Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications.

Time for Change in Digital Breast Tomosynthesis Research.

The purpose of our study was to quantitatively compare the time for interpretation of screening full-field digital mammography (FFDM) images using prior analog film mammograms for comparison versus digitized prior analog mammograms. Images from 100 FFDM studies were interpreted by four radiologists. All FFDM images had comparison analog mammograms obtained a minimum of 1 year earlier that were digitized using a 43-μm film digitizer. Initially, the FFDM images were interpreted using the digitized prior mammogram on two, 5-megapixel monitors and PACS. All available PACS tools could be used. Four weeks later, the same 100 screening FFDMs were interpreted using the original analog mammograms on an alternator at 90° to the monitors used to interpret the screening FFDMs. The interpretation times were recorded and compared. The results were compared and evaluated for statistical significance using statistical software, with statistical significance set at p < 0.05. For each radiologist, the mean reading time for FFDM with digitized priors was significantly shorter in length in comparison with the mean reading time calculated for interpreting FFDM using analog film priors. The differences in times recorded between digitized analog versus analog ranged from 11.31 to 74.18 seconds. The reading times for the four readers ranged from 17.32 to 185.94 seconds, with a mean of 58.56 seconds when using analog film prior mammograms. When using digitized analog prior mammograms, the reading times for the four readers ranged from 11.32 to 109.11 seconds with a mean of 39.76 seconds. The average difference in reading time was calculated to be 18.80 seconds, showing that there is a 32% increase in interpretation speed when using a digitized prior analog for comparison studies as opposed to an analog prior. There is a statistically significant 32.1% average improvement in interpretation time when FFDM screening mammograms use digitized analog comparison mammograms than if FFDM is interpreted with the original analog film mammograms. This should allow more FFDMs to be interpreted in the same amount of time if digitized prior analog mammograms are used.

Several different types of detectors, software, and hardware designs are employed in instruments used to monitor radioactive content of freight shipments in order to detect illicit trafficking of nuclear materials. The instruments can be container monitors, hand-held radiation detectors, mobile analysis systems, or fixed radiation portal monitors. However, within the various groupings (e.g., portal monitors), all instruments are expected to solve the same problem, that is, to detect and identify any radioactive material present according to the prescribed investigative procedure or CONOPS. The best way to compare the performance of different instruments is with a numerical score or Figure of Merit (FOM). The FOM must quantify the performance of the instrument with respect to true positives (TP), false positives (FP), and false negatives (FN). The minimization of FN for certain radionuclides (e.g., uranium and plutonium or other Special Nuclear Material (SNM)) is more important than the minimization of FN for non-threat nuclides (e.g., low level NORM). In a similar way, the minimization of FP for SNM is more important than falsely reporting the common NORM nuclides which represent no threat. System performance depends on the measurement detail (e.g., transit speed through a portal, measurement time in general, amount and distribution of NORM), therefore the test conditions should also be included in the statement of the FOM. The FOM is expected to vary significantly with the above measurement details. A FOM has been developed based on the number of true positives (TP), the number of false important positives (FIP), the number of FP, the number of true positives for SNM (TPSNM), and the number of false positives for SNM (FPSNM). This formula rates the overall performance with extra weight given to FP and FN for SNM. Examples will be shown for testing of portal monitors.

MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization

Linear structures are a major contributor to false-positives (FPs) in detection of clustered microcalcifications (MCs) in mammograms. We propose a unified classifier approach to incorporate the dichotomous effect of linear structures in MC detection, the purpose being to suppress the FPs associated with linear structures. We introduce a dummy variable in the classifier model as in traditional regression analysis, the role of which is to adapt the input features to the classifier according to the presence of linear structures. In the experiment we demonstrate the proposed approach by using Adaboost decision stumps as the unified classifier. The results on a set of 200 mammogram images (all containing clustered MCs) show that it could reduce the FPs in an existing SVM detector by up to 47.3% with the true-positive rate at 85%.

Objective: This study aimed to compare two-dimensional (2D) synthetic mammography (SM) and conventional 2D full-field digital mammography (FFDM) in the evaluation of breast lesions. We aim to determine whether SM, when combined with digital breast tomosynthesis (DBT), can serve as a new standard suitable for clinical adoption. This potential shift could impact existing screening protocols, offering benefits such as improved patient outcomes, maintained diagnostic accuracy, and workflow efficiency. Implementing SM+DBT may reduce radiation exposure, streamline image acquisition, and maintain diagnostic detail. This protocol could also increase patient throughput by minimizing image capture time and may enable more frequent, guideline-aligned screenings, ultimately supporting healthcare delivery efficiency from a clinical perspective. Mammograms from 100 patients wi Material And Method: th suspected breast pathologies were obtained using the Hologic Selenia Dimensions System in COMBO HD mode, generating FFDM, DBT, and SM images. FFDM and SM images were independently assessed using the 5th edition ACR Breast Imaging Reporting and Data System (5th edition BI-RADS) lexicon and compared. Institutional review board approval and patient consent were obtained. Spiculated margins and fine Result: pleomorphic calcifications were more distinctly visualised on SM images. FFDM images provided a more precise visualisation of nipple retraction and skin thickening. There was no significant difference in average glandular dose between FFDM and SM images (p = 0.12). BIRADS categorisation showed complete agreement across both methods in all cases. FFDM and SM images Conclusion: provided equivalent diagnostic performance for breast lesion evaluation. Combining DBT with SM reduced patient radiation dose without compromising diagnostic accuracy. Synthetic mammography may be considered a viable alternative to full-field digital mammogra Advances In Knowledge: phy when combined with digital breast tomosynthesis.

Realistic breast phantoms serve as important tools when evaluating full field digital mammography (FFDM) and digital breast tomosynthesis (DBT) system modifications. Current breast phantoms contain either unrealistic features or uniform backgrounds. The purpose of this work was to introduce a novel, task-based methodology for evaluating FFDM and DBT systems using an anthropomorphic inkjet-printed 3D phantom with clinically relevant signals. The methodology consists of multiple physical components: an anthropomorphic breast phantom, microcalcifications made of two types of material, and masses. A 4 cm compressed thickness breast phantom was first modeled analytically, then realized in a slice-by-slice fashion using inkjet printing with iohexol-doped ink. The microcalcifications (MCs) were made by arranging individual specks of varying sizes into regular patterns. Two types of MCs were used, ranging in diameter from 150 μm to 260 μm: one made from calcium hydroxyapatite (HA) and another from soda lime glass microspheres. Lastly, realistically-shaped masses were created using ink doped with potassium iodide. The phantom was imaged on two commercially available FFDM/DBT systems, Holgic Selenia Dimensions and the GE Senographe Essential. A typical mammographic beam was used (according to the automatic exposure control for each commercial system), and a similar average glandular dose was maintained across the systems. A pilot study consisting of a four-alternative, forced-choice (4AFC) analysis with human observers was performed on the FFDM and DBT acquisitions. The linear attenuation coefficients of the microcalcification models were measured to be similar to reference values. A custom Matlab program was created to extract ROI images from images of the phantom, each containing a signal, in preparation for use with 4AFC software. A pilot 4AFC study showed the visibility of the microcalcifications ranged from easy to difficult, and results informed the final reader study. An anthropomorphic breast phantom was created using inexpensive, easily available materials. The task-based assessment was performed on clinical FFDM and DBT systems. This promising phantom generation methodology can be used to objectively evaluate task performance resulting with FFDM and DBT breast imaging systems.

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

Purpose. Report advances being made in synthetic mammography applied to carbon nanotube-enabled stationary digital breast tomosynthesis (sDBT). Methods. The potential value of adding Laplacian decomposition, feature-enhancement algorithms, and weighted recombination to the tunable forward-projection steps developed previously to generate synthetic mammograms for sDBT was studied in this phantom-based comparison of sDBT to full field digital mammography (FFDM) and moving-source or conventional DBT. Contrast-to-noise ratio (CNR) and the full-width-at-half-maximum (FWHM) of the signal intensity were used to compare the display of microcalcification and mass features in the FFDM image and the synthetic images generated by sDBT and DBT. These findings guided modifications in the sDBT image processing chain, seeking to maximize the display of clinically-important features in the sDBT-based synthetic image. Results. Decomposing each reconstructed image slice into its high, mid, and low-frequency components yielded images emphasizing a different feature of clinical importance: microcalcifications, masses, and background density. Applying feature-enhancement algorithms to these images followed by weighted recombination during forward projection yielded an sDBT-based synthetic image that displayed masses with a higher CNR than the FFDM image and the synthetic image generated by DBT. Additionally, microcalcifications that could be visualized in all three modalities were displayed with a higher CNR in the synthetic images generated by DBT and sDBT compared to the FFDM image. Conclusion. Adding Laplacian decomposition, feature-enhancement, and weighted recombination steps to the image processing chain that generates a synthetic image from information collected by sDBT improved the display of clinicallyimportant features. Advancing the synthetic mammography capability of sDBT is important, as it will help complete the evolution of this promising technology to a viable clinical tool.

One of the limitations of the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is a relatively large number of false-positive (FP) detections. Rectal tubes (RTs) are one of the typical sources of FPs because a portion of a RT, especially a portion of a bulbous tip, often exhibits a cap-like shape that closely mimics the appearance of a small polyp. Radiologists can easily recognize and dismiss RT-induced FPs; thus, they may lose their confidence in CAD as an effective tool if the CAD scheme generates such "obvious" FPs due to RTs consistently. In addition, RT-induced FPs may distract radiologists from less common true positives in the rectum. Therefore, removal RT-induced FPs as well as other types of FPs is desirable while maintaining a high sensitivity in the detection of polyps. We developed a three-dimensional (3D) massive-training artificial neural network (MTANN) for distinction between polyps and RTs in 3D CTC volumetric data. The 3D MTANN is a supervised volume-processing technique which is trained with input CTC volumes and the corresponding "teaching" volumes. The teaching volume for a polyp contains a 3D Gaussian distribution, and that for a RT contains zeros for enhancement of polyps and suppression of RTs, respectively. For distinction between polyps and nonpolyps including RTs, a 3D scoring method based on a 3D Gaussian weighting function is applied to the output of the trained 3D MTANN. Our database consisted of CTC examinations of 73 patients, scanned in both supine and prone positions (146 CTC data sets in total), with optical colonoscopy as a reference standard for the presence of polyps. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. These CTC cases were subjected to our previously reported CAD scheme that included centerline-based segmentation of the colon, shape-based detection of polyps, and reduction of FPs by use of a Bayesian neural network based on geometric and texture features. Application of this CAD scheme yielded 96.4% (27/28) by-polyp sensitivity with 3.1 (224/73) FPs per patient, among which 20 FPs were caused by RTs. To eliminate the FPs due to RTs and possibly other normal structures, we trained a 3D MTANN with ten representative polyps and ten RTs, and applied the trained 3D MTANN to the above CAD true- and false-positive detections. In the output volumes of the 3D MTANN, polyps were represented by distributions of bright voxels, whereas RTs and other normal structures partly similar to RTs appeared as darker voxels, indicating the ability of the 3D MTANN to suppress RTs as well as other normal structures effectively. Application of the 3D MTANN to the CAD detections showed that the 3D MTANN eliminated all RT-induced 20 FPs, as well as 53 FPs due to other causes, without removal of any true positives. Overall, the 3D MTANN was able to reduce the FP rate of the CAD scheme from 3.1 to 2.1 FPs per patient (33% reduction), while the original by-polyp sensitivity of 96.4% was maintained.

To evaluate the tumour visibility and diagnostic performance of digital breast tomosynthesis (DBT) in patients with noncalcified T1 breast cancer. Medical records of 106 females with noncalcified T1 invasive breast cancer who underwent DBT and full-field digital mammography (FFDM) between January 2012 and December 2014 were retrospectively reviewed. To assess tumour visibility (score 1-3), all DBT and FFDM images were reviewed by two radiologists blinded to clinicopathological information. A reference standard was established by an unblinded consensus review of all images. Clinicopathological and imaging variables were analysed based on tumour visibility. After adding 159 negative controls, the diagnostic performance of DBT + FFDM was compared with that of FFDM. The tumour visibility was significantly higher through DBT + FFDM (2.5 vs 1.8; p = 0.002) than FFDM alone. Breast composition was the independent variable for tumour visibility through DBT + FFDM (extremely dense; odds ratio, 0.02; p < 0.001). Sensitivity (p = 0.642), specificity (p = 0.463), positive-predictive value (p = 0.078), and negative-predictive value (p = 0.072) of DBT + FFDM were not significantly superior to those of FFDM in 55 females with extremely dense breast composition, whereas specificity (p = 0.002) and positive-predictive value (p < 0.001) were significantly higher in 210 females with other breast compositions. Addition of DBT to FFDM showed no significant increase in the tumour visibility and diagnostic performance in patients with noncalcified T1 cancer in extremely dense breasts. Addition of DBT to FFDM did not further improve the detection of noncalcified early breast cancers in females with extremely dense breasts.

Microcalcification (MC) clusters in mammograms are one of the primary signs of breast cancer. In the literature, most MC detection methods follow a two-step paradigm: segmenting each MC and analyzing their spatial distributions to form MC clusters. However, segmentation of MCs cannot avoid low sensitivity or high false positive rate due to their variability in size (sometimes <0.1 mm), brightness, and shape (with diverse surroundings). In this paper, we propose a novel self-adversarial learning framework to differentiate and delineate the MC clusters in an end-to-end manner. The class activation mapping (CAM) mechanism is employed to directly generate the contours of MC clusters with the guidance of MC cluster classification and box annotations. We also propose the self-adversarial learning strategy to equip CAM with better detection capability of MC clusters by using the backbone network itself as a discriminator. Experimental results suggest that our method can achieve better performance for MC cluster detection with the contouring of MC clusters and classification of MC types.

Mammography is the only technique currently used for detecting microcalcification (MC) clusters, an early indicator of breast cancer. However, mammographic images superimpose a three-dimensional compressed breast image onto two-dimensional projection views, resulting in overlapped anatomical breast structures that may obscure the detection and visualization of MCs. One possible solution to this problem is the use of cone beam computed tomography (CBCT) with a flat-panel (FP) digital detector. Although feasibility studies of CBCT techniques for breast imaging have yielded promising results, they have not shown how radiation dose and x-ray tube voltage affect the accuracy with which MCs are detected by CBCT experimentally. We therefore conducted a phantom study using a FP-based CBCT system with various mean glandular doses and kVp values. An experimental CBCT scanner was constructed with a data acquisition rate of 7.5 frames/s. 10.5 and 14.5 cm diameter breast phantoms made of gelatin were used to simulate uncompressed breasts consisting of 100% glandular tissue. Eight different MC sizes of calcium carbonate grains, ranging from 180-200 microm to 355-425 microm, were used to simulate MCs. MCs of the same size were arranged to form a 5 x 5 MC cluster and embedded in the breast phantoms. These MC clusters were positioned at 2.8 cm away from the center of the breast phantoms. The phantoms were imaged at 60, 80, and 100 kVp. With a single scan (360 degrees), 300 projection images were acquired with 0.5 x, 1x, and 2x mean glandular dose limit for 10.5 cm phantom and with 1x, 2x, and 4x for 14.5 cm phantom. A Feldkamp algorithm with a pure ramp filter was used for image reconstruction. The normalized noise level was calculated for each x-ray tube voltage and dose level. The image quality of the CBCT images was evaluated by counting the number of visible MCs for each MC cluster for various conditions. The average percentage of the visible MCs was computed and plotted as a function of the MGD, the kVp, and the average MC size. The results showed that the MC visibility increased with the MGD significantly but decreased with the breast size. The results also showed that the x-ray tube voltage affects the detection of MCs under different circumstances. With a 50% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 348(+/-2), 288(+/-7), 257(+/-2) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 355 (+/-1), 307 (+/-7), 275 (+/-5) microm at 6, 12, and 24 mGy, respectively. With a 75% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 367 (+/-1), 316 (+/-7), 265 (+/-3) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 377 (+/-3), 334 (+/-5), 300 (+/-2) microm at 6, 12, and 24 mGy, respectively.

Recall Rate Reduction with Tomosynthesis During Baseline Screening Examinations: An Assessment From a Prospective Trial

Linear structures are a major source of false positives (FPs) in computer-aided detection of clustered microcalcifications (MCs) in mammograms. In this work, we investigate whether it is feasible to improve the performance in MC detection by directly exploiting the FPs associated with linear structures. We analyze the cause of FPs by linear structures and their characteristics with an SVM detector, and design a linear structure detection procedure together with a dual-thresholding scheme to separate the linear structures from other tissue background in a mammogram. The proposed procedure was demonstrated on a set of 200 mammograms containing clustered MCs. The results show that it could effectively reduce the FPs in the SVM detector by as much as 30% with the true detection rate at 85%.