Emission Optical Projection Tomography (eO PT) is a relatively new imag- ing modality that bridges a gap between micro Magnetic Resonance Imag- ing and Confocal Laser Scanning Microscopy. eO PT can be used to image the anatomy and gene expression of intact biological specimens at high resolution and thus provides an alternative to time consuming methods such as serial sectioning. Tomographic image reconstruction for eOPT is currently performed using the Filtered Back Projection algorithm which, while being fast, does not account for the physics of image formation and thus can result in reconstructions of reduced resolution and questionable quantitative consistency. This thesis describes work that was done on eOPT in three areas, including image formation, tomographic reconstruction, and memory savings, the latter of which were required to bring implementation of 3D iterative reconstruction algorithms within reach for the relatively high-resolution eO PT imaging modality. In the area of image formation, measurements were taken to reveal the effects of optical blurring, diffraction and charge-coupled device (CCD) camera noise. Accurate models of each of these phenomena were developed and compared against the measurements. The subject of image reconstruction was first addressed with a modi- fication to the FBP algorithm designed to correct for the quantitative inaccuracies suspected of being introduced by the FBP algorithm when reconstructing specimens consisting of very fine detail. This was done by incorporating the quantitative aspects of the model of image formation into the FBP algorithm. The full model of image formation was incorpo- rated into the iterative Maximum Likelihood Expectation Maximisation (MLEM) algorithm. The third strand of this thesis focuses on various memory saving meth- ods developed to enable the implementation and testing of a variation of MLEM known as the Ordered Subsets Expectation Maximisation (OSEM). , Without such memory saving methods, the implementation of an iterative 3D reconstruction algorithm such as MLEM or OSEM using a full model of image formation would have remained beyond the capacity of modern computers for the foreseeable future, requiring several Terabytes of RAM. Comparisons were made between the quality of and the time required to produce FBP and OSEM reconstructions of the same data sets given the availability of limited computing resources. The feasibility of adopting OSEM reconstructions as an alternative to FBP reconstructions was dis- cussed, based on the use of currently available cutting edge computing hardware.