This paper describes the development and use of multiscale, platelet-based image reconstruction algorithms in medical imaging. Such algorithms are effective because platelets approximate images in certain (piecewise) smoothness classes significantly more efficiently than sinusoids, wavelets, or wedgelets. Platelet representations are especially well-suited to the analysis of Poisson data, unlike most other multiscale image representations, and they can be rapidly computed. We present a fast, platelet-based maximum penalized likelihood algorithm that encompasses denoising, deblurring, and tomographic reconstruction and its applications to photon-limited imaging.