Image interpolation is an important operation that is widely used in medical imaging, image processing, and computer graphics. A variety of interpolation methods are available in the literature. However, their systematic evaluation is lacking. At a previous meeting, we presented a framework for the task independent comparison of interpolation methods based on a variety of medical image data pertaining to different parts of the human body taken from different modalities. In this new work, we present an objective, task-specific framework for evaluating interpolation techniques. The task considered is how the interpolation methods influence the accuracy of quantification of the total volume of lesions in the brain of Multiple Sclerosis (MS) patients. Sixty lesion detection experiments coming from ten patient studies, two subsampling techniques and the original data, and 3 interpolation methods is presented along with a statistical analysis of the results. This work comprises a systematic framework for the task-specific comparison of interpolation methods. Specifically, the influence of three interpolation methods in MS lesion quantification is compared.