Abstract
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. 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. 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. 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.
Highlights
X-ray mammography is the best simple method to detect early stage breast cancer.[1]. Since it is a two-dimensional imaging modality, accuracy of x-ray mammography is limited by overlying tissue structure.[2,3,4]. This overlap effect can be reduced and in some cases essentially eliminated by using digital breast tomosynthesis (DBT) (Refs. 5–8) since this limited angle tomography enables a partial three-dimensional (3D) reconstruction
As we mentioned in Sec. 2.A.1, the DBT reconstruction using SART needs about 3 OS cycles, which means updating the value of each voxel 63 times
This study proposes a new method to improve the quality of DBT images
Summary
X-ray mammography is the best simple method to detect early stage breast cancer.[1]. since it is a two-dimensional imaging modality, accuracy of x-ray mammography is limited by overlying tissue structure.[2,3,4] This overlap effect can be reduced and in some cases essentially eliminated by using digital breast tomosynthesis (DBT) (Refs. 5–8) since this limited angle tomography enables a partial three-dimensional (3D) reconstruction. 5–8) since this limited angle tomography enables a partial three-dimensional (3D) reconstruction This reduction of overlap effects may result in earlier detection and better interpretation of breast cancer in dense breasts.[9,10,11] Projections are acquired by keeping the breast and detector stationary, while the x-ray source moves over a limited angle above the breast. Due to the limited-angle acquisition, the in-slice resolution is much higher than the resolution between slices (depth resolution).[9] Severe image artifacts radiate in directions parallel to the x-ray beam paths These artifacts result in lower lesion contrast and blurred borders of the lesions in off-focus planes that are not in the plane of the largest cross section of a lesion. This problem has been investigated extensively before, mainly focusing on the generation, the evaluation, and reduction of artifacts along the Z axis.[12,13,14,15,16] Perhaps almost as important, to lesion contrast and future quantitative accuracy, are fan-shaped artifacts that are present in planes well removed from the planes of large, high-contrast objects, but those artifacts are not addressed by this algorithm that we propose here
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