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Article ; Online: Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI.

Carass, Aaron / Greenman, Danielle / Dewey, Blake E / Calabresi, Peter A / Prince, Jerry L / Pham, Dzung L

Neuroimage. Reports

2024 Volume 4, Issue 1

Abstract: Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) ...

Abstract	Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.
Language	English
Publishing date	2024-02-02
Publishing country	Netherlands
Document type	Journal Article
ISSN	2666-9560
ISSN (online)	2666-9560
DOI	10.1016/j.ynirp.2024.100195
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep gray matter substructure volumes and depressive symptoms in a large multiple sclerosis cohort.

Hu, Chen / Dewey, Blake E / Mowry, Ellen M / Fitzgerald, Kathryn C

Multiple sclerosis (Houndmills, Basingstoke, England)

2023 Volume 29, Issue 7, Page(s) 809–818

Abstract: Background: Consistent findings on underlying brain features or specific structural atrophy patterns contributing to depression in multiple sclerosis (MS) are limited.: Objective: To investigate how deep gray matter (DGM) features predict depressive ... ...

Abstract	Background: Consistent findings on underlying brain features or specific structural atrophy patterns contributing to depression in multiple sclerosis (MS) are limited. Objective: To investigate how deep gray matter (DGM) features predict depressive symptom trajectories in MS patients. Methods: We used data from the MS Partners Advancing Technology and Health Solutions (MS PATHS) network in which standardized patient information and outcomes are collected. We performed whole-brain segmentation using SLANT-CRUISE. We assessed if DGM structures were associated with elevated depressive symptoms over follow-up and with depressive symptom phenotypes. Results: We included 3844 participants (average age: 46.05 ± 11.83 years; 72.7% female) of whom 1905 (49.5%) experienced ⩾1 periods of elevated depressive symptoms over 2.6 ± 0.9 years mean follow-up. Higher caudate, putamen, accumbens, ventral diencephalon, thalamus, and amygdala volumes were associated with lower odds of elevated depressive symptoms over follow-up (odds ratio (OR) range per 1 Conclusion: Lower DGM volumes were associated with depressive symptom trajectories in MS.
MeSH term(s)	Female ; Male ; Humans ; Multiple Sclerosis/diagnostic imaging ; Multiple Sclerosis/pathology ; Gray Matter/diagnostic imaging ; Gray Matter/pathology ; Depression/etiology ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; Brain/pathology ; Atrophy/pathology
Language	English
Publishing date	2023-01-24
Publishing country	England
Document type	Journal Article
ZDB-ID	1290669-4
ISSN	1477-0970 ; 1352-4585
ISSN (online)	1477-0970
ISSN	1352-4585
DOI	10.1177/13524585221148144
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension.

Park, Woo Yeon / Jeon, Kyulee / Schmidt, Teri Sippel / Kondylakis, Haridimos / Alkasab, Tarik / Dewey, Blake E / You, Seng Chan / Nagy, Paul

Journal of imaging informatics in medicine

2024 Volume 37, Issue 2, Page(s) 899–908

Abstract: The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying ...

Abstract	The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.
Language	English
Publishing date	2024-02-05
Publishing country	Switzerland
Document type	Journal Article
ISSN	2948-2933
ISSN (online)	2948-2933
DOI	10.1007/s10278-024-00982-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: A latent space for unsupervised MR image quality control via artifact assessment

Zuo, Lianrui / Xue, Yuan / Dewey, Blake E. / Liu, Yihao / Prince, Jerry L. / Carass, Aaron

2023

Abstract: Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images. Existing IQC methods for MR imaging generally require human effort to craft ... ...

Abstract	Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images. Existing IQC methods for MR imaging generally require human effort to craft meaningful features or label large datasets for supervised training. The involvement of human labor can be burdensome and biased, as labeling MR images based on their quality is a subjective task. In this paper, we propose an automatic IQC method that evaluates the extent of artifacts in MR images without supervision. In particular, we design an artifact encoding network that learns representations of artifacts based on contrastive learning. We then use a normalizing flow to estimate the density of learned representations for unsupervised classification. Our experiments on large-scale multi-cohort MR datasets show that the proposed method accurately detects images with high levels of artifacts, which can inform downstream analysis tasks about potentially flawed data. Comment: Accepted at the International Society for Optics and Photonics - Medical Imaging (SPIE-MI) 2023
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-02-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: AniRes2D

Wu, Zejun / Remedios, Samuel W. / Dewey, Blake E. / Carass, Aaron / Prince, Jerry L.

Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution

2023

Abstract: Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs) to super-resolve these 2D-acquired LR MR slices. This paper introduces ... ...

Abstract	Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs) to super-resolve these 2D-acquired LR MR slices. This paper introduces AniRes2D, a novel approach combining DDPM with a residual prediction for 2D super-resolution (SR). Results demonstrate that AniRes2D outperforms several other DDPM-based models in quantitative metrics, visual quality, and out-of-domain evaluation. We use a trained AniRes2D to super-resolve 3D volumes slice by slice, where comparative quantitative results and reduced skull aliasing are achieved compared to a recent state-of-the-art self-supervised 3D super-resolution method. Furthermore, we explored the use of noise conditioning augmentation (NCA) as an alternative augmentation technique for DDPM-based SR models, but it was found to reduce performance. Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images. Comment: Accepted for presentation at SPIE Medical Imaging 2024, Clinical and Biomedical Imaging
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-12-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Bile acid metabolites predict multiple sclerosis progression and supplementation is safe in progressive disease.

Ladakis, Dimitrios C / Harrison, Kimystian L / Smith, Matthew D / Solem, Krista / Gadani, Sachin / Jank, Larissa / Hwang, Soonmyung / Farhadi, Farzaneh / Dewey, Blake E / Fitzgerald, Kathryn C / Sotirchos, Elias S / Saidha, Shiv / Calabresi, Peter A / Bhargava, Pavan

medRxiv : the preprint server for health sciences

2024

Abstract: Background: Bile acid metabolism is altered in multiple sclerosis (MS) and tauroursodeoxycholic acid (TUDCA) supplementation ameliorated disease in mouse models of MS.: Methods: Global metabolomics was performed in an observational cohort of people ... ...

Abstract	Background: Bile acid metabolism is altered in multiple sclerosis (MS) and tauroursodeoxycholic acid (TUDCA) supplementation ameliorated disease in mouse models of MS. Methods: Global metabolomics was performed in an observational cohort of people with MS followed by pathway analysis to examine relationships between baseline metabolite levels and subsequent brain and retinal atrophy. A double-blind, placebo-controlled trial, was completed in people with progressive MS (PMS), randomized to receive either TUDCA (2g daily) or placebo for 16 weeks. Participants were followed with serial clinical and laboratory assessments. Primary outcomes were safety and tolerability of TUDCA, and exploratory outcomes included changes in clinical, laboratory and gut microbiome parameters. Results: In the observational cohort, higher primary bile acid levels at baseline predicted slower whole brain, brain substructure and specific retinal layer atrophy. In the clinical trial, 47 participants were included in our analyses (21 in placebo arm, 26 in TUDCA arm). Adverse events did not significantly differ between arms (p=0.77). The TUDCA arm demonstrated increased serum levels of multiple bile acids. No significant differences were noted in clinical or fluid biomarker outcomes. Central memory CD4+ and Th1/17 cells decreased, while CD4+ naïve cells increased in the TUDCA arm compared to placebo. Changes in the composition and function of gut microbiota were also noted in the TUDCA arm compared to placebo. Conclusion: Bile acid metabolism in MS is linked with brain and retinal atrophy. TUDCA supplementation in PMS is safe, tolerable and has measurable biological effects that warrant further evaluation in larger trials with a longer treatment duration.
Language	English
Publishing date	2024-01-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.01.17.24301393
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Article: Joint Image and Label Self-Super-Resolution.

Remedios, Samuel W / Han, Shuo / Dewey, Blake E / Pham, Dzung L / Prince, Jerry L / Carass, Aaron

Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)

2021 Volume 12965, Page(s) 14–23

Abstract: We propose a method to jointly super-resolve an anisotropic image volume along with its corresponding voxel labels without external training data. Our method is inspired by internally trained superresolution, or self-super-resolution (SSR) techniques ... ...

Abstract	We propose a method to jointly super-resolve an anisotropic image volume along with its corresponding voxel labels without external training data. Our method is inspired by internally trained superresolution, or self-super-resolution (SSR) techniques that target anisotropic, low-resolution (LR) magnetic resonance (MR) images. While resulting images from such methods are quite useful, their corresponding LR labels-derived from either automatic algorithms or human raters-are no longer in correspondence with the super-resolved volume. To address this, we develop an SSR deep network that takes both an anisotropic LR MR image and its corresponding LR labels as input and produces both a super-resolved MR image and its super-resolved labels as output. We evaluated our method with 50
Language	English
Publishing date	2021-09-21
Publishing country	Switzerland
Document type	Journal Article
DOI	10.1007/978-3-030-87592-3_2
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Article ; Online: Autoencoder based self-supervised test-time adaptation for medical image analysis.

He, Yufan / Carass, Aaron / Zuo, Lianrui / Dewey, Blake E / Prince, Jerry L

Medical image analysis

2021 Volume 72, Page(s) 102136

Abstract: Deep neural networks have been successfully applied to medical image analysis tasks like segmentation and synthesis. However, even if a network is trained on a large dataset from the source domain, its performance on unseen test domains is not guaranteed. ...

Abstract	Deep neural networks have been successfully applied to medical image analysis tasks like segmentation and synthesis. However, even if a network is trained on a large dataset from the source domain, its performance on unseen test domains is not guaranteed. The performance drop on data obtained differently from the network's training data is a major problem (known as domain shift) in deploying deep learning in clinical practice. Existing work focuses on retraining the model with data from the test domain, or harmonizing the test domain's data to the network training data. A common practice is to distribute a carefully-trained model to multiple users (e.g., clinical centers), and then each user uses the model to process their own data, which may have a domain shift (e.g., varying imaging parameters and machines). However, the lack of availability of the source training data and the cost of training a new model often prevents the use of known methods to solve user-specific domain shifts. Here, we ask whether we can design a model that, once distributed to users, can quickly adapt itself to each new site without expensive retraining or access to the source training data? In this paper, we propose a model that can adapt based on a single test subject during inference. The model consists of three parts, which are all neural networks: a task model (T) which performs the image analysis task like segmentation; a set of autoencoders (AEs); and a set of adaptors (As). The task model and autoencoders are trained on the source dataset and can be computationally expensive. In the deployment stage, the adaptors are trained to transform the test image and its features to minimize the domain shift as measured by the autoencoders' reconstruction loss. Only the adaptors are optimized during the testing stage with a single test subject thus is computationally efficient. The method was validated on both retinal optical coherence tomography (OCT) image segmentation and magnetic resonance imaging (MRI) T1-weighted to T2-weighted image synthesis. Our method, with its short optimization time for the adaptors (10 iterations on a single test subject) and its additional required disk space for the autoencoders (around 15 MB), can achieve significant performance improvement. Our code is publicly available at: https://github.com/YufanHe/self-domain-adapted-network.
MeSH term(s)	Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Retina ; Tomography, Optical Coherence
Language	English
Publishing date	2021-06-19
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2021.102136
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Zs.A 5206: Show issues

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Article: Evaluating the Impact of Intensity Normalization on MR Image Synthesis.

Reinhold, Jacob C / Dewey, Blake E / Carass, Aaron / Prince, Jerry L

Proceedings of SPIE--the International Society for Optical Engineering

2019 Volume 10949

Abstract: Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, ... ...

Abstract	Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled-i.e., normalized-both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.
Language	English
Publishing date	2019-02-22
Publishing country	United States
Document type	Journal Article
ISSN	0277-786X
ISSN	0277-786X
DOI	10.1117/12.2513089
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Book ; Online: Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Zhang, Jinwei / Zuo, Lianrui / Dewey, Blake E. / Remedios, Samuel W. / Hays, Savannah P. / Pham, Dzung L. / Prince, Jerry L. / Carass, Aaron

2023

Abstract: Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their performance ...

Abstract	Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their performance across various sites or scanners, leading to domain generalization errors. While few-shot or one-shot domain adaptation emerges as a potential solution to mitigate generalization errors, its efficacy might be hindered by the scarcity of labeled data in the target domain. This paper seeks to tackle this challenge by integrating one-shot adaptation data with harmonized training data that incorporates labels. Our approach involves synthesizing new training data with a contrast akin to that of the test domain, a process we refer to as "contrast harmonization" in MRI. Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation. Notably, domain adaptation using exclusively harmonized training data achieved comparable or even superior performance compared to one-shot adaptation. Moreover, all adaptations required only minimal fine-tuning, ranging from 2 to 5 epochs for convergence.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing
Subject code	006
Publishing date	2023-10-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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