  Machine unlearning is a promising paradigm for removing unwanted data samples
from a trained model, towards ensuring compliance with privacy regulations and
limiting harmful biases. Although unlearning has been shown in, e.g.,
classification and recommendation systems, its potential in medical
image-to-image translation, specifically in image recon-struction, has not been
thoroughly investigated. This paper shows that machine unlearning is possible
in MRI tasks and has the potential to benefit for bias removal. We set up a
protocol to study how much shared knowledge exists between datasets of
different organs, allowing us to effectively quantify the effect of unlearning.
Our study reveals that combining training data can lead to hallucinations and
reduced image quality in the reconstructed data. We use unlearning to remove
hallucinations as a proxy exemplar of undesired data removal. Indeed, we show
that machine unlearning is possible without full retraining. Furthermore, our
observations indicate that maintaining high performance is feasible even when
using only a subset of retain data. We have made our code publicly accessible.
