X-ray 'Histotomography' built on the basic principles of CT can be used to create 3D images of zebrafish at resolutions one thousand times greater than CT, enabling the visualization of cell nuclei and other subcellular structures in 3D. Noise in the scans caused either through natural Xray phenomena or other distortions can lead to low accuracy in tasks related to detection and segmentation of anatomically significant objects. We evaluate the use of supervised Encoder-Decoder models for noise removal in projection and reconstruction domain images in absence of clean training targets. We propose the use of a Noise-2-Noise architecture with U-Net backbone along with structural similarity index loss as an addendum to help maintain and sharpen pathologically relevant details. We empirically show that our technique outperforms existing methods, with an average peak signal to noise ratio (PSNR) gain of 14. 50dB and 15. 05dB for noise removal in the reconstruction domain when trained without and with clean targets respectively. Using the same network architecture, we obtain a gain in structural similarity index (SSIM) in the projection domain by an average of 0.213 when trained without clean targets and 0.259 with clean targets. Additionally, by comparing reconstructions from denoised projections with those from original projections, we establish that noise removal in the projection domain is beneficial to improve the quality of reconstructed scans.