Image reconstruction is the manipulation of digitized information obtained during body imaging into interpretable pictures that represent anatomical details and diseases. It is a vital step in producing visual results from a Computed Tomography (CT), Magnetic Image Resonance (MRI) etc. scanning devices. In all modalities (CT, MRI etc.) the data is acquired in form of slices or projections. In simple terms, the process of putting the projections together in a two-dimensional (2D) or three dimensional (3D) scans can be called image reconstruction. Back projection and forward projections are two commonly used operations in the image reconstruction pipeline. And since the image reconstruction algorithms can be compute intensive, there is desire to fasten the processing time for it, to get the final scan faster. The scans are what a medical professional such as a radiologist use to provide diagnosis to a patient, hence faster access to scan can result in more timely diagnosis.  With the goal to reduce the processing time for image reconstruction pipeline, the authors worked to analyze and optimize representative back projection and forward projection algorithms. This paper provides details on the various optimization and code migration work that was done to achieve targeted performance metrics using a hardware vendor neutral programming language.