Brain morphometry from contrast-enhanced T1-weighted brain MRIs

Description

Cancer remains the second leading cause of death in the US. However, recent advancements have increased cancer survivorship, now numbering tens of millions. Given this, there is tremendous interest in studying cancer-related cognitive impairment (CRCI) and CRCI due to chemotherapy or “chemobrain”, can afflict up to 78% of survivors. The neural substrates of CRCI are unknown and understanding this may improve survivors’ quality of life. The CRCI neuroimaging literature is still in its infancy and these studies have used small sample sizes from traditional research-dedicated nCE scans. Because conducting well-powered neuroimaging studies is very expensive, adapting clinical CE T1w scans could prove useful for CRCI and many other diseases like dementia. The promary objective of this project is to develop a novel deep learning method to generate nCE images from acquired CE T1w scans to allow accurate brain morphometry and be a plentiful source of inexpensive neuroimaging data.

Students

• Ming Lyu

• Danielle Sim

• Devansh Shah

• Ting Fung Lam

Advisors

• Mark Shiroishi

• Neda Jahanshad

• Paul Thompson

Skills Required by the team

• AI
• Familiarity with MRIs