bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic segmentation of spinal cord nerve rootlets
Authors: Valosek Jan , Mathieu Theo , Schlienger Raphaëlle , Kowalczyk Olivia , Cohen-Adad Julien
Published: November 2024

Motivation: Precise identification of spinal nerve rootlets is relevant for studying functional activity in the spinal cord. Goal(s): Our goal was to develop a deep learning-based tool for the automatic segmentation of spinal nerve rootlets from multi-site T2-w images coupled with a method for the automatic identification of spinal levels. Approach: Active learning was employed to iteratively train a nnUNet model to perform multi-class spinal nerve rootlets segmentation. Results: The code/model is available on GitHub and is currently being validated by several laboratories worldwide. Impact: Currently, most spinal cord fMRI studies use vertebral levels for groupwise registration, which is inaccurate. This new tool enables researchers to identify spinal levels via the automatic segmentation of nerve rootlets, improving fMRI analysis pipeline accuracy.
Deep learning-based comprehensive multi-sequence liver lesion segmentation for accurate tumor burden assessment
Authors: Zhang Xiaolan , Wu Botong , Zheng Chao
Published: November 2024

Motivation: Accurate lesion segmentation is crucial for tumor burden assessment and subsequent patient-specific treatment prediction. Goal(s): To develop and validate a deep learning-based automated segmentation model for accurate hepatocellular carcinoma (HCC) lesion detection across various imaging sequences. Approach: A total of 2800 patients with focal liver lesions (FLLs) were included for developing automated segmentation models. The automated segmentation models were trained involving preprocessing, lesion detection using mask R-CNN, and lesion segmentation using a 3D-UNet framework. Results: The 3D-UNet framework is used for lesion segmentation, achieving a DSC accuracy ranging from 78.23% to 85.14% and volume ratios from 0.92 to 1.51 across different sequences. Impact: The model demonstrates promising potential in accurately segmenting HCC lesions.
Deep Learning Based Automated Brain Segmentation from Computed Tomography Scans
Authors: Son Won Jun , Ahn Sung Jun , Lee Hyunyeol
Published: November 2024

Motivation: While computed tomography (CT) imaging has been actively employed in clinical practice, its limited contrast for brain tissues makes it challenging to achieve precise brain segmentation. Goal(s): In this study, we developed a deep learning (DL)-based method enabling brain tissue segmentation from CT image. Approach: MRI-derived tissue labels were provided as ground truth to a DL network, where U-Net and VGG16 interact to each other for model optimization by means of a perceptual loss. Results: Results demonstrate the effectiveness of incorporating the perceptual loss to the model in preserving image details, and in terms of evaluation scores. Impact: The presented method, upon further validation and optimization, is expected to be a valuable means to a range of brain imaging studies where MRI is somehow not available.
CNN for Automatic Estimation of Paraspinal Skeletal Muscle Index for Assessment of Sarcopenia and Correlation with Liver Frailty
Authors: Esparza Juan , Shrestha Utsav , Makhani Salima , Satapathy Sanjaya , Tipirneni-Sajja Aaryani
Published: November 2024

Motivation: Paraspinal muscle mass estimation for liver transplant candidacy is practically limited by tedious segmentation. Goal(s): Develop an automatic segmentation algorithm using a convolutional neural network (CNN) for segmentation of abdominal paraspinal muscles to calculate skeletal muscle index in cirrhotic patients. Approach: A U-Net CNN was trained on spin echo images and evaluated with Dice coefficient. Skeletal muscle index of original and predicted masks was compared with independent t-test, ANOVA and a Bland-Altman plot. Results: Dice coefficient was >0.88, with a mean bias of <1% between CNN SMI and manual SMI, while not being statistically significant. SMI and liver frailty were not directly associated. Impact: Faster and precise segmentation of abdominal paraspinal muscles to calculate muscle mass in cirrhotic patients would reduce the time burden, thereby increasing practicality for MRI skeletal muscle index estimation.
Deep learning-based quantification of myocardial oxygen extraction fraction and blood volume in health: reproducibility, sex, and homogeneity
Authors: Li Ran , Eldeniz Cihat , Schindler Thomas , Peterson Linda , Woodard Pamela , Zheng jie
Published: November 2024

Motivation: A previously developed MRI method for quantitative myocardial oxygen extraction mapping (mOEF) showed promising results, but image quality suffered from distortion and inhomogeneity artifacts. Goal(s): The objective of this study is to evaluate a new CMR method for in vivo measurement of mOEF utilizing on a deep-learning quantification approach in healthy controls. Approach: A new pulse sequence and a novel deep learning-based analysis method were created and evaluated on a group of healthy subjects. Results: This investigation yielded dramatically improved image quality, which allowed reliable evaluation of reproducibility and distribution of mOEF within the heart. Impact: Our study, involving 20 healthy volunteers, showcased outstanding reproducibility in the measurements, suggesting its potential for translation into imaging studies for patients with myocardial metabolic dysfunction.
Accelerating KalmanNet using POD: Real-time 3D MR-thermometry for monitoring microwave ablation procedures
Authors: Schröer Simon , Gutberlet Marcel , Löning Joaquin , Horstmann Dominik , Belker Othmar , Wacker Frank , Hensen Bennet
Published: November 2024

Motivation: Noise is a challenge for real-time MR-thermometry. The Extended Kalman Filter (EKF) has been shown to reduce noise successfully in temperature maps. Goal(s): We modified KalmanNet, a recurrent neural network emulating an EKF, using a proper orthogonal decomposition (POD) to reduce computational cost. Approach: POD-KalmanNet was applied to microwave ablations of 14 bioprotein phantoms. Mean squared errors (MSE) and Sørensen-Dice-Coefficient (DSC) were compared between noisy and filtered data. Results: Results show a highly significant reduction of MSE (p < 0.001) and a significant increase of DSC (p < 0.05) for filtered images compared to noisy images. POD-KalmanNet could enable real-time filtering of MR-Thermometry. Impact: KalmanNet was modified using proper orthogonal decomposition. This modification allows lower memory usage and inference times. It makes the application of KalmanNet to 3D temperature maps practically feasible. Reducing noise in 3D temperature maps can improve outcomes of thermoablation procedures.
Fast Probabilistic Parameter Estimation for Quantitative MRI using Variational Autoencoders
Authors: Yang Fan , Zhang Hui , Parker Christopher
Published: November 2024

Motivation: Mapping MRI signal into tissue parameters aims to identify robust physiologic-phenotypic associations. However, conventional methods are computationally expensive, limiting their applicability in research or clinical practice. Goal(s): To develop fast and robust techniques for estimating quantitative tissue parameters under a probabilistic framework using MRI signals. Approach: Train and evaluate the performance of variational autoencoders and compare its capabilities with state-of-the-art deep learning methods on both synthesized and real MRI data. Results: Compared to existing autoencoder-based methods, both synthetic and real data experiments show enhanced performance of VAEs on tissue parameter estimation. Parameter maps produced from real data show higher similarity to gold-standard maps. Impact: We show that variational autoencoders can be trained for fast inference of quantitative parameter estimation MRI data quantification in qMRI.
High quality brain segmentation from synthetic MPRAGE images at 7T MRI
Authors: Fortin Marc-Antoine , Stirnberg Rüdiger , Völzke Yannik , Lamalle Laurent , Pracht Eberhard , Löwen Daniel , Stöcker Tony , Goa Pål Erik
Published: November 2024

Motivation: Brain segmentation and multiparameter mapping (MPM) are important for neurodegenerative disease characterization. Acquiring sub-millimeter images increases scan time and patient discomfort. At 7T, B1+ inhomogeneities challenge brain segmentation. Goal(s): The quality of brain segmentations produced from FastSurferVINN was evaluated and compared between a 7T MPRAGE protocol and two synthetic MPRAGE approaches. Approach: MPRAGE and MPM images were acquired on 16 subjects across three 7T sites using pTx pulses. MPRAGElike and synMPRAGE images were generated from MPM. All images were segmented with FastSurferVINN. Results: FastSurferVINN seems to be a robust technique to segment sub-millimeter 7T images. MPRAGElike generated superior segmentations compared to synMPRAGE. Impact: Neuroscientists with Multi-Parameter Mapping sequences in their imaging protocol can approximate an MPRAGElike image (preferably over synthetic synMPRAGE). Acquiring an MPRAGE sequence solely for brain segmentation can be avoided resulting in a considerable amount of scan time saved.
Point-Guided 3D U-SAM: MRI abdominal Segmentation Model using 3D Interactive U-Net and Segment Anything Model
Authors: Sugimoto Yuta , Fujita Naoto , Tamada Daiki , Funayama Satoshi , Ichikawa Shintaro , Goshima Satoshi , Terada Yasuhiko
Published: November 2024

Motivation: In abdominal MRI segmentation tasks, the need for high-quality support information for Segment Anything Model (SAM)-driven segmentation in limited data scenarios has motivated the search for an architecture with high performance and minimal support information requirements. Goal(s): Our objective is to design a user-friendly architecture for segmentation, focusing on using only support information within the region of interest. We aim to verify its high-performance capabilities. Approach: We developed Point-Guided 3D U-SAM, combining SAM and 3D U-Net with point-based support input. We compared its segmentation performance with existing methods. Results: The model excelled in abdominal MRI segmentation across various contrast levels, ensuring high performance. Impact: Point-Guided 3D U-SAM, which combines Segment Anything Model (SAM) and 3D U-Net with point-based inputs, would advance semi-automated organ segmentation, particularly where contrast is poor, such as MRCP, and in abdominal imaging, significantly reducing manual effort in clinical segmentation.
Thresholding to remove fluid from FreeSurfer segmentations of quantitative brain maps – a simple solution to a painful problem
Authors: Kukran Simran , Smith Joely , Statton Ben , Dixon Luke , Thust Stefanie , Dragonu Iulius , Cardona Sarah , Finnegan Mary , Quest Rebecca , Bangerter Neal , Koh Dow Mu , Lally Peter , Orton Matthew , Sollars Matthew Grech
Published: November 2024

Motivation: Segmentation of quantitative maps is required to compute anatomical region mean relaxation times. FreeSurfer is designed to automatically segment conventional weighted images. Voxels of CSF contaminate tissue segmentations in some healthy volunteers, skewing the mean T1 or T2. Goal(s): To remove contaminant fluid from tissue regions of interest in T1 and T2 maps. Approach: Mean T1 or T2 of CSF in ventricles is used as a maximum threshold within brain tissue. Results: Thresholding prior to 2D erosion of masks removes contaminant CSF and prevents erroneous variation between 10 healthy volunteers. Impact: A simple threshold-based correction of FreeSurfer segmentation applied to quantitative T1 and T2 maps. The same threshold can be applied to all subjects in one step, eliminating the need for laborious manual adjustment.
Intraoperative fiber tractography during pediatric posterior fossa tumor surgery - initial experience.
Authors: Jellema Pien , Luca Alberto , Hoving Eelco , Baarsen Kirsten , Wijnen Jannie , Lequin Maarten
Published: November 2024

Motivation: Intraoperative fiber tractography could help to elucidate the nature of cerebellar mutism syndrome after tumor resection in pediatric posterior fossa tumor (pPFT) patients. Goal(s): To explore the feasibility of reconstructing eloquent fiber tracts with a short intraoperative diffusion MRI scan in pPFT patients. Approach: We reconstructed tracts intraoperatively in four pPFT patients and compared their structural properties to preoperative scans obtained with the same settings. Results: It is in general possible, but challenging, to reconstruct the eloquent tracts in pre- and intraoperative data of pPFT patients. However, further optimization of the fiber tractography model is required to increase their quality. Impact: The impact of the mechanical manipulation of the surgical procedure on the eloquent pathways in pediatric posterior fossa tumor patients is investigated through the development of a reliable fiber tractography model for intraoperative diffusion MRI.
Probabilistic Tractography of Key Language Tracts: MNI-based Spherical ROIs Improve Sensitivity and Specificity
Authors: Ansell Jane , Lowe Melissa , Hadjigeorgiou Vasilis , Jarosz Jozef , Borri Marco
Published: November 2024

Motivation: Pre-surgical tractography aids neurosurgical planning around key language tracts. Standard reconstruction approaches lead to a trade-off between sensitivity and specificity, and may not characterise all aspects of the target tract. Goal(s): Increase sensitivity and specificity of reconstruction methods for the inferior fronto-occipital fasciculus and arcuate fasciculus and language tracts. Approach: A new set of three spherical seed, end, and supplementary waypoint regions of interest (ROIs), defined in MNI space, were implemented using volunteer datasets, and results compared to a reference atlas. Results: The new 3-ROI approach achieved greater overlap with the reference tracts (higher Dice coefficient), with increased sensitivity and/or specificity. Impact: Improvements in sensitivity and specificity of reconstructed language tracts using 3-ROI based tractography optimise the characterisation of tracts in healthy volunteers. Further evaluation should now be done to allow extension to clinical cases.
On the impact of the free pool T2 on quantitative MT‑derived T1 and Macromolecular Proton Fraction values in the MP2RAGE
Authors: Soustelle Lucas , Hertanu Andreea , Troalen Thomas , Ranjeva Jean-Philippe , Guye Maxime , Duhamel Guillaume , Girard Olivier
Published: November 2024

Motivation: T1 estimations using the MP2RAGE methodology are biased by Magnetization Transfer (MT) effects. A quantitative MT-based solution was previously proposed in which the inversion efficiency of the preparation pulse (Q) was fixed; we sought to alleviate this strong hypothesis. Goal(s): To better understand the influence of free water T2 (T2,f) on Q in the scope of MP2RAGE-T1 brain mapping. Approach: A better modeling of the MP2RAGE involving a discretized preparation pulse is employed, and tested for fixed and free T2,f values. Results: The T1 bias is highly dependent on the T2,f values, calling for a better and accurate estimation of this parameter. Impact: Quantitative MT-derived T1 estimation in the MP2RAGE methodology remains dependent on the estimated free water T2 values because of its impact on the inversion efficiency pulse.
Subspace modeling enables time-efficient B1-corrected volumetric water-specific relaxometry with a long echo train CASPR trajectory
Authors: Stelter Jonathan , Weiss Kilian , Meineke Jakob , Spieker Veronika , Zhang Weitong , Schnabel Julia , Braren Rickmer , Kainz Bernhard , Karampinos Dimitrios
Published: November 2024

Motivation: The acquisition time of volumetric relaxometry techniques is constrained by the sampling efficiency and confounding factors such as fat or the B1 inhomogeneity. Goal(s): To develop a method for volumetric T1- and T2-mapping that improves efficiency while accounting for confounding factors. Approach: A Cartesian acquisition with spiral profile ordering is employed to increase sampling efficiency. A subspace reconstruction is proposed to perform B1- and water-specific T1- and T2-mapping considering the exact k-space sampling pattern. Results: Simulation, phantom and in-vivo experiments were performed to evaluate the proposed method with regard to its quantification performance and its B1 sensitivity at 3T. Impact: Subspace reconstruction in combination with a CASPR trajectory can improve the scan's efficiency and sampling flexibility while correcting for confounders such as fat or B1. The technique may be valuable to develop volumetric relaxometry in clinically acceptable scan times.
T2* relaxometry in the human cervical spinal cord at 7T
Authors: Beghini Laura , Vannesjo S. Johanna
Published: November 2024

Motivation: In spinal cord T2* relaxometry, only mono-exponential models have previously been used, neglecting the potential presence of multiple compartments. Goal(s): Determine if a mono- or bi-exponential model is better for T2* relaxometry in the cervical spinal cord white matter (WM) and grey matter (GM). Approach: Mask the ventral half of the GM and WM in a 7-echoes GRE acquisition. Fit a mono-/bi-exponential model to the average signal in the masks for each slice. Compute R2, F-tests and p-values. Results: A bi-exponential model gave statistically better results in WM. No relevant difference between the models was measured in GM for most slices. Impact: A bi-exponential model is statistically better than a mono-exponential one for T2* relaxometry in spinal cord white matter, but not grey matter. The model can be used for contrast optimization, e.g. to improve lesion detection in demyelinating diseases.
Temporal changes in tissue plasticity and microvasculature as a gauge of recovery in stroke
Authors: Annio Giacomo , Mangin Gabrielle , Nicoletti Antonino , Caligiuri Giuseppina , Schregel Katharina , Sinkus Ralph
Published: November 2024

Motivation: Stroke is major global cause of death. However markers of recovery are still lacking. Goal(s): Vasculature affects shear waves propagation acting as a scattering source, ultimately affecting waves dispersion properties measurable with MRE. Approach: In this study we use multi frequency MRE to find the fingerprint of vascular remodelling after stroke in the dispersion properties of waves, and explored the temporal profile of vascular and tissue remodelling and its relashionship with recovery. Results: We show for the first time in-vivo that MRE senses tissue integrity as well as vascular organization. We show that such parameters have a good prognostic value. Impact: We show that MRE could disentangle tissue constitutive properties and vasculature, and thereby could provide new insights in the understanding of tissue plasticity after stroke and potentially constitute a relevant marker in the context of stroke therapy.
White matter hyperintensities: A MRI marker of brain injury in patients with Cardiovascular diseases
Authors: Wang Wanbing , Lyu Jinhao , Li Yanhua , Zhang Xinghua , Yan Fei , Duan Qi , Lou Xin
Published: November 2024

Motivation: Cardiovascular disease (CVD) is closely related to cerebrovascular injury. However, even when the ejection fraction (EF) is normal, the cardiac microvascular system may still be abnormal. Goal(s): Explore the relationship between CVD with preserved EF and cerebrovascular injury.Approach: The heart and brain MRI of the participants were scanned with 3.0T MRI scanner. Different cardiac ejection parameters and high signal images of white matter were obtained. Results: Patients with CVD, when EF is normal, are still associated with WMH and are still a risk factor for brain injury. Impact: t is crucial to pay early attention to the early indicators of cerebrovascular abnormalities in patients with heart failure, as timely intervention is essential for reducing the risk of stroke.
Field-dependent Regional BOLD Responses to Varied Transient Hypoxic Stimuli in Mice
Authors: Choi SangHan , Le Thuy Thi , Lee Chan Hee , Im Geun Ho , Uludağ Kamil , Kim Seong-Gi
Published: November 2024

Motivation: Heterogenous BOLD responses are expected across brain regions due to different baseline blood volumes. However, there are very few experimental data to show regional differences of ΔR2*. Goal(s): To determine regional ΔR2* by graded hypoxia and to elucidate field dependency of regional ΔR2* to transient hypoxic stimuli. Approach: Three hypoxic conditions (90%N2, 95%N2, 100%N2) were administered to anesthetized mice in two different magnetic field strengths of 9.4T and 15.2T. Results: The peak ΔR2* map revealed heterogenous BOLD responses across regions. Field-dependent ΔR2* ratio to 15.2T to 9.4T is close to the ratio of the magnetic field strengths. Impact: We quantitatively determined regional BOLD responses in whole brain to hypoxia, which is dependent on baseline blood volume. Our result can serve as reference data for normalizing sensitivity of BOLD responses evoked by neuronal activity or vascular stimuli across regions.
Alterations of amide proton transfer weighted (APTw) signal after revascularization in patients with moyamoya disease
Authors: Xia Chao , Zeng Jiaxin , Li Hongxia , Wei Xia , Liu Zhiqin , Li Ziyu , Li Xing , Yu Wei , Liu Yi , Ai Kai , Hu Na , Lui Su
Published: November 2024

Motivation: Assessing alterations of the brain microenvironment in patients with moyamoya disease after revascularization (MMD) would develop optimal postoperative management and improve the prognosis. Goal(s): This study aimed to explore alterations of amide proton transfer weighted (APTw) signal in patients with MMD after receiving revascularization surgery. Approach: MMD patients who underwent unilateral revascularization surgery were prospectively collected. Baseline computed tomography perfusion (CTP) and APTw imaging were performed. Patients were followed up with CTP and APTw imaging about 1 year after revascularization. Results: APTw values in both surgical and contralateral hemispheres at follow-up were found to be significantly higher than those at baseline. Impact: APTw imaging is a potential assessment tool to quantitatively evaluate alterations of acid-base metabolism in the brain microenvironment of patients with MMD after revascularization, which provides additional information to help neurosurgeons develop optimal postoperative management and follow-up protocols for MMD.
Evaluating the Neuroprotective Effects of Caplacizumab in Thrombotic Thrombocytopenic Purpura with Quantitative MRI and CT
Authors: Hannan Fahad , Mendes Daniel , Ting-Yim Lee , Thiessen Jonathan , Huang Susan
Published: November 2024

Motivation: Thrombotic Thrombocytopenic Purpura patients suffer from high risk of cognitive related complications post-remission that can decrease quality-of-life and in some cases death. Goal(s): We investigate a new treatment drug Caplacizumab to determine whether it can reduce neurocognitive burden. Approach: We use quantitative MRI and CT-perfusion to investigate patients that have received Caplacizumab to patients that have not. MRI and CT data look at a variety of different metrics to investigate brain health. Results: Preliminary results from CT-perfusion indicate better blood brain barrier recovery in Caplacizumab group, which may decrease cognitive burden. MRI results show significant decrease of myelin in patients without Caplacizumab. Impact: The results of this study can open the door for a new treatment for Thrombotic Thrombocytopenic Purpura patients that may ensure they do not suffer from cognitive decline or cerebrovascular disease related death.
Multi-domain and Uni-domain Fusion for domain-generalizable fMRI-based phenotypic prediction
Authors: Chen Pansheng , An Lijun , Wulan Naren , Zhang Chen , Zhang Shaoshi , Ooi Leon Qi Rong , Kong Ru , Wu Jianxiao , Chopra Sidhant , Bzdok Danilo , Eickhoff Simon , Holmes Avram , Yeo B.T. Thomas
Published: November 2024

Motivation: Resting-state functional connectivity (RSFC) is widely used to predict phenotypes in individuals. However, predictive models may fail to generalize to new datasets due to differences in population, data collection, and processing across datasets. Goal(s): To resolve the dataset difference issue, we aimed to generalize knowledge from multiple diverse source datasets and translate the model to new target data. Approach: Here we proposed Multi-domain and Uni-domain Fusion (MUF) method that combines cross-domain learning and intra-domain learning, to capture both domain-general information and domain-specific information. Results: The results show that our MUF outperformed 4 strong baseline methods on 6 target datasets. Impact: Our MUF method is adept at addressing the challenges introduced by different population profiles, fMRI processing pipelines, and prediction tasks. We offer a robust and universal learning strategy for domain-generalization in fMRI-based phenotypic prediction.
Understanding the Trustworthiness of Saliency Maps for Biomarker Research in 3D Medical Imaging Classification
Authors: Shi Yixiong , Wang Chenyu , Liu Dongnan , Cai Weidong , Cabezas Mariano
Published: November 2024

Motivation: It is significant to understand if saliency maps can be considered as potential biomarkers by providing reliable anatomical information in 3D medical imaging classification. Goal(s): We found saliency maps can provide different (even mutually exclusive) information with randomised models. It is necessary to estimate the robustness of saliency maps under the stochastic training process. Approach: We introduced a novel method by re-organising the saliency scores in the saliency maps and quantify the inter-map difference for estimating the robustness of saliency maps. Results: All selected explanation methods were not able to exhibit strong performance in the estimation of robustness of saliency maps. Impact: Our estimation provides evidence that saliency maps are not competent to maintain the robustness under the stochastic training process. Researchers should be critically careful when utilising saliency maps as biomarkers for interpretation.
Comparison of Variational Autoencoders for Magnetic Resonance Spectroscopy Data Synthesis
Authors: van de Sande Dennis , Kleinveld Justin , Amirrajab Sina , Veta Mitko , Breeuwer Marcel
Published: November 2024

Motivation: Scarcity of MRS datasets hinders deep learning model development, often leading to reliance on simulations which show difficulties with replicating in-vivo characteristics. Goal(s): The goal is to evaluate different variational autoencoders to enhance MRS datasets and improve deep learning model development for MRS. Approach: This study assesses different models for generating MRS data. Additionally, interpolation is done between multiple pairs of real spectra to improve the diversity of the synthetic data. Results: One model shows great potential in terms of reconstruction quality and generative performance. The incorporation of interpolation further enhances the diversity in synthetic spectra, particularly in relation to residual water signals. Impact: The demonstrated potential of variational autoencoders for MRS data generation will help in generating synthetic data that is similar to in-vivo data. This will help the development of other deep learning models for MRS applications.
Synthesising ultra-strong gradients diffusion MRI with high-resolution convolutional neural networks
Authors: Mancini Matteo , McNabb Carolyn , Cercignani Mara , Jones Derek , Palombo Marco
Published: November 2024

Motivation: Ultra-strong gradients scanners allow to explore microstructure, but these systems are not widespread because of their associated challenges. Goal(s): Our goal is to leverage image synthesis and deep learning to design a neural network able to predict high b-values data from low b-values. Approach: .We implemented a U-net architecture and tailored a loss function able to learn tissue-based features in a patch-based fashion. We trained it on a large dataset and tested it quantitatively and qualitatively. Results: .Qualitative and quantitative results showed a remarkable agreement between synthetic high-b values and the ground-truth. A preliminary test with a microstructural model also gave encouraging results. Impact: Being able to synthesise high b-value data from clinical data could unleash the availability of advanced microstructural models to study the human brain and body, with applications in fundamental research and in the clinical settings.
Machine Learning Assisted Prediction of Cartilage Proteoglycan Content Using MR Fingerprinting
Authors: Kantola Ville , Nykänen Olli , Casula Victor , Karjalainen Ville-Pauli , Nissi Mikko , Nieminen Miika
Published: November 2024

Motivation: Early signs of cartilage degeneration include changes in proteoglycan content, which cannot be diagnosed using standard clinical imaging tools. Goal(s): Prediction of cartilage proteoglycan content from quantitative MR fingerprinting data at 3T. Approach: Gaussian process regression (GPR) models were trained to predict optical density of safranin-O stained cartilage sections, representing proteoglycan content, from MRF data on a voxel-by-voxel basis. Results: The trained GPR models reached very high accuracy (mean correlation of 0.81 with a respective NRMSE of 11.7%) and had clearly enhanced performance when compared to linear models. Impact: Non-invasive prediction of proteoglycan content in cartilage using MR fingerprinting at clinical field strength is feasible, holding promise for direct clinical imaging of cartilage composition in the future.
Robust Fitting Methods for Knee Cartilage T1ρ Quantification with Varied SNR – Preliminary results from a Multi-vendor Multi-site study
Authors: Zhang Zhiyuan , Kim Jeehun , Latery Richard , Winalski Carl , Liu Jing , Link Thomas , Peng Qi , Ying Leslie , Hardy Peter , Li Xiaojuan
Published: November 2024

Motivation: High-resolution T1ρ mapping is desired for improved sensitivity to small lesions and less partial volume averaging effects. However, the low image SNR can induce bias in T1ρ quantification. Goal(s): To develop and evaluate advanced fitting methods of high-resolution T1ρ mapping in a multi-site multi-vendor setting. Approach: High-resolution T1ρ mapping in volunteers were collected with a harmonized protocol from three sites and three MR platforms. Data were fitted with nonlinear least-squares (NLS) fitting, noise-corrected NLS fitting (NCNLS), and maximum-likelihood estimation (MLE). Results: :NLS overestimated T1ρ while NCNLS and MLE reduced the overestimation. The results were consistent across all sitesImpact: Advanced fitting methods can reduce fitting errors induced by low SNR for high-resolution T1ρ mapping, which may provide improved diagnosis of osteoarthritis.
Repeatability of Quantitative T1, T2 and T1ρ Mapping of Knee Cartilage with 3D-MR Fingerprinting
Authors: Zhang Xiaoxia , Moura Hector , Monga Anmol , Zibetti Marcelo , Kijowski Richard , Regatte Ravinder
Published: November 2024

Motivation: 3D-MRF sequence for simultaneous multi-parametric mapping has the potential to provide a more time-efficient comprehensive evaluation of the knee cartilage. Goal(s): However, evaluation of 3D-MRF repeatability of knee cartilage is limited. Approach: 3D-MRF and conventional sequences for knee cartilage were acquired four times on fourteen healthy subjects. Multi-parametric maps were computed and repeatability was evaluated. Results: High inter- and intra-subject repeatabilities were found using the 3D-MRF sequence over seven days with good agreement to conventional sequences. Impact: The 3D-MRF sequence showed high T1, T2, and T1ρ repeatability on knee cartilage on different days and good agreement with conventional methods.
ROI based Multi-parameter Quantitative Network(RMQ-Net) with Uncertainty-awareness for Quantitative UTE MRI Study of Cartilage
Authors: Lu Xing , Du Kevin , Ma Yajun , Athertya Jiyo , Malhi Bhavsimran Singh , Chang Eric , Bukata Susan , Chung Christine
Published: November 2024

Motivation: Quantitative MRI (qMRI) studies of cartilage regions need both regional segmentation and pixel-wise fitting analysis, which can be time-consuming and subject to inter-individual variability. Goal(s): To design a deep neural network for simultaneous qMRI mapping and accurate tissue segmentation. Approach: By leveraging different scan sequences, we proposed a RMQ-net with Uncertainty-awareness(UA) module, or UA-QMR-net. A majority-voting strategy was applied for robust cartilage segmentation and accelerated qMRI analysis. Results: The results demonstrated that the UA-RMQ-net achieved higher performance than the original RMQ-net for both UTE-T1 and UTE-T1r analyses of articular cartilage. Impact: By leveraging information from different scan sequences, the proposed UA-RMQ-net could obtain higher performance for accelerated qMRI analysis.
Healthy Knee Cartilage T2 Relaxation properties at 0.55T using Radial TSE approach: A comparison with 3.0T MAPSS
Authors: Bhattacharjee Rupsa , Han Fei , Su Pan , Yang Yang , Link Thomas , Majumdar Sharmila
Published: November 2024

Motivation: Baseline healthy-knee-cartilage reference comparisons across 0.55T and 3.0T can be utilized for comparing healthy to diseased cartilage in early-to-moderate OA at 0.55T. Goal(s): To compute and compare T2-baseline relaxation time measurements of healthy knee cartilage obtained at 0.55T using Radial-TSE approach with 3.0T using MAPSS, paired with DL-based cartilage segmentation. Approach: Phantom and Healthy Knee-cartilage-compartmental T2 relaxation values are compared using Exp2 and Sepg3 approaches with Radial TSE at 0.55T and 3.0T MAPSS. Results: We demonstrated healthy knee cartilage reference T2-values at 0.55T using Radial-TSE. Average T2-increase from 3.0T to 0.55T yields a wider range for detecting voxel-by-voxel granular changes of the cartilage. Impact: Baseline healthy-knee-cartilage reference Radial-TSE-T2 at 0.55T and T2-MAPSS-3.0T comparisons can be utilized for assessing healthy and diseased cartilage in early-to-moderate OA at 0.55T. Research findings at 3.0T could be translated at low-field economic scanners with a wider range of early-detection.
Improved Fat Suppression in 3-D Double-Echo Steady-State Sequence with Water Excitation using an Adapted Dixon Technique
Authors: Wen Yan , Fung Maggie , Tan Ek Tsoon , Campbell Gracyn , Wang Kang , Sneag Darryl
Published: November 2024

Motivation: In 3-D double-echo steady-state sequence with water excitation, fat suppression may be incomplete due to system imperfections, such as B0 and B1 inhomogeneity. Goal(s): This study describes implementing an adapted Dixon reconstruction to further suppress residual fat signal. Approach: The images between default and proposed reconstructions were compared in 16 patients undergoing routine upper extremity MR neurography. Results: The preliminary results demonstrated the feasibility of using the proposed reconstruction to remove residual fat signals incompletely suppressed by the water excitation. Impact: A benefit of the further suppressed fat provided by the proposed reconstruction was the improved depiction of small, yet clinically important peripheral nerves (particularly subcutaneous sensory branches surrounded by fat).
Phantom and workflow for validation and quality assurance of musculoskeletal MR intensive-variable biomarkers
Authors: Waterton John , Tibiletti Marta , Cashmore Matt , Hall Matt , Lamb Rachel , McHugh Damien , Walker Adrian , O'Connor James , Parker Geoff
Published: November 2024

Motivation: We seek better validation and QA of musculoskeletal intensive-variable MR biomarkers. Goal(s): We aimed to devise and evaluate a phantom with suitable form factor and composition for practical use in this setting. Approach: “Expected” temperature- and B0-dependent R1 values were calculated from prior data and verified by inversion recovery (IR). Practical QA use cases for hand and knee trials were exemplified using product-sequence 3D variable flip angle (3DVFA) R1measurements on three vendors’ scanners. Results: There was little deviation between “Expected” and IR-measured R1 (rms 3.8%). 3DVFA R1 exhibited bias (mean +23%) which must be subtracted during within-study QA. Impact: This work will improve QA in real-world musculoskeletal clinical trials which use intensive-variable MR biomarkers, and will help validate such biomarkers.
Fast and large coverage lower extremity perfusion evaluation with dual-echo EPI enabled blood oxygen level dependent technique
Authors: Qu Jianxun , Lin Tianye , Suo Shiteng , Fan Xiaoyuan , Feng Feng
Published: November 2024

Motivation: Lower extremity reactive hyperemia BOLD have been used to evaluate the skeletal muscle perfusion status. The existing methods, such as multi-echo GRE and single-echo EPI, have limitations in terms of spatial coverage and signal quantification. Goal(s): The study aims to apply dual-echo multi-band EPI to achieve high temporal and spatial resolution, and large coverage in BOLD experiment. Approach: The study applied dual-echo EPI to sample the T2* along the reactive hyperemia experiment. Images at TE of zero were synthesized to investigate the intrinsic signal change. Results: The proposed method can provide semi-quantitative indicators of the perfusion status of the lower extremity skeletal muscle. Impact: This work demonstrates the advantages of dual echo EPI acquisition for lower extremity reactive hyperemia experiments. Compared to the commonly used acquisition strategies, the proposed one can be used to enhance the quality and efficiency of lower extremity perfusion imaging.
Non-Contrast MR Perfusion Imaging of the Hip
Authors: Miyazaki Mitsue , Vucevic Diana , Malis Vadim , Mesa Anya , Bae Won
Published: November 2024

Motivation: Avascular necrosis of the hip is due to disturbed blood supply related to various causes. Early evaluation currently requires contrast enhanced MR perfusion imaging. Goal(s): Evaluate human hip perfusion using non-contrast MR perfusion technique. Approach: Bilateral hips of healthy volunteers were imaged coronally at 3-T using a 2D spin labeling MR perfusion technique using tag-on tag-off acquisition at varying TI and processing to determine tagged blood signal into the acetabulum, femoral head, and femoral neck. Results: We found distinct perfusion signal in the acetabulum but the signal in the femoral head and neck were more subdued. Impact: This study demonstrates the feasibility of non-contrast MR perfusion imaging of the human hip, with future application in evaluation of avascular necrosis.
Structural and Functional Alterations with Proprioception Deficits in Chronic Ankle Instability
Authors: Lu Rong , Xue Xiao'ao , Xia Tian , Tang Weijun , Li Qing , Fu Caixia , Chu Ying-Hua , Hwang Tingfang
Published: November 2024

Motivation: Understanding the neuropathological mechanisms of chronic ankle instability (CAI) is clearly of interest to both the practicing clinician and researchers on sports medicine. Goal(s): Ankle proprioception deficits occur in patients with CAI. The underlying central neuropathology is unclear. This study examined cerebellar vermis structure/function in CAI patients vs controls and associations with proprioception deficits. Approach: This research is a cross-sectional laboratory investigation. Results: Patients with CAI showed reduced gray matter volume (VBM) and increased functional connectivity (fALFF) in cerebellar vermis subregions IV/V compared to controls. Proprioceptive deficits in CAI patients were associated with lower VBM and higher fALFF values in these subregions. Impact: We hope the similar strategies could be applied to modulate our observed ﻿compensatory overactivation of vermis, make cerebellum more ﻿more efficient when handling the increased proprioceptive demands, and finally bring optimal clinical function to the patients with CAI.
Reducing inter scanner variability using Voxel-based relaxometry and Z-score normalization: 6 years Longitudinal Study of Hip-OA patients
Authors: THAHAKOYA RAFEEK , HAN MISUNG , ROACH KOREN , BHATTACHARJEE RUPSA , JIANG FEI , PEDOIA VALENTINA , SAUZA RICHARD , MAJUMDAR SHARMILA
Published: November 2024

Motivation: It is important to examine and correct inter-scanner variability of T1rho/T2 values to improve the accuracy of longitudinal studiesGoal(s): To evaluate the inter-scanner variability of T1rho/T2 values and measure longitudinal changes in hip cartilage T1rho/T2 values after calibration. Approach: TCalibrate the hip cartilage T1rho/T2 values using Voxel-based relaxometry and Z-score normalization techniquesResults: The T1rho/T2 values showed that there was no statistically significant cartilage region (p<0.05) between the scanners. The longitudinal results showed an increase in number of subjects showing positive slope was increased in T1rho/T2 values after calibration (36.84 and 34.21%) compared without calibration (15.8% for both T1rho/T2). Impact: The technique used for reducing the inter-scanner variability of T1rho/T2 values may benefit the longitudinal assessments of hip OA, specifically for evaluating disease progression over time on different scanners/before and after scanner upgrades
Validating accelerated T1rho dispersion measurement with samples from spontaneous human osteoarthritis
Authors: Pala Swetha , Paajanen Antti , Ristaniemi Aapo , Nippolainen Ervin , Afara Isaac , Nykänen Olli , Nissi Mikko
Published: November 2024

Motivation: Validation of compressed sensing accelerated T1rho dispersion measurement of spontaneously degenerated human cartilage tissue samples. Goal(s): To study the effect the data reduction has on the ability to detect differences between the intact and degenerated articular cartilage specimens at different spin-lock amplitudes. Approach: T1rho dispersion measurement with compressed sensing technique for reconstruction. Results: Four-fold acceleration of T1rho dispersion measurement by compressed sensing approach was feasible without loss in the sensitivity to osteoarthritic changes within the articular cartilage. Differences were significant between intact and OA groups in the superficial and transitional zones, and T1rho correlated moderately with the reference methods. Impact: Compressed sensing allowed reducing the scan-time required for measurement of T1rho dispersion, while still retaining the ability to detect degenerative changes in articular cartilage. Thus, the study brings T1rho dispersion measurements closer to clinical viability.
Feasibility of high-resolution deep learning reconstruction synthetic T2 maps contrast images in diagnosis of joint degeneration
Authors: wang kejun , liu weiyin , Zha Yunfei
Published: November 2024

Motivation: Combing T2 mapping with routine knee MRI increase diagnostic efficacy especially early identification of joint degeneration. Goal(s): To explore the feasibility of high-resolution deep-learning reconstruction (DLR) synthetic MRI with equivalent to higher diagnosis performance using conventional routine knee MRI as reference. Approach: To test reliability of T2 mapping for in vitro phantom and in vivo human knees and explore the image quality of DLR synthetic contrast MR images in comparison with conventional MR images. Results: DLR synthetic MRI offer reliable T2 mapping and provide sufficient image quality for diagnosis of knee. Impact: There has been always a demand for high-resolution knee MRI acquisition and straightforward diagnosis in clinics. DLR synthetic MRI is a rapid-acquisition and high-image-quality contrast images and quantitative maps and may improves diagnosis, prognosis and follow-ups.
Integrated PET/MR Imaging Using Synthetic MRI for Improved Stroke Lesion Characterization and Metabolism Prediction
Authors: Cui Bixiao , Zhang Yifei , Shan Yi , Yang Hongwei , Lu Jie
Published: November 2024

Motivation: To explore synthetic MRI's potential for improved stroke lesion characterization and metabolic activity prediction. Goal(s): To Enhance stroke lesion visualization and to estimate regional metabolism via quantitative relaxation values in synthetic MRI. Approach: 10 stroke patients underwent integrated PET/MR scanning. We compared tissue contrast in synthetic tailored contrast-enhanced composite images with conventional T2 FLAIR. Relaxometry values were used to build predictive models for PET SUV. Results: Composite images significantly improved stroke lesion visibility compared to traditional methods. Relaxometry values successfully predicted metabolic activity within the lesion. Impact: This study demonstrates the potential of synthetic MRI in stroke patients, offering improved
Assessment of a long label duration in multi-delay pseudo-continuous arterial spin labelling scheme
Authors: Obara Makoto , Togao Osamu , Mikayama Ryoji , Wada Tatsuhiro , Tokunaga Chiaki , Vaclavu Lena , van Osch Matthias J.P. , Ishida Shota , van de Ven Kim , Ito Yuki , Hamano Hiroshi , Ueda Yu , Jihun Kwon , Yoneyama Masami , Van Cauteren Marc
Published: November 2024

Motivation: Clinical usefulness of multi-delay (MD) as well as long label duration (LD) pseudo-continuous arterial spin labelling (pCASL) have been suggested. However, MD-pCASL scheme including long LD has not been established. Goal(s): Long LD combined MD-pCASL scheme was assessed in terms of quantification. The possibility of scan acceleration was also studied. Approach: MD-pCASL with maximum LD 3 sec for 11 (3min 13sec) and 5 delays (1min 38sec) were assessed, and compared to a scheme with maximum LD 2 sec. Results: Reasonable quantitative values were obtained by both LD 3 sec schemes, while providing higher SNR. Impact: The potential of a MD-ASL scheme with long label duration has been demonstrated in terms of improved SNR, reasonable quantitative parameters, and acceleration of scan time. Advantages of the scheme are especially expected for neurovascular applications.
Amide proton transfer MRI can reflect tissue-level reperfusion status and predict functional outcome in patients with ischemic stroke
Authors: Zhang Chi , Yong Xingwang , Hsu Yi-cheng , Zhang Yi , Lu Shanshan
Published: November 2024

Motivation: Amide proton transfer (APT) imaging can clarify the metabolic microenvironment after ischemia. Few clinical studies to date have examined its use in ischemic tissue after reperfusion therapy, where the pH may return to normal physiological level. Goal(s): We used a numerical fitting of extrapolated semi-solid magnetization transfer reference (NEMR) method, which has been introduced to improve the detection of ischemia, to assess the tissue microenvironment after reperfusion therapy. Approach: Prospectively, case-control studyResults: We found both APT signal and mismatch pattern between APT and DWI (APT<DWI) could reflect tissue-level reperfusion status and provide complementary information to perfusion imaging for predicting 90-day neurological outcomes. Impact: We demonstrated the utility of APT signal and mismatch pattern between APT and DWI (APT<DWI) as effective biomarkers to characterize the tissue microenvironment and predict 90-day functional outcomes after reperfusion in patients with ischemic stroke.
Cerebral blood flow from arterial spin labeling may serve as a marker for clinical outcome following large vessel occlusion and thrombectomy
Authors: Sollmann Nico , Hoffmann Gabriel , Buerkle Johannes , Zimmer Claus , Wunderlich Silke , Rühling Sebastian , Schwarting Julian , Maegerlein Christian , Berndt-Mück Maria , Boeckh-Behrens Tobias , Kaczmarz Stefan , Petzsche Moritz Hernandez
Published: November 2024

Motivation: Determination of a non-invasive imaging biomarker for outcome following ischemic stroke. Goal(s): To investigate whether cerebral blood flow from arterial spin labeling can be used as an imaging biomarker for assessment of clinical outcome following stroke with mechanical thrombectomy. Approach: Prospective study with pseudo-continuous arterial spin labeling acquired few days after mechanical thrombectomy, combined with outcome evaluation few days and 3-months post-stroke. Results: Percent cerebral blood flow difference (i.e., between infarct masks in relation to the mirrored mask in the unaffected hemisphere), pre-stroke modified Rankin Scale, and infarct volume were associated with functional independence (i.e., modified Rankin Scale 0-2 at 3-months post-stroke). Impact: Percent cerebral blood flow difference from the infarct territory may predict functional independence in patients with ischemic stroke and mechanical thrombectomy. Thus, this quantitative parameter may be used as an early non-invasive imaging biomarker to predict clinical outcome.