Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Heidelberg University Mannheim, Mannheim, Germany.
National Academy of Science of Belarus, Institute of Applied Physics, Minsk, Belarus.
Br J Radiol. 2019 Nov;92(1103):20190345. doi: 10.1259/bjr.20190345. Epub 2019 Sep 5.
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).
Thirty-two clinical DBT data sets with malignant and benign findings, = 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.
For masses, the image quality of HBI reconstructions was superior to that of FBP in terms of conspicuity,clarity of lesion borders and spicules ( < 0.01). HBI and FBP were not significantly different in calcification conspicuity. Overall, HBI reduced noise and supressed artifacts surrounding the lesions better ( < 0.01). The visibility of fibroglandular parenchyma increased using the HBI method ( < 0.01). On average, five cases per radiologist were downgraded from BI-RADS breast density category C/D to A/B.
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.
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.
比较两种重建方法（广泛使用的滤波反投影（FBP）重建和迭代启发式贝叶斯推理重建（贝叶斯推理重建加全变分应用方法，HBI））的图像质量和乳腺密度。
使用 FBP 和 HBI 对 32 个有恶性和良性发现的临床 DBT 数据集进行重建，恶性发现 = 27，良性发现 = 17。三名有经验的放射科医生使用 5 分视觉分级量表独立评估图像，并根据美国放射学院乳腺成像报告和数据系统图谱，第五版对乳腺密度进行分类。图像质量指标包括病灶的显著性、病灶边界和刺突的清晰度、噪声水平、病灶周围的伪影、实质和乳腺密度的可见度。
对于肿块，HBI 重建的图像质量在显著性、病灶边界和刺突的清晰度方面优于 FBP（ < 0.01）。在钙化显著性方面，HBI 和 FBP 没有显著差异。总体而言，HBI 可以更好地降低噪声并抑制病灶周围的伪影（ < 0.01）。使用 HBI 方法，纤维腺体实质的可见度增加（ < 0.01）。平均每位放射科医生有 5 例从 BI-RADS 乳腺密度类别 C/D 降级为 A/B。
HBI 与 FBP 相比，显著提高了病灶的可见度。HBI 对乳腺实质的可见度增加，导致乳腺密度评分降低。应用 HBIR 算法应提高乳腺断层合成术的诊断性能，并减少致密乳腺患者额外成像的需求。
与广泛使用的滤波反投影（FBP）重建相比，迭代启发式贝叶斯推理（HBI）图像重建可显著提高乳腺断层合成术的图像质量，使乳腺癌的可见度更好，并降低感知的乳腺密度。应用 HBI 应提高乳腺断层合成术的准确性，并减少不必要的乳腺活检数量。它还可能降低患者的辐射剂量，这在筛查环境中尤为重要。
