To cope with Malta's rising population, two new MRI scanners are to be installed, according to a report posted on 18 January in the Times of Malta.
Health and Elderly Minister Jo Etienne Abela said that a new MRI scanner, funded by the National Social Development and Social Fund at a cost of €2 million, will be installed at St Vincent De Paul Residence, the government's largest home for the elderly. Another MRI machine is being procured for Gozo General Hospital. The move is expected to significantly reduce the island's waiting list for MRI scans, currently standing at 15,750 patients, which is double the figure from six months ago, he said.
The surge in demand for MRI scans is linked to the expanding population, Abela said, adding that the three existing MRI scanners at Mater Dei Hospital have been operating "around the clock, seven days a week." In response to the overwhelming demand, the government has also been outsourcing MRI services to the private sector.
To tackle the long waiting list, the new MRI scanner at St Vincent De Paul Residence will specifically focus on the diagnosis and treatment of elderly individuals, noted the Times of Malta. With the additional machine for Gozo General Hospital, located on the second largest island of the Maltese archipelago, the government hopes to spare patients the need to leave Gozo for scans.
Abela confirmed that the tendering process for Gozo General's machine had been completed, and a bidder had been selected. Meanwhile, the implementation of a portable MRI machine is to serve as a temporary solution, he said.
As highlighted by the Times of Malta in 2019, this initiative aligns with broader efforts to address lengthy waiting times for medical appointments after a study by the European Commission that noted an average waiting time of 40 weeks for Malta.



















![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)