  Despite the ever-increasing interest in applying deep learning (DL) models to
medical imaging, the typical scarcity and imbalance of medical datasets can
severely impact the performance of DL models. The generation of synthetic data
that might be freely shared without compromising patient privacy is a
well-known technique for addressing these difficulties. Inpainting algorithms
are a subset of DL generative models that can alter one or more regions of an
input image while matching its surrounding context and, in certain cases,
non-imaging input conditions. Although the majority of inpainting techniques
for medical imaging data use generative adversarial networks (GANs), the
performance of these algorithms is frequently suboptimal due to their limited
output variety, a problem that is already well-known for GANs. Denoising
diffusion probabilistic models (DDPMs) are a recently introduced family of
generative networks that can generate results of comparable quality to GANs,
but with diverse outputs. In this paper, we describe a DDPM to execute multiple
inpainting tasks on 2D axial slices of brain MRI with various sequences, and
present proof-of-concept examples of its performance in a variety of evaluation
scenarios. Our model and a public online interface to try our tool are
available at: https://github.com/Mayo-Radiology-Informatics-Lab/MBTI
