Recently, to easily extend the helical field-of-view (FOV), the segmented helical computed tomography (SHCT) method was proposed, as well as the corresponding generalized backprojection filtration (G-BPF) type algorithm. Similar to the geometric relationship between helical and circular CT, SHCT just becomes full-scan multiple source-translation CT (F-mSTCT) when the pitch is zero and the number of scan cycles is one. The strategy of G-BPF follows the idea of the generalized Feldkamp approximate cone-beam algorithm for helical CT, i.e., using the F-mSTCT cone-beam BPF algorithm to approximately perform reconstruction for SHCT. The image quality is limited by the pitch size, which implies that satisfactory quality could only be obtained under the conditions of small pitches. To extend the analytical reconstruction for SHCT, an effective single-slice rebinning (SSRB) method for SHCT is investigated here. Transforming the SHCT cone-beam reconstruction into the virtual F-mSTCT fan-beam stack reconstruction task with low computational complexity, and then some techniques are developed to address the challenges involved. By using the basic BPF reconstruction with derivating along the detector (D-BPF), our experiments demonstrate that SSRB has fewer interlayer artifacts, higher z-resolution, more uniform in-plane resolution, and higher reconstruction efficiency compared to G-BPF. SSRB could promote the effective application of deep learning in SHCT reconstruction.