Generalized Filtered Back-Projection Reconstruction in Breast Tomosynthesis

2006 ◽  
pp. 167-174 ◽  
Author(s):  
Bernhard E. H. Claus ◽  
Jeffrey W. Eberhard ◽  
Andrea Schmitz ◽  
Paul Carson ◽  
Mitchell Goodsitt ◽  
...  
Keyword(s):  
Back Projection ◽  
Filtered Back Projection ◽  
Projection Reconstruction ◽  
Breast Tomosynthesis

Generalized filtered back-projection for digital breast tomosynthesis reconstruction

2012 ◽  
Author(s):  
Klaus Erhard ◽  
Michael Grass ◽  
Sebastian Hitziger ◽  
Armin Iske ◽  
Tim Nielsen
Keyword(s):  
Digital Breast Tomosynthesis ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Breast Tomosynthesis

An investigation of filter choice for filtered back-projection reconstruction in PET

1998 ◽  
Vol 45 (3) ◽  
pp. 1133-1137 ◽  
Author(s):  
T.H. Farquhar ◽  
A. Chatziioannou ◽  
G. Chinn ◽  
M. Dahlbom ◽  
E.J. Hoffman
Keyword(s):  
Back Projection ◽  
Filtered Back Projection ◽  
Projection Reconstruction

Simulation Analysis of the Sensitivity in Magnetic Induction Tomography Based on Perturbation Theory

2013 ◽  
Vol 749 ◽  
pp. 371-376
Author(s):  
Yang Xuan ◽  
Xu Wang ◽  
Cheng An Liu ◽  
Dan Yang
Keyword(s):  
Perturbation Theory ◽  
Salt Solution ◽  
Magnetic Induction ◽  
Imaging Modality ◽  
Reconstruction Algorithm ◽  
Target Object ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Projection Reconstruction ◽  
Magnetic Induction Tomography

Magnetic induction tomography (MIT) is a noninvasive and contactless imaging modality which aims at the reconstruction of the electrical conductivity in objects from alternating magnetic fields. Filtered back projection reconstruction algorithm is widely used in biomedical imaging field, and tried to use in MIT. Finite element analysis model has been established based on Scharfetter coil-coil model and perturbation theory, then simulated coaxial coil system by ANSYS software, the perturbation aroused by a target object moving on vertical coil axis. The sensitivity of a target object moves in vacuum and a salt solution were calculated respectively, the characteristics of the perturbation sensitivity in a salt solution were analyzed. The conditions of filtered back projection reconstruction algorithm in MIT were discussed.

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.

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