A Better Way to Compare Images Taken at Different Institutions

Magnetic Resonance Imaging (MRI) is an essential tool for medical clinicians, providing detailed views of the inner of the human body as well as valuable information on pathologies.

However, The Variability of Image Acquisition Protocols Beteen Different Institutions Poshes Significant Challenges to Achieving Consistency and Reliable Interpretation, Particularly in Multi-Cener Research.

To solve this problem, the New Study has Been Carried by Dr. Gregory Lodygensky, the clinical Professor at Université de Montreal and Clinician-Research at Its affiliatd Sainte-Justine Hospital, with teacher-reset jose dolz and Christian disrosiers of the École de Technologie supérieureure (ETS).

Published in Medical Image Analysisthey Study Proposes Modifying Mris from Different Hospital Hospitals To Make Them More Similar, Enabling More Relable and Accurate Comparisons.

Harmonization of Mri Results is a Central Issue for Research and Health-Care Quality. Each Hospital, Clinic or Research Institute Has Own Private Mri Style, Depending on the Equipment, Imaging Protocols and Parameters They Use.

This leads to variable in contrast, brightness and other image characteristics, and poses to Major obstacle in clinical research when data from seven research centers are pooled.

Three Key Steps

Developed by Farzad Beizaee, The Study’s First Author and An ETS Doctoral Candidate, The New Harmonization Method Involves Three Key Steps:

• First, the model is created that “Learns” how images in the source timain (for example, mri images from the private machine at Sainte-Justine) are organized or distributed.
• Once the distribution of the source domain is well understood, the aim is to “re-format” mris from other centers to eliminate variations caused by changes in parameters or the use of another machine, while at the same time preserving inherent patiental.
• LASTLY, WHEN THE MODEL IS USED ON NEW Images (For Example, From an Unfamily Machine), It Must Adapt and Ensure That the New Images Still respect the Distribution It Learned in the First Stage.

To Validate Their Model, The Researchers Teded the New Approach on Mri Brain Images Held in Databases in the United States and From Neonatal Imaging Consortium Built in Collaboration with Researchers in Australia.

These data were used to perform two different tasks: Firstly, to segment brain images Into different parts in adults and newborns to check whether brain struture remained consistency before and after harmonization, and dryly, to estate brain act in newborns.

The Results Highlighted the Higher Performance of This Technique Compred with existing Harmonization Methods, Demonstating it adaptability for a variety of Tasks and Population Groups. NOTABLY, THE TOOL WAS SUCCESSFULLY VALIDATED ON THE MRI OF A NEWBORN’S BRAIN THIS HAD LESIONS, TASK THAT ALL ORHER AVAILABLE MODELS FAIL TO THE SINCE THEY ARE TRAINED ON HEALTHY 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 From Different Scanners,” Said 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 research, he and His team will exploit applying this approach on a larger scale, facilitating the comparison and analysis of research date and further improving the accuracy and relying of medical diagnosis.