Multimodal image registration between pre-operative and intra-operative imaging enables the fusion of clinically important information during many surgical and interventional tasks. The registration of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images assists prostate biopsy and focal therapy, arguably having transformed prostate cancer patient care to a less invasive and more localized diagnostic, monitoring and treatment pathway. Though, even with great progress having been made by the community in the past two decades, challenges remain in this application. First, paired MRI and TRUS data from a sizable patient cohort are not routinely stored in clinical practice, and publicly-accessible data is scarce and low-quality. Second, annotating anatomical and pathological landmarks on both images - critical in representing corresponding locations for validation - requires expert domain knowledge and experience from multiple disciplines including urology, radiology and pathology. In addition to its prevalence-warranted clinical importance, this is also a unique application that saw a wide range of registration algorithms proposed and housed intriguing debates such as rigid-versus-nonrigid and FLE-versus-TRE. Both feature- and intensity-based classical methods and unsupervised or segmentation-driven learning methods have been innovated with some most technically interesting approaches in the field such as biomechanical regularisation and statistical motion modelling. The mu-Reg challenge aims to provide well-curated, yet real-world clinical data, with more than a hundred paired MR and TRUS images, annotated carefully by researchers and clinicians with more than 15 years of experience working with this application. The outcome of the challenge includes one of the first multimodal imaging data, facilitated with expert annotations for validation, for benchmarking advancement in registration methodology, as well as for future research in managing the most common non-skin cancer in men. --- The training and validation data may be used within the research remit of this challenge and in further research-related publications. The training and validation data are not to be used commercially. However, if the desired use is unclear, the organizers ask that those accessing the data refrain from further use or distribution outside of this challenge. --- Challenge information is accessible at: https://muregpro.github.io/