Toronto Radiology

10/18/2025

This study authored by Toronto Radiology's Dr. Suradech Suthiphosuwan and co-led by Drs. Timothy Reynold Lim and Dr. Jiwon Oh, aims to see whether people with Radiologically Isolated Syndrome (RIS)—a condition where brain scans show signs of multiple sclerosis (MS) but the person has no symptoms—have changes in a specific brain structure called the choroid plexus (ChP). The researchers wanted to know:
1. Is the ChP larger in people with RIS?
2. Is a larger ChP linked to other signs of brain damage or inflammation?
3. Can ChP size help predict who with RIS might later develop MS symptoms?

Link to article: https://rpb.li/H7YMy

10/04/2025

It is with great sadness and profound respect that we inform the healthcare community of the passing of our esteemed colleague, Dr. Walter Montanera. Anyone who had the privilege of crossing paths with Walter knows how much of an inspiration he was. A colleague, a mentor, a caregiver, a trailblazer, a friend. He will forever be etched in our hearts.

https://radiology.to/in-memoriam-dr-walter-montanera/..

07/28/2025

The neuroradiology division of Toronto Radiology has published a guide on the utilization of low field portable MRI in a clinical setting. The aim of this guide is to equip current and prospective users of the Hyperfine Swoop® MRI system with practical strategies to maximize image quality, ensure accurate interpretation, and navigate common pitfalls with confidence.

Their findings show that low-field portable MRI offers a valuable bedside neuroimaging solution for critically ill or non-transportable patients but an understanding of its limitations is key to its successful implementation.

https://radiology.to/publications/tips-and-challenges-for-clinical-use-and-interpretation-of-low-field-portable-mri-in-neuroimaging/

.Michael'sHospital

06/28/2025

We are thrilled to announce the winners of the 2025 St. Michael’s Hospital Department of Medical Imaging physician awards. These prestigious awards, determined by the votes of peers and colleagues, honour exceptional contributions in the fields of research and teaching. This year’s recipients, Dr. Amy Chen and Dr. Guan Huang, have demonstrated remarkable dedication and innovation in their respective areas.

https://radiology.to/celebrating-excellence-in-medical-imaging-2025-award-winners/

.MichaelsHospital

06/20/2025

Toronto Radiology is proud to present this new publication, led by Drs. Errol Colak and Anish Kirpalani, where they aim to validate the effectiveness of an RSNA 2023 Abdominal Trauma AI Challenge award-winning machine learning model for detecting splenic injuries on CT scans.

https://radiology.to/publications/external-validation-of-an-rsna-2023-abdominal-trauma-ai-challenge-high-performing-machine-learning-model-in-the-detection-and-grading-of-splenic-injuries-on-ct/..

05/26/2025

The latest publication from Toronto Radiology's Dr. Matias F. Callejas and Dr. Djeven Deva, aimed to assess the prevalence and characteristics of pericardial calcification in adult patients with cystic fibrosis.

https://radiology.to/publications/association-between-cystic-fibrosis-and-pericardial-calcification-detected-at-chest-ct-in-adults/....

Purpose of the StudyThis study aimed to assess the prevalence and characteristics of pericardial calcification in adult patients with cystic fibrosis (CF),

05/12/2025

Don't miss the latest publication by Toronto Radiology's Drs. Amy Chen and Errol Colak, evaluating the use of machine learning to detect cervical spine fractures on CT that radiologists may miss. Our results underscore the potential for AI to serve as a safety net in diagnostic imaging, particularly in high-risk trauma settings.

Proud to share this collaborative work with colleagues from across institutions. Read the full publication here:

🔗 https://radiology.to/publications/machine-learning-to-detect-cervical-spine-fractures-missed-by-radiologists-on-ct

03/29/2025

Follow along with Toronto Radiology's Dr. Amy Chen and her popular series which showcases neuroradiology cases from the Toronto area. See how well you do with this collection of interesting cases.

Find cases here: https://radiology.to/case-study/neuroradiology-case-of-the-week

03/17/2025

Join Us for an Insightful Neuroradiology Case Conference!

Date: Tuesday, March 18th
Time: 10 AM EST

This collaborative educational event as part of an ongoing monthly series brings radiologists from Boston, São Paulo, and Toronto together. We will explore complex neuroradiology cases, providing valuable insights and fostering a deeper understanding of the field. Don’t miss this opportunity to learn from experts and engage in meaningful discussions.

02/19/2025

Artificial intelligence has the potential to revolutionize gastrointestinal imaging, from improving disease detection to enhancing diagnostic accuracy. This comprehensive publication explores how AI is being applied in GI imaging, its current advancements, and the potential impact on patient care.

Learn more details about the publication here: https://radiology.to/publications/artificial-intelligence-in-gastrointestinal-imaging-advances-and-applications/

02/14/2025

Join us for our monthly International Neuroradiology Case Conference!
Date: Tuesday, February 18th
Time: 9 AM EST

This collaborative educational event as part of an ongoing monthly series brings radiologists from São Paulo, Toronto and Boston together. We will explore complex neuroradiology cases, providing valuable insights and fostering a deeper understanding of the field. Don’t miss this opportunity to learn from experts and engage in meaningful discussions.

Follow the link in the poster to join!

02/10/2025

🔍 Check out this publication by Toronto Radiology!

"What is Explainable AI in Radiology?" explores how AI models make decisions in medical imaging and why Explainable AI (XAI) is essential for accuracy, trust, and clinical adoption.

Discover how XAI is shaping the future of radiology and empowering clinicians with deeper insights.

📖 Read the publication here: https://radiology.to/publications/what-is-explainable-ai-in-radiology/