New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers.
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ABSTRACT:
SUBMITTER: Rodriguez-Ruiz A
PROVIDER: S-EPMC6088454 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
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New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers.
Rodriguez-Ruiz Alejandro A Teuwen Jonas J Vreemann Suzan S Bouwman Ramona W RW van Engen Ruben E RE Karssemeijer Nico N Mann Ritse M RM Gubern-Merida Albert A Sechopoulos Ioannis I
Acta radiologica (Stockholm, Sweden : 1987) 20171218 9
Background The image quality of digital breast tomosynthesis (DBT) volumes depends greatly on the reconstruction algorithm. Purpose To compare two DBT reconstruction algorithms used by the Siemens Mammomat Inspiration system, filtered back projection (FBP), and FBP with iterative optimizations (EMPIRE), using qualitative analysis by human readers and detection performance of machine learning algorithms. Material and Methods Visual grading analysis was performed by four readers specialized in bre ...[more]
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Project description:To analyze the effects of projection-view (PV) distribution on the contrast and spatial blurring of microcalcifications on the tomosynthesized slices (X-Y plane) and along the depth (Z) direction for the same radiation dose in digital breast tomosynthesis (DBT).A GE GEN2 prototype DBT system was used for acquisition of DBT scans. The system acquires PV images from 21 angles in 3° increments over a ±30° range. From these acquired PV images, the authors selected six subsets of PV images to simulate DBT of different angular ranges and angular increments. The number of PV images in each subset was fixed at 11 to simulate a constant total dose. These different PV distributions were subjectively divided into three categories: uniform group, nonuniform central group, and nonuniform extreme group with different angular ranges and angular increments. The simultaneous algebraic reconstruction technique (SART) was applied to each subset to reconstruct the DBT slices. A selective diffusion regularization method was employed to suppress noise. The image quality of microcalcifications in the reconstructed DBTs with different PV distributions was compared using the DBT scans of an American College of Radiology phantom and three human subjects. The contrast-to-noise ratio (CNR) and the full width at half maximum (FWHM) of the line profiles of microcalcifications within their in-focus DBT slices (parallel to detector plane) and the FWHMs of the interplane artifact spread function (ASF) in the Z-direction (perpendicular to detector plane) were used as image quality measures.The results indicate that DBT acquired with a large angular range or, for an equal angular range,with a large fraction of PVs at large angles yielded superior ASF with smaller FWHM in the Z-direction. PV distributions with a narrow angular range or a large fraction of PVs at small angles had stronger interplane artifacts. In the X-Y focal planes, the effect of PV distributions on spatial blurring depended on the directions. In the X-direction (perpendicular to the chestwall), the normalized line profiles of the calcifications reconstructed with the different PV distributions were similar in terms of FWHM; the differences in the FWHMs between the different PV distributions were less than half a pixel. In the Y-direction (x-ray source motion), the normalized line profiles of the calcifications reconstructed with PVs acquired with a narrow angular range or a large fraction of PVs at small angles had smaller FWHMs and thus less blurring of the line profiles. In addition, PV distributions with a narrow angular range or a large fraction of PVs at small angles yielded slightly higher CNR than those with a wide angular range for small, subtle microcalcifications; however, PV distributions had no obvious effect on CNR for relatively large microcalcifications.PV distributions affect the image quality of DBT. The relative importance of the impact depends on the characteristics of the signal and the direction (perpendicular or parallel) relative to the direction of x-ray source motion. For a given number of PVs, the angular range and the distribution of the PVs affect the degree of in-plane and interplane blurring in opposite ways. The design of the scan parameters of tomosynthesis systems would require proper consideration of the characteristics of the signals of interest and the potential trade-off of the image quality of different types of signals.
| S-EPMC3203126 | biostudies-other
Project description:This paper describes a new image reconstruction method for digital breast tomosynthesis (DBT). The new method incorporates detector blur into the forward model. The detector blur in DBT causes correlation in the measurement noise. By making a few approximations that are reasonable for breast imaging, we formulated a regularized quadratic optimization problem with a data-fit term that incorporates models for detector blur and correlated noise (DBCN). We derived a computationally efficient separable quadratic surrogate (SQS) algorithm to solve the optimization problem that has a non-diagonal noise covariance matrix. We evaluated the SQS-DBCN method by reconstructing DBT scans of breast phantoms and human subjects. The contrast-to-noise ratio and sharpness of microcalcifications were analyzed and compared with those by the simultaneous algebraic reconstruction technique. The quality of soft tissue lesions and parenchymal patterns was examined. The results demonstrate the potential to improve the image quality of reconstructed DBT images by incorporating the system physics model. This paper is a first step toward model-based iterative reconstruction for DBT.
| S-EPMC5772655 | biostudies-literature
Project description:In principle, a stationary digital breast tomosynthesis (s-DBT) system has better image quality when compared to continuous motion DBT systems due to zero motion blur of the source. The authors have developed a s-DBT system by using a linear carbon nanotube x-ray source array. The purpose of the current study was to quantitatively evaluate the performance of the s-DBT system; and investigate the dependence of imaging quality on the system configuration parameters.Physical phantoms were used to assess the image quality of each configuration including inplane resolution as measured by the modulation transfer function (MTF), inplane contrast as measured by the signal difference to noise ratio (SdNR), and depth resolution as measured by the z-axis artifact spread function. Five parameters were varied to create five groups of configurations: (1) total angular span; (2) total number of projection images; (3) distribution of exposure (mAs) across the projection images; (4) entrance dose; (5) detector pixel size.It was found that the z-axis depth resolution increased with the total angular span but was insensitive to the number of projection images, mAs distribution, entrance dose, and detector pixel size. The SdNR was not affected by the angular span or the number of projection images. A decrease in SdNR was observed when the mAs was not evenly distributed across the projection images. As expected, the SdNR increased with entrance dose and when larger pixel sizes were used. For a given detector pixel size, the inplane resolution was found to be insensitive to the total angular span, number of projection images, mAs distribution, and entrance dose. A 25% increase in the MTF was observed when the detector was operating in full resolution mode (70 μm pixel size) compared to 2 × 2 binned mode (140 μm pixel size).The results suggest that the optimal imaging configuration for a s-DBT system is a large angular span, an intermittent number of projection views, and a uniform mAs distribution over all views. With the detector operating at full resolution, a stationary DBT system can achieve an inplane resolution of 5.1 cycles per mm, which is significantly better than continuous motion DBT systems.
| S-EPMC3600660 | biostudies-other
Project description:ObjectivesTo perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm.Materials and methodsOncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale.ResultsFifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%.ConclusionsDLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm.Clinical relevance statementDeep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm.Key points• Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.
| S-EPMC10957592 | biostudies-literature
Project description:Rationale and objectivesA linear array of carbon nanotube-enabled x-ray sources allows for stationary digital breast tomosynthesis (sDBT), during which projection views are collected without the need to move the x-ray tube. This work presents our initial clinical experience with a first-generation sDBT device.Materials and methodsFollowing informed consent, women with a "suspicious abnormality" (Breast Imaging Reporting and Data System 4), discovered by digital mammography and awaiting biopsy, were also imaged by the first generation sDBT. Four radiologists participated in this paired-image study, completing questionnaires while interpreting the mammograms and sDBT image stacks. Areas under the receiver operating characteristic curve were used to measure reader performance (likelihood of correctly identifying malignancy based on pathology as ground truth), while a multivariate analysis assessed preference, as readers compared one modality to the next when interpreting diagnostically important image features.ResultsFindings from 43 women were available for analysis, in whom 12 cases of malignancy were identified by pathology. The mean areas under the receiver operating characteristic curve was significantly higher (p < 0.05) for sDBT than mammography for all breast density categories and breast thicknesses. Additionally, readers preferred sDBT over mammography when evaluating mass margins and shape, architectural distortion, and asymmetry, but preferred mammography when characterizing microcalcifications.ConclusionReaders preferred sDBT over mammography when interpreting soft-tissue breast features and were diagnostically more accurate using images generated by sDBT in a Breast Imaging Reporting and Data System 4 population. However, the findings also demonstrated the need to improve microcalcification conspicuity, which is guiding both technological and image-processing design changes in future sDBT devices.
| S-EPMC6635094 | biostudies-literature
Project description:Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.
| S-EPMC8608738 | biostudies-literature
Project description:We are developing a dual modality tomosynthesis breast scanner in which x-ray transmission tomosynthesis and gamma emission tomosynthesis are performed sequentially with the breast in a common configuration. In both modalities projection data are obtained over an angular range of less than 180° from one side of the mildly compressed breast resulting in incomplete and asymmetrical sampling. The objective of this work is to implement and evaluate a maximum likelihood expectation maximization (MLEM) reconstruction algorithm for gamma emission breast tomosynthesis (GEBT).A combination of Monte Carlo simulations and phantom experiments was used to test the MLEM algorithm for GEBT. The algorithm utilizes prior information obtained from the x-ray breast tomosynthesis scan to partially compensate for the incomplete angular sampling and to perform attenuation correction (AC) and resolution recovery (RR). System spatial resolution, image artifacts, lesion contrast, and signal to noise ratio (SNR) were measured as image quality figures of merit. To test the robustness of the reconstruction algorithm and to assess the relative impacts of correction techniques with changing angular range, simulations and experiments were both performed using acquisition angular ranges of 45°, 90° and 135°. For comparison, a single projection containing the same total number of counts as the full GEBT scan was also obtained to simulate planar breast scintigraphy.The in-plane spatial resolution of the reconstructed GEBT images is independent of source position within the reconstructed volume and independent of acquisition angular range. For 45° acquisitions, spatial resolution in the depth dimension (the direction of breast compression) is degraded with increasing source depth (increasing distance from the collimator surface). Increasing the acquisition angular range from 45° to 135° both greatly reduces this depth dependence and improves the average depth dimension resolution from 10.8 to 4.8 mm. The 135° acquisition results in a near-isotropic, spatially uniform 3D resolution of approximately 4.3 mm full width at half maximum. Background nonuniformity (cupping) artifacts arise primarily from angular incompleteness for small angular range acquisition but primarily from gamma ray attenuation at larger angular range. However, background artifacts can be largely eliminated if both prior information regularization and AC are applied. An artificial decrease in lesion voxel value with increasing lesion depth can also be substantially reduced through a combination of AC and RR. In experiments using compressible gelatin breast phantoms, lesion contrast and SNR are about 2.6-8.8 times and 2.3-5.6 times higher, respectively, in GEBT than in planar breast scintigraphy depending on the acquisition angle, the gamma camera trajectory, and the lesion location. In addition, the strong reduction in lesion contrast and SNR with increasing lesion depth that is observed in planar breast scintigraphy can be largely overcome in GEBT.The authors have demonstrated a promising EM-based reconstruction scheme for use in GEBT. Compared to planar breast scintigraphy GEBT provides superior and less position-dependent lesion contrast, lesion SNR, and spatial resolution as well as more accurate quantification of lesion-to-background activity concentration ratio.
| S-EPMC3523572 | biostudies-other
Project description:The limitations of mammography are well known and are partly related to the fact that with conventional imaging, the three-dimensional volume of the breast is imaged and presented in a two-dimensional format. Because normal breast tissue is similar in x-ray attenuation to some breast cancers, clinically relevant malignancies may be obscured by normal overlapping tissue. In addition, complex areas of normal tissue may be perceived as suspicious. The limitations of two-dimensional breast imaging lead to low sensitivity in detecting some cancers and high false-positive recall rates. Although mammographic screening has been shown to reduce breast cancer deaths by approximately 30%, controversy exists over when and how often screening mammography should occur. Digital breast tomosynthesis (DBT) is rapidly being implemented in breast imaging clinics around the world as early clinical data demonstrate that it may address some of the limitations of conventional mammography. With DBT, multiple low-dose x-ray images are acquired in an arc and reconstructed to create a three-dimensional image, thus minimizing the impact of overlapping breast tissue and improving lesion conspicuity. Early studies of screening DBT have shown decreased false-positive callback rates and increased rates of cancer detection (particularly for invasive cancers), resulting in increased sensitivity and specificity. In our clinical practice, we have completed more than 2 years of using two-view digital mammography combined with two-view DBT for all screening and select diagnostic imaging examinations (over 25,000 patients). Our experience, combined with previously published data, demonstrates that the combined use of DBT and digital mammography is associated with improved outcomes for screening and diagnostic imaging. Online supplemental material is available for this article.
| S-EPMC4319526 | biostudies-literature
Project description:Background Breast Imaging Reporting and Data System (BI-RADS) breast density categories assigned by interpreting radiologists often influence decisions surrounding supplemental breast cancer screening and risk assessment. The landscape of mammographic screening continuously evolves, and different mammographic screening modalities may result in different perception of density, reflected in different assignment of BI-RADS density categories. Purpose To investigate the effect of screening mammography modality on BI-RADS breast density assessments. Materials and Methods Data were retrospectively analyzed from 24 736 individual women (42.3% [10 455 of 24 736] white women, 57.7% [14 281 of 24 736] black women; mean age, 56.3 years; age range, 40.0-74.9 years) who underwent from one to seven mammographic screening examinations from September 2010 through February 2017 (60 766 examinations). Three screening modalities were used: digital mammography alone (8935 examinations); digital mammography with digital breast tomosynthesis (DBT; 30 779 examinations); and synthetic mammography with DBT (21 052 examinations). Random-effects logistic regression analysis was performed to estimate the likelihood of assignment to high versus low BI-RADS density category according to each modality, adjusted for ethnicity, age, body mass index (BMI), and radiologist. The interactions of modality with ethnicity and BMI on density categorization were also tested with the model. Results Women screened with DBT versus digital mammography alone had lower likelihood regarding categorization of high density breasts (digital mammography and DBT vs digital mammography: odds ratio, 0.69 [95% confidence interval: 0.61, 0.80], P < .001; synthetic mammography and DBT vs digital mammography: odds ratio, 0.43 [95% confidence interval: 0.37, 0.50], P < .001). Lower likelihood of high density was also observed at synthetic mammography and DBT compared with digital mammography and DBT (odds ratio, 0.62; 95% confidence interval: 0.56, 0.69; P < .001). There were interactions of modality with ethnicity (P = .007) and BMI (P = .003) on breast density assessment, with greater differences in density categorization according to modality observed for black women than for white women and groups with higher BMI. Conclusion Breast density categorization may vary by screening mammographic modality, and this effect appears to vary by ethnicity and body mass index. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.
| S-EPMC6493215 | biostudies-literature
Project description:ImportanceMany US radiologists have screening mammography recall rates above the expert-recommended threshold of 12%. The influence of digital breast tomosynthesis (DBT) on the distribution of radiologist recall rates is uncertain.ObjectiveTo evaluate radiologists' recall and cancer detection rates before and after beginning interpretation of DBT examinations.Design, setting, and participantsThis cohort study included 198 radiologists from 104 radiology facilities in the Breast Cancer Surveillance Consortium who interpreted 251?384 DBT and 2?000?681 digital mammography (DM) screening examinations from 2009 to 2017, including 126 radiologists (63.6%) who interpreted DBT examinations during the study period and 72 (36.4%) who exclusively interpreted DM examinations (to adjust for secular trends). Data were analyzed from April 2018 to July 2019.ExposuresDigital breast tomosynthesis and DM screening examinations.Main outcomes and measuresRecall rate and cancer detection rate.ResultsA total of 198 radiologists interpreted 2?252?065 DM and DBT examinations (2?000?681 [88.8%] DM examinations; 251?384 [11.2%] DBT examinations; 710?934 patients [31.6%] aged 50-59 years; 1?448?981 [64.3%] non-Hispanic white). Among the 126 radiologists (63.6%) who interpreted DBT examinations, 83 (65.9%) had unadjusted DM recall rates of no more than 12% before using DBT, with a median (interquartile range) recall rate of 10.0% (7.5%-13.0%). On DBT examinations, 96 (76.2%) had an unadjusted recall rate of no more than 12%, with a median (interquartile range) recall rate of 8.8% (6.3%-11.3%). A secular trend in recall rate was observed, with the multivariable-adjusted risk of recall on screening examinations declining by 1.2% (95% CI, 0.9%-1.5%) per year. After adjusting for examination characteristics and secular trends, recall rates were 15% lower on DBT examinations compared with DM examinations interpreted before DBT use (relative risk, 0.85; 95% CI, 0.83-0.87). Adjusted recall rates were significantly lower on DBT examinations compared with DM examinations interpreted before DBT use for 45 radiologists (35.7%) and significantly higher for 18 (14.3%); 63 (50.0%) had no statistically significant change. The unadjusted cancer detection rate on DBT was 5.3 per 1000 examinations (95% CI, 5.0-5.7 per 1000 examinations) compared with 4.7 per 1000 examinations (95% CI, 4.6-4.8 per 1000 examinations) on DM examinations interpreted before DM use (multivariable-adjusted risk ratio, 1.21; 95% CI, 1.11-1.33).Conclusions and relevanceIn this study, DBT was associated with an overall decrease in recall rate and an increase in cancer detection rate. However, our results indicated that there is wide variability among radiologists, including a subset of radiologists who experienced increased recall rates on DBT examinations. Radiology practices should audit radiologist DBT screening performance and consider additional DBT training for radiologists whose performance does not improve as expected.
| S-EPMC7292996 | biostudies-literature