Evidence map›Paper›PMID 39006427›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Diffusion model enables quantitative CBF analysis of Alzheimer's Disease.

Qinyang Shou, Steven Cen, Nan-Kuei Chen, John M Ringman, Junhao Wen, Hosung Kim, Danny Jj Wang, Alzheimer’s Disease Neuroimaging Initiative

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Qinyang ShouLaboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-3343-3895
Steven CenDepartment of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-7859-8909
Nan-Kuei ChenDepartment of Biomedical Engineering, University of Arizona.ORCID 0000-0001-6564-4219
John M RingmanDepartment of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-5429-0777
Junhao WenLaboratory of AI & Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0003-2077-3070
Hosung KimLaboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-2269-8644
Danny Jj WangLaboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-0840-7062
Alzheimer’s Disease Neuroimaging Initiative

Funding

Regulatory and Human Study Operations (RHSO) Core CU19AG065169 · NIA · UNIVERSITY OF ARIZONA · PI WORLEY, PAUL F · 2021 to 2025
$59.8M
USCADRC Diversity Supplement PachicanoP30AG066530 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HELENA Chang CHUI · 2020 to 2026
$27.8M
Laminar Perfusion ImagingR01EB032169 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Danny JJ WANG · 2021 to 2026
$2.5M
Multiband ASL for Neurodevelopment StudyR01EB028297 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, DANNY JJ · 2019 to 2023
$2.2M
Development of High-Speed and Quantitative Neuro MRI Technologies for Challenging Patient PopulationsR01NS102220 · NINDS · UNIVERSITY OF ARIZONA · PI CHEN, NAN-KUEI · 2018 to 2022
$2.1M
High Capacity, High Performance Storage System for NeuroscienceS10OD032285 · OD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2022 to 2022
$1.7M
BBB Permeability Imaging in CADASILR01NS114382 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, DANNY JJ · 2020 to 2024
$1.2M
NIA NIH HHS P30 AG066530NIA NIH HHS U19 AG065169NIBIB NIH HHS R01 EB028297NIBIB NIH HHS R01 EB032169NIH HHS S10 OD032285NINDS NIH HHS R01 NS102220NINDS NIH HHS R01 NS114382
6 · The paper itself

Abstract

Objectives: Cerebral blood flow (CBF) measured by arterial spin labeling (ASL) is a promising biomarker for Alzheimer's Disease (AD). ASL data from multiple vendors were included in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. However, the M0 images were missing in Siemens ASL data, prohibiting CBF quantification. Here, we utilized a generative diffusion model to impute the missing M0 and validated generated CBF data with acquired data from GE. Methods: A conditional latent diffusion model was trained to generate the M0 image and validate it on an in-house dataset ( Results: The trained diffusion model generated the M0 image with high fidelity (Structural similarity index, SSIM=0.924±0.019; peak signal-to-noise ratio, PSNR=33.348±1.831) and caused minimal bias in CBF values (mean difference in whole brain is 1.07±2.12ml/100g/min). Both generated and acquired CBF data showed similar differentiation patterns by AD stages, similar classification performance, and decreasing slopes with AD progression in specific AD-related regions. Generated CBF data also improved accuracy in classifying AD stages compared to qualitative perfusion data. Interpretation/Conclusion: This study shows the potential of diffusion models for imputing missing modalities for large-scale studies of CBF variation with AD.

Identifiers

PMID39006427
PMCPMC11245071

What OpenQuestion holds

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Registered trials

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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.