The circular Radon transform (CRT) is widely employed as an imaging model for wave-based tomographic bioimaging modalities like ultrasound reflectivity tomography. A complete set of CRT data function is known to have redundancies. However, no explicit non-iterative image reconstruction method is known for inverting temporally-truncated data. To address this, a learning-based approach is proposed to establish a filtered backprojection (FBP) method for use with the half-time CRT data function. The proposed method approximates a mapping that is known to exist in theory; therefore, it is fundamentally different than many deep-learning based reconstruction methods that seek to establish a non-existent mapping. Thus, the proposed method performs well on unforeseen data. The learned half-time FBP achieves image quality comparable to a conventional full-time FBP method although it uses half of the complete data.