MRI Software Advances

Our strength lies in enhancing the capabilities of Magnetic Resonance Imaging (MRI), which includes software developments, post-processing and artificial intelligence.


Through its national and international connections, Mātai is bringing together a number of health-related research projects through a biomedical imaging component. 

Biomedical imaging is instrumental to almost all aspects of medicine. Imaging procedures are tailored to the clinical problem, and is fundamental for diagnosis of a number of diseases  (i.e. stroke, neurodegenerative, musculoskeletal, cardiac, kidney, and liver disease).

Mātai’s strength lies in the development and deployment of novel image acquisition and image-processing techniques and protocols, for our further understanding of these diseases.

We will facilitate research groups and companies around New Zealand to make health-related discoveries, by providing access to these imaging protocols, technical skillsets, and through its clinical & scientific collaborations.


AI (also called deep learning, machine learning or artificial neural networks) is increasingly becoming a more error-free approach to diagnostics in medical imaging. Mātai will build AI capabilities in biomedical imaging to automatically detect and classify abnormalities.

A key area of research will be to identify which input data (for example which particular MR imaging contrast is a relevant biomarker for concussion) best enable deep learning methods to make accurate predictions.

AI offers a huge paradigm shift by supporting the radiologist to boost workflow efficiency and by improving care and patient throughput. Already it has been shown to be more effective than radiologists in the early detection of Multiple Sclerosis and Alzheimer’s disease based on MRI data.

A key area of research for Mātai Lab will be to use AI to identify which input data best enables AI to make accurate diagnostic predictions. For example, which particular MRI imaging contrasts are relevant biomarkers for concussion. We foresee this approach to both reduce the scan time needed to diagnose concussion and enable a faster, more accurate and early diagnosis. Another related area of research will be how to tailor existing AI approaches to best work with the data we obtain using the latest MRI technology.


With our cornerstone expertise in image processing Mātai will work on novel cloud-based image processing tools and predictive algorithms.

We will also collaborate with imaging vendors to build a cloud-based pipeline for advanced image processing and interpretation. Such products will be globally applicable for use in early and improved diagnosis of brain injury, including sports-related concussion.

Other tools we will develop will aid radiologists in image interpretation. This is a heavily underdeveloped area, with a need for image processing software that can register and post-process data from longitudinal studies – taking the burden off radiologists who currently must interpret a large amount of data on a daily basis.