bluuebunny/crossref_metadata_2025 huggingface dataset:
Causal discovery of morphological changes in thoracic aorta: Investigation on a large epidemiological non-contrast-enhanced MRI cohort
Authors: Fay Louisa , Hepp Tobias , Yang Bin , Gatidis Sergios , Kuestner Thomas
Published: November 2024

Motivation: The thoracic aorta is often affected by life-threatening, undetected morphological changes. Prior works primarily focused on factors correlating with aortic aneurysms but lack the investigation of causal influences related to morphological changes. Goal(s): Our goal is to perform automatic aortic shape analysis inline on the scanner. We investigate causal dependencies between metadata and thoracic aortic diameter in approx. 30,000 non-contrast-enhanced MRA. Approach: We apply a deep learning framework for shape analysis and Peter-Clark-algorithm to investigate causal influences on the thoracic aorta. Results: We found that sex, age, height, BMI, hypertension, and vascular-stiffness causally impact the aorta’s diameter, whereas diabetes lacks a causal relationship. Impact: This study reveals causal influences on morphological changes of the thoracic aorta using a large epidemiological dataset (~30,000 non-contrast-enhanced-MRA). A deep-learning-based framework supports the identification of causal factors impacting the aortic diameter and thereby, enabling early detection of life-threatening risks.
Clinical Validation of the InnerEye Hippocampal Segmentation Tool
Authors: Schroder Anna , Salhab Hamza , Moggridge James , Micallef Caroline , Wu Jiaming , Vos Sjoerd , Bristow Melissa , Pérez-García Fernando , Alvarez-Valle Javier , Yousry Tarek , Thornton John , Barkhof Frederik , Alexander Daniel , Grech-Sollars Matthew
Published: November 2024

Motivation: Accurate segmentation of the hippocampus provides an important biomarker in neurodegenerative diseases, e.g., Alzheimer’s disease. However, currently available tools are not robust to disease-related atrophy. Goal(s): We aim to demonstrate the accuracy of our InnerEye hippocampal segmentation tool on clinical data. Approach: We fine-tuned our existing model on manually segmented data and externally validated the model on a clinical dataset of patients referred to a dementia clinic. We compare our model to three commonly used segmentation tools. Results: Our model provides significant improvements over currently available tools when tested on an external, clinical dataset. Impact: The hippocampal segmentation model presented in this work provides significant improvements over currently available tools in an external, clinical dataset. Segmentation performance was increased, while run-times were decreased. These results support the tool as a viable alternative in clinical settings.
Impact of Fiber Segmentation Methods on Fiber Quantification Reliability
Authors: Li Lingyu , Tong QiQi , Zhu Silei , Lu Chenxi , He Hongjian
Published: November 2024

Motivation: The reliability of fiber quantification depends on the quality of the fiber tract reconstruction, which is influenced by the method used for fiber segmentation. Goal(s): Our objective was to assess the reliability of different fiber segmentation methods and determine the most suitable strategy for the specific bundle. Approach: We compared the distributions of intra-class coefficient (ICC) for different measurements and tracts across three widely used fiber segmentation methods. This analysis was conducted using the traveling subject dataset. The results are presented along with examples, followed by a discussion of the underlying reasons. Results: We summarized the advantageous strategies for main tracts. Impact: This strategy aims to enhance the stability of study results by facilitating the selection of more efficient segmentation methods for conducting fiber-specific quantification in the future.
Observation of morphochemical changes and water molecule dynamics of Trypoxylus dichotomus in the pupal stage using 9.4-T MRI
Authors: Ikegami Shoto , Harada Ren , Takei Kyoya , Osaku Kenji , Oda Yoshiki , Niihara Kinuko , Yoshida Masafumi , Inoue Takashi A. , Honda Keiichi , Kuroda Kagayaki
Published: November 2024

Motivation: Clarification of tissue destruction and reconstruction process of holometabolous insects might lead to innovative technology for regenerative medicine.Goal(s): Observation of Morpholochemical changes and water molecule dynamics in the pupal body. Approach: We applied T2W, DTI and PRESS to the pupal body of Trypoxylus dichotomus.Results: From the pupation, the digestive tract swelled and became a liquid reservoir. It has high ADC and FA in the Head-Foot direction. After the pupation, the structure was narrower and elongated. The results suggest that destructed tissues were stored temporarily in the form of reservoir, and through the liquid channel around the reservoir to reconstruction tissues.Impact: In the pupal body, the tissue absorbed into the digestive tract while some of the nervous system and muscles are retained formed a liquid reservoir, used to form adult tissues such as flight muscles through certain liquid channel.
Validating a novel normalization method - 'Pscore' to assess individual deviations using diffusion MRI data from the Human Connectome Project
Authors: Hafiz Rakibul , Irfanoglu M. Okan , Nayak Amritha , Pierpaoli Carlo
Published: November 2024

Motivation: Conventional methods like Zscores are often used to evaluate individual patients against a normative distribution. This can lead to biased estimations when distributions are skewed. We showed this in a pilot study using 48 controls and proposed a novel metric - 'Pscore', to address this bias. We aimed to reproduce these results systematically using a larger dataset. Goal(s): Validate the 'Pscore' approach on a large-scale high resolution neuroimaging dataset. Approach: Diffusion MRI data from the Human Connectome Project (HCP) was used to test various normalization methodsResults: Pscores demonstrate symmetric distributions and no systematic biases observed in Zscores of all diffusion MRI derived metrics. Impact: The non-Gaussian nature of neuroimaging data has implications for building normative databases and their use to assess abnormalities in individual subjects. The proposed 'Pscore' approach reliably addresses this, which implies its usefulness for individual assessments even in smaller neuroimaging datasets.
Atlas-based analysis of diffusion imaging may predict efficacy of constraint-induced movement therapy in stroke rats
Authors: Zhao Xinxin , Li Ce , Bai Yulong , Xu Jianrong , Zhou Yan
Published: November 2024

Motivation: Atlas-based diffusion imaging of whole brain white matter analysis might be helpful in evaluating obscure motor function recovery in chronic stroke. Goal(s): Our goal was to evaluate the potential of diffusion imaging atlas-based in predicting motor function recovery in stroke rats with constraint-induced movement therapy. Approach: We used atlas-based analysis of diffusion imaging to conduct multi-parameter measurements of white matter fibers in the entire brain, combined with catwalk-automated gait analysis to test quantificationally evaluated the motor function. Results: Atlas-based analysis of diffusion observations indicated that treatment with constraint-induced movement therapy improved microstructural integrity of axon and myelin. Impact: Atlas-based analysis of diffusion imaging were intended to advance non-invasive imaging approaches for understanding microstructural changes of axon and myelin in chronic stroke.
Optimizing data sampling pattern and analysis algorithm in T2-based water suppression diffusion MRI (T2wsup-dMRI)
Authors: Kimura Tokunori
Published: November 2024

Motivation: It is very important to suppress CSF partial-volume effects in tissue-specific quantitative parameters of T2, T1, PD, ADC, and FA etc. in brain MRI. Goal(s): To assess and confirm the optimal method to improve the accuracy and precision of quantitative parameters in our proposed method of T2wsup-dMRI. Approach: Evaluated the tissue SNRs of quantitative parameters for in-vivo brain MRI data with the several combinations of data sampling pattern in (TE, b) space and analysis algorithm. Results: The combination of Triangle-pattern and 2d-single and bi-exponential combined LSQ fitting (2dSi&BiExpLSQ) was the best from the views of SNR, hardware, and computing costs. Impact: The combination of Triangle sampling pattern and 2dSi&BiExpLSQ fitting algorithm in T2wsup-dMRI provides high quality maps with minimum hardware and computing costs for obtaining multi-quantitative parameter mapping especially in clinical brain diffusion MRI.
The assessment of the within-participant reliability of fractional anisotropy measurements of different spinal cord regions.
Authors: Al-shaari Hussein , Fulford Jonathan , Heales Christine
Published: November 2024

Motivation: DTI-MRI reliability studies are performed to validate findings, improve clinical applications, assure scientific precision, and advance methodology. Goal(s): The aim was to assess the within-participant reliability of diffusion tensor imaging (DTI) measurements, specifically fractional anisotropy (FA) determinations, within different regions of the (CSC). Approach: In total, 20 healthy controls were recruited over two months. Each participant was scanned twice. The within-participants coefficients of variation (CV%) was used to evaluate the reliability of FA metric between (C2-C5) for the WM, and WM sub-regions. Results: In general, the CV%s were low for the WM, DC, VC and LC regions demonstrating higher reproducibilityImpact: Results from this study indicated high within-participants reliability and demonstrated that FA may be highly effective in assessing CSC changes. Previous studies have used semi-automated or manual segmentation methods, while this study used automatic-segmentation by SCT, which provides tract-based analysis.
Short association fibre tractography predicts retinotopy in higher visual areas from retinotopy in lower visual areas
Authors: Attar Fakhereh Movahedian , Kirilina Evgeniya , Edwards Luke J. , Haenelt Daniel , Pine Kerrin , Trampel Robert , Chaimow Denis , Weiskopf Nikolaus
Published: November 2024

Motivation: High spatial resolution diffusion weighted imaging (DWI) tractography may enable mapping of cortical topological structure based on white matter connectivity organisation as an additional method to functional MRI techniques. Goal(s): We investigated whether retinotopic mapping of primary visual cortex (V1) combined with short association fibres tractography could predict retinotopic organisation in the higher visual areas (V2, V3). Approach: We used sub-millimetre resolution DWI tractography combined with functional retinotopic mapping in a group of participants in vivo. Results: Our results demonstrated high accuracy and precision, especially at group level. Prediction was less reliable in the anatomical direction with the most pronounced gyral bias effects. Impact: Sub-millimetre resolution diffusion weighted imaging tractography enabled mapping of cortical topology by exploiting the topological structure of underlying short association fibre connections, opening the door to applications exploring brain reorganisation in response to injury or pathology from a new perspective.
Superficial White Matter Classification using Diffusion MRI Tractography with Spiking Neural Networks
Authors: Sattar Aamir , Li Cheng , Zhang Fan , He Jianzhong , Zheng Hairong , Wang Shanshan
Published: November 2024

Motivation: The investigation of superficial white matter (SWM) poses challenges due to its small size, variability, delicate structure, high curvature, and fiber crossings in diffusion MRI tractography. Goal(s): Our goal is to develop an innovative methodology for classifying SWM streamline clusters using diffusion MRI tractography, leveraging brain-inspired learning-based techniques. Approach: A dual-phase method with Spiking Neural Networks (SNNs) and leaky integrate and fire (LIF) neurons is developed for the classification of 199 SWM clusters. Results: Experiments were conducted using two open-source datasets, and our method achieves accurate SWM classification results with an accuracy of 93.73%. Impact: The findings of this study on SWM classification hold great potential for facilitating analyses of SWM within neuroscientific research, contributing to understanding the complexities and alterations in SWM associated with various health conditions and neurological disorders.
SH-CASA: A Novel Algorithm for Denoising Diffusion MRI Data using Spherical Harmonics
Authors: Zucchelli Mauro , Papageorgakis Christos , Dipasquale Ottavia , Casagranda Stefano
Published: November 2024

Motivation: The diffusion MRI (dMRI) signal exhibits a low signal-to-noise ratio. Goal(s): This study endeavors to enrich the quality of dMRI data by employing a pioneering denoising technique, which combines Component Analysis with Standard-deviation Attenuation (CASA) and Spherical Harmonics (SH). Approach: Comparative analysis is conducted between the denoising capabilities of SH-based decomposition and the original PCA-based CASA technique using synthetic and in-vivo data. Results: The findings demonstrate that both denoising methods notably enhance image and tractography quality. In the case of synthetic data, SH-CASA displayed the most substantial improvement. Impact: Our novel denoising technique, SH-CASA, significantly enhances both the quality of raw diffusion MRI data and the resulting tractography. This holds particular significance in clinical settings where rescanning the patient is not always feasible.
Filtering of spurious streamlines via streamline orientation and pathway density
Authors: Delinte Nicolas , Macq Benoit
Published: November 2024

Motivation: Tract extraction from whole-brain tractograms requires either an extensive knowledge of inclusion and exclusion zones or manual efforts to obtain clean tracts. Goal(s): Automated filtering of spurious streamlines can accelerate the tract extraction process. The algorithm should be versatile, while preserving tract shape and minimizing parameter adjustments. Approach: We developed a filtering algorithm based on streamline direction and density along an average trajectory. Our method was compared to four other filtering implementations. Results: Our algorithm is applicable to a wide variety of tracts with a high and low streamline count. It offers efficient filtering and provides a conservative filtering preserving tract morphology. Impact: We introduced an efficient filtering algorithm, removing spurious streamlines while preserving tract morphology across tracts of low and high density with default parameters. Additionally, the computed average trajectory enables the analysis of metrics in multi-fixel models along the tract pathway.
Analytical T1, T2, proton density, and magnetic field inhomogeneity quantification in the brain using phase-cycled bSSFP
Authors: Plähn Nils , Safarkhanlo Yasaman , Bonanno Gabriele , Mackowiak Adèle , Açikgöz Berk , Peper Eva , Bastiaansen Jessica
Published: November 2024

Motivation: Addressing the need for simplified, time-efficient, and unbiased quantitative imaging in human brain. Goal(s): Evaluating novel analytical method, ORACLE, to simultaneously quantify T1, T2, proton density (PD), and magnetic field inhomogeneity (B0) in human brain based on balanced steady-state free precession (bSSFP) profiles. Approach: Acquiring bSSFP data for simultaneous multi-parameter quantification and reference multi-echo spin-echo, MP2RAGE and dual-echo gradient-echo data in 4 human subjects. Results: Quantifications using bSSFP profiles and ORACLE was consistent with reference methods, although magnetization transfer effects led to a 15% consistent underestimation of T1. Proposed method has faster acquisition and parametric estimation comparing to other methods combined. Impact: This novel analytical time-efficient method extracts a wide range of quantitative parameters from bSSFP profiles, which can be a valuable alternative to existing reference methods, multi-echo spin-echo, MP2RAGE and dual-echo gradient-echo, that quantify one parameter at a time.
Improving in-vivo myelin and iron mapping from relaxation rates maps by incorporating relaxation rate changes from in-vivo to ex-vivo conditions
Authors: Fritz Francisco , Streubel Tobias , Mordhorst Laurin , Mushumba Herbert , Püschel Klaus , Morozova Maria , Morawski Markus , Jäger Carsten , Kirilina Evgeniya , Weiskopf Nikolaus , Mohammadi Siawoosh
Published: November 2024

Motivation: Estimating myelin and iron concentration maps from in-vivo MRI yields biased estimates because MR-to-microstructure linear mappings are derived from fixed-postmortem human brain tissue. Goal(s): We assessed whether taking into account the changes of relaxation rates from in-vivo to hydrated fixed ex-vivo specimens would allow the use of current MR-to-microstructure linear mappings for in-vivo MRI. Approach: We introduced a pipeline that accounts for the major relaxation-rate changes during fixation and hydration, and compared the estimated MRI-based myelin parameters to their counterparts from light microscopy in the human corpus callosum. Results: We found that including these changes significantly improved the accuracy of the myelin estimates. Impact: We proposed a new method that significantly improves the MRI-based myelin and iron maps estimation from in-vivo longitudinal and effective transverse relaxation rates.
Evaluating the Impact of Glymphatic Clearance Dysfunction on Cognitive Function in Cerebral Small Vessel Disease
Authors: Yang Jie , Zhong Jianquan , Xiao Rui , Wu Hengping , Yuan Chao , Chen Meining , Hao Feng
Published: November 2024

Motivation: The pathogenesis of cerebral small vessel disease (SVD) is unclear, which may be related to glymphatic clearance. Goal(s): To assess the association between glymphatic clearance function and the extent of cerebral damage in SVD, along with the effects on cognitive function. Approach: We derived the ALPS index from DTI data to evaluate lymphatic function, and we utilized MR imaging to determine the SVD score as a measure of cognitive function. Results: We found a significantly decrease in ALPS index in SVD patients compared to healthy controls, and a significantly negative correlation between the ALPS index in the left hemisphere and SVD severity. Impact: Glymphatic clearance dysfunction may be present in patients with SVD and may be associated with decreased cognitive function.
Comparison of whole-brain cerebrovascular reactivity mapping measured by resting-state and carbogen-based BOLD fMRI for moyamoya disease
Authors: Yeh Tzu-Chen , Wu Hung-Yi , Chen Ting-Yi , Yu Pei-Chen , Cheng Chou-Ming , Chou Chi-Che
Published: November 2024

Motivation: Mapping of cerebrovascular reactivity (CVR) using resting-state BOLD fMRI (RS-fMRI) was raised in 2017 and 2021 with verification using the moyamoya disease (MD). But pathological hemodynamics of MD was missing by RS-fMRI. Goal(s): Arterial stealing phenomenon can be demonstrated by carbogen-based fMRI (CO2-CVR), but not by RS-fMRI. Approach: Both RS-CVR and CO2-CVR using a 3T MR and an automatic delivery system of carbogens were acquired for eleven subjects and ten patients with MD. Bland-Altman plots was applied for both RS-CVR and CO2-CVR. Results: By using CO2-CVR, unique negative CVR of MD verified the arterial stealing phenomenon which was missed by RS-CVR. Impact: Arterial stealing phenomenon of moyamoya disease/syndrome can be demonstrated as negative cerebrovascular reactivity by carbogen-based fMRI (CO2-CVR), but not by resting-state fMRI (RS-CVR).
Vascularization's Impact on Neuronal Metrics in Human Visual Cortex
Authors: Schellekens Wouter , Winawer Jonathan , Petridou Natalia
Published: November 2024

Motivation: We aimed to unravel the intricate relationship between neuronal metrics and vascularization in human visual cortex, addressing the need for a deeper understanding of these factors' impact on fMRI data. Goal(s): Our study sought to determine if differences exist in pRF sizes between micro- and macro-vascular compartments and the influence of extra- and intra-vascular effects. Approach: We obtained pRF estimates across cortical depth using different fMRI scan sequences (SE/GE) at varying field strengths (7T/3T). Results: While our findings confirmed typical pRF size trends and vascularization-dependent amplitude effects across cortical depth, we did not find that vascularization or magnetic field strength affected pRF sizes. Impact: This study's findings challenge the conventional understanding of how vascularization affects neuronal metrics in functional brain imaging. The research underscores the complexity of neurovascular interactions and their implications for the interpretation of fMRI data.
Combining domain knowledge and foundation models for one-shot spine labeling
Authors: Anand Deepa , Saxena Ashish , Bhushan Chitresh , Shanbhag Dattesh
Published: November 2024

Motivation: Spine labelling is a step crucial for several important tasks such as MRI scan planning or associating image regions with mentions in clinical reports and others. Automating it can lead to significant benefits but developing automated solutions requires extensive annotations of vertebra labels. Goal(s): To automate spine labelling without extensively training a DL model with manual annotations. Approach: We adapted a vision foundational model-based approach that combines spine domain knowledge to predict spine labels. Results: Our spine labelling method gives an average accuracy of 79% and 86% for cervical and lumbar high resolution T1 images, respectively. Impact: Leveraging spatially relevant landmarks (disc) and vision foundation deep learning model, spine labels are predicted using one-shot localization. The proposed method doesn’t require any prior data for model training.
An Interpretable Deep Learning Approach for Identifying Working Memory-related Regions in fMRI using Three Large Cohorts
Authors: Zhao Tianyun , Tubiolo Philip , Williams John , Van Snellenberg Jared , Huang Chuan
Published: November 2024

Motivation: fMRI is allows studying human brain activity in vivo, but standard analyzing fMRI cannot capture nonlinear relationships between activity and variables. Utlizing deep learning (DL) models may capture such relationships, providing new insight into mechanisms underlying human health and disease.Goal(s): To evaluate our interpretable DL pipeline in fMRI analysis using three large cohorts to demonstrate its generalizability and reproducibility. Approach: We built a VGG-like network to predict task performance and generate saliency maps that can show brain regions important for task performance using three independent datasets. Results: The DL generated saliency maps are consistent between each dataset. Impact: We demonstrated that interpretable deep learning can be used as a reliable and generalizable tool to gain insight into brain regions whose activation impacts task performance.
Distribution-free uncertainty estimation in multi-parametric quantitative MRI through conformalized quantile regression
Authors: Birk Florian , Mahler Lucas , Steiglechner Julius , Wang Qi , Scheffler Klaus , Heule Rahel
Published: November 2024

Motivation: When using black-box regression models in diagnostic imaging, it is critical to quantify the uncertainty of such model predictions. Goal(s): Implementing and understanding uncertainty quantification for multi-parametric quantitative MRI. Do prediction intervals reflect model uncertainty? Approach: Train conditional quantile regression deep neural networks with subsequent conformalization steps for multi-parametric quantitative mapping without making distributional assumptions about the data. Results: Conformalized relaxometry and magnetic field prediction intervals reflect model uncertainty. Conformalized quantile regression was successfully implemented and provides supportive information about intrinsic model uncertainty which is mandatory for clinical decision making. Impact: A novel method for quantifying uncertainty of supervised machine learning models for multi-parametric quantitative MRI was successfully tested in silico and in vivo. Conformalized quantile regression allows prediction of confidence intervals without making assumptions about the training data distribution.
Physics-informed Variational Auto-Encoder to generate synthetic multi-echo chemical shift-encoded liver MR images
Authors: Meneses Juan , Gana Juan , Galgani Jose , Tejos Cristian , Chen Zhaolin , Uribe Sergio
Published: November 2024

Motivation: Deep Learning (DL)-based methods to quantify liver PDFF have had robustness difficulties due to the lack of large and heterogeneous training datasets with known results. Goal(s): To create a DL algorithm to synthesize realistic multi-echo liver MR images given a set of arbitrary MR scan parameters. Approach: To use a physics-driven approach to create a DL-based generative model able to synthesize realistic liver CSE-MR images with different compositions and geometries. Results: Our framework enabled a reliable customization of MR scan parameters, by directly adjusting them in the physical model. Feasibility of training a DL method purely based on synthetic data was also demonstrated. Impact: We successfully generated realistic multi-echo liver MR images with diverse geometries and compositions, which can be used to efficiently train DL-based methods for liver PDFF quantification. The physics-driven nature of our model enables the customization of MR scan parameters.
CDGAN：Cross Datasets Generative Adversarial Network for MR multi-contrast Image Synthesis
Authors: Wang Guowen , Wang Silei , He Yuebin , Lin Liangjie , Cai Shuhui , Cai Congbo , Chen Zhong
Published: November 2024

Motivation: Multi-contrast MR images usually take a long time to scan, resulting in only a part of the valuable contrasts being obtained. Current deep learning methods face challenges when applied to domain adaptation across datasets or when tasked with generating high-quality images of various contrast. Goal(s): Our purpose is to synthesize diverse contrast MRIs across different datasets. Approach: we propose a cross-dataset generative adversarial network (CDGAN).The synthesized MR modalities of one specific object not only conform to the characteristics of the modalities themselves, but also have the same structure. Results: This method effectively addresses the issue of synthesizing across datasets. Impact: The method demonstrates a significant improvement in the quality of generated images when tested on different datasets.
Assessment of Synthetic 3T Image Generator for Accurate CSF Volume Measurement in T1-w, T2-w, and FLAIR Sequences Compared to Low-Field Imaging
Authors: Islam Kh Tohidul , Zhong Shenjun , Zakavi Parisa , Kavnoudias Helen , Farquharson Shawna , Durbridge Gail , Barth Markus , McMahon Katie , Parizel Paul , Egan Gary , Dwyer Andrew , Law Meng , Chen Zhaolin
Published: November 2024

Motivation: Addressing limited access to high-field MRI systems, our study investigates whether a Synthetic 3T Image Generator can enhance low-field MRI to match high-field image quality, crucial for accurate cerebrospinal fluid (CSF) volume analysis. Goal(s): We aimed to validate the efficacy of the Synthetic 3T generator in improving CSF volume measurements on low-field MRI, in comparison to high-field T1-w, T2-w, and FLAIR sequences. Approach: A cGAN was employed to enhance 64mT MRI data to synthetic 3T images, for evaluation in comparison to high-field MRIs. Results: The synthetic 3T images demonstrated significant improvements in CSF volume estimation across all sequences when compared to low-field images. Impact: The synthetic 3T MRI enhancements could advance neuroimaging in resource-limited settings, improve diagnostic precision for brain injuries and potentially broaden the application of neurological and psychiatric patient care worldwide and expand neuroimaging research opportunities.
Automated pipeline for creating personalized biomechanical knee models and computing personalized cartilage pressures during gait
Authors: Marusich Kathryn , Clouthier Allison , Ong Carmichael , Bartsch Anna , Kogan Feliks , Gold Garry , Chaudhari Akshay , Gatti Anthony
Published: November 2024

Motivation: Automated analysis of MRI and biomechanics data can provide personalized information about cartilage pressures. Goal(s): Our goal was to develop an automated pipeline to create a personalized biomechanical knee joint model from MRI data, to simulate personalized knee mechanics during gait in comparison to knee mechanics of a generic knee joint geometry. Approach: Bone and cartilage geometry was automatically segmented from knee MRI scans via deep learning. Gait simulations were performed on musculoskeletal models with personalized and generic knee models. Results: Personalizing knee joint geometries affected cartilage pressure distributions in the joint but maintained peak cartilage pressures and contact forces. Impact: Biomechanical models personalized with MRI data enable understanding of how bone geometry influences cartilage pressures during gait, which may lead to better tailoring and evaluation of interventions.
In Vivo Reproducibility of T2 and T1rho Relaxation Times in Multiple Coils and Sequences
Authors: Vainberg Yael , Gatti Anthony , S. Anoosha Pai , Kogan Feliks
Published: November 2024

Motivation: T2 and T1rho reproducibility is essential for evaluating the small changes that occur during early osteoarthritis that are predictive of disease progression. Goal(s): To evaluate the reproducibility of T2 and T1rho relaxation times in multiple coils and with multiple imaging sequences. Approach: We studied the reproducibility of T1rho and T2 relaxation times with a MAPSS and qDESS sequence in a Transmit-Receive knee coil and two different flexible extremity coil arrays. Results: Intra-coil CVs of less than 5% (QIBA Goal) in qDESS T2 measurements with all coils tested, MAPSS T2 measurements with both flexible-coil-arrays and MAPSS T1rho measurements only with the Air coil. Impact: Preliminary findings suggest that flexible, receive-only coil arrays show similar or improved reproducibility for evaluating cartilage T2 and T1rho relaxation times compared to standard T/R knee coils.
Mapping Hip Cartilage Over Time: A Pipeline for Longitudinal Analysis of Quantitative MRI Using Radial Sections
Authors: Abbas Batool , Gerig Guido , Lattanzi Riccardo
Published: November 2024

Motivation: Scientific literature on osteoarthritis of the hip is very sparse, especially compared to the knee, because MRI assessment of the hip is incredibly challenging. Goal(s): Our goal was to determine clinical feasibility for cartilage assessment of the hip captured through radial imaging planes. Approach: We acquired 2D radial images of the hip joint at three different time points and assessed them using standard image processing techniques. Results: We demonstrate that 2D radial imaging is a constructive approach for mitigating partial volume artifacts and the resulting images can be effectively processed for quantitative and qualitative analyses of the hip joint cartilage. Impact: We demonstrate a processing pipeline for analysis of hip cartilage longitudinally acquired through radial imaging planes. The proposed methods could facilitate the clinical translation of quantitative radial imaging for assessment of the hip cartilage for pre-symptomatic indicators of disease.
T2 MR Fingerprinting in global and focal knee cartilage
Authors: Sitarcikova Diana , Cloos Martijn A. , Janacova Veronika , Hristoska Barbara , Gologan Malina , Trattnig Siegfried , Juras Vladimir
Published: November 2024

Motivation: Simultaneous measurement of multiple parameters during single acquisition saves measurement and post-processing time with reduced motion artifacts. Goal(s): To evaluate and compare T2 mapping of global and focal knee articular cartilage via MR fingerprinting and conventional CPMG sequences. Approach: Volunteer and patient knees were scanned with protocol including MRF and CPMG T2 mapping. The two T2 mapping methods were compared in global cartilage, focal cartilage lesions and morphologically normal appearing cartilage segments. Results: The average bias between the two methods was 17.09 ± 6.3 ms, and correlation was moderate to very high in global cartilage and high in focal cartilage. Impact: T2 mapping with MR fingerprinting is reliable in global and focal articular knee cartilage in terms of segment bulk T2 value assessment when compared to conventional method. Future study will concentrate on longitudinal change in T2 during patient follow-up.
Comparing Different Deep Learning (DL) Models for Joint Estimation of Proton Density and T1$$$ \rho$$$ Maps in the Knee Joint
Authors: Singh Dilbag , Regatte Ravinder R. , Zibetti Marcelo V. W.
Published: November 2024

Motivation: Estimating proton density (PD) and T1$$$\rho$$$ maps in the knee joint is time-consuming with nonlinear least squares (NLS) algorithms. Deep learning (DL) methods can do it faster. Goal(s): Find the best DL model for this task, comparing different DL models. Approach: We compared UNet, DenseNet, Encoder-Decoder, and Convolutional Neural Network (CNN). The proposed models directly transform k-space data into T1$$$\rho$$$ and PD maps, eliminating the need for traditional exponential fitting. Results: UNet and Encoder-Decoder-based models obtained the best performance, using short training and prediction times and minimal memory requirements. The proposed models are 129 times faster than the benchmark NLS method. Impact: This study compared different aspects of four DL models for joint PD and T1$$$\rho$$$ maps in the knee cartilage, indicating the most recommended models for this task.
Feasibility of Accelerated MRI in Temporomandibular Joints Using AI-assisted Compressed Sensing Technique
Authors: Ye Zheng , Lv Xinyang , Xie Yuanyuan , Li Zhenlin , Xiong Xin
Published: November 2024

Motivation: Patients with temporomandibular joint (TMJ) disorders often cannot endure long MRI examination due to facial pain, thus necessitating accelerated MRI. Goal(s): To investigative the feasibility of AI-assisted compressed sensing (ACS) accelerated MRI technique in TMJ, and compare its performance with parallel imaging (PI) protocol and standard protocol. Approach: Forty-four participants with 88 TMJs were qualitatively and quantitatively evaluated. Diagnostic agreement of joint effusion and disc displacement were analyzed. Results: Overall image quality, SNR, and most structures visibility of ACS protocol were significantly higher than standard protocol, and similar to PI protocol. Diagnostic agreement was excellent with kappa values ranging from 0.81 to 1.00. Impact: This study demonstrated that ACS accelerated MRI is feasible in TMJ with reduced acquisition times, good image quality, and excellent diagnostic precision, which holds great promise in clinical practice and is especially helpful for patients with TMJ disorders.
Texture Analysis of the Infrapatellar Fat Pad Within and Between Collegiate Basketball and Swimming Athletes
Authors: Consolini Jack , Breighner Ryan , Majumdar Sharmila , Gold Garry , Potter Hollis , Koff Matthew
Published: November 2024

Motivation: MRI-based texture features have been used to identify abnormalities within the infrapatellar fat pad (IPFP) of middle-aged to elderly adults, yet no existing investigations have evaluated IPFP quality in young athletes. Goal(s): To quantify and compare texture features of the IPFP in collegiate athletes comprised of men and women basketball players and swimmers. Approach: Image texture features of the IPFP were calculated from bilateral MRIs acquired at 2 time points. (baseline and 1 yr. follow-up) for collegiate men and women basketball and swimming athletes. Results: The IPFP of collegiate basketball players significantly differed from the swimmers, with greater global and local image homogeneity. Impact: While the texture features of swimmer IPFPs remained consistent across sexes and timepoints, swimmers had significantly greater texture heterogeneity than basketball players at baseline. Observed variability indicates the need for further longitudinal investigation of IPFP structure within low impact sports.
Application of High-resolution MR reconstruction technique, Precise IQ Engine (PIQE) for Flow Sensitive Black Blood (FSBB) Imaging
Authors: Okuchi Sachi , Fushimi Yasutaka , Fujimoto Koji , Miyake Kanae , Numamoto Hitomi , Monzen Yuichiro , Nakajima Satoshi , Sakata Akihiko , Otani Sayo , Sakurama Azusa , Tagawa Hiroshi , Wang Yang , Ikeda Satoshi , Ito Shuichi , Umehana Masaki , Ma Yongping , Kondo Hiroki , Imai Rimika , Nakamoto Yuji
Published: November 2024

Motivation: To apply the Precise IQ Engine (PIQE), a new high-resolution MR reconstruction technique, for Flow Sensitive Black Blood (FSBB) imaging. Goal(s): To evaluate the usefulness of PIQE comparing the super-resolution FSBB images reconstructed from low-resolution FSBB images with short acquisition time using PIQE, low-resolution FSBB images, and high-resolution FSBB images. Approach: On three FSBB images, cerebral microbleed (CMB) was identified and image quality was assessed for 121 patients who underwent FSBB images for CMB detection. Results: PIQE made CMBs, blurred due to low resolution, clearly visible. PIQE-FSBB has image quality comparable to high-resolution FSBB even with shorter scan time. Impact: PIQE, a technique that reconstructs high-resolution images from low-resolution images, was applied for FSBB imaging. The super-resolution FSBB reconstructed from low-resolution FSBB with short acquisition time had good quality, and was comparable to high-resolution FSBB for cerebral microbleeds detection.
Investigation of the heterogeneity of post-stroke apathy using resting-state fMRI
Authors: Fang Yirong , Zhang Yong , Wang Peng
Published: November 2024

Motivation: Post-stroke apathy (PSA) is a highly heterogeneous disorder, which presents obstacles to the effective diagnosis and treatment. Goal(s): We intended to identify distinct subtypes of PSA and revealed the underlying neurological substrates of PSA heterogeneity. Approach: By comparison between stroke patients and normal controls, abnormal alterations of resting-state functional connectivity (RSFC) were determined and correlated with apathy syndrome. PSA subtypes were identified based on the significant canonical correlates using hierarchical clustering. Results: The best clustering scheme was four clusters and we observed significant group differences in graph theoretic metrics. Impact: Various subtypes displayed distinct characteristics in terms of RSFC and graph theoretic metrics. Our study emphasizes the importance of RSFC of neural network in the evaluation of PSA heterogeneity and provides new insights for future research and therapeutic targets.
Hemodynamic analysis of non-stenotic middle cerebral artery in patients with cerebral ischemia based on 4D Flow MRI
Authors: Chen Yue , Sun Jiali , Sui Ying , Lin Shuai , Wang Wei , Lian Jianxiu , Ma Yuefei
Published: November 2024

Motivation: The change of hemodynamics in patients with symptomatic cerebral ischemia are not clear. Goal(s): This study aims to explore the difference of hemodynamic characteristics in non-stenotic middle cerebral artery (MCA) between healthy subjects and cerebral ischemia patients. Approach: The multiple hemodynamic parameters in the proximal, curved and distal segments of MCA were quantitatively analyzed in these two groups by using 4D flow MRI. Results: The results showed that the peak blood flow velocity was lower and the circumferential WSS reduced in MCA, the energy loss impaired in the M1 segment of MCA when compared cerebral ischemia patients with healthy subjects. Impact: This study was based on 4D flow MRI to explore the hemodynamic characteristics of cerebral ischemia in non-stenosis MCA, which is helpful to reveal the mechanism of cryptogenic stroke and provides a possibility for future prediction of stroke risk.
Interactive database of spinal cord morphometry
Authors: Valosek Jan , Bédard Sandrine , Keřkovský Miloš , Rohan Tomáš , Cohen-Adad Julien
Published: November 2024

Motivation: Spinal cord morphometric measures are commonly used to evaluate spinal cord pathologies. Yet, their interpretation is challenged by considerable intra- and inter-subject variability. Goal(s): Develop a method for automatic normalization relative to a healthy cohort to reduce inter-subject variability. Approach: Morphometric measures computed from an open-access dataset (N=203) were linearly interpolated using a newly proposed approach into the spinal cord template to build a normative database. Results: The methodology is open-source and allows normalization based on sex, MRI vendors, and age decades, thereby minimizing inter-subject variability associated with demographic and biological factors. Impact: This new morphometric database will allow researchers to normalize based on sex, MRI vendors, and age decades, thereby minimizing inter-subject variability associated with demographic and biological factors. The method is open-source and available in Spinal Cord Toolbox.
Evaluation of Intravoxel Incoherent Motion in the Spinal Cord of Multiple Sclerosis Patients
Authors: Johnson Brian , Heales Christine
Published: November 2024

Motivation: Previous perfusion-weighted imaging studies in multiple sclerosis (MS) have revealed alterations of cerebral perfusion, yet these types of studies have not been translated to studying MS in the spinal cord. Goal(s): Evaluate the use of intravoxel incoherent motion (IVIM) as a non-contrast MRI technique to assess perfusion in the spinal cord of MS. Approach: A cross-sectional analysis was conducted to determine spinal cord, white matter, and gray matter differences in IVIM-derived indices between the healthy and MS cohorts. Results: Spinal cord white matter perfusion fraction (p=0.082) and pseudo-diffusion (p=0.055) measurements came close to statistical significance between MS patients and healthy controls. Impact: This is the first study utilizing IVIM in the spinal cord and the findings suggest that IVIM has potential as a tool for assessing the microcirculation of the human spinal cord in MS.
Intravoxel incoherent motion (IVIM) MRI in the cervical cord: application to traumatic spinal cord injury
Authors: Lebret Anna , Frese Sabina , Lévy Simon , Curt Armin , Callot Virginie , Freund Patrick , Seif Maryam
Published: November 2024

Motivation: Traumatic spinal cord injury (SCI) leads to a perfusion deficit in the cervical cord, a factor that significantly contributes to neurodegeneration. Characterizing changes in perfusion after injury holds potential to better understand progressive SCI-induced neurodegeneration. Goal(s): To determine blood perfusion changes in the cervical cord of tetraplegic SCI patients using intravoxel incoherent motion (IVIM) MRI. Approach: Cardiac-gated IVIM MRI was applied to the cervical cord in 21 SCI patients and 38 healthy controls (HC). Results: SCI patients showed significant changes in IVIM parameters in the grey matter of the cervical cord compared to HC, indicating remote perfusion deficit above the injury site. Impact: The characterization of spinal cord perfusion changes in SCI provides valuable insights into tissue-specific changes which can serve as a foundation for the development of targeted treatment strategies.
Substantia Nigra and Nigrosome1 identification in Parkinson’s disease and healthy controls: comparison of manual and automated approach at 3T
Authors: Caligiuri Maria Eugenia , Biondetti Emma , Quattrone Andrea , Chiarelli Antonio , Chimento Ilaria , Bonacci Maria Celeste , Buonocore Jolanda , Wise Richard , Quattrone Aldo
Published: November 2024

Motivation: Quantification of iron overload in relevant brain regions is crucial for Parkinson’s disease (PD) diagnosis and monitoring.Goal(s): To evaluate and compare manual and automated approaches for substantia nigra (SN) and nigrosome1 (N1) identification and iron content assessment using quantitative susceptibility mapping (QSM)Approach: Histogram analysis to assess distributions of iron content in SN and N1, and compare them in patients with PD and controlsResults: Histogram analysis of automatically defined SN regions of interest in PD can be a promising tool to complement current diagnostic procedures. Nigrosome1 identification is confirmed to be more challengeing, and should be carefully checked by expert raters.Impact: Lean and accurate quantification of iron overload in Substantia Nigra and Nigrosome1 in parkinsonian patients could represent an added value to routine clinical evaluation, enhancing accuracy of early diagnosis, possibly in pre-morbid conditions, as well as optimal treatment monitoring
Alzheimer's brain and control brain show distinct ultrashort echo time magnetization transfer ratio (UTE-MTR): an ex vivo study
Authors: Zeng Chun , Malhi Bhavsimran Singh , Lo James , Moazamian Dina , Shin Soo Hyun , Athertya Jiyo , Cheng Xin , Ma Yajun , Li Yongmei , Hiniker Anne , Du Jiang
Published: November 2024

Motivation: Alzheimer’s disease is a degenerative brain disorder. Conventional MRI primarily focuses on quantifying the volume of the hippocampus and gray matter (GM). Goal(s): This study employed ultrashort echo time magnetization transfer ratio (UTE-MTR) mapping to identify myelin changes in white matter (WM) and grey matter (GM) within brain samples from AD and regular donors. Approach: Six ROIs were selected for each specimen, comprising three WM regions and three GM regions. Results: The UTE-MTR values of both WM and GM from AD donors were lower than those of controls, indicating the capability of UTE-MTR to detect myelin loss in individuals with AD. Impact: Decreased UTE-MTR values of WM and GM were found in AD brains compared to control brains, indicating myelin loss in AD brain regions. This biomarker provides valuable assistance for the diagnosis of AD.
Longitudinal Dynamic fMRI Analysis of Music vs Singing Perception in Preterm Infants
Authors: Boehringer Andrew , de Almeida Joana Sa , Filippa Manuela , Loukas Serafeim , Lordier Lara , Van de Ville Dimitri , Hüppi Petra
Published: November 2024

Motivation: Instrumental music and singing differ due to the vocal component of the sung melody and present distinct neural processing pathways, though its developmental origins remain unclear. Goal(s): This study explores the early development of music vs singing brain processing. Approach: Dynamic PPI-CAPs approach was applied to longitudinal fMRI data of preterm infants at 33- and 40-weeks gestational age. Results: Singing elicited a stronger activation of salience and language-related networks, while music elicited limbic networks. Default-mode network and right temporal gyrus were elicited for both stimuli longitudinally. Visual network is also involved for both stimuli but only at term-equivalent age. Impact: This study explores the longitudinal early development of neural processing networks in the context of instrumental and vocal music stimuli and shows that preterm infants already present a specialized processing for these different stimuli.
Evaluation of AI-based Direct Contrast Synthesis in the Pediatric Brain
Authors: Nagaraj Usha , Meineke Jakob , Tkach Jean , Leach James , Doneva Mariya
Published: November 2024

Motivation: Direct contrast synthesis (AI-DCS) enables the derivation of synthetic images from a trained neural network. Its utility in clinical practice has yet to be adequately explored. Goal(s): To evaluate the image quality and diagnostic confidence of these synthetic images in the brain in the pediatric population. Approach: The study radiologist compares the image quality and diagnostic confidence of AI-DCS images to standard of care (SOC) and SyMRI. Results: AI-DCS demonstrates better overall image quality and diagnostic confidence than SyMRI on T1, T2 and FLAIR in majority of patients. AI-DCS demonstrates better overall image quality on FLAIR when compared to SOC. Impact: AI-DCS demonstrates improved image quality and diagnostic confidence compared to SyMRI and comparable image quality to the current standard of care.