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Article ; Online: ESPRESO: An algorithm to estimate the slice profile of a single magnetic resonance image.

Han, Shuo / Remedios, Samuel W / Schär, Michael / Carass, Aaron / Prince, Jerry L

2023 Volume 98, Page(s) 155–163

Abstract: To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in- ... ...

Abstract	To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in-plane direction, and the through-plane resolution is in part characterized by the protocol's slice profile which acts as a through-plane point spread function (PSF). Although super-resolution (SR) has been shown to improve the visualization and down-stream processing of 2D multi-slice MR acquisitions, previous algorithms are usually unaware of the true slice profile, which may lead to sub-optimal SR performance. In this work, we present an algorithm to estimate the slice profile of a 2D multi-slice acquisition given only its own image volume without any external training data. We assume that an anatomical image is isotropic in the sense that, after accounting for a correctly estimated slice profile, the image patches along different orientations have the same probability distribution. Our proposed algorithm uses a modified generative adversarial network (GAN) where the generator network estimates the slice profile to reduce the resolution of the in-plane direction, and the discriminator network determines whether a direction is generated or real low resolution. The proposed algorithm, ESPRESO, which stands for "estimating the slice profile for resolution enhancement of a single image only", was tested with a state-of-the-art internally supervised SR algorithm. Specifically, ESPRESO is used to create training data for this SR algorithm, and results show improvements when ESPRESO is used over commonly-used PSFs.
MeSH term(s)	Magnetic Resonance Imaging/methods ; Algorithms ; Phantoms, Imaging ; Radionuclide Imaging ; Image Processing, Computer-Assisted
Language	English
Publishing date	2023-01-24
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	604885-7
ISSN	1873-5894 ; 0730-725X
ISSN (online)	1873-5894
ISSN	0730-725X
DOI	10.1016/j.mri.2023.01.012
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Article ; Online: No ultrasounds detected from fungi when dehydrated.

Phillips, Neil / Remedios, Samuel W / Nikolaidou, Anna / Baracskai, Zlatko / Adamatzky, Andrew

Ultrasonics

2023 Volume 135, Page(s) 107111

Abstract: Many organisms (including certain plant species) can be observed to emit sounds, potentially signifying threat alerts. Sensitivity to such sounds and vibrations may also play an important role in the lives of fungi. In this work, we explore the potential ...

Abstract	Many organisms (including certain plant species) can be observed to emit sounds, potentially signifying threat alerts. Sensitivity to such sounds and vibrations may also play an important role in the lives of fungi. In this work, we explore the potential of ultrasound activity in dehydrating fungi, and discover that several species of fungi do not emit sounds (detectable with conventional instrumentation) in the frequency range of 10kHz to 210kHz upon dehydration. Over 5 terabytes of ultrasound recordings were collected and analysed. We conjecture that fungi interact via non-sound means, such as electrical or chemical.
MeSH term(s)	Sound ; Vibration ; Fungi ; Ultrasonography
Language	English
Publishing date	2023-07-31
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	200839-7
ISSN	1874-9968 ; 0041-624X
ISSN (online)	1874-9968
ISSN	0041-624X
DOI	10.1016/j.ultras.2023.107111
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Article: Efficient Quality Control with Mixed CT and CTA Datasets.

Remedios, Lucas W / Cai, Leon Y / Hansen, Colin B / Remedios, Samuel W / Landman, Bennett A

Proceedings of SPIE--the International Society for Optical Engineering

2022 Volume 12032

Abstract: Deep learning promises the extraction of valuable information from traumatic brain injury (TBI) datasets and depends on efficient navigation when using large-scale mixed computed tomography (CT) datasets from clinical systems. To ensure a cleaner signal ... ...

Abstract	Deep learning promises the extraction of valuable information from traumatic brain injury (TBI) datasets and depends on efficient navigation when using large-scale mixed computed tomography (CT) datasets from clinical systems. To ensure a cleaner signal while training deep learning models, removal of computed tomography angiography (CTA) and scans with streaking artifacts is sensible. On massive datasets of heterogeneously sized scans, time-consuming manual quality assurance (QA) by visual inspection is still often necessary, despite the expectation of CTA annotation (artifact annotation is not expected). We propose an automatic QA approach for retrieving CT scans without artifacts by representing 3D scans as 2D axial slice montages and using a multi-headed convolutional neural network to detect CT vs CTA and artifact vs no artifact. We sampled 848 scans from a mixed CT dataset of TBI patients and performed 4-fold stratified cross-validation on 698 montages followed by an ablation experiment-150 stratified montages were withheld for external validation evaluation. Aggregate AUC for our main model was 0.978 for CT detection, 0.675 for artifact detection during cross-validation and 0.965 for CT detection, 0.698 for artifact detection on the external validation set, while the ablated model showed 0.946 for CT detection, 0.735 for artifact detection during cross-validation and 0.937 for CT detection, 0.708 for artifact detection on the external validation set. While our approach is successful for CT detection, artifact detection performance is potentially depressed due to the heterogeneity of present streaking artifacts and a suboptimal number of artifact scans in our training data.
Language	English
Publishing date	2022-04-04
Publishing country	United States
Document type	Journal Article
ISSN	0277-786X
ISSN	0277-786X
DOI	10.1117/12.2607406
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Article: Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network.

Chou, Yiyu / Chang, Catie / Remedios, Samuel W / Butman, John A / Chan, Leighton / Pham, Dzung L

Frontiers in neuroscience

2022 Volume 16, Page(s) 768634

Abstract: Manual classification of functional resting state networks (RSNs) derived from Independent Component Analysis (ICA) decomposition can be labor intensive and requires expertise, particularly in large multi-subject analyses. Hence, a fully automatic ... ...

Abstract	Manual classification of functional resting state networks (RSNs) derived from Independent Component Analysis (ICA) decomposition can be labor intensive and requires expertise, particularly in large multi-subject analyses. Hence, a fully automatic algorithm that can reliably classify these RSNs is desirable. In this paper, we present a deep learning approach based on a Siamese Network to learn a discriminative feature representation for single-subject ICA component classification. Advantages of this supervised framework are that it requires relatively few training data examples and it does not require the number of ICA components to be specified. In addition, our approach permits one-shot learning, which allows generalization to new classes not seen in the training set with only one example of each new class. The proposed method is shown to out-perform traditional convolutional neural network (CNN) and template matching methods in identifying eleven subject-specific RSNs, achieving 100% accuracy on a holdout data set and over 99% accuracy on an outside data set. We also demonstrate that the method is robust to scan-rescan variation. Finally, we show that the functional connectivity of default mode and salience networks identified by the proposed technique is altered in a group analysis of mild traumatic brain injury (TBI), severe TBI, and healthy subjects.
Language	English
Publishing date	2022-03-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2022.768634
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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: Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers.

Remedios, Samuel W / Butman, John A / Landman, Bennett A / Pham, Dzung L

Lecture notes-monograph series

2020 Volume 12444

Abstract: Multi-site training methods for artificial neural networks are of particular interest to the medical machine learning community primarily due to the difficulty of data sharing between institutions. However, contemporary multi-site techniques such as ... ...

Abstract	Multi-site training methods for artificial neural networks are of particular interest to the medical machine learning community primarily due to the difficulty of data sharing between institutions. However, contemporary multi-site techniques such as weight averaging and cyclic weight transfer make theoretical sacrifices to simplify implementation. In this paper, we implement federated gradient averaging (FGA), a variant of federated learning without data transfer that is mathematically equivalent to single site training with centralized data. We evaluate two scenarios: a simulated multi-site dataset for handwritten digit classification with MNIST and a real multi-site dataset with head CT hemorrhage segmentation. We compare federated gradient averaging to single site training, federated weight averaging (FWA), and cyclic weight transfer. In the MNIST task, we show that training with FGA results in a weight set equivalent to centralized single site training. In the hemorrhage segmentation task, we show that FGA achieves on average superior results to both FWA and cyclic weight transfer due to its ability to leverage momentum-based optimization.
Language	English
Publishing date	2020-09-26
Publishing country	United States
Document type	Journal Article
ISSN	0749-2170
ISSN	0749-2170
DOI	10.1007/978-3-030-60548-3_17
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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: HACA3: A unified approach for multi-site MR image harmonization.

Zuo, Lianrui / Liu, Yihao / Xue, Yuan / Dewey, Blake E / Remedios, Samuel W / Hays, Savannah P / Bilgel, Murat / Mowry, Ellen M / Newsome, Scott D / Calabresi, Peter A / Resnick, Susan M / Prince, Jerry L / Carass, Aaron

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

2023 Volume 109, Page(s) 102285

Abstract: The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In ... ...

Abstract	The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects. Code is available at https://github.com/lianruizuo/haca3.
MeSH term(s)	Humans ; Brain/pathology ; Magnetic Resonance Imaging/methods ; White Matter ; Aging ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-08-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	639451-6
ISSN	1879-0771 ; 0895-6111
ISSN (online)	1879-0771
ISSN	0895-6111
DOI	10.1016/j.compmedimag.2023.102285
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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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Book ; Online: Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

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

2023

Abstract: Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation. Current state-of-the-art automatic MS lesion segmentation methods ... ...

Abstract	Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation. Current state-of-the-art automatic MS lesion segmentation methods utilize modified U-Net-like architectures. However, in the literature, dedicated architecture modifications were always required to maximize their performance. In addition, the best-performing methods have not proven to be generalizable to diverse test datasets with contrast variations and image artifacts. In this work, we developed an accurate and generalizable MS lesion segmentation model using the well-known U-Net architecture without further modification. A novel test-time self-ensembled lesion fusion strategy is proposed that not only achieved the best performance using the ISBI 2015 MS segmentation challenge data but also demonstrated robustness across various self-ensemble parameter choices. Moreover, equipped with instance normalization rather than batch normalization widely used in literature, the model trained on the ISBI challenge data generalized well on clinical test datasets from different scanners.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-12-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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