bluuebunny/crossref_metadata_2025 huggingface dataset:
Brain long distance functional connectivity mediates the relationship between fatigue and PTSD after COVID-19 infection.
Authors: Luo Zhaoyao , Yuan Huijie , Bao Wenrui , Niu Xuan , Mu Junya , Li Haining , Lu Tao , Ma Shaohui , Wei Xiaocheng , Zhang Ming
Published: November 2024

Motivation: Brain network alterations in acute phase of coronavirus disease 2019 patients (acute-COVID) with fatigue has not yet been elucidated. Goal(s): Our goal was to determine the characteristics of alterations in brain network and the neurobiological mechanisms in acute-COVID patients with fatigue. Approach: We utilized graph theory analysis to assess brain network alterations in COVID-19 patients. Results: Results demonstrated the abnormalities in brain network in acute-COVID patients with fatigue, involing left insular gyrus and right inferior frontal gyrus. Morever, the normalized characteristic path length of global brain in acute phase of COVID-19 could predict the development of chronic symptoms. Impact: Our functional MRI findings highlight abnormal brain network metrics in acute phase COVID-19 patients with fatigue, offering valuable neuroimaging marker for early clinical intervention in long-COVID patients.
Robust Rat Liver Lobe Segmentation in Low-SNR T2-weighted Datasets Using a 2.5D Approach
Authors: Hsu Wei-Chan , Zhao Wan-Ting , Herrmann Karl-Heinz , Güllmar Daniel , Wei Weiwei , Dahmen Uta , Lawonn Kai , Reichenbach Jürgen
Published: November 2024

Motivation: Our project was motivated by the lack of an efficient way of segmenting ligated and non-ligated liver lobes in portal vein ligation (PVL) experiments. Goal(s): Our goal was to demonstrate that a 2.5D segmentation approach can achieve precise and robust lobe segmentation in experimental PVL volumetry to reduce manual annotation work. Approach: We stacked adjacent slices as input and trained a U-Net to segment the rat liver lobes using 15 rat T2-weighted datasets. Results: An average Dice score of 0.707 was reached by 5-fold cross validation on 15 datasets, showing the robustness in low-SNR MR images with high intensity variation. Impact: We demonstrate the 2.5D approach is robust in segmenting liver lobes with varied intensity in low-SNR MR images. The framework can greatly reduce manual annotation work even with limited datasets.
MRI-Based Deep Learning for Automatic Segmentation of Punctate White Matter Injury in Neonates
Authors: Sun Qinli , Xia Yuwei , Wang Miaomiao , Li Xianjun , Liu Congcong , Zhao Huifang , Bai Pengxuan , Ge Yao , Shi Feng , Yang Jian
Published: November 2024

Motivation: Punctate white matter injury (PWMI) in neonates is characterized by small lesions and significant sample variability, posing a challenge for quantification. Goal(s): We introduce a novel approach that combines the 3D nnU-Net framework for semantic segmentation of PWMI using neonatal brain MR images. Approach: The PWML automatic segmentation models, based on 3D-T1WI, was developed utilizing V-Net, VB-Net, 2D nnU-Net and 3D nnU-Net. Automatic localization of lesions and quantitative analysis of the brain regions were further realized by segmentation of dHCP template brain regions. Results: The automatic segmentation model demonstrated robust performance, achieving a median Dice Similarity Coefficientn of 0.865 on the test set. Impact: This innovation offers an automatic and accurate segmentation of PWMI regions, potentially providing clinicians with a powerful tool for the automatic localization and classification model construction, quantitative analysis and grading prognostic study of PWMI in neonates.
Quantitative Thyroid Volume Analysis in Healthy, Hypo and Hyperthyroid Individuals Using T2-Weighted MRI and Artificial Intelligence
Authors: Nguyen Thanh-Duc , Garg Saurabh , Akbari Nasrin , Lee Soojin , Datta Madhurima , Rajendran Arun , Basar Saqib , Niotis Kellyann , Chodakiewitz Yosef , Attariwala Raj , Hashemi Sam
Published: November 2024

Motivation: Assess the feasibility of calculating thyroid volumes using T2-weighted MR imaging. Goal(s): Quantify thyroid volume using deep learning methods in healthy, hypo and hyperthyroid patients. Approach: We assessed MRIs of 469 healthy, 606 hypothyroid, and 203 hyperthyroid individuals, matched for age, weight, and height. Results: Findings indicated altered thyroid volumes in healthy (11.02 ml), hyperthyroid (9.42 ml), and hypothyroid (8.35 ml), BMI-normalized volumes also differed: healthy (0.445), hyperthyroid (0.387), and hypothyroid (0.337). There is a moderate association between thyroid volume and weight (0.41, p=3.2e-25) and a weaker link with height (0.17, p=3.9e-05). Impact: MRI-based analysis of thyroid volumes in healthy, hyper and hypothyroid patients using deep learning, revealing varied absolute and BMI-based normalized volumes.
Quantitative Analysis of DCE-MRI Data using DL model based on Signal Intensity vs Concentration Curves
Authors: Prajapati Piyush , Kandpal Ankit , Jafari Raufiya , Gupta Rakesh , Singh Anup
Published: November 2024

Motivation: Quantitative analysis of dynamic-contrast-enhanced MRI(DCE-MRI) is valuable approach for mapping tumor physiology; however, traditional non-linear-least squares(NLLS) methods are slow and provide noisy maps. Deep-learning(DL) approach offers solutions, yet reported models rely on signal-intensity-time-curves(SIC) which are MRI-acquisition protocol dependent. Goal(s): To develop DL network(CNNCON) that uses concentration-time-curves(CTCs) to estimate perfusion-parameters(GTKM) and compare with SIC-based DL network(CNNSIGNAL). Approach: Two CNN networks were developed using CTC and SIC data(simulations and in-vivo). Performance of models was evaluated on simulated data with different protocols and experimental data. Results: The CNNCONC outperforms NLLS & CNNSIGNAL in terms of speed, accuracy and smoothness of maps. Impact: The proposed DL framework improves DCE-MRI analysis by providing more accurate and robust results in less time. It eliminates protocol dependence and holds the potential for routine clinical use in the diagnosis and treatment planning of brain tumor patients.
Automatic Right Ventricular Segmentation of Cardiac Cine Magnetic Resonance Images Based on a Novel Multi-atlas Two-Stage U-net
Authors: Wang Lijia , Su Hanlu
Published: November 2024

Motivation: Right ventricular(RV) segmentation is of great significance for the clinical diagnosis of heart diseases. However, due to the complex structure, RV segmentation is still challenging. Goal(s): Fully automatic and accurate segmentation of the right ventricle. Approach: A new deep atlas network that combines atlas prior knowledge with Deformable Multi-scale Two-Stage U-net(DMTSU-net) is proposed to extract and fuse multi-scale RV features in Cine Cardiac Magnetic Resonance (CCMR) images. Results: Compare with 8 classical methods, the segmentation results of DMTSU-net are mostly close to the gold standard and significantly correlate with it on all evaluation indices in 15 testing datasets. Impact: The proposed framework integrates prior information of atlases into a deep neural network to achieve accurate segmentation, which is promising for clinical heart disease diagnosis.
Clustering for Anatomical Quantification and Evaluation (CAQE): Unsupervised brain segmentation with quantitative MRI
Authors: Balaji Sharada , Obajtek Marek , Vavasour Irene , Dvorak Adam , Gilbert Guillaume , Tam Roger , Laule Cornelia , Li David , Traboulsee Anthony , MacKay Alex , Kolind Shannon
Published: November 2024

Motivation: Traditional image segmentation uses conventional MRI to classify tissue types based on image intensity. However, segmenting using microstructural data may provide more specific classification of tissue. Goal(s): Cluster and segment brain tissue using quantitative MRI measures, to classify tissue based only on microstructural features without spatial input. Approach: Measures denoting myelin content, anisotropy and tissue heterogeneity were clustered and used to label test datasets based on microstructural features alone, using an unsupervised approach. Results: Segmentations were more informative than traditional segmentation, and consistent between healthy subjects. Differences between clusters reflect microstructural feature differences which would otherwise be invisible with conventional imaging. Impact: The CAQE framework can be used for segmentation of tissue based on quantitative measures alone, providing better delineation of regions based on microstructural features. This allows for future comparisons between healthy and damaged tissue, to visualise and interpret pathological changes.
The use of multiparametric brain tumor segmentation for investigating 1H MRSI-detected, fasting-induced ketone body accumulation in glioma
Authors: Alcicek Seyma , Divé Iris , Pilatus Ulrich , Prinz Vincent , Steinbach Joachim P. , Forster Marie-Thérèse , Hattingen Elke , Ronellenfitsch Michael W. , Wenger Katharina J.
Published: November 2024

Motivation: The evaluation of MR spectroscopy imaging findings with multiparametric brain tumor segmentation might facilitate the understanding of altered tumor metabolism induced by intervention on an individual patient/tumor level. Goal(s): In this study, we used this approach to elucidate the glioma metabolism under nutritional intervention. Approach: The concentrations of ketone bodies in brain tumor after 72-hour-fasting were correlated with the volume of glioma sub-regions for 13 brain tumor patients. Results: The outcome indicates that the accumulation of ketone bodies in solid tumors and necrotic areas after fasting might be a result of neovascularization and the blood-brain barrier compromise. Impact: Here, we report on the validation of a dedicated, multi-voxel MRSI protocol with fully automated multiparametric segmentation of glioma sub-regions for monitoring fasting-induced changes.
Harmonization of multi-site diffusion MRI data of Human Connectome Project Lifespan and Disease Studies from 2545 subjects
Authors: Cetin-Karayumak Suheyla , Zurrin Ryan , Cho Kang Ik Kevin , Pieper Steven , O'Donnell Lauren , Rathi Yogesh
Published: November 2024

Motivation: The Human Connectome Project (HCP) is a multi-site neuroimaging initiative that studies brain connections across the lifespan and in diseases. Scanner variability, especially in diffusion MRI (dMRI) data, can introduce bias and prevent reliable pooling of data. Goal(s): We present our harmonization efforts on the dMRI data from 11 HCP datasets using a well-validated harmonization algorithm based on rotation-invariant spherical harmonics. Approach: Using several diffusion measures, we demonstrate that harmonization removes significant statistical differences between datasets. Results: Harmonized HCP dMRI data will be shared in the NIMH Data Archive and facilitate large-scale analysis and potentially enhance our understanding of neurological and psychiatric disorders. Impact: Harmonizing diffusion MRI data from 11 Human Connectome Project scanners enables more reliable cross-site brain connectivity analyses. Leveraging large-scale harmonized diffusion MRI data can enhance statistical power, paving the way for advanced neurological and psychiatric insights within the HCP study.
Low-rank based motion correction followed by automatic frame selection in diffusion tensor CMR
Authors: Wang Fanwen , Ferreira Pedro , Munoz Camila , Wen Ke , Luo Yaqing , Huang Jiahao , Wu Yinzhe , Pennell Dudley , Scott Andrew , Nielles-vallespin Sonia , Yang Guang
Published: November 2024

Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration difficult, and the need to manually reject frames corrupted by motion. Goal(s): To develop a semi-automatic post-processing pipeline for robust DT-CMR registration and automatic frame selection. Approach: We used low intrinsic rank averaged frames as the reference to register other low-ranked frames. A myocardium-guided frame selection rejected the frames with signal loss, through-plane motion and poor registration. Results: The proposed method outperformed our previous noise-robust rigid registration on helix angle data quality and reduced negative eigenvalues in healthy volunteers. Impact: This improved image registration and frame selection algorithm may enable groupwise deformable registration on DT-CMR, paving the way towards clinical translation.
Structural Volumetric and Periodic Table DTI Patterns in Complex Normal Pressure Hydrocephalus – Towards Principles of a Translational Taxonomy
Authors: Keong Nicole , Lock Christine , Toh Emma
Published: November 2024

Motivation: The Periodic Table of DTI Elements organizes neural tract patterns by their diffusivity and neural properties but mainly describes white matter injury. Goal(s): Our goal was to expand the utility of this methodology by interrogating other subcortical and cortical structures of interest and comparing them to brain metrics in controls derived from an open-access dataset. Approach: We examined brain volumes and DTI profiles in Complex Normal Pressure Hydrocephalus (CoNPH) vs. healthy controls from ADNI. Results: Lateral ventricular volumes were significantly higher but most other volumes significantly lower in CoNPH vs. controls. Most DTI metrics (supratentorial and infratentorial) were significantly higher in CoNPH. Impact: The Periodic Table of DTI Elements can demonstrate white matter and cortical injury and alterations of grey matter structures. The strategy can be used to compare cohorts with controls-in-common from an open-access dataset for wider community use.
Quantifying uncertainty in diffusion MRI: a comparative study at different magnetic fields
Authors: Cipriano Emilio , Bosco Paolo , Lancione Marta , Biagi Laura , Tosetti Michela
Published: November 2024

Motivation: Diffusion MRI (dMRI) is a very noisy MRI technique. The low signal-to-noise ratio introduces uncertainties in diffusion metrics and structural connectivity measures. Goal(s): We searched for a method to investigate the uncertainty in dMRI measurements and to compare them across different magnetic field strengths. Approach: We assessed uncertainties using wild and residual bootstrapping techniques in a group of 7 healthy subjects undergoing optimized acquisition protocols at 1.5T, 3T, and 7T. Results: 7T diffusion metrics and uncertainties are comparable with 3T and exhibited significant differences with 1.5T. The enhanced 7T spatial resolution demonstrated capabilities in representing structural connectivity with greater complexity and reduced uncertainty. Impact: The findings support the hypothesis that 7T dMRI can offer new insights into structural connectivity and may be particularly valuable for single-subject studies, thanks to its ability to detect more connections and reduce uncertainty compared to clinical fields.
Subject-specific analysis approach to longitudinal tracing of heterogeneous white matter abnormalities in sports-related concussion
Authors: Yang Ho-Ching , Dzemidzic Mario , Wen Qiuting , Riggen Larry , Broglio Steven , McCrea Michael , McAllister Thomas , Harezlak Jaroslaw , Wu Yu-Chien
Published: November 2024

Motivation: Unlike groupwise approaches, subject-specific analysis has the potential to pinpoint highly individualized abnormalities in white matter structure caused by sport-related concussion (SRC). Goal(s): Develop a subject-specific analysis approach to investigate the heterogeneity and longitudinal changes in white matter microstructures after SRC. Approach: The DTI metrics were voxel-by-voxel Z-transformed using a normal distributed template created from non-contact sport controls. The extreme Z maps were obtained, and averaged extreme Z-scores were compared across three study time points. Results: The existence of heterogeneity in the concussed brains can be appreciated in the projected extreme Z maps and their longitudinal trajectories. Impact: We developed a subject-specific analysis pipeline to demonstrate heterogeneity in sport-related concussion with respect to anatomical locations and recovery trajectories.
Evaluation of diffusion MRI methods and fiber tracking algorithms for optimal imaging of the anterior visual pathway
Authors: Kumar Gurucharan Marthi Krishna , Hao Ziqi , Mendola Janine , Shmuel Amir
Published: November 2024

Motivation: Diffusion-weighted MRI of the anterior-visual-pathway (AVP) is challenging due to the small diameter of the optic nerve and susceptibility artifacts. Goal(s): To compare the performance of diffusion MRI acquisition, tractography, reconstruction, and fiber-tracking methods in reconstructing the AVP. Approach: The overlap between a mask based on track-density and the T1-weighted imaged AVP was used to evaluate the accuracy of the reconstruction. Results: Readout segmented EPI (RESOLVE) and the combined use of Constant Solid Angle (CSA) reconstruction with Parallel Transport Tractography (PTT) scored best. Impact: Our findings support an optimal selection of methods for imaging the AVP in clinical research and radiology clinics.
Creation of a cross-population white matter atlas for concurrent mapping of brain connections between Eastern and Western populations
Authors: Li Yijie , Zhang Wei , Wu Ye , Yin Li , Chen Yuqian , Cetin-Karayumak Suheyla , Cho Kang Ik , Zekelman Leo , Rushmore Jarrett , Rathi Yogesh , Makris Nikos , O'Donnell Lauren , Zhang Fan
Published: November 2024

Motivation: Existing white matter atlases are usually created based on a certain population, which may omit subtle differences across populations from different cultures. Goal(s): This study presents a fine-scale white matter atlas that is created concurrently using high-quality diffusion MRI data from both Eastern and Western populations. Approach: The curated atlas includes a cluster-level parcellation of 800 fiber clusters from the entire brain and an anatomical tract parcellation of 53 major long-range white matter connections. Results: Comparative assessment between the two populations within the atlas shows highly visually similar white matter geometry but significant differences when measuring streamline counts in the fiber parcels. Impact: We propose a diffusion MRI tractography atlas that enables concurrent white matter parcellation across Eastern and Western populations. While the white matter geometry is visually similar, the number of streamlines in the fiber parcels differs significantly between the two populations.
Multi-modal multi-scale imaging reveals that long-association cortico-cortical systems are composed of short-range relay fibers
Authors: Maffei Chiara , Dann Evan , Jones Robert , Celestine Marina , Wang Hui , Haber Suzanne , Yendiki Anastasia
Published: November 2024

Motivation: Obtaining accurate anatomical information of connectional neuroanatomy across scales is crucial to improve in-vivodiffusion MRI techniques and advance our understanding of the brain white matter circuitries. Goal(s): To reveal the mesoscopic organization of the SLF-I, a major cortico-cortical fiber association system of the human brain. Approach: We combine multi-scale, multi-species, multi-modality connectional data from humans and macaques to investigate the structural connectivity of medial fronto-parietal cortical regions. Results: We provide preliminary novel evidence that the SLF-I is composed of a succession of shorter relay fibers, which, in lower-resolution dMRI tractography result erroneously in a long, direct association bundle. Impact: The mesoscopic anatomical validation of major white matter pathways in the human brain will increase the accuracy of their reconstruction in vivo, and open new avenues for our understanding of the functional substrates of these different connections.
ATM: Anatomy to Tract Mapping
Authors: Tan Yee-Fan , Liu Siyuan , Phan Raphaël C.-W. , Ting Chee-Ming , Yap Pew-Thian
Published: November 2024

Motivation: Conventional diffusion tractography relies on error-prone voxel-to-voxel tracing and typically demands diffusion MRI with high signal-to-noise ratio, spatial and angular resolution, which can be challenging to acquire. Goal(s): To generate bundle-specific streamlines from anatomical MRI. Approach: We present a deep learning framework for anatomy to tract mapping (ATM), allowing bundle-specific streamlines to be generated from anatomical MRI. ATM generates streamlines without resorting to voxel-to-voxel tracing, hence sidesteps challenges involved in tracing across complex configurations such as crossings, kissing, and bending and the bottlenecks where multiple bundles converge toward before re-emerging. Results: ATM effectively captures bundle shapes and generates bundle-specific streamlines from T1-weighted MRI. Impact: We demonstrate that tract streamlines can be estimated directly from anatomical MRI. This allows (1) tractography in the absence of diffusion MRI and (2) anatomy tractography to guide diffusion tractography.
Tractography of Human Intervertebral Disc Using High Resolution Diffusion Tensor Imaging
Authors: Wei Zhao , Yang Wenhui , Moazamian Dina , Jerban Saeed , Bydder Graeme , Du Jiang , Chang Eric , Ma Yajun
Published: November 2024

Motivation: Evaluation of the fiber microstructure is valuable for assessing IVD degeneration. Goal(s): To investigate the three-dimensional fiber structure and orientation of the intact IVD. Approach: A high resolution DTI protocol was used to reconstruct the 3D fiber structure of an IVD specimen at the microstructural level on a 3T MRI scanner. Results: The concentric lamella structure and interlamellar fibers were observed in the AF. The AF fiber orientations exhibited circumferential variability around the AF ring. The nucleus pulposus fibers exhibited a tri-directional distribution with perpendicular orientations. The major fibers in the cartilaginous endplate were horizontally orientated in the anterior-posterior direction. Impact: The primary fiber orientations within human IVD tissues, including those of the annulus fibrosus, nucleus pulposus, and cartilaginous endplate, were observed using high resolution DTT, which may be a promising tool for IVD degeneration and regeneration study.
Fast full-brain relaxometry in the clinic using MR-STAT
Authors: Schilder Martin , Mandija Stefano , Jacobs Sarah , Kleinloog Jordi , Liu Hanna , van der Heide Oscar , Köktaş Beyza , D'Agata Federico , Keil Vera , Snijders Tom , Vonken Evert-Jan , Dankbaar Jan Willem , Hendrikse Jeroen , van den Berg Cornelis , van der Kolk Anja , Sbrizzi Alessandro
Published: November 2024

Motivation: This is the first work assessing quantitative values (T1 and T2) from Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) as a fast relaxometry technique in clinical setting. Goal(s): To assess MR-STAT as viable option for fast relaxometry in the clinic. Approach: We applied MR-STAT to investigate the quantitative T1 and T2 values of brain tissue at 3T in a heterogeneous cohort of 50 subjects (10 healthy volunteers, mixed-pathology 40 patients). Results: Quantitative values in normal appearing brain tissue were comparable to earlier literature. Furthermore, individual case examples (glioma, multiple sclerosis) confirmed the ability to discern pathological tissue in T1 and T2 values. Impact: Voxel values in clinical MRI are subjective. Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) quickly quantifies MR tissue properties and gives voxels quantitative values. This work demonstrates MR-STAT as fast relaxometry technique in a clinical population and assesses quantitative values.
Myelin water fraction mapping of in-vivo and ex-vivo human brains at 3T and 7T
Authors: Xu Guojun , Zhao Zhiyong , Zhu Qinfeng , Cao Zuozhen , Shen Yiqi , Shen Yao , Li Sihui , Zhu Keqing , Zhang Jing , Wu Dan
Published: November 2024

Motivation: Multi-echo gradient-echo (mGRE) MRI enabled non-invasive quantification of myelin water fraction (MWF) of the human brain. Goal(s): The MWF may depend on field strength that changes the T2* decay and the results need to be verified by histological staining. Approach: We performed mGRE-based MWF on in-vivo and ex-vivo human brain at high resolution and revealed the accuracy of the measurements using histological staining at both 3T and 7T. Results: The MWF-derived from 7T was systematically higher than those from 3T and the in-vivo and ex-vivo measurements showed good agreement. The MWF at 3T and 7T both demonstrated good correlations with myelin basic protein. Impact: These findings indicated the MWF mapping could reliably depict the myelin content in the human brain, although the measurement were field-strength dependent.
Improved chemical exchange detection using magnetization transfer (MT) effect independent composite metric (R2-R1rho): a preliminary study
Authors: Qian Yurui , Zhou Ziqin , Hou Jian , Yu Ziqiang , Jiang Baiyan , Chen Weitian
Published: November 2024

Motivation: Composite metric R2-R1rho (1/T2–1/T1rho) could be utilized for probing chemical exchange.. However, a persistent challenge lies in the confounding influences of magnetization transfer (MT) effects on R2, which was not fully discussed in literatures. Goal(s): In this study, we reported our investigation on this effect and proposed an acquisition strategy to improve the specificity of the composite metric (R2-R1rho) to thethe chemical exchange effect. Approach: We demonstrated our findings and the proposed method using simulation, phantom, and in vivo experiments. Results: From our preliminary results, composite metric (R2-R1rho) to detect chemical exchange without the influence from magnetization transfer effect. Impact: Using improved composite metric (R2-R1rho) to detect chemical exchange without the influence from magnetization transfer effect.
Diffusion Time Dependent Radial Diffusivity & Myelin qMRI in Ex Vivo Ferret Spinal Cord
Authors: Alderson Hannah , Does Mark , Harkins Kevin
Published: November 2024

Motivation: There remains a gap in knowledge surrounding what microstructural features impact similar myelin qMRI metrics. Goal(s): The goal of this work is to evaluate the relationship between a novel axon diameter surrogate and myelin qMRI metrics. Approach: Ex vivo ferret spinal cords were imaged at 15T using multiple spin echo, selective inversion recovery and diffusion tensor imaging sequences. Results: BPF was found to have a negative correlation with the axon diameter surrogate measure. MET2 derived metrics were found to have no correlation with the axon diameter surrogate measure. Impact: The diffusion time dependence of the radial diffusivity is thought to report on axon diameter towards detecting microstructural changes as a result of pathology. This work provides further evaluation of this metric and other relaxation parameters of white matter.
NAD+ proton T1 and T2 relaxation measured in vivo in the human brain at 7T using single-slice spectrally-selective downfield MRS
Authors: Swago Sophia , Wilson Neil , Elliott Mark , Nanga Ravi Prakash Reddy , Reddy Ravinder , Witschey Walter
Published: November 2024

Motivation: NAD+ is a key metabolite in aging and disease, but its absolute in vivo tissue quantification requires correction for T1 and T2 relaxation effects. Goal(s): Our goal was to determine T1 and T2 of NAD+ in human brain in vivo at 7T. Approach: We utilized spectrally-selective downfield spectroscopy with slice localization. Results: We measured an average T1 of 164.6±28.1ms and an average T2 of 33.5±10.3ms across three NAD+ downfield resonances. Impact: Our measurements of NAD+ T1 and T2 in human brain can be used as correction factors to quantify absolute concentration of NAD+, a potential biomarker to study metabolic derangements in many diseases.
Investigating the relation between cardiac-induced brain tissue strain and both global boundary conditions and local microstructure
Authors: Ingeberg Marius Burman , van Houten Eli , Froeling Martijn , Zwanenburg Jaco
Published: November 2024

Motivation: Recent developments enabled to measure brain tissue strain as induced by arterial pulsations in detail. This opens the opportunity to study how these strains are affected by the brain’s macroscopic environment and its local microstructure. Goal(s): To explore to what extent the strain principal strain directions can be explained by both global boundary conditions and local tissue microstructure. Approach: Systolic 3D strain measurements of the brain were compared with a brain model and DTI measurements. Results: The first principal strain showed good agreement with the brain model and consistent spatial patterns were observed in comparisons between third principal strain and DTI data. Impact: Our results help confirm previous ideas on how the brain swells during cerebral arterial pulsations while also providing a first view into the relationship between the direction of the Poisson effect and brain microstructure, opening up avenues for further research.
Non-Linear Echo Combination Allows for Reduced Scan Times in T2* Weighted Imaging at 1.5T
Authors: Burns Brian
Published: November 2024

Motivation: Scan times for multi-echo T2*w sequences are too long at 1.5T. Goal(s): Shorten scan times by reducing the number of echoes needed for a specific contrast. Approach: During this ISMRM, we introduced a novel echo combination method that increases contrast over the standard Root Sum-of-Squares (RSOS) technique. We applied this method to 7 and 6 echo 3D SPGR protocols and compared the results to a 9 echo protocol that used RSOS. Results: Contrast was comparable or better in the 6 and 7 echo protocols using our technique, compared to the 9 echo protocol with RSOS, resulting in a scan time reduction of 23-26%. Impact: Our echo combination technique allows fewer echoes in multi-echo T2*w imaging to match the contrast of higher echo protocols resulting in a 23-26% scan time reduction. This alleviates one of the primary concerns clinicians have using T2*w imaging at 1.5T.
4D flow analysis of whole-brain cerebral blood flow in patients with Moyamoya disease before and after direct revascularization—A pilot study
Authors: Yuting Wang , mingfang Luo , Zhenyu Wang , Xiao Hu , Meining Chen , Yishuang Wang , Bo He
Published: November 2024

Motivation: The hemodynamic changes after superficial temporal artery-middle cerebral artery (STA-MCA) bypass in patients with Moyamoya disease (MMD) remain unclear. 4D flow is expected to provide more information about the hemodynamic parameters of patients with MMD before and after surgery. Goal(s): To clarify the hemodynamics by comparing flow parameters obtained by 4D flow magnetic resonance imaging (MRI). Approach: Patients with MMD were prospectively recruited for 4D flow magnetic resonance scanning before and after bypass surgery. Results: After bypass surgery, the contralateral internal carotid artery blood flow decreased and cerebral blood flow was redistributed. Impact: The results indicate that 4D flow MRI is a feasible, noninvasive alternative for evaluating bypass grafts in the early postoperative period. These flow parameter values may help improve the way patients are evaluated after bypass surgery.
Cerebral arterial venous blood flow dynamics under the influence of free and deep breathing
Authors: LIU Pan , Owashi Kimi , Monnier Heimiri , Capel Cyrille , Metanbou Serge , Balédent Olivier
Published: November 2024

Motivation: The hydrodynamic characteristics of arterial venous cerebral blood flow (AVCBF) in different breathing patterns remain insufficiently understood. Goal(s): Quantifying the effects of free and deep breathing on multiple parameters of CBF using real-time phase contrast MRI. Approach: We applied RT-PC on 14 healthy volunteers to continuously quantify AVCBF dynamics in the main cerebral vessels during free and deep breathing. A time-domain analysis investigated the influence of breathing change on the AVCBF parameters: mean flow, stroke volume, cardiac period. Results: Deep-breathing decreased global AVCBF dynamics. Mean flows, stroke volumes and cardiac periods decreased but their percentage change between inspiratory and expiratory periods increased. Impact: This study quantified the effects of free- and deep-breathing on cerebral blood flow dynamics. It should contribute to a better understanding of cerebral hemodynamics and its relation with breathing, providing a valuable reference for clinical applications and physiological studies.
Signal changes of amide proton transfer weighted imaging in patients with moyamoya disease: a prospectively longitudinal study
Authors: Li Hongxia , Xia Chao , Liu Zhiqin , Zeng Jiaxin , Wei Xia , Li Ziyu , Li Xing , Yu Wei , Sun Yuan , Liu Yi , Ai Kai , Hu Na , Lui Su
Published: November 2024

Motivation: Identifying the dynamic changes of the brain microenvironment in patients with moyamoya disease (MMD) would improve disease management and treatment. Goal(s): To explore the alteration of amide proton transfer weighted (APTw) signal in patients with MMD during the follow-up. Approach: MMD patients who had not received revascularization were prospectively included. All patients underwent cranial computed tomography perfusion (CTP) and APTw imaging at baseline and at follow-up. Results: APTw signal in bilateral internal carotid arteries blood supply area significantly increased in MMD patients without surgery during follow-up, and the change of APTw signal was more sensitive than that of CTP. Impact: APTw imaging could reflect dynamic alterations of the brain microenvironment of patients with MMD over time, and the changes of APTw signal were more sensitive than CTP, which is helpful to improve clinical management.
How Does the Function of “Small” Vessels Change in Small Vessel Disease?: Insights from Multimodal Cerebral Hemodynamics Imaging at High-fields
Authors: Wu Yue , Bai Li , Zhang Jinyuan , Sun Chengyue , Kong Qingle , Li Zhixin , Sun Dongbiao , Ling Chen , An Jing , Zhuo Yan , Xue Rong , Yuan Yun , Zhang Zihao
Published: November 2024

Motivation: Given the limitations in resolution and sensitivity, in vivo studies of microvascular function in small vessel disease(SVD) have been notably scarce. Goal(s): Advanced cerebral-hemodynamic techniques have made it possible to unveil functional alterations in SVD explored in this study. Approach: Utilizing a comprehensive microvascular-functional measurement, including 7T-high-resolution phase-contrast and 3T-ASL modeling, we examined the hemodynamics change and its associations with severity of SVD reflected by multidomain cognitive impairments. Results: Flow velocity in lenticulostriate arteries emerged as the most sensitive indicator, while ASL-derived arterial-transit-time(ATT) and cerebral-blood-flow(CBF), reflecting capillary functions, exhibited reduced sensitivity. Our exploration unveiled insights into microvascular pathology and compensatory mechanisms in SVD. Impact: Utilizing cutting-edge cerebrovascular MRI techniques, multiple microvascular hemodynamic metrics provide novel insights into small vessel disease(SVD) pathology in-vivo, revealing the functional damage and compensatory mechanisms. Also, flow velocity in small arteries is proved a promising imaging marker for SVD progression.
Federated image-to-image MRI translation from heterogeneous multiple-sites data
Authors: Fiszer Jan , Ciupek Dominika , Malawski Maciej , Pieciak Tomasz
Published: November 2024

Motivation: Applying machine learning (ML) in MRI necessitates the development of large and diverse datasets, which is a challenging process. Federated learning (FL) is a new frontier in ML that offers the possibility of multi-site data aggregation. Goal(s): In our study, we examine a traditional deep convolutional neural network applied to multiple sources with that of the FL technique using different aggregation methods. Approach: As a proof-of-concept, we employ four publicly available MRI datasets and carry out image-to-image translation between T1- and T2-weighted scans. Results: Our findings suggest that the FL generalizes the model more effectively than using models trained at each site separately. Impact: Our research demonstrated the crucial role of federated learning in medical imaging. It also emphasized the significance of selecting an appropriate aggregation algorithm considering the data type and degree of heterogeneity.
Federated training of deep learning models for prostate cancer segmentation on MRI: A simulation study
Authors: Wang Kuancheng , Sompalle Pranav , Tonetti Alexander , Zhang Zelin , Einat Tal , Ashush Ori , Madabhushi Anant , Patel Malhar , Shiradkar Rakesh
Published: November 2024

Motivation: Site and scanner specific variations in prostate MRI impact performance of deep learning (DL) based models. Federated learning allows for privacy preserving training of DL models without the need for data sharing. Goal(s): In this study, we train DL models for prostate cancer segmentation on MRI using the Rhino Health federated computing platform. Approach: We adopt 3D UNet architecture to train the DL models on 2 publicly available datasets. Results: DL models trained using a federated approach result in more generalizable models compared to those trained on single site data. Impact: Successful development of deep learning based prostate cancer segmentation models on MRI using federated learning will result in reproducible and generalizable models. These can enhance clinical adoption and potentially improve downstream diagnostic and treatment workflows for prostate cancer.
Synthesizing high-resolution brain MR T1-MPRage-like Images from low-dose CT
Authors: Chen Yasheng , Ying Chunwei , Wang Tongyao , Ford Andria , Lee Jin-Moo , Dhar Rajat , An Hongyu
Published: November 2024

Motivation: Using deep learning to improve the soft tissue contrast in low-dose brain CT similar to MRI. Goal(s): Converting low-dose brain CT to high-resolution T1-MPRAGE-like Images. Approach: A ResUNet-based deep learning approach is developed to learn the complex transformation from low-dose brain CT to its corresponding T1-MPRAGE. Results: With the proposed approach, we obtained high-resolution MR T1-MPRAGE-like images with superior soft-tissue contrasts from noisy low-dose brain CT images. Impact: By transferring brain CT to T1-MPRAGE-like images, our approach provides superior soft tissue contrast from low-dose brain CT images. Our method would allow tissue-specific analysis using noisy non-contrast CT.
MR Contrast-Invariant Deep Learning Method for Synthetic CT Generation
Authors: Kaushik Sandeep , Cozzini Cristina , Wyatt Jonathan , McCallum Hazel , Maxwell Ross , Menze Bjoern , Wiesinger Florian
Published: November 2024

Motivation: Deep learning models are sensitive to image contrast variations. We explore the feasibility of training a single model to process multiple MR contrasts. Goal(s): To generate synthetic CT images from different MR image contrasts using a image contrast agnostic model. Approach: A multi-task deep convolutional neural network has been trained using a variety of MR image contrasts. Results: We demonstrate generation of synthetic CT images from multiple MR images with superior qualitative accuracy and encouraging quantitative accuracy. Impact: The ability to generate synthetic CT from a variety of MR contrasts brings flexibility of choice of MR sequence in MR guided radiation therapy clinical setup. It improves the model robustness to scan parameter variations leading to a consistent outcome.
T2* Cartilage Mapping in Early Axial Spondyloarthritis: Diagnostic Accuracy and Correlation with Clinical Characteristics and Sacroiliitis
Authors: Huang Hongjie , Zhang Yuyang , Zhuang Feifei , Liu Xi , Wu Keyi , Wang Feng , Zhao Xiance , Cao Dairong
Published: November 2024

Motivation: Cartilage degradation has been recognized as an early and crucial feature in axSpA. Goal(s): The objectives of the present study were (1) to determine the performance of T2* cartilage mapping in diagnosing and assessing disease activity in early axSpA, (2) to investigate the interaction of cartilage damage with clinical characteristics and sacroiliitis. Approach: Sacroiliac joints cartilage T2* values were higher in patients with early axSpA compared to controls without axSpA. Results: The combination of T2* values and ‘positive MRI’ improve diagnostic efficiency of axSpA. Sacroiliac joints cartilage damage correlates with age, disease activity, acute sacroiliitis, and structural damage. Impact: T2* relaxation time may be a promising imaging biomarker for diagnosing and differentiating disease activity in early axSpA. T2* mapping could be a recommended addition to routine MRI protocol of SIJs.
Fully Automated Whole-Leg multiparametric quantitative MRI processing, segmentation and analysis.
Authors: Froeling Martijn , Schlaffke Lara , Heskamp Linda
Published: November 2024

Motivation: Quantitative magnetic resonance imaging (qMRI) is a common tool for assessing neuromuscular disorders, but its quantitative parameters often lack specificity and generally do not directly relate to muscle function. Goal(s): In our ongoing MOTION study, we are collecting whole-leg qMRI data and assessing muscle structure, function, and lifestyle in a large cross-sectional cohort to identify confounding factors in qMRI evaluation. Approach: To streamline data analysis for this cohort, we've developed a fully automated muscle-Bids-based data analysis pipeline, including automated muscle segmentation. Results: Here, we introduce our data analysis pipeline, demonstrated using repeated scans of one volunteer. Impact: The implementation of fully automated qMRI data processing streamlines large-scale studies and enhances its integration into clinical workflows. This standardization we expect to reduces variability for more dependable and reproducible outcomes.
Accelerating acquisition of readout-segmented echo planar imaging with a simultaneous multi-slice technique for diagnosing knee lesions
Authors: Liu Simin , Zhang Yao , Ran Jun , Liu Wei , Yin Ting , Li Xiaoming
Published: November 2024

Motivation: knee injury is common in clinical practice. DTI enables quantitative assessment of tissue changes in pathological joints, but its clinical application is limited due to prolonged scan time. Goal(s): This study aims to explore the feasibility of using SMS technique in RESOLVE- DTI in knee joint to reduce acquisition time without compromising image quality. Approach: There protocols, Conventional RESOLVE-DTI with 12 directions, and SMS-RESOLVE-DTI with 12 and 20 directions, were used to fully assess the image quality and quantitative parameters. Results: The use of SMS technique greatly reduced the acquisition time without compromise image quality and quantitative evaluation accuracy. Impact: The SMS technique greatly shortens the acquisition time of RESOLVE-DTI and provides similar image quality, allowing clinical doctors to simultaneously evaluate cartilage and ligaments of the knee joint in one scan. SMS-RESOLVE-DTI has high clinical application potential.
Deep Learning Reconstruction for Turbo Spin Echo Imaging to Accelerate Ankle MRI: A Multi-Reader Study
Authors: Xie Yuxue , Li Xiangwen , Hu Yiwen , Fu Caixia , Li Qing , Nickel Dominik , Tao Hongyue , Chen Shuang
Published: November 2024

Motivation: Ankle MRI usually requires a long examination time. A faster scan with adequate image qualities is desired in clinical practice. Goal(s): To evaluate a deep-learning reconstruction accelerated turbo spin echo (DLR-TSE) sequence in ankle application. Approach: Four radiologists independently assessed the image quality and reviewed structural abnormalities of 56 consecutive patients on DLR-TSE and compared these results with those of conventional TSE. Results: Overall, DLR-TSE achieved superior image qualities with a 57.4% reduction in total acquisition time compared with conventional TSE images. There were no differences in the differentiation of anatomic details, diagnostic confidence, or assessments of structural abnormalities between the two techniques. Impact: Deep learning reconstruction can accelerate the turbo spin echo imaging without compromising the image quality or lesion detectability in ankle MRI.
Accelerated 3D IR-FS-UTE Knee Imaging with High Short-T2 Contrast in Less Than 5 minutes
Authors: Zhong Zheng , Oscanoa Julio , Li Miaowen , Liu Qi , Ye Yongquan , Xu Jian
Published: November 2024

Motivation: 3D DIR-UTE can provide high short T2 contrast that is useful in knee imaging, however with a prohibited long acquisition time. Goal(s): The goal is to maintain high short-T2 contrast while significantly reducing the acquisition time. Approach: The approach involves employing an accelerated IR-prepared fat-saturation UTE sequence along with advanced compressive sensing reconstruction. Results: This approach achieved a threefold acceleration without sacrificing image quality and produced high short-T2 contrast, making structures like the meniscus and ligament clearly visible. Impact: This technique holds potential for various musculoskeletal applications, such as in the early detection of conditions such as osteochondral junction alterations, osteoarthritis and meniscal tears.
Correlation between cartilage degeneration in IR-ZTE MR imaging and histological findings in knee osteoarthritis
Authors: Chiang Shih-Wei , Wang Chao‑Ying , Hsu Yi-Chih , Huang Guo-Shu
Published: November 2024

Motivation: The recently developed IR-ZTE MR technique is being proposed to have osteochondral junction (OCJ) region detection capability. The signal source in IR-ZTE imaging of the OCJ remains to be investigated. Validation by histology would provide strong corroboration of the signal sources. Goal(s): Our goal was to evaluate the correlation between the knee cartilage signal using IR-ZTE and degree of histological degeneration. Approach: Three patients were imaged using IR-ZTE before total knee replacement. Histological analysis was performed using the OARSI osteoarthritis cartilage histopathology assessment system. Results: Results demonstrated that the signal changes of IR-ZTE was correlated strongly degree of histological degeneration in knee cartilage. Impact: IR-ZTE signal seems to serve as an in vivo indicator of the cartilage degeneration in knee OA progression.
Clinical feasibility of accelerated diffusion weighted imaging with deep learning reconstruction for patients with acute neurologic symptoms
Authors: Yim Younghee , Park Sang Ik , Lee Jung Bin , Park Min-young
Published: November 2024

Motivation: Timely diagnosis of patient with acute neurologic symptom is critical. Detecting small lesions on DWI can be challenging and experience plays a significant role, especially when motion artifacts affect image quality. Goal(s): Our goal was to shorten acquisition time and provide highly sensitive images of small lesions, particularly for emergency clinicians unfamiliar with DWI. Approach: We included 80 patients, comparing quantitative and qualitative analyses between conventional and deep-learning DWI. We assessed diagnostic performance among experienced neuroradiologists and primary care physician. Results: Results showed similar image quality between two sequences, but deep-learning DWI exhibited superior lesion conspicuity. Diagnostic accuracy remained consistent between the two. Impact: Deep-learning DWI offers comparable image quality with significant shorter acquisition time. It also enhances detection of tiny brain lesion, providing diagnostic confidence to less experienced clinicians in emergency situation.