| 1. |
- Timberg, Pontus, et al.
(författare)
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Breast density assessment using breast tomosynthesis images
- 2016
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Ingår i: Breast Imaging : 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings - 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319415451 - 9783319415468 ; 9699, s. 197-202
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Konferensbidrag (refereegranskat)abstract
- In this work we evaluate an approach for breast density assessment of digital breast tomosynthesis (DBT) data using the central projection image. A total of 348 random cases (both FFDM CC and MLO views and DBT MLO views) were collected using a Siemens Mammomat Inspiration tomosynthesis unit at Unilabs, Malmö. The cases underwent both BI-RADS 5th Edition labeling by radiologists and automated volumetric breast density analysis (VBDA) by an algorithm. Preliminary results showed an observed agreement of 70% (weighted Kappa, κ = 0.73) between radiologists and VBDA using FFDM images and 63% (κ = 0.62) for radiologists and VBDA using DBT images. Comparison between densities for FFDM and DBT resulted in high correlation (r = 0.94) and an observed agreement of 72% (κ = 0.76). The automated analysis is a promising approach using low dose central projection DBT images in order to get radiologist- like density ratings similar to results obtained from FFDM.
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| 2. |
- Aho Fält, Tobias, et al.
(författare)
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Simulated Dose Reduction for Abdominal CT With Filtered Back Projection Technique: Effect on Liver Lesion Detection and Characterization
- 2019
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Ingår i: American Journal of Roentgenology: diagnostic imaging and related sciences. - 0361-803X. ; 212:1, s. 84-93
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE. Previous studies have shown the possibility to reduce radiation dose in abdominal CT by 25–50% without negatively affecting detection of liver lesions. How radiation dose reduction affects characterization of liver metastases is not as well known. The objective of this study was to investigate how different levels of simulated dose reduction affect the detection and characterization of liver lesions, primarily hypovascular metastases. A secondary objective was to analyze the relationship between the lesion size and contrast-to-noise ratio (CNR) and the detection rate.MATERIALS AND METHODS. Thirty-nine patients (19 with metastases and 20 without) were retrospectively selected. The following radiation dose levels (DLs) were simulated: 100% (reference level), 75%, 50%, and 25%. Five readers were asked to mark liver lesions and rate the probability of malignancy on a 5-grade Likert scale. Noninferiority analysis using the jackknife free-response ROC (JAFROC) method was performed as well as direct comparison of detection rates and grades.RESULTS. JAFROC analysis showed noninferior detection and characterization of metastases at DL75 as compared with DL100. However, the number of benign lesions and false-positive localizations rated as “suspected malignancy” was significantly higher at DL75.CONCLUSION. Radiation dose can be reduced by 25% without negatively affecting diagnosis of hypovascular liver metastases. Characterization of benign lesions, however, is impaired at DL75, which may lead to unnecessary follow-up examinations. Finally, increased image noise seems to affect the detection of small lesions to a degree that cannot be explained solely by the reduction in CNR.
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| 3. |
- Arboleda, Carolina, et al.
(författare)
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Towards clinical grating-interferometry mammography
- 2019
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; , s. 1-7
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, as several research works have demonstrated in a pre-clinical setting, since it is able to provide attenuation, differential phase contrast, and scattering images simultaneously. In order to translate this technique to the clinics, it has to be adapted to cover a large field-of-view within a clinically acceptable exposure time and radiation dose. Methods: We set up a grating interferometer that fits into a standard mammography system and fulfilled the aforementioned conditions. Here, we present the first mastectomy images acquired with this experimental device. Results and conclusion: Our system performs at a mean glandular dose of 1.6 mGy for a 5-cm-thick, 18%-dense breast, and a field-of-view of 26 × 21 cm2. It seems to be well-suited as basis for a clinical-environment device. Further, dark-field signals seem to support an improved lesion visualization. Evidently, the effective impact of such indications must be evaluated and quantified within the context of a proper reader study. Key Points: • Grating-interferometry-based mammography (GIM) might facilitate breast cancer detection, since it is sensitive to refraction and scattering and thus provides additional tissue information. • The most straightforward way to do grating-interferometry in the clinics is to modify a standard mammography device. • In a first approximation, the doses given with this technique seem to be similar to those of conventional mammography.
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| 4. |
- Bertani, Valeria, et al.
(författare)
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Mammographic calcifications undergoing percutaneous biopsy : outcome in women with and without a personal history of breast cancer
- 2023
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Ingår i: Radiologia Medica. - : Springer Science and Business Media LLC. - 0033-8362 .- 1826-6983. ; 128:2, s. 149-159
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To compare the positive predictive values (PPVs) of BI-RADS categories used to assess pure mammographic calcifications in women with and without a previous history of breast cancer (PHBC). Materials and methods: In this retrospective study, all consecutive pure mammographic calcifications (n = 320) undergoing a stereotactic biopsy between 2016 and 2018 were identified. Mammograms were evaluated in consensus by two radiologists according to BI-RADS and blinded to patient history and pathology results. Final pathologic results were used as the standard of reference. PPV of BI-RADS categories were compared between the two groups. Data were evaluated using standard statistics, Mann–Whitney U tests and Chi-square tests. Results: Two hundred sixty-eight patients (274 lesions, median age 54 years, inter-quartile range, 50–65 years) with a PHBC (n = 46) and without a PHBC (n = 222) were included. Overall PPVs were the following: BI-RADS 2, 0% (0 of 56); BI-RADS 3, 9.1% (1 of 11); BI-RADS 4a, 16.2% (6 of 37); BI-RADS 4b, 37.5% (48 of 128); BI-RADS 4c, 47.3% (18 of 38) and BI-RADS 5, 100% (4 of 4). The PPV of BI-RADS categories was similar in patients with and without a PHBC (P =.715). Calcifications were more often malignant in patients with a PHBC older than 10 years (47.3%, 9 of 19) compared to 1–2 years (25%, 1 of 4), 2–5 years (20%, 2 of 10) and 5–10 years (0%, of 13) from the first breast cancer (P =.005). Conclusion: PPV of mammographic calcifications is similar in women with or without PHBC when BI-RADS classification is strictly applied. A higher risk of malignancy was observed in patients with a PHBC longer than 10 years.
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| 5. |
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| 6. |
- Dahlblom, Victor, et al.
(författare)
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Artificial intelligence detection of missed cancers at digital mammography that were detected at digital breast tomosynthesis
- 2021
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Ingår i: Radiology: Artificial Intelligence. - : Radiological Society of North America (RSNA). - 2638-6100. ; 3:6
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT. Materials and Methods: In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010–2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1–100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics. Results: The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18). Conclusion: Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.
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| 7. |
- Dahlblom, Victor, et al.
(författare)
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Malmö Breast ImaginG database: objectives and development
- 2023
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Ingår i: Journal of Medical Imaging. - 2329-4302. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- PurposeWe 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; and 3. develop and validate image-based radiological breast cancer risk profiles.ApproachThe 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.ResultsTo 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.ConclusionsWe 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.
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| 8. |
- Dustler, Magnus, et al.
(författare)
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Binary implementation of fractal Perlin noise to simulate fibroglandular breast tissue
- 2018
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Ingår i: Proceedings of SPIE : Medical Imaging 2018: Physics of Medical Imaging - Medical Imaging 2018: Physics of Medical Imaging. - : SPIE. ; 10573, s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Software breast phantoms are important in many applications within the field of breast imaging and mammography. This paper describes an improved method of using a previously employed in-house fractal Perlin noise algorithm to create binary software breast phantoms. The Perlin Noise algorithm creates smoothly varying structures of a frequency with a set band limit. By combining a range of frequencies (octaves) of noise, more complex structures are generated. Previously, visually realistic appearances were achieved with continuous noise values, but these do not adequately represent the breast as radiologically consisting of two types of tissue - fibroglandular and adipose. A binary implementation with a similarly realistic appearance would therefore be preferable. A library of noise volumes with continuous values between 0 and 1 were generated. A range of threshold values, also between 0 and 1, were applied to these noise volumes, creating binary volumes of different appearance, with high values resulting in a fine network of strands, and low values in nebulous clusters of tissue. These building blocks were then combined into composite volumes and a new threshold applied to make them binary. This created visually complex binary volumes with a visually more realistic appearance than earlier implementations of the algorithm. By using different combinations of threshold values, a library of pre-generated building blocks can be used to create an arbitrary number of software breast tissue volumes with desired appearance and density.
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| 9. |
- Dustler, Magnus, et al.
(författare)
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High-attenuation artifact reduction in breast tomosynthesis using a novel reconstruction algorithm
- 2019
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Ingår i: European Journal of Radiology. - : Elsevier BV. - 0720-048X. ; 116, s. 21-26
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To assess the effect on reducing the out-of-plane artifacts from metal objects in breast tomosynthesis (BT)using a novel artifact-reducing reconstruction algorithm in specimen radiography. Methods and Materials: The study was approved by the Regional Ethical Review Board. BT images of 18 partial- and whole mastectomy specimens from women with breast cancer were acquired before and after a needle was inserted close to the lesion. The images were reconstructed using both a standard reconstruction algorithm, and a novel algorithm; the latter uses pre-segmentation to remove highly attenuating artifact-inducing objects from projection images before reconstruction. Images were separately reconstructed with and without segmentation, and combined into an artifact-reduced reconstruction. Standard and artifact-reduced BT-algorithms were compared visually and quantitatively using clinical images of mastectomy specimens and a physical anthropomorphic phantom. Six readers independently assessed the visibility of the lesion with and without artifact-reduction in a side-by-side comparison. A quantitative analysis was performed, comparing the signal-difference to background ratio (SDBR)and artifact spread function (ASF)between the two reconstruction methods. Results: The magnitude of out-of-plane artifacts was clearly reduced with the novel reconstruction compared to BT-images without artifact reduction. Lesion masking by artifacts was largely averted; tumour visibility was comparable to standard BT images without a needle. In 76 ± 8% (standard deviation)of cases overall, readers could confidently state needle location. The same figure was 94 ± 6% for whole mastectomy cases, compared to 62 ± 17% for partial mastectomies. With metal artifact reduction, SDBR increased by 97% in the phantom, and by 69% in the mastectomies. The artifact spread function was substantially narrower. Conclusion: Artifact reduction in BT using a novel reconstruction method enables qualitatively and quantitatively improved clinical use of BT when metal artifacts can be a limiting factor such as in tomosynthesis-guided biopsy.
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| 10. |
- Dustler, Magnus, et al.
(författare)
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Realism of mammography tissue patches simulated using perlin noise : A forced choice reading study
- 2021
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Ingår i: Medical Imaging 2021 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510640191 ; 11595
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Konferensbidrag (refereegranskat)abstract
- Software breast phantoms are central to the optimization of breast imaging, where in many cases the use of real images would be inefficient - or impossible. Establishing the realism of such phantoms is critical. For this study, patches of simulated breast tissue with different composition - fatty, scattered, heterogenous and dense tissue - were generated using a method based on Perlin noise. The composition of the patches is controlled by numerical parameters derived from input by radiologists and medical physicists with experience of breast imaging. Separate Perlin noise-based methods were used to simulate skin pores, high-frequency noise (representing quantum and electronic noise) and ligaments and vascular structures. In a forced choice reading study, the realism of the simulated tissue patches compared to patches from real mammograms was determined. Patches of 200-500 pixels were extracted from radiolucent, linear, nodular or homogenous (10 per category) mammograms randomly selected from a previously acquired dataset. Eighteen simulated patches in the same size range were added. Four readers, two radiologists and two medical physicists were shown the images in random order and asked to rate them as real or simulated. All readers accepted a substantial fraction of simulated images as real, ranging from 22% to 72%. Only two readers showed a significant difference in the number of images rated real in the real and simulated groups, 22% vs 73% (P=.0003) and 33% vs 63% (P=.04), respectively. These results suggest that the method employed can create images that are almost indistinguishable from patches of real mammograms.
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