---
title: "Dublin City University Trains MRI to Spot Alzheimer's Without Expensive PET Scans"
subtitle: "A BiomedCLIP-based teacher learns from paired PET-MRI scans, then teaches an MRI-only student to flag amyloid buildup without expensive PET imaging."
url: "https://alphasignal.ai/news/dublin-city-university-trains-mri-to-spot-alzheimer-s-without-expensive-pet"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-04-14T00:00:00.000Z"
updated: "2026-08-23T21:59:50.605Z"
type: "Paper"
topics:
  - "Image"
  - "Development"
subtopics:
  - "Vision Language"
upvotes: 1094
source_url: "https://arxiv.org/abs/2604.12574"
comments: 0
truncated: true
---

# Dublin City University Trains MRI to Spot Alzheimer's Without Expensive PET Scans

> A BiomedCLIP-based teacher learns from paired PET-MRI scans, then teaches an MRI-only student to flag amyloid buildup without expensive PET imaging.

Detecting the amyloid-beta plaques that mark early Alzheimer's disease usually means booking a PET scan, which is expensive, radioactive, and unavailable in most clinics. A team from Dublin City University has now shown that a knowledge distillation setup can teach a standard MRI model to predict amyloid positivity on its own, hitting an [AUC of 0.74 on OASIS-3](https://arxiv.org/abs/2604.12574) and 0.68 on ADNI without any PET or clinical variables at inference time.

## Why PET has become a bottleneck

Amyloid PET is costly, invasive, and thin on the ground, which rules it out for population-level screening. With FDA approvals of lecanemab and donanemab (both of which require confirmed amyloid pathology before treatment), projected demand for amyloid PET is growing roughly 20-fold.

MRI is the obvious alternative: non-invasive, cheap, and already part of routine dementia workups. The catch is that MRI can't directly image amyloid, and prior MRI-only models have been mediocre. A 3D EfficientNet baseline from Kim et al. hit just AUC 0.61 on T1w scans. Models that do better tend to lean on non-imaging covariates like APOE genotype or cognitive scores, but those are missing in a huge fraction of real records. Routine APOE testing isn't recommended by ACMG guidelines and is absent for 26% of NACC participants, and cognitive measures are missing from 89% of EHR dementia records.

## The three-phase teacher-student setup

The core idea is cross-modal knowledge distillation. A BiomedCLIP-based teacher learns PET-MRI alignment through cross-modal attention and triplet contrastive learning with PET-informed (Centiloid-aware) online negative sampling. An MRI-only student then mimics the teacher through feature-level and logit-level distillation. BiomedCLIP is a Vision Transformer pretrained on 15 million biomedical image-text pairs, which the authors adapt using LoRA on the last six transformer blocks.

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## Sources

- [Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI](https://arxiv.org/abs/2604.12574)
- [arXiv HTML full paper](https://arxiv.org/html/2604.12574v1)
- [GitHub repository](https://github.com/FrancescoChiumento/pet-guided-mri-amyloid-detection)
- [Detecting Beta-Amyloid via Cross-Modal Knowledge Distillation from PET to MRI (MIUA 2025)](https://doras.dcu.ie/31156/1/MIUA2025_Francesco_Chiumento.pdf)