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Deep Learning for Anatomical Brain M...

Lijun, An,

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Deep Learning for Anatomical Brain MRI Harmonization /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning for Anatomical Brain MRI Harmonization // An Lijun.
作者:	Lijun, An,
面頁冊數:	1 electronic resource (164 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
標題:	Tomography. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31777272
ISBN:	9798346775164

Deep Learning for Anatomical Brain MRI Harmonization /
Lijun, An,

Deep Learning for Anatomical Brain MRI Harmonization /An Lijun. - 1 electronic resource (164 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

Mega-analyses have significantly advanced neuroimaging research by increasing statistical power, enhancing generalizability, and detecting subtle effects. Mega-analyses work by combining large amounts of data from multiple datasets or sites. Harmonization is a crucial postprocessing step to reduce unwanted inter-site heterogeneity when combining MRI from multiple datasets. Most MRI harmonization algorithms do not explicitly consider predictive downstream applications, which might potentially limit downstream performance. Furthermore, many deep learning harmonization approaches overlook the incorporation of covariates. Since covariate distribution differences are unavoidable across datasets, neglecting covariates during harmonization can inadvertently remove covariate differences instead of reducing undesired inter-site heterogeneity. In this thesis, we focused on addressing the aforementioned research gaps.First, we proposed a goal-specific brain MRI harmonization framework goal-specific conditional variational auto-encoder (gcVAE). MRI harmonization is an ill-posed problem; gcVAE utilizes predictive downstream application performance to regularize the harmonization procedure. Our framework can be flexibly integrated with various deep learning based harmonization models, such as the recently proposed conditional variational autoencoder (cVAE) harmonization model. Using three large-scale datasets with a total of 2787 participants and 10085 anatomical T1 scans, we demonstrated that our approach outperformed existing approaches in preserving relevant biological information while removing site differences.Second, we proposed deep learning models, covariate variational autoencoder (coVAE) and residual batching effects harmonization (ResBat), that account for covariate distribution differences across datasets. coVAE extended cVAE by concatenating covariates with latent representations, while ResBat adopted a residual framework inspired by the classic combating batching effects when combining batches (ComBat) harmonization algorithm. ResBat preserved the effects of covariates using interpretable models and eliminated unwanted inter-site heterogeneity in residuals through a deep learning model. We found that coVAE introduced spurious associations between anatomical MRI and randomly shuffled covariates during harmonization, while ResBat effectively mitigated this false-positive issue. Furthermore, we found that ResBat outperformed a wide suite of classical and deep learning baselines on various evaluation metrics.Overall, this thesis emphasizes the importance of appropriately framing neuroimaging problems under the deep learning paradigm. Ignoring predictive downstream applications, neglecting covariates or improperly incorporating covariates might lead to unsatisfactory outcomes. By addressing these concerns, this thesis contributes to the advancement of neuroimaging harmonization methodology and highlights the importance of adequately modeling these factors in future harmonization studies.

English

ISBN: 9798346775164Subjects--Topical Terms:

836553
Tomography.

Deep Learning for Anatomical Brain MRI Harmonization /
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