scholarly journals Evaluation of a new image reconstruction method for digital breast tomosynthesis: effects on the visibility of breast lesions and breast density

2019 ◽  
Vol 92 (1103) ◽  
pp. 20190345 ◽  
Author(s):  
Julia Krammer ◽  
Sergei Zolotarev ◽  
Inge Hillman ◽  
Konstantinos Karalis ◽  
Dzmitry Stsepankou ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Image Reconstruction ◽  
Image Quality ◽  
Breast Density ◽  
Breast Imaging ◽  
Reconstruction Method ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Breast Tomosynthesis

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.

scholarly journals A Bayesian Hyperparameter Inference for Radon-Transformed Image Reconstruction

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Hayaru Shouno ◽  
Madomi Yamasaki ◽  
Masato Okada
Keyword(s):  
Bayesian Inference ◽  
Image Reconstruction ◽  
Prior Information ◽  
Additive White Gaussian Noise ◽  
Estimation Accuracy ◽  
Reconstruction Method ◽  
Model Framework ◽  
Poissonian Noise ◽  
Observation Noise

We develop a hyperparameter inference method for image reconstruction from Radon transform which often appears in the computed tomography, in the manner of Bayesian inference. Hyperparameters are often introduced in Bayesian inference to control the strength ratio between prior information and the fidelity to the observation. Since the quality of the reconstructed image is controlled by the estimation accuracy of these hyperparameters, we apply Bayesian inference into the filtered back-projection (FBP) reconstruction method with hyperparameters inference and demonstrate that the estimated hyperparameters can adapt to the noise level in the observation automatically. In the computer simulation, at first, we show that our algorithm works well in the model framework environment, that is, observation noise is an additive white Gaussian noise case. Then, we also show that our algorithm works well in the more realistic environment, that is, observation noise is Poissonian noise case. After that, we demonstrate an application for the real chest CT image reconstruction under the Gaussian and Poissonian observation noises.

AN IMPROVED 3DRP PET IMAGE RECONSTRUCTION METHOD WITH REBINNED DIRECT IMAGE ESTIMATE

2006 ◽  
Vol 18 (05) ◽  
pp. 237-245
Author(s):  
WEI-MIN JENG ◽  
HSUAN-HUI WANG
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Three Dimensional ◽  
Simulation Software ◽  
Direct Image ◽  
Projection Data ◽  
Reconstruction Method ◽  
Two Dimensional ◽  
Dimensional Image

The quality of traditional two-dimensional image reconstruction for PET has been efficiently improved by three-dimensional image reconstruction, but the sensitivity of the data and the quality of the image are restricted by the limit of modality physics. In analytical image reconstruction algorithm, 3DRP method compensates the unmeasured events by forward projection based on the initial direct image estimate. However, the original 3DRP method merely depends on the parallel projections without taking into account the oblique projections. In our proposed 3DRP-SSRB method, we improve the first image estimate by incorporating the rebinned oblique data. SSRB method was used to perform the rebinning operation to make uses of the oblique projection data to improve the sensitivity information. And then project the improved image estimate forward and reconstruct the final image. Conflicting parameters of reconstructed image quality of 3DRP are experimented by simulated three-dimensional phantom study with regard to both system sensitivity and image quality factors. PET simulation software package was used to conduct the experiment along with the MATLAB software to evaluate the effectiveness of two-dimensional FBP, 3DRP, and our proposed 3DRP-SSRB methods. The result demonstrated its better image quality by having better mean squared error numbers in most of output image slices.

scholarly journals Influence of Image Reconstruction Method on Image Quality in Digital Breast Tomosynthesis

2021 ◽  
Vol 77 (1) ◽  
pp. 14-22
Author(s):  
Mami Nishikawa ◽  
Kaori Tominaga ◽  
Tokitaka Ueno ◽  
Shiori Yasukawa ◽  
Kana Hiroshige ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Digital Breast Tomosynthesis ◽  
Reconstruction Method ◽  
Breast Tomosynthesis ◽  
Image Reconstruction Method
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