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.