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Magnetic resonance imaging (MRI) is an important instrument for medical clinicians, offering detailed views of the inside of the human physique in addition to useful data on pathologies.
Nonetheless, the variability of picture acquisition protocols between totally different establishments poses important challenges to reaching constant and dependable interpretation, notably in multi-center analysis.
To unravel this downside, a brand new examine has been carried by Dr. Gregory Lodygensky, a medical professor at Université de Montreal and clinician-researcher at its affiliated Sainte-Justine Hospital, with professor-researchers Jose Dolz and Christian Desrosiers of the École de technologie supérieure (ETS).
Revealed in Medical Picture Evaluation, their examine proposes modifying MRIs from totally different hospitals to make them extra comparable, enabling extra dependable and correct comparisons.
Harmonization of MRI outcomes is a central problem for analysis and health-care high quality. Every hospital, clinic or analysis institute has its personal specific MRI type, relying on the gear, imaging protocols and parameters they use.
This results in variability in distinction, brightness and different picture traits, and poses a significant impediment in medical analysis when information from a number of analysis facilities are pooled.
Three key steps
Developed by Farzad Beizaee, the examine’s first writer and an ETS doctoral candidate, the brand new harmonization methodology entails three key steps:
First, a mannequin is created that “learns” how photographs within the supply area (for instance, MRI photographs from a specific machine at Sainte-Justine) are organized or distributed.
As soon as the distribution of the supply area is properly understood, the intention is to “re-format” MRIs from different facilities to get rid of variations attributable to modifications in parameters or using one other machine, whereas on the identical time preserving inherent affected person variations.
Lastly, when the mannequin is used on new photographs (for instance, from an unfamiliar machine), it should adapt and be certain that the brand new photographs nonetheless respect the distribution it realized within the first stage.
To validate their mannequin, the researchers examined the brand new method on MRI mind photographs held in databases in the US and from a neonatal imaging consortium inbuilt collaboration with researchers in Australia.
These information have been used to carry out two totally different duties: firstly, to section mind photographs into totally different components in adults and newborns to examine whether or not mind construction remained constant earlier than and after harmonization, and secondly, to estimate mind age in newborns.
The outcomes highlighted the superior efficiency of this system in contrast with present harmonization strategies, demonstrating its adaptability for a wide range of duties and inhabitants teams. Notably, the instrument was efficiently validated on the MRI of a new child’s mind that had lesions, a process that every one different out there fashions fail to do since they’re skilled on photographs of wholesome brains.
“Thanks to this model, we can now interpret data from several thousands of families and children who are monitored at various hospitals—data that come from different scanners,” stated Lodygensky. “The analysis of these large cohorts in children and adults was hampered by the major harmonization problem, which has now been resolved.”
In future collaborations and analysis, he and his group will discover making use of this method on a bigger scale, facilitating the comparability and evaluation of analysis information and additional bettering the accuracy and reliability of medical diagnoses.
Extra data:
Farzad Beizaee et al, Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization, Medical Picture Evaluation (2025). DOI: 10.1016/j.media.2025.103483
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College of Montreal
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‘Harmonizing’ the MRIs: A greater approach to evaluate photographs taken at totally different establishments (2025, February 28)
retrieved 28 February 2025
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