reduced the cupping artifact (HU range: 17.0 HU compared with 31.4-32.2). An HU shift in the negative direction was seen with MBIR. CONCLUSIONS: Quantitative image quality parameters in oncologic chest CT are improved with MBIR compared with FBP and simpler iterative reconstruction algorithms. Artifacts at low doses are reduced. A shift in HU values was shown; thus, absolute HU values should be used with care.

Comparing five different iterative reconstruction algorithms for computed tomography in an ROC study.

OBJECTIVES: The purpose of this study was to evaluate lesion conspicuity achieved with five different iterative reconstruction techniques from four CT vendors at three different dose levels. Comparisons were made of iterative algorithm and filtered back projection (FBP) among and within systems. METHODS: An anthropomorphic liver phantom was examined with four CT systems, each from a different vendor. CTDIvol levels of 5 mGy, 10 mGy and 15 mGy were chosen. Images were reconstructed with FBP and the iterative algorithm on the system. Images were interpreted independently by four observers, and the areas under the ROC curve (AUCs) were calculated. Noise and contrast-to-noise ratios (CNR) were measured. RESULTS: One iterative algorithm increased AUC (0.79, 0.95, and 0.97) compared to FBP (0.70, 0.86, and 0.93) at all dose levels (p < 0.001 and p = 0.047). Another algorithm increased AUC from 0.78 with FBP to 0.84 (p = 0.007) at 5 mGy. Differences at 10 and 15 mGy were not significant (p-values: 0.084-0.883). Three algorithms showed no difference in AUC compared to FBP (p-values: 0.008-1.000). All of the algorithms decreased noise (10-71%) and improved CNR. CONCLUSIONS: Only two algorithms improved lesion detection, even though noise reduction was shown with all algorithms. KEY POINTS: Iterative reconstruction algorithms affected lesion detection differently at different dose levels. One iterative algorithm improved lesion detectability compared to filtered back projection. Three algorithms did not significantly improve lesion detectability. One algorithm improved lesion detectability at the lowest dose level.

New developed DR detector performs radiographs of hand, pelvic and premature chest anatomies at a lower radiation dose and/or a higher image quality.

A newly developed Digital Radiography (DR) detector has smaller pixel size and higher fill factor than earlier detector models. These technical advantages should theoretically lead to higher sensitivity and higher spatial resolution, thus making dose reduction possible without scarifying image quality compared to previous DR detector versions. To examine whether the newly developed Canon CXDI-70C DR detector provides an improved image quality and/or allows for dose reductions in hand and pelvic bone examinations as well as premature chest examinations, compared to the previous (CXDI-55C) DR detector version. A total of 450 images of a technical Contrast-Detail phantom were imaged on a DR system employing various kVp and mAs settings, providing an objective image quality assessment. In addition, 450 images of anthropomorphic phantoms were taken and analyzed by three specialized radiologists using Visual Grading Analysis (VGA). The results from the technical phantom studies showed that the image quality expressed as IQFINV values was on average approximately 45 % higher with the CXDI-70C detector compared to the CXDI-55C detector. Consistently, the VGA results from the anatomical phantom studies indicated that by using the CXDI-70C detector, diagnostic image quality could be maintained at a dose reduction of in average 30 %, depending on anatomy and kVp level. This indicates that the CXDI-70C detector is significantly more sensitive than the previous model, and supports a better clinical image quality. By using the newly developed DR detector a significant dose reduction is possible while maintaining image quality.

No evidence for shedding of circulating tumor cells to the peripheral venous blood as a result of mammographic breast compression.

This pilot study aimed to investigate whether mammographic compression procedures might cause shedding of tumor cells into the circulatory system as reflected by circulating tumor cell (CTC) count in peripheral venous blood samples. From March to October 2012, 24 subjects with strong suspicion of breast malignancy were included in the study. Peripheral blood samples were acquired before and after mammography. Enumeration of CTCs in the blood samples was performed using the CellSearch(®) system. The pressure distribution over the tumor-containing breast was measured using thin pressure sensors. The median age was 66.5 years (range, 51-87 years). In 22 of the 24 subjects, breast cancer was subsequently confirmed. The difference between the average mean tumor pressure 6.8 ± 5.3 kPa (range, 1.0-22.5 kPa) and the average mean breast pressure 3.4 ± 1.6 kPa (range, 1.5-7.1 kPa) was statistically significant (p < 0.001), confirming that there was increased pressure over the tumor. The median pathological tumor size was 19 mm (range, 9-30 mm). Four subjects (17 %) were CTC positive before compression and two of these (8 %) were also CTC positive after compression. A total of seven CTCs were isolated with a mean size of 8 × 6 µm(2) (range of the longest diameter, 5-12 µm). The study supports the view that mammography is a safe procedure from the point of view of tumor cell shedding to the peripheral blood.

Investigation of viewing procedures for interpretation of breast tomosynthesis image volumes: a detection-task study with eye tracking.

OBJECTIVES: To evaluate the efficiency of different methods of reading breast tomosynthesis (BT) image volumes. METHODS: All viewing procedures consisted of free scroll volume browsing and three were combined with initial cine loops at three different frame rates (9, 14 and 25 fps). The presentation modes consisted of vertically and horizontally orientated BT image volumes. Fifty-five normal BT image volumes in mediolateral oblique view were collected. In these, simulated lesions were inserted, creating four unique image sets, one for each viewing procedure. Four observers interpreted the cases in a free-response task. Time efficiency, visual attention and search were investigated using eye tracking. RESULTS: Horizontally orientated BT image volumes were read faster than vertically when using free scroll browsing only and when combined with fast cine loop. Cine loops at slow frame rates were ruled out as inefficient. CONCLUSIONS: In general, horizontally oriented BT image volumes were read more efficiently. All viewing procedures except for slow frame rates were promising when assuming equivalent detection performance.

Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence.

Purpose: Breast cancer screening is predominantly performed using digital mammography (DM), but digital breast tomosynthesis (DBT) has higher sensitivity. DBT demands more resources than DM, and it might be more feasible to reserve DBT for women with a clear benefit from the technique. We explore if artificial intelligence (AI) can select women who would benefit from DBT imaging. Approach: We used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately double read DM and DBT. We retrospectively analyzed DM examinations (n=14768) with a breast cancer detection system and used the provided risk score (1 to 10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. Results: If using a threshold of 9.0, 25 (26%) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61% would be detected, with only 1797 (12%) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, whereas the false-positive recalls would be increased with 58 (21%). Conclusion: Using DBT only for selected high gain cases could be an alternative to complete DBT screening. AI can analyze initial DM images to identify high gain cases where DBT can be added during the same visit. There might be logistical challenges, and further studies in a prospective setting are necessary.

Malmö Breast ImaginG database: objectives and development.

Purpose: We describe the design and implementation of the Malmö Breast ImaginG (M-BIG) database, which will support research projects investigating various aspects of current and future breast cancer screening programs. Specifically, M-BIG will provide clinical data to:1.investigate the effect of breast cancer screening on breast cancer prognosis and mortality;2.develop and validate the use of artificial intelligence and machine learning in breast image interpretation; and3.develop and validate image-based radiological breast cancer risk profiles. Approach: The M-BIG database is intended to include a wide range of digital mammography (DM) and digital breast tomosynthesis (DBT) examinations performed on women at the Mammography Clinic in Malmö, Sweden, from the introduction of DM in 2004 through 2020. Subjects may be included multiple times and for diverse reasons. The image data are linked to extensive clinical, diagnostic, and demographic data from several registries. Results: To date, the database contains a total of 451,054 examinations from 104,791 women. During the inclusion period, 95,258 unique women were screened. A total of 19,968 examinations were performed using DBT, whereas the rest used DM. Conclusions: We describe the design and implementation of the M-BIG database as a representative and accessible medical image database linked to various types of medical data. Work is ongoing to add features and curate the existing data.

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.

Breast compression in mammography: pressure distribution patterns.

BACKGROUND: Breast compression is important in mammography in order to improve image quality, better separate tissue components, and reduce absorbed dose to the breast. In this study we use a method to measure and visualize the distribution of pressure over a compressed breast in mammography. PURPOSE: To measure and describe the pressure distribution over the breast as a result of applied breast compression in mammography. MATERIAL AND METHODS: One hundred and three women aged 40.7-74.3 years (median, 48.9 years) invited for mammographic screening consented to take part in this study. They were subjected to two additional breast compressions of the left breast (standard force and approximately 50% reduction). Pressure images of the compressed breast were obtained using force sensing resistor (FSR) sensors placed underneath the compression plate. Subjects rated their experience of pain on a visual analogue scale (VAS). RESULTS: Four pressure patterns were identified, fitting 81 of the 103 breasts, which were grouped accordingly. The remaining 22 breasts were found to correspond to a combination of any two patterns. Two groups (43 breasts) showed pressure mainly over the juxtathoracic part of the breast, had significantly greater breast thickness (P = 0.003) and had a lower mean pressure over dense tissue (P < 0.0001) than those with more evenly distributed pressure. Reducing compression force increased average breast thickness by 1.8 mm (P < 0.0001). CONCLUSION: The distribution of pressure differed greatly between breasts. In a large proportion of breasts the compression plate did not provide optimal compression of the breast, the compression force being absorbed in juxtathoracic structures.

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.