| 1. |
- Andersson, Jonas, 1975-, et al.
(författare)
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Estimating Patient Organ Dosewith Computed Tomography: A Review of Present Methodologyand Required DICOM Information : A Joint Report ofAAPM Task Group 246 and the European Federationof Organizations for Medical Physics (EFOMP)
- 2019
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Rapport (refereegranskat)abstract
- The purpose of this report is (1) to summarize the current state of the art in estimating organ doses from CT examinations and (2) to outline a road map for standardized reporting of essential parameters necessary for estimation of organ doses from CT imaging in the DICOM standard. To address these purposes, the report includes a comprehensive discussion of (1) the various metrics, concepts, and methods that may be used to achieve estimates of patient organ dose and (2) the DICOM standard for CT.This Joint Report of the American Association of Physicists in Medicine (AAPM) Task Group 246 and the European Federation of Organizations for Medical Physics (EFOMP) contains three major sections and an appendix. Section 2 (with additional material in the appendix) provides a review of basic CT dosimetry metrics, their uses and limitations in the context of organ dosimetry, and the DICOM information currently associated with parameters that affect CT dose metrics and, consequently, organ dose estimates. Section 3 provides an overview of present and emerging organ dose estimation methods reported in the literature, e.g., for the lens of the eye, breast tissue, colon, and skin. Finally, the report concludes with section 4, which provides a discussion on the sources and magnitudes of uncertainty for different organ dose estimation methods.Ongoing efforts to facilitate routine standardized estimation of patient organ doses from CT are dependent, in large part, on the availability of the DICOM Radiation Dose Structured Report (RDSR), which provides a host of information pertinent to radiation dose calculations. This report, therefore, includes detailed information on DICOM header content in CT images and how it can be used in organ dose estimation. The RDSR markedly expands the abilities of the clinical medical physicist to estimate doses at the patient, device, and protocol level
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| 2. |
- Boita, Joana, et al.
(författare)
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Development and content validity evaluation of a candidate instrument to assess image quality in digital mammography : A mixed-method study
- 2021
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Ingår i: European Journal of Radiology. - : Elsevier BV. - 0720-048X. ; 134
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality. Methods: Interviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*). Results: Twelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items. Conclusions: By following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.
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| 3. |
- Boita, Joana, et al.
(författare)
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How does image quality affect radiologists’ perceived ability for image interpretation and lesion detection in digital mammography?
- 2021
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 31:7, s. 5335-5343
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Boita, Joana, et al.
(författare)
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Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis
- 2021
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Ingår i: European Journal of Radiology. - : Elsevier BV. - 1872-7727 .- 0720-048X. ; 139
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo validate a candidate instrument, to be used by different professionals to assess image quality in digital mammography (DM), against detection performance results.MethodsA receiver operating characteristics (ROC) study was conducted to assess the detection performance in DM images with four different image quality levels due to different quality issues. Fourteen expert breast radiologists from five countries assessed a set of 80 DM cases, containing 60 lesions (40 cancers, 20 benign findings) and 20 normal cases. A visual grading analysis (VGA) study using a previously-described candidate instrument was conducted to evaluate a subset of 25 of the images used in the ROC study. Eight radiologists that had participated in the ROC study, and seven expert breast-imaging physicists, evaluated this subset. The VGA score (VGAS) and the ROC and visual grading characteristics (VGC) areas under the curve (AUCROC and AUCVGC) were compared.ResultsNo large differences in image quality among the four levels were detected by either ROC or VGA studies. However, the ranking of the four levels was consistent: level 1 (partial AUCROC: 0.070, VGAS: 6.77) performed better than levels 2 (0.066, 6.15), 3 (0.061, 5.82), and 4 (0.062, 5.37). Similarity between radiologists’ and physicists’ assessments was found (average VGAS difference of 10 %).ConclusionsThe results from the candidate instrument were found to correlate with those from ROC analysis, when used by either observer group. Therefore, it may be used by different professionals, such as radiologists, radiographers, and physicists, to assess clinically-relevant image quality variations in DM.
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| 5. |
- Moriakov, Nikita, et al.
(författare)
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Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
- 2019
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Ingår i: MEDICAL IMAGING 2019. - : SPIE-INT SOC OPTICAL ENGINEERING. - 9781510625440
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Konferensbidrag (refereegranskat)abstract
- Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular range gives rise to different artifacts, which manufacturers try to solve in several ways. In this study we propose an extension of the Learned Primal Dual algorithm for digital breast tomosynthesis. The Learned Primal-Dual algorithm is a deep neural network consisting of several 'reconstruction blocks', which take in raw sinogram data as the initial input, perform a forward and a backward pass by taking projections and back-projections, and use a convolutional neural network to produce an intermediate reconstruction result which is then improved further by the successive reconstruction block. We extend the architecture by providing breast thickness measurements as a mask to the neural network and allow it to learn how to use this thickness mask. We have trained the algorithm on digital phantoms and the corresponding noise-free/noisy projections, and then tested the algorithm on digital phantoms for varying level of noise. Reconstruction performance of the algorithms was compared visually, using MSE loss and Structural Similarity Index. Results indicate that the proposed algorithm outperforms the baseline iterative reconstruction algorithm in terms of reconstruction quality for both breast edges and internal structures and is robust to noise.
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| 6. |
- Rodriguez-Ruiz, Alejandro, et al.
(författare)
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Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study
- 2019
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 29:9, s. 4825-4832
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload. Methods and materials: A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis. Results: Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (− 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > − 0.05) for any threshold except at the extreme AI score of 9. Conclusion: It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload. Key Points: • There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer. • The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists’ breast cancer detection performance. • When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.
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| 7. |
- Rodriguez-Ruiz, Alejandro, et al.
(författare)
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One-view digital breast tomosynthesis as a stand-alone modality for breast cancer detection : do we need more?
- 2018
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 28:5, s. 1938-1948
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To compare the performance of one-view digital breast tomosynthesis (1v-DBT) to that of three other protocols combining DBT and mammography (DM) for breast cancer detection. Materials and methods: Six radiologists, three experienced with 1v-DBT in screening, retrospectively reviewed 181 cases (76 malignant, 50 benign, 55 normal) in two sessions. First, they scored sequentially: 1v-DBT (medio-lateral oblique, MLO), 1v-DBT (MLO) + 1v-DM (cranio-caudal, CC) and two-view DM + DBT (2v-DM+2v-DBT). The second session involved only 2v-DM. Lesions were scored using BI-RADS® and level of suspiciousness (1–10). Sensitivity, specificity, receiver operating characteristic (ROC) and jack-knife alternative free-response ROC (JAFROC) were computed. Results: On average, 1v-DBT was non-inferior to any of the other protocols in terms of JAFROC figure-of-merit, area under ROC curve, sensitivity or specificity (p>0.391). While readers inexperienced with 1v-DBT screening improved their sensitivity when adding more images (69–79 %, p=0.019), experienced readers showed similar sensitivity (76 %) and specificity (70 %) between 1v-DBT and 2v-DM+2v-DBT (p=0.482). Subanalysis by lesion type and breast density showed no difference among modalities. Conclusion: Detection performance with 1v-DBT is not statistically inferior to 2v-DM or to 2v-DM+2v-DBT; its use as a stand-alone modality might be sufficient for readers experienced with this protocol. Key points: • One-view breast tomosynthesis is not inferior to two-view digital mammography.• One-view DBT is not inferior to 2-view DM plus 2-view DBT.• Training may lead to 1v-DBT being sufficient for screening.
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| 8. |
- Rodriguez-Ruiz, Alejandro, et al.
(författare)
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Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography : Comparison With 101 Radiologists
- 2019
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 1460-2105 .- 0027-8874. ; 111:9, s. 916-922
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.
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| 9. |
- Sechopoulos, Ioannis, et al.
(författare)
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Radiation dosimetry in digital breast tomosynthesis : report of AAPM Tomosynthesis Subcommittee Task Group 223.
- 2014
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Ingår i: Medical physics. - : Wiley. - 2473-4209 .- 0094-2405. ; 41:9
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Tidskriftsartikel (refereegranskat)abstract
- The radiation dose involved in any medical imaging modality that uses ionizing radiation needs to be well understood by the medical physics and clinical community. This is especially true of screening modalities. Digital breast tomosynthesis (DBT) has recently been introduced into the clinic and is being used for screening for breast cancer in the general population. Therefore, it is important that the medical physics community have the required information to be able to understand, estimate, and communicate the radiation dose levels involved in breast tomosynthesis imaging. For this purpose, the American Association of Physicists in Medicine Task Group 223 on Dosimetry in Tomosynthesis Imaging has prepared this report that discusses dosimetry in breast imaging in general, and describes a methodology and provides the data necessary to estimate mean breast glandular dose from a tomosynthesis acquisition. In an effort to maximize familiarity with the procedures and data provided in this Report, the methodology to perform the dose estimation in DBT is based as much as possible on that used in mammography dose estimation.
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| 10. |
- Teuwen, Jonas, et al.
(författare)
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Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
- 2021
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415. ; 71
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Tidskriftsartikel (refereegranskat)abstract
- The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <±3%; dose <±20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.
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