In this study, we investigate the performance of advanced 2D acquisition geometries--Pentagon and T-shaped--in digital breast tomosynthesis (DBT) and compare them against the conventional 1D geometry. Unlike the conventional approach, our proposed 2D geometries also incorporate anterior projections away from the chest wall. Implemented on the Next-Generation Tomosynthesis (NGT) prototype developed by X-ray Physics Lab (XPL), UPenn, we utilized various phantoms to compare three geometries: a Defrise slab phantom with alternating plastic slabs to study low-frequency modulation; a Checkerboard breast phantom (a 2D adaptation of the Defrise phantom design) to study the ability to reconstruct the fine features of the checkerboard squares; and the 360° Star-pattern phantom to assess aliasing and compute the Fourier-spectral distortion (FSD) metric that assesses spectral leakage and the contrast transfer function. We find that both Pentagon and T-shaped scans provide greater modulation amplitude of the Defrise phantom slabs and better resolve the squares of the Checkerboard phantom against the conventional scan. Notably, the Pentagon geometry exhibited a significant reduction in aliasing of spatial frequencies oriented in the right-left (RL) medio-lateral direction, which was corroborated by a near complete elimination of spectral leakage in the FSD plot. Conversely T-shaped scan redistributes the aliasing between both posteroanterior (PA) and RL directions thus maintaining non-inferiority against the conventional scan which is predominantly affected by PA aliasing. The results of this study underscore the potential of incorporating advanced 2D geometries in DBT systems, offering marked improvements in imaging performance over the conventional 1D approach.
The mathematical underpinnings of a novel reconstruction algorithm are presented that can facilitate 4D tomosynthesis for the purpose of guiding needle breast biopsies in real-time. Conventional tomosynthesis reconstruction algorithms produce motion artifacts when applied to a continuous tomosynthesis acquisition of a moving biopsy needle. The novel algorithm proposed in this work successfully overcomes this by using differences in slow-scan data to identify variational regions in the reconstructed volume, and adaptively reconstruct those regions to eliminate motion. The algorithm has been tested using simulated images, where reconstructed images of a moving needle had significantly better clarity than the conventional algorithm.
Tomosynthesis has become a vital interventional tool for breast biopsy procedures. It is used to orient, advance and confirm the biopsy needle’s movement. However, at the end of a procedure, success is determined only after the biopsy sample shows the presence of the targeted lesion. Contrarily, failures, such as a target miss, are realized only after healthy tissue has been incorrectly excised. If real-time 4D tomosynthesis is made possible, it could not only guide and confirm the needle advancement but also anticipate any inadvertent target displacement and prevent healthy tissue damage. This study explores three classes of novel reconstruction algorithms that facilitate real-time 4D tomosynthesis guided biopsy procedures namely, Image-Processed algorithm, Segmented algorithm and Difference-Exploiting algorithm. A conventional tomosynthesis reconstruction algorithm applied to an incrementally moving needle shows a blurred needle tip - a consequence of superimposing and averaging the back-projections where the tip exists at different positions. The Image-Processed algorithm contrast-enhances all the back-projections before reconstruction thereby curbing the blurring and producing a more discernible needle tip. Pixel-based Segmented and Difference-Exploiting algorithms reconstruct individual pixels differently. The Segmented algorithm uses only the latest back-projection to reconstruct the pixels of the needle thereby capturing its most recent position. The Difference-Exploiting algorithm utilizes the superimposed differences of back-projections that helps in selectively identifying those elements, like the moving needle, that show a variation. Reconstructing these elements differently compared to other static elements of the breast allows capturing them in real-time. This work details the formulation of the three algorithms.
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