title: Improved digital breast tomosynthesis images using automated ultrasound.
abstract: OBJECTIVE
  Digital breast tomosynthesis (DBT) offers poor image quality along the depth direction. This paper presents a new method that improves the image quality of DBT considerably through the a priori information from automated ultrasound (AUS) images.
  
  
  METHODS
  DBT and AUS images of a complex breast-mimicking phantom are acquired by a DBT/AUS dual-modality system. The AUS images are taken in the same geometry as the DBT images and the gradient information of the in-slice AUS images is adopted into the new loss functional during the DBT reconstruction process. The additional data allow for new iterative equations through solving the optimization problem utilizing the gradient descent method. Both visual comparison and quantitative analysis are employed to evaluate the improvement on DBT images. Normalized line profiles of lesions are obtained to compare the edges of the DBT and AUS-corrected DBT images. Additionally, image quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) are calculated to quantify the effectiveness of the proposed method.
  
  
  RESULTS
  In traditional DBT image reconstructions, serious artifacts can be found along the depth direction (Z direction), resulting in the blurring of lesion edges in the off-focus planes parallel to the detector. However, by applying the proposed method, the quality of the reconstructed DBT images is greatly improved. Visually, the AUS-corrected DBT images have much clearer borders in both in-focus and off-focus planes, fewer Z direction artifacts and reduced overlapping effect compared to the conventional DBT images. Quantitatively, the corrected DBT images have better ASF, indicating a great reduction in Z direction artifacts as well as better Z resolution. The sharper line profiles along the Y direction show enhancement on the edges. Besides, noise is also reduced, evidenced by the obviously improved SDNR values.
  
  
  CONCLUSIONS
  The proposed method provides great improvement on the quality of DBT images. This improvement makes it easier to locate and to distinguish a lesion, which may help improve the accuracy of the diagnosis using DBT imaging.
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Yuan
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China.
authors.forename: Sidan
authors.initials: S
authors.lastname: Du
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Oliver D
authors.initials: OD
authors.lastname: Kripfgans
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Xueding
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Paul L
authors.initials: PL
authors.lastname: Carson
authors.affiliation: School of Physics, Nanjing University, Nanjing 210093, China.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Liu
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging; D000465:Algorithms; D016477:Artifacts; D001940:Breast; D047688:Breast Cyst / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods; D008327:Mammography / Q000295:instrumentation / Q000379:methods*; D008954:Models, Biological; D019047:Phantoms, Imaging; D014056:Tomography, X-Ray / Q000295:instrumentation / Q000379:methods*; D016217:Ultrasonography, Mammary / Q000295:instrumentation / Q000379:methods*