Evaluation of a new image reconstruction method for digital breast tomosynthesis: effects on the visibility of breast lesions and breast density
| Autor: | Inge Hillman, Konstantinos Karalis, Juergen Hesser, Sergei Zolotarev, Dzmitry Stsepankou, Valeriy Vengrinovich, Tony Svahn, Julia Krammer |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Image quality Breast Neoplasms Image processing Iterative reconstruction Bayesian inference 030218 nuclear medicine & medical imaging 03 medical and health sciences Bayes' theorem 0302 clinical medicine parasitic diseases Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Computer vision Breast density Projection (set theory) Aged Breast Density Retrospective Studies Aged 80 and over Full Paper business.industry Visibility (geometry) Bayes Theorem General Medicine Middle Aged Tumor Burden 030220 oncology & carcinogenesis Female Artificial intelligence Artifacts business Algorithms Mammography |
| Zdroj: | Br J Radiol |
| ISSN: | 1748-880X 0007-1285 |
| DOI: | 10.1259/bjr.20190345 |
| Popis: | Objective: To compare image quality and breast density of two reconstruction methods, the widely-used filtered-back projection (FBP) reconstruction and the iterative heuristic Bayesian inference reconstruction (Bayesian inference reconstruction plus the method of total variation applied, HBI). Methods: Thirty-two clinical DBT data sets with malignant and benign findings, n = 27 and 17, respectively, were reconstructed using FBP and HBI. Three experienced radiologists evaluated the images independently using a 5-point visual grading scale and classified breast density according to the American College of Radiology Breast Imaging-Reporting And Data System Atlas, fifth edition. Image quality metrics included lesion conspicuity, clarity of lesion borders and spicules, noise level, artifacts surrounding the lesion, visibility of parenchyma and breast density. Results: For masses, the image quality of HBI reconstructions was superior to that of FBP in terms of conspicuity,clarity of lesion borders and spicules (p < 0.01). HBI and FBP were not significantly different in calcification conspicuity. Overall, HBI reduced noise and supressed artifacts surrounding the lesions better (p < 0.01). The visibility of fibroglandular parenchyma increased using the HBI method (p < 0.01). On average, five cases per radiologist were downgraded from BI-RADS breast density category C/D to A/B. Conclusion: HBI significantly improves lesion visibility compared to FBP. HBI-visibility of breast parenchyma increased, leading to a lower breast density rating. Applying the HBIR algorithm should improve the diagnostic performance of DBT and decrease the need for additional imaging in patients with dense breasts. Advances in knowledge: Iterative heuristic Bayesian inference (HBI) image reconstruction substantially improves the image quality of breast tomosynthesis leading to a better visibility of breast carcinomas and reduction of the perceived breast density compared to the widely-used filtered-back projection (FPB) reconstruction. Applying HBI should improve the accuracy of breast tomosynthesis and reduce the number of unnecessary breast biopsies. It may also reduce the radiation dose for the patients, which is especially important in the screening context. |
| Databáze: | OpenAIRE |
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