bluuebunny/crossref_metadata_2025 huggingface dataset:
Categorizing liver stiffness in children and adults through deep learning and multiparametric MRI with segmented liver and spleen data
Authors: Dillman Jonathan , Ali Redha , Li Hailong , Zhang Huixian , Pan Wen , Reeder Scott , Harris David , Masch William , Alsam Anum , Shanbhogue Krishna , Bernieh Anas , Ranganathan Sarangarajan , Parikh Nehal , He Lili
Published: November 2024

Motivation: To address the limited accessibility of Magnetic Resonance Elastography (MRE) for liver stiffness assessment. Goal(s): To develop AI-based pipeline for categorizing subjects into no/mild (<3 kPa) and moderate/severe (≥3 kPa) liver stiffening using multiparametric MRI images. Approach: Our model contains two main components: segmentation and classification. We employed a Swin-UNETR model to segment liver and spleen tissues from multiparametric MRI images. Then, we developed a Swin Transformer-based model for liver stiffness stratification. We used multi-site ten-fold cross-validation to evaluate our models’ performance. Results: Our best model achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.84 for liver stiffness categorization. Impact: Offering an accessible and accurate method for liver stiffness categorization, our research may enhance patient care, reduce healthcare costs, and expand the availability of this vital diagnostic tool, benefiting clinicians, researchers, and, ultimately, patients with liver disease, worldwide.
Decoding Brain Dynamic Functional Connectivity Implicated in ADHD via Graph Neural Networks and Transformers
Authors: Girish Deepank , Chan Yi Hao , Xia Jing , Rajapakse Jagath
Published: November 2024

Motivation: Few studies have investigated the potential of using dynamic functional connectivity for Attention Deficit Hyperactivity Disorder (ADHD) diagnosis and biomarker discovery. Goal(s): The goal of this study is to effectively capture spatiotemporal dynamic features in resting state fMRI data for detection of ADHD subjects. Approach: We present a novel ensemble framework that combines the strengths of Graph Convolutional Networks (GCN), Graph Isomorphism Networks (GIN), and Transformers. Results: On ADHD-200 dataset, our framework outperforms other state-of-the-art models for ADHD detection. By using explainable AI, we generated biomarkers for ADHD which are consistent with the existing literature. Impact: Innovative integration of GCN, GIN, and Transformers in our proposed framework enables analysis of effective dynamic functional connectivity for ADHD diagnosis. The classification performance outperforms existing state-of-the-art models and generated biomarkers further affirm its usefulness of our methods.
Denoising AutoEncoder as a Pre-processor for Knee MRI Analysis
Authors: Chen Shengjia , Cigdem Ozkan , Zhang Chaojie , Rajamohan Haresh Rengaraj , Cho Kyunghyun , Kijowski Richard , Deniz Cem
Published: November 2024

Motivation: Pre-processing MR images is a necessary step prior to image analysis due to variability of intensity scale in MR images. Goal(s): To develop a deep learning algorithm for standardizing knee MR images prior to analysis. Approach: We developed a denoising autoencoder with VNet architecture achieving on-the-fly image pre-processing (Bias field correction and intensity normalization) and denoising. Image quality was evaluated using SNR, NMSE, PSNR, and SSIM. Results: Our approach achieved an improved SNR with an efficient runtime compared to conventional pre-processing methods. Impact: Our DL-based knee MRI pre-processing tool generates standardized MRI outputs for image analysis and DL model development. This tool can be incorporated into a wide range of image analysis pipelines for the knee.
4D Flow MRI Velocity Enhancement and Anti-Aliasing Using Divergence Free Potential in Neural Networks
Authors: Bisbal Javier , Sotelo Julio , Mella Hernan , Mura Joaquín , Tejos Cristián , Arrieta Cristóbal , Uribe Sergio
Published: November 2024

Motivation: 4D flow MRI suffers from different sources of noise and aliasing artifacts. However, the existing techniques for enhancing velocities in 4D flow MRI encounter reliability problems with varying flow patterns or acquisition parameters. Goal(s): Our goal was to develop a velocity enhancement an anti-aliasing technique for 4D Flow MRI that can be easily applied to diverse flow types. Approach: We incorporate a vector potential into a neural network to predict velocity fields that strictly adhere to the divergence-free condition. Results: Results from simulated 4D flow MRI images demonstrate significant noise reduction and aliasing correction. Impact: The proposed Physics-Informed Neural Network enables the recovery of noise-free and aliasing artifact-free velocity fields using divergence-free terms in the network without the need for tuning hyperparameters in the training function, enhancing the applicability of these networks to different datasets.
Angiogram-aware deep learning methods for artifact correction of contrast enhanced MR angiography
Authors: Asaduddin Muhammad , Kim Eung Yeop , Park Sung-Hong
Published: November 2024

Motivation: CE MRA data is susceptible to motion and noise artifact due to its longer acquisition time. Conventional intensity-based registration are often unreliable, necessitating better artifact correction methods. Goal(s): To provide better artifact correction methods for CE MRA using generative deep learning and angiogram-aware loss functionApproach: two deep learning architectures were trained with/without angiogram-aware loss function. Network accuracy was evaluated based on CE MRA dynamic scans and angiogram. Results: motion correction was successfully performed, resulting in angiograms with PSNR=37.9±4.3 and SSIM=0.97±0.04. angiogram-aware loss function improved the correction accuracy by up to 13 points in PSNR and 17 points in SSIM. Impact: We developed accurate deep learning solutions for CE MRA artifact correction, potentially reducing the need for repeated MRA scans. We also showed that angiogram-aware loss function, which considers the last processing steps of CE MRA data, can improve correction accuracy.
Image Registration using Averaging VoxelMorph with CNN Edge Detector
Authors: Lei Xuan , Schniter Philip , Chen Chong , Liu Yingmin , Ahmad Rizwan
Published: November 2024

Motivation: Image registration followed by averaging is a common technique to improve the quality of free-breathing single-shot cardiac images. However, registering images becomes challenging when the SNR is low. Goal(s): Improve image registration for free-breathing cardiac MRI. Approach: We train a network, called AvgMorph, to register all source images to one target image. In addition, we use the output of a sophisticated deep learning-based edge detector to compute loss. Results: We validate AvgMorph using a realistic MRXCAT digital phantom for late gadolinium enhancement. AvgMorph outperforms existing methods in terms of NMSE, SSIM, and perceptual quality metrics. Impact: Pairwise registration of free-breathing images is suboptimal. We propose a network to register all source images to a single target image and utilize a loss function computed to edge maps rather than the images themselves.
A Complexity Guidance based Dynamic Activation Network for Phase Image Processing
Authors: Liao Zeyu , Bao Lijun
Published: November 2024

Motivation: Existing phase processing methods often require users to trade off between time and precision. Therefore, a phase processing network that can dynamically activate different parts based on input samples is of great research value. Goal(s): We hope that the proposed network can adaptively determine whether to begin with VOI extraction, i.e., removing the brain skull, and provide different solutions for samples of different complexity. Approach: We combine dynamic neural network and deformable convolution in the network design to realize dynamic activation and verify it on MRI phase data. Results: Our dynamic activation based network (DANet) implements adaptive phase processing and achieve competitive performance. Impact: Our methodological framework can be applied across various field related to phase signal processing, such as Optical Interferometry (OI), Magnetic Resonance Imaging (MRI), Fringe Projection Profilometry (FPP), and Interferometric Synthetic Aperture Radar (InSAR).
Masked U-net: Bridging the gap between real and synthetic data for mapping reconstruction in multiple overlapping-echo detachment imaging
Authors: Zeng JunBo , Ye Ming , Zhou Yudan , Yang Qinqin , Wu Zhigang , Lin Liangjie , Cai Congbo , Cai Shuhui
Published: November 2024

Motivation: The synthetic training data used for mapping reconstruction of deep learning can simulate the required features, but it is difficult to fully simulate the unnecessary characteristics existing in real-world data. Goal(s): This work aims to enable the model to extract the essential mapping relationships for mapping reconstruction and eliminate the interference of non-ideal factors in real data. Approach: We propose a mask pre-training method called Masked U-net that allows the model to learn appropriate inductive biases on quantitative images. Results: The proposed method can better extract relevant features and reduce the interference of irrelevant factors in real data. Impact: The proposed method bridges the gap between the real data and synthetic data, improves the quality of deep learning reconstruction driven by synthetic training data, and achieves important application in T2/T2* mapping reconstruction of multiple overlapping-echo detachment (MOLED) imaging.
Renal multiple post label times arterial spin label (ASL) read out by turbo spin echo (TSE)
Authors: Zhang Yizhe , Tao Quan , Chen Zelong , Wu Zhigang , Zhang Wenyan , Zhang Qianqian , Deng Kan , Wu Peng , Xu Yikai , Feng Yanqiu
Published: November 2024

Motivation: Renal imaging using echo planar imaging (EPI) readout may be sensitive to field inhomogeneities and induced ghost artifact. Besides, single inversion time is not enough for renal blood flow (RBF) quantification. Investigation of more stable clinical arterial spin label (ASL) imaging methods is required. Goal(s): We aimed to design a stable clinical ASL sequence to quantify RBF accurately. Approach: Multiple inversion time (TIs) readout by turbo spin echo (TSE) was processed, and the repeatability of quantification was evaluated in volunteers (n=7). Results: Multiple TIs combined with TSE readout of FAIR-ASL provided more accurate RBF measurement and highly repeatability compare with pcASL-EPI. Impact: Multi-PLDs readout by TSE may provide a stable clinical ASL-FAIR sequences to accurately quantify RBF.
Velocity Selective Arterial Spin Labeling at 7T
Authors: Roefs Emiel , Hirschler Lydiane , Petridou Natalia , van Osch Matthias
Published: November 2024

Motivation: Velocity selective (VS)-ASL can be of great added benefit for fMRI at ultra-high field because of its specificity and ATT-insensitivity. Goal(s): Our goal was to show the feasibility of VS-ASL at 7T with minimal TR, which involves minimizing SAR and timing parameters within the sequence. Approach: VS-ASL was implemented using BIR4 labeling module and compared to the robust FAIR ASL. Image quality was measured based on quantified ASL-signal and tSNR-efficiency. Results: Good agreement between FAIR and VSSinv-ASL was found. VSSinv-ASL was used to create intrinsic BGS without additional SAR. A shortest TR of 4500ms was achieved which could potentially be shorted with pTx. Impact: We showed the feasibility of Velocity Selective (VS)-ASL at UHF and the application in fMRI. VS-ASL showed comparable but less homogeneous tSNR as FAIR. VS-ASL is of added benefit for fMRI at ultra-high field because of its specificity and ATT-insensitivity.
Improving pCASL CBF mapping in neonates and adults with enhanced background suppression and complex subtraction
Authors: Hu Zhiyi , Jiang Dengrong , Shepard Jennifer , Uchida Yuto , Oishi Kenichi , Liu Peiying , Lin Doris , Yedavalli Vivek , Tekes Aylin , Golden W. Christopher , Lu Hanzhang
Published: November 2024

Motivation: Standard arterial spin labeling (ASL) uses modest background-suppression to enhance SNR, which may not be optimal in reducing noise. Goal(s): Our goal was to further improve the SNR in ASL and reliably measure the cerebral blood flow (CBF) in low-perfusion scenarios such as neonates. Approach: We utilized an enhanced background suppression to minimize the signal from static background tissue. Complex subtraction of control/labeled signals was done to correct the magnetization sign-switching in between. Results: Enhanced background suppression combined with complex subtraction improved the reliability of CBF measurement in both neonates and adults, particularly benefiting the quality of neonatal CBF mapping. Impact: The improvement of SNR in ASL through enhanced background suppression coupling complex subtraction can facilitate the quality of cerebral blood flow measurement. The benefits are more pronounced in low-perfusion scenarios, such as bolstering the reliability of neonatal ASL.
Dynamic B0 field shimming for improving pseudo-continuous arterial spin labeling at 7 Tesla
Authors: Ji Yang , Li Hongwei , Woods Joseph , Okell Thomas
Published: November 2024

Motivation: Increased B0 inhomogeneity along the length of brain-feeding arteries at 7 Tesla is one major issue for pseudo-continuous arterial spin labeling (PCASL), which reduces the labeling efficiency, leading to loss of perfusion signal. Goal(s): Our goal is to improve PCASL at 7 Tesla by specifically improving B0 field homogeneity of the vessels within the inversion region. Approach: We propose a vessel-specific dynamic B0 shimming method to optimize labeling efficiency without compromising the static shim over the imaging region. Results: Preliminary perfusion images indicate the superior performance of our proposed 3D dynamic shimming method over global or 2D-based correction methods. Impact: Our proposed dynamic B0 shimming method demonstrates strong potential in improving the robustness and effectiveness of PCASL, allowing the high sensitivity and spatial resolution of 7T ASL to be fully utilized.
Optimizing velocity-selective inversion pulses for improved labeling efficiency and robustness
Authors: Guo Jia
Published: November 2024

Motivation: Current Fourier Transform-based velocity-selective inversion (VSI) pulses are sensitive to field inhomogeneities, leading to labeling errors or inefficiency. Goal(s): To improve the labeling robustness and efficiency of the VSI pulses for more robust and high-SNR perfusion measurement. Approach: A new design with 6-segment FT-VSI pulse was implemented and tested in healthy subjects using dual-module VSI labeling. Results: Compared with the existing VSI pulse, the new pulse significantly improved the labeling robustness against field inhomogeneities and the overall labeling efficiency, leading to >15% higher ASL signal (p<0.0002) and >20% higher temporal SNR (p<0.009). Impact: This new VSI pulse can effectively improve the labeling robustness against field inhomogeneities, while increasing the labeling efficiency and reducing the SAR. These features are especially beneficial with dm-VSASL implementation and in ultra-high field applications for delay-insensitive ASL perfusion imaging.
Simultaneous Assessment of Microstructural integrity and Metabolite Profile from Superior Longitudinal Fasciculus in Normal Healthy Brains
Authors: Rajan Archith , Hanaoka Mauro , de Godoy Laiz Laura , Gewolb Daniel , Dutweiler Elizabeth , Sheriff Sulaiman , Poptani Harish , Mohan Suyash , Chawla Sanjeev
Published: November 2024

Motivation: Superior longitudinal fasciculus (SLF) is critical in multiple normal functions. It is imperative to co-localize white-matter tract of SLF with metabolite maps to facilitate simultaneous analysis of microstructural integrity and metabolite alterations. Goal(s): To evaluate relationships among metabolite ratios and diffusion MRI derived parameters along the path of SLF in normal healthy adults. Approach: The associations between whole brain spectroscopy derived Cho/Cr, Cho/NAA ratios and diffusion MRI derived FA, MD, fICVF, fIso and ODI parameters were assessed along the length of SLF segments II-III. Results: Strong significant correlations between Cho/Cr and Cho/NAA ratios and FA, MD, ficvf were observed. Impact: Diffusion MRI and whole brain spectroscopy could be used to study the covariation of white matter microstructure and metabolism. Multiparametric normative tract profiles established over larger cohorts could serve as the basis for early detection of white matter anomalies.
GABA in the anterior cingulate cortex mediates the association of white matter hyperintensities with executive function
Authors: Fu Xiaona , Sun Peng , Zhang Xinli , Zhu Dongyong , Qin Qian , Lu Jue , Wang Jing
Published: November 2024

Motivation: The alteration of cortical GABA levels in WMH patients and whether it mediates the association of WMH volume with executive function remain unclearGoal(s): We investigated the cortical GABA levels, and whether cortical GABA mediates the association between WMH and executive function in the WMH people. Approach: We used the independent samples test,general linear model,partial correlation analyses and mediation analysis. Results: Patients with moderate to severe WMH showed lower GABA+/Cr in the anterior cingulate cortex (ACC) and worse executive function than mild WMH patients. And the GABA level in ACC mediates the association between white matter hyperintensities and executive function in WMH patients. Impact: The GABA+/Cr level in the the anterior cingulate cortex had a critical role. And it mediates the association between white matter hyperintensities and executive function in the WMH patients.
Application of B1+ corrected data-driven myelin water imaging for the diagnosis of multiple sclerosis pathology in normal appearing tissue
Authors: Zlotzover Sharon , Radunsky Dvir , Reichman Dominique Ben-Ami , Shrot Shai , Hoffmann Chen , Ben-Eliezer Noam
Published: November 2024

Motivation: Multicomponent T2 (mcT2) analysis is the go-to tool for mapping myelin in vivo. Resolving T2 spectra, however, is highly challenging due to substantial ambiguity in the multidimensional space of microstructural configurations. Goal(s): Accurate and reproducible myelin water imaging. Approach: A spatially-global data-driven mcT2 analysis was employed, relying on the identification of tissue-specific mcT2 configurations prior to performing voxel-wise analysis. A new scheme was developed for correcting transmit field (B1+) inhomogeneities. Results: Successful application of the data-driven technique is demonstrated on numerical phantom, healthy volunteers, and for identifying pathology in normal-appearing tissue of subjects with multiple sclerosis. Impact: The data-driven approach constitutes a new paradigm for multi-component T2 fitting, yielding unprecedented accuracy and high robustness. Application on MS patients’ data highlights the potential of data-driven MWF values as a biomarker for pathology in normal appearing tissue.
Development of numerical phantom converting from electron microscopic analysis to multi-component water fraction for MRI simulator.
Authors: Ohshiro Ryuji , Kanazawa Yuki , Haga Akihiro , Harada Masafumi
Published: November 2024

Motivation: To improve the accuracy of myelin water fraction (MWF) calculations.Goal(s): To develop multi-component phantom for MRI simulators and to validate the accuracy depending on pulse sequences and imaging parameter settings. Approach: First, five different electron micrographs of the normal central nervous system (CNS) were divided into two regions (my and ax/ie), and the percentage of each component was calculated. Second, each proton density (PD) was set as a percentage of the divided area. Results: We developed multi-component water phantom and could demonstrate the SPGR signal variations to B0 inhomogeneity on an MRI simulator using our multi-component water fraction phantom. Impact: Our multi-component phantom derived from electron microscopic analysis may be useful to evaluate the differences in MWF values between each signal model, e.g., qMT and mcDESPOT.
Exploring Correlations Between Imaging Biomarkers of Macromolecular, Myelin Water, and Myelin Content in the Brain
Authors: Lo James , Zheng Chun , Athertya Jiyo , Malhi Bhavsimran , Shin Soo Hyun , Bydder Graeme , Du Jiang , Ma Yajun
Published: November 2024

Motivation: Myelin imaging metrics have varying degrees of association with histology but have not yet been correlated with each other. Goal(s): To explore correlations between macromolecular protein, myelin water, and non-aqueous myelin content on a 3T clinical scanner. Approach: Seven healthy volunteers were scanned using an MT-Cones sequence to provide for two-pool MT modeling of macromolecular proton fraction (MMF), a STAIR-STE-Cones sequence to quantify myelin water fraction (MWF), and a STAIR-UTE-Cone to estimate non-aqueous myelin proton fraction (MPF). Results: Strong, significant correlations were found between these three myelin imaging biomarkers with R values of 0.856, 0.876, and 0.775, respectivelyImpact: Strong positive correlations were found between the three imaging biomarkers of macromolecular, myelin water, and non-aqueous myelin components. This may help in understanding the value of these myelin imaging biomarkers in the assessment of neuroinflammatory and neurodegenerative diseases.
Comparison of white matter bundle structure between humans and macaques.
Authors: Ouchi Kazuya , Yoshimaru Daisuke , Takemura Aya , Higo Noriyuki , Yamamoto Shinya , Hayashi Ryusuke , Sugase-Miyamoto Yasuko , Tsurugizawa Tomokazu
Published: November 2024

Motivation: Comparative studies on brain structure between humans and macaques across entire brain have not been thoroughly conducted. Goal(s): The goal of this study is to show the similarities and differences of structural connectivity (SC) in humans and macaques. Approach: SC was calculated from diffusion-weighted imaging. Based on the SC, the similarity and Least Absolute Shrinkage and Selection Operator (LASSO) were used. Results: High similarity of SC between and within species was observed. LASSO yielded feature extraction of 12 connections. Impact: This study clearly showed the similarity of white matter structure between and within species in humans and macaques. The method of this study is quantitative and provides valuable insights in translational research.
The changes of brain metabolism after spinal manipulation in patients with chronic nonspecific low back pain: a MRS study
Authors: Tan Wenli , Chen Hui , Huo Miao , Liu Mengxiao
Published: November 2024

Motivation: Bridge the knowledge gap in understanding the central mechanisms of chronic low back pain (CLBP) and the effects of spinal manipulation (SM). Goal(s): To use 1H-MRS to investigate metabolic differences in the posterior cingulate cortex (PCC) during spinal manipulation for chronic low back pain (CLBP). Approach: The approach involved recruiting CLBP patients and healthy controls, performing 1H-MRS in the posterior cingulate cortex (PCC) before and after spinal manipulation (SM), and correlating metabolite changes with clinical outcomes. Results: CLBP patients exhibited altered brain metabolism in the posterior cingulate cortex (PCC), with changes in specific metabolites, and these abnormalities improved after spinal manipulation (SM). Impact: The research provides insights into the central mechanisms of chronic low back pain (CLBP) and the potential therapeutic benefits of spinal manipulation, which could inform better pain management strategies.
More Features in the Mahalanobis Distance Lead to Improved Diagnostic Classification of Autistic and Non-autistic Individuals
Authors: Vazquez Luis , Dean Doug , Prigge Molly , King Carolyn , Morgan Jubel , Adluru Nagesh , Lainhart Janet , Zielinski Brandon , King Jace , Alexander Andrew , Guerrero-Gonzalez Jose
Published: November 2024

Motivation: Heterogeneity of neuroimaging findings remains a challenge to identify specific brain-related phenotypes in ASD. Quantitative metrics of individual deviation across brain measurements are needed for parsing variation and identifying individuals who may or may not have abnormal brain structure. Goal(s): This study aims to quantify individual brain differences in individuals with and without ASD. Approach: We investigated the Mahalanobis distance to characterize multidimensional brain measures of microstructure in individuals with and without ASD in a set of white matter regions. Results: We found multivariate Mahalanobis distance is superior to univariate comparisons at distinguishing between individuals with and without ASD. Impact: Normative modeling and multivariate approaches may provide informative metrics for parsing heterogeneity in the multivariate patterns of brain development in autistic individuals.
Association between cerebral blood flow and cerebral small vessel disease: a comparison between ASL and phase-contrast MRI
Authors: Xie Linyun , Zheng Jiaxin , Zhang Yao , Hong Hui , Xu Shan , Cui Lei , Wang Shuyue , Li Jixuan , Liu Lingyun , Lin Miao , Xiao Luo , Li Kaicheng , Zeng Qingze , Zhang Minming , Zhang Ruiting , Zhao Li , Huang Peiyu
Published: November 2024

Motivation: Chronic hypoperfusion is a central mechanism of cerebral small vessel disease (CSVD), but studies using different methods have shown different results. Goal(s): To compare the associations between CSVD and CBF measured by phase-contrast MRI and ASL in the same cohort. Approach: 69 subjects were totally recruited in the study. WMH, FW, and tFA were used to reflect the disease severity. The associations between CSVD and CBF measured by two methods were investigated. Results: Compared to CBFPC, CBFASL showed a negative association with FW and a positive association with tFA. The ratio of CBFASL to CBFPC was also correlated with FW and tFA. Impact: Future studies need to consider the differences between CBF measured by ASL and phase-contrast MRI and choose a more appropriate method according to the research purpose.
Iron Deposition Characteristics in Normal-appearing White Matter: A Community Study Using Quantitative Susceptibility Mapping Method
Authors: Gao Yian , Li Meng , Zhang Qihao , Li Jing , Feng Mengmeng , Xin Haotian , Sui Chaofan , Liang Changhu , Guo Lingfei
Published: November 2024

Motivation: The factors that influence iron deposition in the normal-appearing white matter (NAWM) of the brain have yet to be thoroughly examined. Goal(s): This study aimed to measure brain iron levels in a community population and identify factors affecting NAWM iron levels. Approach: Brain iron load was assessed using quantitative susceptibility mapping. Results: Age, hypertension, T2DM, smoking, BMI, and APOE4 affect iron metabolism in certain NAWM brain regions. Moreover, the mean susceptibility values of the corpus callosum are significantly related to some cognitive tests. Impact: We could identify the potential factors that could affect iron levels in the white matter and accurately map iron in this part of the brain. This information could help us better control confounding variables in future research.
White matter changes in intrauterine fetal brain with mild to moderate isolated ventriculomegaly based on diffusion magnetic resonance imaging.
Authors: Zhang Chao , Chen Ruike , Sun Cong , Ding Feier , Wu Dan , Wang Guangbin
Published: November 2024

Motivation: There is a controversial argue about the prognosis of fetuses with isolated ventriculomegaly in clinical practice. Goal(s): Application of diffusion Magnetic Resonance Imaging (dMRI) in fetuses with isolated ventriculomegaly to explore structure changes of white matter fibers. Approach: Collecting brain dMRI data in isolated ventriculomegaly and healthy fetuses, analyzing differences in white matter structure between the two groups using diffusion tensor imaging (DTI) and fixel based analysis (FBA) methods. Results: Significant differences were found in DTI or FBA parameters of white matter fibers including the inferior longitudinal fasciculus, corpus callosum, fornix, sagittal stratum and corticospinal tract between the two groups. Impact: There are changes in the brain white matter structure in intrauterine fetuses with isolated ventriculomegaly, which provides a certain objective basis for potential risks of neurodevelopmental disorders and prenatal counseling.
White matter microstructure alterations in systemic lupus erythematosus patients: A Quantitative synthetic MRI Study
Authors: Deng Kemei , Jiang Muliang , Wu Chengli , cui wei , Long Liling
Published: November 2024

Motivation: Neuropsychiatric disorders are common symptoms in systemic lupus erythematosus (SLE) patients. However, investigations into altered white matter (WM) microstructure caused by SLE were insufficient. Goal(s): Investigate WM microstructure alterations and their association with anxiety and depression in SLE patients using synthetic MRI (SyMRI) technique. Approach: T1, T2 and myelin content, acquired by SyMRI, were compared between 52 SLE patients and 24 health controls. Results: Longer T1 relaxation time and lower myelin content were found in several WM regions, and anxiety in SLE patients was found to be correlated with a decrease in myelin content in the fornix. Impact: The demyelination of the fornix may be a significant factor leading to anxiety in patients with systemic lupus erythematosus.
Separation of vascular and nonvascular components in the white matter lesion in cerebral small vessel disease
Authors: Chen Ya-Fang , Chen Chih-Hao , Tang Sung-Chun , Wu Wen-Chau
Published: November 2024

Motivation: White matter lesion (WML) is a neuroimaging signature of cerebral small vessel disease (CSVD), commonly comprising both vascular and nonvascular components for which assessment methods remain to be developed. Goal(s): To investigate the feasibility of using multimodal MR imaging to separate vascular and nonvascular components in WML. Approach: Twelve patients with CSVD were prospectively included and underwent 3T MR imaging. Magnetization transfer ratio (MTR) was derived from magnetization transfer imaging. Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were derived from diffusion tensor imaging. Results: MTR was found to correlate with ADC and FA differently in WML as compared to normal-appearing white matter. Impact: Appropriate separation of vascular/nonvascular components in WML can help differential diagnosis and treatment formation of CSVD. The sensitivity of combined MTR/FA/ADC can be further verified by comparing CSVD with other neurological diseases that present WML but have nonvascular pathogenesis.
Structural and Oxygen Metabolic Magnetic Resonance Imaging of long-COVID and ME/CFS
Authors: Xu Xiang , Belani Puneet , Jiang Dengrong , Leforestier Rodophe , Lu Hanzhang , Natelson Benjamin
Published: November 2024

Motivation: Many long-COVID patients experience fatigue and post exertional malaise which are primary symptoms of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Understanding the similarities and differences between long-COVID and classic ME/CFS could provide insights into the disease mechanisms. Goal(s): To measure brain anatomy and oxygen metabolism between the two groups. Approach: Clinical standard and advanced MRI techniques measuring the venous oxygenation were applied. The fatigue level was assessed by questionnaires. Results: No differences in brain anatomy were observed between the groups, but the long-COVID group had significant lower venous oxygenation than the healthy control group and the classic CFS group. Impact: Many long-COVID patients fulfill diagnostic criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Compared to structural changes, metabolism-related parameters, such as venous oxygenation of the brain, may be more sensitive to changes related to long-COVID and CFS disease mechanisms.
Altered Volumes of the Limbic Structures in COVID-19 Patients with neuroPASC revealed by Ultrahigh Field 7T MRI
Authors: Rashid Shams , Saju Sera , Khegai Oleksandr , Jette Nathalie , Belani Puneet , Pawha Puneet , Kirsch Claudia , Seifert Alan , Zhu Yijuan , Becker Jacqueline , Delman Bradley , Balchandani Priti
Published: November 2024

Motivation: Post-Acute Sequelae of COVID-19 of neurological origin (neuroPASC), such as brain fog and anxiety, are not well understood. Goal(s): To study volumetric changes in the brain’s limbic structures in COVID-19 patients with neuroPASC such as brain fog. Approach: 29 COVID-19 patients with neuroPASC and 29 matched controls underwent ultrahigh field brain MRI at 7T. Hippocampal subfields and amygdala subnuclei were segmented with FreeSurfer and studied across neuroPASC subgroups. Results: Several subunit volumes were significantly decreased in COVID patients compared to controls across neuroPASC subgroups. Sex-based differences were also present. Impact: There may be subtle changes in the volumes of the brain’s limbic structures in COVID patients with neuroPASC compared to controls. These changes may be related to neuroPASC symptoms and may depend on sex differences.
Subtle Brain Structure Alternations in Young Adult Patients infected with SARS-CoV-2 Omicron and Mild Symptom: A Case Control Study Using MRI
Authors: Liu Jiachen , Shen Rui , Yu Shuwan , Xu Ning , Zhao Chenyang , Qiao Huiyu , Ning Zihan , Han Hualu , Zhao Xihai
Published: November 2024

Motivation: The existence of brain structural abnormalities in young adults with mild Omicron infections remains unclear. Goal(s): This study aimed to investigate short-term brain structure changes in this specific patient group. Approach: Young adult patients and age- and sex-matched controls were enrolled, and brain structures were assessed on T1-MPRAGE images. One-way ANOVA, Kruskal-Wallis tests, and logistic regression were employed to determine differences among groups. Results: While whole brain volume didn't significantly differ among groups, subtle alterations in various regions within cerebral cortex, white matter, and cerebrospinal fluid were observed during the acute phase, with a tendency of recovery over time. Impact: Short-term structural changes in brain were observed in this study. The findings suggest cerebral impairments at the early stage of infection with SARS-CoV-2 Omicron even in individuals with mild symptoms.
Post-COVID Fatigue Relates to Bioenergetic Dysfunctions in the Posterior Cingulate Gyrus
Authors: Yoo Hye Bin , Lee Hyeong Hun , Huang Serene , Lim Jeong Hoon
Published: November 2024

Motivation: Persistent fatigue after recovery from SARS-CoV-2 shows pathologies comparable to chronic fatigue syndrome or myalgic encephalomyelitis (CFS/ME). It is unknown if disruptions in mitochondrial functions caused by SARS-CoV-2 persists in post COVID fatigue as dysregulated mitochondrial homeostasis. Goal(s): We aim to investigate if post-COVID fatigue relates to perturbations of mitochondrial function in the brain representing signs of neuroinflammation, redox imbalance, and neuronal dysfunctions. Approach: Proton MR spectroscopy was performed on post-COVID fatigue patients targeting at posterior cingulate gyrus (PCG), one of the most metabolically active regions. Results: We found reduced level of antioxidants and neuronal activity in post-COVID fatigue patients. Impact: Proton MR spectroscopy in PCG of post-COVID fatigue patients shows signs of redox imbalance and reduced neuronal activity, suggesting of long-term dysregulations in mitochondrial homeostasis persisting after SARS-CoV-2 infection. SARS-CoV-2 infection may lead to further neurodegenerations post-recovery.
Neurochemistry, microstructure, and perfusion in long-COVID: An interim analysis from a multisite study
Authors: Silva Ana , Joers James , Gundry Katherine , Lorente Alfredo , Thotland Jeromy , Deelchand Dinesh , Park Young , Li Xiufeng , Manousakis Georgios , Metzler Abby , Lenglet Christophe , Eberly Lynn , Kendall June , Toledano Michel , Kantarci Orhun , Atik Merve , Senjem Matthew , Zeydan Burcu , Byrne Keenan , Juttukonda Meher , Salat David , Sherman Janet , Pollak Lauren , Mukerji Shibani , Harrold G , Yasar Sevil , Nabi Mehreen , Rehman Sana , Karmonik Christof , Gillani Syed , Flores Valerie , Davis Rachel , Ashizawa Tetsuo , Barker Peter , Ratai Eva , Kantarci Kejal , Öz Gülin
Published: November 2024

Motivation: There is an urgent need to understand the biological basis of long-COVID. Goal(s): To elucidate long-term consequences of SARS-CoV-2 infection in the brain using advanced MRI. Approach: In a multisite study, we collected single-voxel MRS, diffusion MRI, pCASL and T2 FLAIR MRI, to investigate the neurochemistry, microstructure, perfusion, and white matter hyperintensities (WMH) in participants suffering from long-COVID. Results: Participants with long-COVID have long-lasting effects (that persist years after acute infection) in the neurochemistry, microstructure, and perfusion, primarily in the brainstem, midbrain and diencephalon, and their connecting tracts, without the presence of overt white matter pathology. Impact: Multi-modal advanced MRI demonstrates long-lasting effects of COVID-19 in the brain, including neurochemical and microstructural abnormalities and perfusion deficits. These insights may facilitate the treatment and care of people suffering from long-COVID.
The long-term cerebral change of patient transformed from mildly into severely emotional problem after multiple COVID-19 infections: 3-6 months
Authors: Cui Yuanyuan , Wang Yunmeng , Zhang Youhan , Dong Shuwen , Yu Qingyang , Xiao Yi , Liu Shiyuan
Published: November 2024

Motivation: It was unclear that if the brain function would be changed after multiple COVID-19 infections when the psychological burden incremented. Goal(s): To evaluate the long-term brain brain change of the patients who transformed from mildly emotional problem after the first COVID-19 infection into severely one after multiple infections. Approach: The patients with mildly emotional abnormality after once infection (control group) and others transformed from mild into severe abnormality after multiple COVID infections (patients group) were included. The parameters derived from BOLD and DTI were compared after 3-6 months. Results: The patients group had a decreased FA and increased ALFF in some brain areas. Impact: The abnormalities in these brain areas could help clinicians to understand the potential machine of the transformation of mild emotion problem into severe emotion problem after another COVID infection.
Graph theory demonstrates lower nodal efficiency and transitivity in the Default Mode Network 6-12 months after admission for COVID-19 pneumonia
Authors: Madzime Joanah , Hoare Jacqueline , Kadenge Betty , Heany Sarah , Meintjes Ernesta
Published: November 2024

Motivation: Neural functional networks provide insights into the intrinsic function and integrity of brain areas. Goal(s): The aim of the current study was to investigate the levels of functional integration and segregation of brain areas within the default mode network (DMN) in a cohort of adults 6-12 months after admission for SARS-CoV-2 pneumonia. Approach: We used a functional atlas and graph-theoretical framework to model the topology of brain areas within the DMN. Results: We found reduced functional integration within the DMN of adults previously admitted with SARS-CoV-2. Affected brain areas are involved in higher-cognitive memory processing functions. Impact: Investigating the brain’s functional organization in the context of post-acute infection with SARS-CoV-2 can offer a window into the neuropathology of long COVID-19 symptoms.
3D-QALAS in COVID: a whole brain voxel-based investigation of relaxometry
Authors: Naeyaert Maarten , Radwan Ahmed , De Ridder Filip , Sunaert Stefan , Raeymaekers Hubert
Published: November 2024

Motivation: The effect of COVID-19 on the brain is not fully understood. Simultaneous 3D-acquisition of relaxometry parameters can indicate where microstructure has changed. Goal(s): To locate differences in T1, T2 and proton density in COVID-19 patients. Approach: 3D-QALAS was performed on 17 volunteers and 17 patients. Relaxometry and segmentation maps were calculated and warped to a common space to compare both groups. Results: T2 changes associated with COVID-19 were observed in left cerebellar white and grey matter and in WM of the brain stem and thalamus, along with increased right temporal and occipital T1 and PD, and decreased frontal T1, T2 and PD values. Impact: A voxel-based relaxometry analysis using 3D-QALAS data, including WM and PD, was performed for the first time, based on the hMRI toolbox. Different patterns of parameter changes were observed in several brain regions, possibly indicating different types of microstructural changes.
Automatic segmentation of the fetal hippocampus using 3D deep convolution neural networks
Authors: Wu Yao , Kapse Kushal , Mastracchio Christina , Teramoto Hironori , Araki Stephanie , Saulino Patricia , Kasper Merrick , Andescavage Nickie , Vezina Gilbert , Limperopoulos Catherine
Published: November 2024

Motivation: The ability to accurately segment the fetal hippocampus is critical to advancing our understanding of the origins of prenatal memory and emotional processing difficulties. Current manual methods are laborious and subjective. Goal(s): We aim to automate left and right fetal hippocampal segmentation in 3D MR images. Approach: We applied a 3D U-Net based model to automatically segment the left and right fetal hippocampus in 3D MR images. Results: Our dataset comprised 131 fetuses with 191 MRI scans. The results demonstrated high accuracy and efficiency, particularly for this challenging-to-segment structure, illuminating the potential of deep convolutional neural networks in this application. Impact: This study's automatic fetal hippocampal segmentation with deep learning has the potential to advance in utero brain development research and biomarker studies. The potential impact includes improving early diagnostics, in-utero neuro-surveillance, and future targeted therapeutics.
Deep Learning Myocardial Segmentation in 3D Whole-Heart Joint T1/T2 mapping: Comparison of nnU-Net and MA-SAM
Authors: Rivera Carlota , Velasco Carlos , Hua Alina , Botnar René , Prieto Claudia
Published: November 2024

Motivation: The significant amount of data collected from a single 3D whole-heart joint T1/T2 mapping sequence substantially increases the time required for segmenting and analyzing the quantitative maps, therefore, automating the segmentation process could result in a significant reduction. Goal(s): To automate the segmentation of myocardium using state-of-the-art segmentation networks. Approach: Two segmentation networks, nnUNet and MA-SAM, are trained and compared for myocardial segmentation of whole-heart joint T1/T2 mapping in healthy subjects and patients. Results: nnUNET and MA-SAM achieved good quality segmentations with DICE score higher than ~0.856 with smoothed masks. nnU-Net achieved better results in term DICE and required the shortest training time. Impact: State-of-the-art nnUNET and MA-SAM networks achieve accurate automatic myocardial segmentation of whole-heart joint T1/T2 mapping. This can significantly reduce the laborious task of manual segmentation and could help to accelerate the analysis and therefore the diagnosis of myocardium-related disease.
A-Eye: quality control and deep learning segmentation of the complete eye in MRI
Authors: Barranco Jaime , Kebiri Hamza , Esteban Óscar , Sznitman Raphael , Langner Sönke , Stachs Oliver , Luyken Adrian , Stachs Philipp , Franceschiello Benedetta , Cuadra Meritxell Bach
Published: November 2024

Motivation: Reliable large-scale MREye segmentation. Goal(s): Quality control of eye MRI and deep learning segmentation validation. Approach: We automatically extract Image Quality Metrics (IQMs) and use them as features to train a model in a supervised framework with expert rating annotations as target. Multi-class 3D MREye segmentation is done for the first time using the deep-learning-based approach nnUNet. Results: None of the models achieved the required levels of sensitivity and specificity necessary for our MREye application. nnUNet for MREye segmentation tasks yielded promising outcomes, robust to a variety of MRI quality. Impact: MREye does not escape the evidence that insufficient data quality threatens the reliability of analysis outcomes. We pioneer manual and automated quality control on MREye and benchmark deep learning eye segmentation.
Lesion Instance Segmentation in Multiple Sclerosis: Assessing the Efficacy of Statistical Lesion Splitting
Authors: Wynen Maxence , Gordaliza Pedro , Stölting Anna , Maggi Pietro , Cuadra Meritxell Bach , Macq Benoit
Published: November 2024

Motivation: Accurate white matter lesion (WML) counting and delineation are crucial for multiple sclerosis (MS) diagnosis and prognosis. Though being a critical step in clinical research and automated tools relying on lesion-centered patches, no previous work studied post-processing methods to transform voxel-wise segmentations into lesion instance masks in MS. Goal(s): In this study, we compare the conventional connected components (CC) method to a confluent lesion splitting (CLS) method that was used but never validated. Approach: CC and CLS's performances are evaluated using three common lesion segmentation tools (LSTs): SPM, SAMSEG, and nnU-Net. Results: CLS lacks generalization, sacrifices specificity for sensitivity and worsens segmentation quality. Impact: Our results underscore the need for the development of a novel instance segmentation methodology that accounts for (i) the potential large distance between voxels and the center of the lesions to which they belong and (ii) confluent lesions.
Semi-supervised segmentation method based on a small number of labeled left atria
Authors: Liu Yiwei , Zhang Shaoze , Zhao Xihai , He Zuo-Xiang
Published: November 2024

Motivation: The study aims to propose segmentation models and explore the impact of the proposed model on small models and the impact of the proportion and amount of different labeled data on the results. Goal(s): The goal of this study is to achieve better results with less labeled data. Approach: The study builds a model and evaluates the performance of the model on different sizes of left atria labeled data. Results: The method proposed in this study can improve the effect of small models, and reducing the labeled data proportion has a greater impact on the model performance than reducing the training data. Impact: This study provides help for how to improve the medical image segmentation performance of small models, improve the efficiency of manual labeling, and achieve better segmentation results with fewer manual annotations.
Feasibility of predicting MRI tissue heating (MRSaiFE) using experimental training data
Authors: Zhang Mengying , Panjwani Nawal , Motovilova Elizaveta , Dyke Jonathan , Robb Fraser , Winkler Simone
Published: November 2024

Motivation: MRI poses safety risks due to tissue damage via SAR hotspots. We have previously developed MRSaiFE, an AI-based SAR prediction tool. Goal(s): This study expands MRSaiFE with experimental, in vivo, training data. Approach: Images from a subject are segmented into a numerical model that is simulated to obtain SAR. The MRSaiFE input is the scanned image, and the predicted output SAR is obtained from training on the simulated SAR. Results: Good agreement (0.4% MSE, 6% RMSE, and 81% SSIM) demonstrates feasibility of using 1) experimental training data and 2) scanned input images, enabling future prediction from in vivo localizers. Impact: By replacing conservative SAR margins with patient-specific values, MRSaiFE offers potential for enhanced sensitivity, resolution, or reduced scan time. Additionally, it could notably enhance safety in patients with medical implants, hyperthermia treatments, and in MRI procedures at ultra-high fields.