At MIT’s preclinical setting (Preclinical Modeling, Imaging and Testing, PMIT), the available shared biomedical imaging instrumentation, such as magnetic resonance imaging (MRI) or x-ray micro-computed tomography (microCT) scanners, produces diverse and large data sets on a daily basis. The acquisition of an image can be fast or slow depending on the acquisition protocols and whether we are interested in a 2D slice, a 3D volume or a 4D dataset over time. The time from acquisition to visualization of the image heavily depends on the size of the dataset, the image reconstruction algorithm and the computing power available. Although image acquisition and visualization are typically tied to the manufacturer of each specific platform, image quantification is more user dependent and can suffer a significant computational burden when performing non-linear mathematical operations on a pixel-by-pixel basis over millions of high-resolution images. The quantification of an image, namely the extraction of precise numerical information from the image that is representative of a biological process tied to disease and therapy, can take days to derive for users that choose high-level, easy-to-use numerical analysis software. We will present a case study of vast improvements in quantitative image processing of large preclinical MRI datasets using Julia libraries and expand on PMIT’s efforts to develop a Julia-based platform for intelligent preclinical evaluation of therapeutics from their development at bench to their visualization in a living subject.