ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Generative diffusion model enables quantification of calibration-free arterial spin labeling perfusion magnetic resonance imaging data in an Alzheimer's disease cohort.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Generative diffusion model enables quantification of calibration-free arterial spin labeling perfusion magnetic resonance imaging data in an Alzheimer's disease cohort.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
introductionM0 images were missing in Siemens ASL data in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, prohibiting cerebral blood flow (CBF) quantification.
methodsA conditional latent diffusion model was trained and evaluated on in-house datasets, then applied to the Siemens data in ADNI-3. Regional CBF differences by Alzheimer's disease (AD) stages, their accuracy for AD classification, and CBF trajectory slopes were compared between generated data (Siemens) and acquired data (General Electric).
resultsThe diffusion model generated M0 images with high fidelity (SSIM = 0.918 ± 0.023, PSNR = 31.361 ± 2.537) and minimal CBF bias (mean difference is 0.21 ± 1.58 mL/100 g/min). Both generated and acquired CBF showed similar spatial patterns and decreasing trends with AD progression in specific AD-related regions. Generated CBF also improved accuracy in classifying AD stages compared to qualitative perfusion images.
conclusionThis study shows the potential of diffusion models for imputing missing modalities in large-scale studies exploring the use of ASL as a biomarker of AD. Highlights: Using latent diffusion model, we can generate M0 image from control image in arterial spin labeling (ASL) with high fidelity.The generated M0 can be used for cerebral blood flow (CBF) quantification in Alzheimer's Disease Neuroimaging Initiative dataset.The performance of classification between Alzheimer's disease (AD) patients and cognitive normal people is better when using generated CBF maps than using non-quantitative perfusion images.ASL CBF decreases with AD progression in key AD-related brain regions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.