bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the robustness of estimating B1+maps by a neural network at different UHF sites.
Authors: Hadjikiriakos Kimon , Krüger Felix , Zimmermann Felix , Riemann Layla , Degenhardt Katja , Hammernik Kerstin , Grimm Johannes , Schmidt Simon , Metzger Greg , Ladd Mark , Schaeffter Tobias , Schmitter Sebastian
Published: November 2024

Motivation: B1+ -maps needed for subject-specific pTx can be derived from localizers using a neural network (NN), omitting separate B1+-mapping. Ideally, a single, general NN applicable to all UHF sites is highly desired. Goal(s): Investigate the robustness of a neural network predicted B1+-field maps. Utilizing receive profiles from an 8Tx/32Rx head coil as neural network input. Approach: Comparing the performance on data from identical and different commercial head coils across multiple MRI sites. Results: Achieving SSIMs as high as 96% and RMSEs as low as 2.7%, with error mapping predominantly localizing discrepancies at the cranial margins, suggesting that larger datasets could enhance Gaussian convergence. Impact: The study suggests that a single NN trained by a large B1+ library for one type of pTx head coil may be disseminated to other UHF sites that use the same coil. This will enable a fast, streamlined pTx calibration.
Open-Source Algorithm for 3D Gradient Nonlinearity Characterization Using a Rigid Arrangement of NMR Field Probes
Authors: Shafiekhani Mojtaba , Littin Sebastian , Chu Ying Hua , Hsu Yi-Cheng , Zaitsev Maxim , Wehkamp Niklas
Published: November 2024

Motivation: MRI users rely on vendor-specific means to correct for distortions due to gradient nonlinearity. This study endeavors to measure gradient nonlinearities without a dedicated phantom for MR systems where accurate correction parameters are unavailable. Goal(s): Develop a nonproprietary gradient nonlinearity measurement procedure. Approach: We conduct NMR field probe measurements to determine the gradient nonlinearity. A dedicated algorithm detects inconsistencies between the apparent probe positions and seeks the optimal spherical harmonic coefficients correcting for distortions. Results: We demonstrate the feasibility of the proposed measurement and a reduction of distortions in MR images. Impact: The proposed nonproprietary approach for measuring gradient nonlinearity induced distortions without a dedicated phantom has the potential to promote accuracy and reproducibility of imaging studies across different MRI systems, if the sites are equipped with a field camera.
Open-Source Simulator for Spatial Encoding Effects in Highly Variable B0 and Gradient Fields
Authors: Tibrewala Radhika , Collins Christopher , Paska Jan , Assländer Jakob , Sodickson Daniel
Published: November 2024

Motivation: Design of accessible scanners may require accounting for substantial magnetic field inhomogeneities, which challenge assumptions used in MRI simulators. Goal(s): To capture the encoding effects of strong field variations accurately and efficiently, predicting distortions of input images. Approach: We discretized magnetic fields as usual but extended the MR signal simulation at each grid point from the 0th-order approximation, which assumes a locally-constant field, to a 1st-order approximation, which assumes a locally-linear field. Results: The 1st-order approximation, which has an analytic solution, better captures strongly varying fields and enables simulations on a coarser grid, greatly improving computational efficiency. Impact: The simulator enables evaluation of scanner designs with strongly varying magnetic fields. Simulated images can be utilized to generate training data efficiently from existing ground-truth images, enabling exploration of machine-learning techniques prior to the construction of prototype systems.
Development and dynamic in vivo evaluation of a multi-channel stretchable self-tuning coil array
Authors: Motovilova Elizaveta , Ching Terry , Vincent Jana , Tan Ek Tsoon , Taracila Victor , Robb Fraser , Hashimoto Michinao , Sneag Darryl , Winkler Simone
Published: November 2024

Motivation: Motivated by the limitations of traditional RF receive coils, this study aims to demonstrate in vivo imaging using a conformal and stretchable, self-tuning liquid metal coil array. Goal(s): This study’s goal was to demonstrate improved signal-to-noise ratio (SNR) with the stretchable array compared to commercial coils. Approach: We designed and fabricated a one-dimensional 6-channel stretchable coil array and tested it in vitro and in healthy volunteers using standard knee imaging sequences. Results: In vitro and in vivo experiments demonstrated an SNR improvement of 4.7x over a dedicated commercial knee coil. Impact: Our self-tuning stretchable coil array allows for maximized SNR and improved image quality due to its conformal fit and minimized distance from the target anatomy. This concept could also allow for dynamic imaging, leading to enhanced, clinically relevant, MRI applications
Functional connectome phenotype of multiple cerebrovascular disease markers and its interaction with plasma p-tau181 on downstream outcomes
Authors: Chong Joanna Su Xian , Ji Fang , Hilal Saima , Chong Joyce Ruifen , Lau Jia Ming , Tan Boon Yeow , Venketasubramanian Narayanaswamy , Lai Mitchell Kim Peng , Chen Christopher Li-Hsian , Zhou Juan Helen
Published: November 2024

Motivation: Cerebrovascular disease (CeVD) is assessed by several MRI markers, but their impact on brain functional connectivity (FC) remains unclear. Goal(s): To examine how multiple CeVD markers influence FC, and how CeVD-related FC changes interact with Alzheimer’s disease pathology to influence downstream outcomes. Approach: We studied multivariate associations between four CeVD markers and whole-brain FC in 529 participants, and how this CeVD-related FC phenotype interacted with plasma p-tau181 to influence longitudinal brain atrophy and cognitive decline. Results: We identified a FC phenotype linked to high CeVD burden across all markers. This phenotype and p-tau181 contributed additively, but not synergistically, to atrophy and cognitive decline. Impact: Using a multivariate approach, our study demonstrated that CeVD exerted widespread, non-MRI marker-specific effects on the whole-brain functional connectome. Further, we showed that AD and CeVD have additive but not synergistic effects on neurodegeneration and cognitive changes over time.
Hierarchical auditory processing integrating macroscopic and mesoscopic neural networks
Authors: Jiang Yuwei , Mu Yangjiayi , Wang He
Published: November 2024

Motivation: The invasive microscopic research on the brain can only be widely conducted in animal models. Detecting signals in laminar level using noninvasive MRI helps us bridge knowledge between macro- and micro-research. Goal(s): To understand the neural mechanism of hierarchical auditory processing in both macroscopic and mesoscopic brain cortex in-vivo. Approach: We used the 7T MRI to perform the whole-brain-fMRI and layer-fMRI with ultra-high-resolution when human was listening to a hierarchical auditory sequence paradigm. Results: The responses to the hierarchical auditory processing are not only along a pathway from auditory to frontal cortices, but also various across laminar cortex. Impact: We used the layer-fMRI to find that the auditory processing is distributed across cortical layers in a hierarchical manner. The detection of auditory processing in both macroscopic and mesoscopic levels allows us to build predictive coding model in multiple dimensions.
Language Lateralization on resting state fMRI Vs task fMRI, in neurosurgical cases
Authors: GUPTA SANTOSH , SRIRAM RITHIKA
Published: November 2024

Motivation: Understanding hemispheric language dominance is crucial for surgery, yet task-based fMRI has many challenges. Resting-state fMRI shows promise, but its ability to depict language lateralization is still evolving, with varied results in literature. Goal(s): To assess language dominance on rest fMRI in neurosurgical patients by different methods and seeing concordance with task-based fMRI. Approach: We explored three calculation techniques for lateralization: first using individual seed volumes, second using voxel activation in ipsilateral hemisphere, and third using net voxel activation generated in bilateral hemispheres Results: Maximum of 54.55% concordance of rest-fMRI with task-based fMRI was seen for language dominance, with one of the methods. Impact: Our results emphasize that rest fMRI for language dominance should be used with caution and as an adjunct to task fMRI in neurosurgical patients.
Noninvasive quantification of ketamine-induced structural plasticity in mice using multishell diffusion weighted imaging
Authors: Ewbank Sedona , Hart Alex , Casey Austen , McNab Jennifer , Heifets Boris , Airan Raag
Published: November 2024

Motivation: To monitor treatment, we need noninvasive quantitation of the microstructural changes known to accompany rapid-acting antidepressant therapy. Goal(s): Our goal was to develop a noninvasive biomarker of the structural plasticity induced by rapid-acting antidepressants. Approach: Neurite orientation dispersion and density imaging (NODDI) was assessed before and after treating male and female mice with ketamine, with or without opioid blockade. Results: Ketamine induced alterations of orientation dispersion index (ODI) and neurite density index (NDI) in brain regions involved in mood regulation and reward processing, particularly in females. Curiously, opioid blockade induced trendwise increases in NDI and ODI across multiple brain regions, independent of ketamine. Impact: Our preliminary results indicate that multishell diffusion MRI tracks the brain microstructural changes known to be induced by ketamine and related rapid-acting antidepressants, enabling a noninvasive quantitative biomarker that could be used to track individual patient response to antidepressant therapy.
Accelerated Cortical Thickness Mapping Using Deep Learning
Authors: Chen Jia-Xiu , Huang Teng-Yi , Liao Yu-Chen , Yu Jui-Jung
Published: November 2024

Motivation: The long processing time of current CT mapping methods hampers their use in clinical research. A faster and reliable CT mapping alternative is needed. Goal(s): To create a deep-learning model that reduces CT mapping time without compromising accuracy or the ability to classify Alzheimer's disease. Approach: We trained a 3D U-Net-based model on T1-weighted MRI datasets to produce CT maps, generating two model variants—one using skull-stripped and the other using both whole-brain and skull-stripped images. Performance was benchmarked against FreeSurfer. Results: The complete Unet-based CT mapping workflow, inclusive of preprocessing, was executed in under a minute without relying on GPU acceleration. Impact: The developed deep-learning-based method, executed within a minute, could accelerate neurological research related to CT values by providing fast and reliable procedure for CT mapping.
Time course of structural neuroplasticity induced by procedural motor learning: DTI vs SANDI
Authors: Griffa Guillermina , Palombo Marco , Yeffal Abraham , Lee Hong-Hsi , Huang Susie Y. , Della-Maggiore Valeria
Published: November 2024

Motivation: Performance gains during motor sequence learning (MSL) are linked to increased hippocampal activity and fast changes in mean diffusivity. Yet, DTI is not biologically specific. Goal(s): To identify the biological compartment(s) driving changes in gray-matter microstructure during MSL. Approach: We applied SANDI to multi-shell-DWI acquired in 28 subjects on the Connectome scanner before, 30 min, and 24h post MSL training. Results: MSL-induced transient changes in DTI were associated with a temporary increase in soma fraction at 30 min, compatible with a fast homeostatic response (e.g. astrocytic swelling). Long-lasting DTI changes were associated with an increment in neurite fraction compatible with structural remodelling. Impact: Our work may broaden the scope of understanding of human hippocampal memory and help pinpoint the biological substrates of plasticity. Moreover, it may set the basis for developing a biologically meaningful biomarker of neuroplasticity to detect early signs of neuropathology.
Combining Inhomogeneous Magnetization Transfer and Myelin Water Imaging at 9.4T: Methodology and Limitations
Authors: Novoselova Masha , Yung Andrew , Kozlowski Piotr
Published: November 2024

Motivation: Myelin imaging techniques are often qualitative and lack specificity, hindering precise understanding of white matter and neurodegenerative diseases. Goal(s): This study aimed to enhance myelin imaging by combining Inhomogeneous Magnetization Transfer (ihMT) and Myelin Water Imaging (MWI) at 9.4T. Approach: Incorporating ihMT saturation pulses with the multi-echo spin-echo MWI readout acquisition facilitates myelin quantification through multi-contrast mechanisms. ihMT images protons with extended dipolar relaxation times, while MWI extracts myelin water contributions from T2 distributions. Results: Results revealed irregularities due to high-field-induced shortening of myelin water T2 values. Therefore, full T2 distributions could not be resolved, impeding further analyses such as myelin quantification. Impact: This study presents a myelin-specific imaging approach through the fusion of two quantitative techniques at 9.4T. Unresolved myelin water T2 peaks caused by the high field strength present a challenge, underscoring the need for further exploration at lower field strengths.
Evaluation of pituitary tumor texture using Synthetic MRI
Authors: Cao Luqiang , Liu Weiyin , Xu Lin , Chen Wen , Zhang Yu , Xiao Zhongyan
Published: November 2024

Motivation: Surgery selection mainly relies on texture of pituitary adenomas. There is rarely a non-invasive imaging method to identify texture of pituitary adenomas that greatly determines surgical selection and outcomes. Goal(s): To explore the predictive performance of synthetic MRI in types of pituitary tumors. Approach: T1, T2 and PD values of every tumor were measured and divided into the solid and soft tumor groups according to the histology results of surgical samples. Results: Synthetic MRI-computed T2 and PD values were significantly higher in the soft pituitary tumor group than in the solid group (P<0.05) with cutoff values of 110.83 ms and 87.3 p.u., respectively. Impact: Both T2 and PD value can assist surgeons in determination of surgical treatments (e.g. transsphenoidal resection or craniotomy) and prediction of surgical outcomes, such as resection completeness, indicating synthetic MRI could a strong imaging marker.
Assessing the Effect of Universal Pulses on EDGE-MP2RAGE at 7T
Authors: Sarlls Joelle , Saib Gael , Mauconduit Franck , Gras Vincent , Talagala S Lalith
Published: November 2024

Motivation: To optimize an EDGE-MP2RAGE acquisition at 7T for epilepsy studies. Goal(s): To determine if it is beneficial to use Universal Pules within EDGE-MP2RAGE at 7T and to find the optimum TI1 to produce the best visulation of the gray-white matter boundary. Approach: Collect EDGE-MP2RAGE data with conventional RF and Universal Pulses and analyze the gray and white matter signal across the brain. Results: Overall, it does seem to be beneficial to use Univeral Pulses within EDGE-MP2RAGE, although it does not produce more consistent gray and white matter signal in all brain regions. Impact: Utilizing Universal Pulses within EDGE-MP2RAGE with the determined optimum TI1=760ms at 7T can improve continuity of the gray-white matter boundary compared to conventional RF pulses, which may allow for detection of focal cortical dysplasia in epilepsy patients.
A Fully Automated Pipeline for the Determination of the Iron Microstructure Coefficient (IMC) from Multi-Echo GRE Data
Authors: Mir Saleha , Salman Fahad , Jakimovski Dejan , McGranor Cheryl , Zivadinov Robert , Schweser Ferdinand
Published: November 2024

Motivation: This work aims to automate analysis of the newly introduced Iron Microstructure Coefficient (IMC) to facilitate understanding of iron cellular distribution in neurological diseases for large cohort studies. Goal(s): The goal is to develop and test an automated and widely applicable pipeline for IMC analysis using quantitative susceptibility and R2* maps obtained from the same multi-echo GRE sequence. Approach: The pipeline inputs magnetic susceptibility and R2* maps, T1-w data, templates, and regions of interest (ROIs). The output is the IMC value per ROI per subject. Results: The pipeline successfully executed in 50 minutes without segmentation failure for a cohort of 28 test subjects. Impact: The automated pipeline accelerates data processing for IMC, providing enhanced standardization in a robust, reproducible, and user-friendly manner. It facilitates large-scale research, driving significant advancements in our understanding of neurological diseases, with the goal of improving accurate diagnosis for patients.
‘Repeat it with me’ 2022-23 Reproducibility Team Challenge: Sensitivity Analysis of the Bloch Equations
Authors: Gunwhy Ebony , Pilgrim-Morris Jemima , Scholand Nick , Uecker Martin
Published: November 2024

Motivation: Inverse problems in MRI require estimation of the Bloch equation partial derivatives, however robust computation is challenging. Sensitivity analysis offers accurate and numerically stable derivatives to overcome this. Goal(s): To replicate and validate previous work, and examine functionality of the BART toolbox and use of collaborative platforms such as GitHub for reproducing research. Approach: A direct replication was attempted following methodology from a previous ISMRM abstract, and an accompanying preprint and GitHub repository. Results: Both replicators successfully recreated all abstract figures. Replication of difference quotient and sensitivity analysis derivatives was achieved within the predefined normalised root mean square error tolerances. Impact: Successful replication further validates novel work in computing partial derivatives of the Bloch Equations. Collaborative platforms such as GitHub can improve existing software and resources when reproducing research. This enables wider dissemination, enhancing ease-of-use for other researchers in future applications.
Towards Practical SEMSI at 0.55 Tesla
Authors: Li Bochao , Keskin Kübra , Cui Sophia , Hargreaves Brian , Nayak Krishna
Published: November 2024

Motivation: Spectrally-encoded multi-spectral imaging (SEMSI) is an approach for distortion-free MRI around metallic implants, but requires multiple-readouts per TR to be clinically feasible. Goal(s): To determine the benefits of SEMSI over SEMAC at 0.55T, using parameters that are consistent with a future multi-readout SEMSI implementation. Approach: We used single-readout SEMSI to prospectively mimic the performance of multi-readout SEMSI with high slew rate and readout bandwidth. Results: We observe that SEMSI simultaneously achieves the expected SNR improvement and artifacts reduction compared to SEMAC. Impact: SNR improvement is important at 0.55T to examine the tissues near the metallic implants. This prospective study confirms the improvement performance of multi-readout SEMSI which will further improve SNR and scan efficiency.
Multi-center, multi-vendor validation of PDFF and T1 mapping in an optimized PDFF-T1 phantom
Authors: Starekova Jitka , Weingartner Sebastian , Rutkowski David , Bae Won , Do Hung , Madhuranthakam Ananth , Malis Vadim , Mukherjee Sujoy , Lin Sheng Qing , Serai Suraj , Yokoo Takeshi , Reeder Scott , Brittain Jean , Hernando Diego
Published: November 2024

Motivation: Chemical-shift-encoded (CSE)-based proton-density fat-fraction (PDFF) is a highly validated biomarker of liver fat. T1 mapping has been proposed as a biomarker of hepatic fibrosis. However, the reproducibility of PDFF and T1 in the setting of concomitantly varying fat and T1 is poorly understood. Goal(s): To validate the reproducibility of CSE-based PDFF and MOLLI-based T1 mapping with concomitantly varying fat and T1. Approach: We conducted a four-center, four-vendor validation study using a quantitative PDFF-T1 phantom. Results: CSE-based PDFF had good reproducibility, although with increased bias and variability at long T1 values. The reproducibility of MOLLI-based T1 was affected substantially by the presence of fat. Impact: CSE-based PDFF demonstrated good reproducibility across four centers/vendors, at both 1.5T and 3T. Increased T1 and increased PDFF led to reduced MOLLI-based T1 reproducibility. This multi-center multi-vendor PDFF-T1 phantom validation approach may enable evaluation of improved quantitative MRI methods.
Advancing 3D DWI Imaging with the 3D Radial-EPI(RAZER) Trajectory
Authors: Kim Seong-Eun , Odeen Henrick Carl Axel , Malmberg Micheal Micheal , Parker Dennis
Published: November 2024

Motivation: Non-Cartesian sampling offers advantages in motion robustness and speed, crucial in 3D DWI. Goal(s): We aimed to improve 3D DWI using RAZER trajectory to improve the 3D DWI imaging with reducing scan time while preserving imaging quality. Approach: We implemented 3D DW RAZER sequence with integrated radial and EPI sampling and conducted 3D DWI studies in a phantom and human carotid arteries. Results: The RAZER DWI sequence offered a clearer depiction of the carotid arterial structure and reliable ADC measurements without geometric distortion when compared to 2D ss-DW EPI, all within a scan time of less than two minutes. Impact: This study introduces a motion-robust 3D DWI with faster imaging capabilities, potentially improving vulnerable plaque identification. It addresses conventional DWI limitations, providing a valuable resource for the detection of possible events and improved patient outcomes in ischemic stroke.
ASN: Adaptive Segmentation Network for Visual Pathway Identification in Multi-parametric MR Images
Authors: Diakite Alou , Li Cheng , Xie Lei , Feng Yuanjing , Han Hua , Zheng Hairong , Wang Shanshan
Published: November 2024

Motivation: Accurate visual pathway (VP) segmentation is critical for clinical diagnosis and surgical planning. Current deep learning-based methods struggle to capture significant context information, impacting the segmentation precision. Goal(s): Improve multi-parametric MRI-based VP segmentation by designing an Adaptive Segmentation Network (ASN). Approach: ASN uses adaptive convolution (AC) to dynamically adjust the kernel based on complementary context, facilitating the integration of contextual information. A spatial attention block selectively extracts relevant regions‘ features in each MRI sequence and fuses them. Results: ASN's effectiveness is validated by segmenting the VP in MR images from two MRI sequences. It surpasses state-of-the-art techniques in VP segmentation. Impact: The introduction of ASN, a new multi-parametric MR images segmentation approach, demonstrates superior performance in visual pathway (VP) segmentation in MR images, surpassing existing state-of-the-art techniques. This novel method effectively incorporates context information, leading to improved segmentation performance.
Digging deeper into the pervasive problem of non-compliance in MR datasets
Authors: Sinha Harsh , Raamana Pradeep
Published: November 2024

Motivation: Large MRI datasets from multiple sites are not monitored for protocol compliance and dataset integrity. Goal(s): We previously demonstrated the pervasiveness of protocol non-compliance in MR datasets using our open source tool mrQA. We aim to produce deeper insights with vertical audit and analyze the common patterns of non-compliance. Approach: We processed the large and open ABCD study verifying relationships between sequences in their protocol. Results: We observed issues on non-compliance in coil, shim setting, and pixel spacing. We also observed significant disparities across vendors, scanners and sites. This underscores the necessity for tools such as mrQA that can identify non-compliance across vendors/sites. Impact: Non-compliance in acquisition parameters is a pervasive problem in MR datasets. It is impractical to “hope” for protocol compliance across sites, and scanners. Our tool, mrQA can enable researchers to continuously monitor and identify non-compliant scans in a practical manner.
Quality assessment of MR images: Does deep learning outperform machine learning with handcrafted features on new sites?
Authors: Kaur Prabhjot , Thornton John , Barkhof Frederik , Yousry Tarek , Vos Sjoerd , Zhang Hui
Published: November 2024

Motivation: Deep learning (DL) outperforms conventional machine learning (ML) that relies on handcrafted feature-based in many vision tasks, but its superiority in assessing brain MRI image quality for new sites/scanners is unclear. Goal(s): Compare DL and conventional ML for quality assessment of brain MRI images from new sites/scanners. Approach: One popular and widely accepted DL and one conventional ML method are evaluated on a multi-site dataset using leave-one-site-out approach using a binary quality label (good/bad). Results: Averaged balanced accuracy (BA) for the DL and conventional ML approaches are comparably poor (0.60+-0.12 and 0.54+-0.12, respectively) and does not exceed 0.76, suggesting room for improvement. Impact: Widespread adoption of automated quality assessment of brain MRI images is limited by a lack of generalizability. By comparing popular DL and conventional ML approaches, we find comparable but limited generalizability. This underscores the need for future algorithm development.
Fast and Generalized Motion Correction in Brain MRI using 3D Radial Trajectory and Projection Moment Analysis
Authors: Li Bowen , She Huajun
Published: November 2024

Motivation: Traditional projection moment analysis in 3D radial MRI failed to get specific rigid-body motion parameters with stationary multichannel coils. Goal(s): Our goal was to develop a method to extract rigid-body motion parameters directly through projection moment analysis. Approach: A PCA-based coil compression, together with projection information from different channels were used to estimate rigid-body motion parameters. A recursive least-squares model was used to recursively estimate motion parameters for every single spoke. Simulation and scanning of moving object were performed to demonstrate its capability in brain scan. Results: The proposed method can correct motion in brain successfully and quickly. Impact: The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients like children, in addition to many other applications including extremity MRI.
Nonlinear Susceptibility Inversion Deep Learning Model for Robust Quantitative Susceptibility Mapping
Authors: Guo Hongyu , Zhang Zheng
Published: November 2024

Motivation: Quantitative susceptibility mapping (QSM) estimates the spatial distribution of tissue susceptibility by solving a challenging ill-posed dipole inversion problem, which heavily affects the accuracy of tissue susceptibility quantification.Goal(s): To generate high-quality QSM images.Approach: In this study, we present a deep learning method for susceptibility inversion that utilizes a nonlinear susceptibility inversion model, NSIDL.Our approach integrated the Proximal Gradient Descent (PGD)[1] algorithm and embedding the physical model in the network. Results: NSIDL was compared to traditional and deep learning methods, and it was found that NSIDL can effectively suppress streaking artifacts, mitigate noise amplification, and prevent excessive smoothing.Impact: This study introduced the NSIDL deep learning method, which improved the accuracy of tissue magnetic sensitivity quantification. The improvement of QSM performance can help clinical doctors make more informed decisions based on reliable sensitivity measurements.
3D MR spirometry in healthy volunteers at 1.5 T using a UTE sequence with a flexible k-space sampling pattern
Authors: Reitmann Anna , Massire Aurélien , Mauconduit Franck , Barrau Nathalie , Duwat Adrien , Brun Anne-Laure , Mellot François , Vignaud Alexandre , Ciuciu Philippe , Grenier Philippe , Maitre Xavier
Published: November 2024

Motivation: To study lung function using 3D MR spirometry. Goal(s): To optimize 3D dynamic lung MRI with flexible k-space sampling for UTE acquisition. Approach: A non-Cartesian UTE sequence is developed to execute arbitrary trajectories stored in a readily available external gradient file library. 3D MR spirometry was then performed on freely-breathing healthy volunteers at 1.5 T. Results: High-quality 4D lung images are obtained, enabling the extraction of relevant respiratory biomarkers. Image quality can then be optimized by directly playing back the shapes and durations of the readout gradients from the files. Impact: A non-Cartesian, center-out MR sequence that allows out-of-the-box UTE capabilities with flexible k-space trajectories is developed. The result is high-quality 4D lung imaging in free-breathing and supine conditions. The lung functional biomarkers are expected to be sensitive to pathology.
Ultrafast breast MRI for predicting lymph node metastases in breast cancers
Authors: Li Yang , Liu Haifa , Wang Qi , Xu Qian , Wang Mengzhu , Grimm Robert , Qi Mingwei
Published: November 2024

Motivation: Studies of ultrafast dynamic contrast-enhanced (DCE)-MRI were previously focused primarily on differentiating between benign and malignant breast tumors, with little research on lymph node metastases. Goal(s): We searched for noninvasive biomarkers to predict lymph node metastases in patients with breast cancer using ultrafast DCE-MRI. Approach: Ultrafast DCE-MRI was performed using a GRASP sequence, and the proprietary kinetic indicators were calculated to quantitatively diagnose lymph node metastases. Results: The relative peak enhancement of patients with lymph node metastasis was significantly higher than that of those without metastasis (area under the curve: 0.671)Impact: In this feasibility study, we preliminarily explored the role of ultrafast breast MRI in diagnosing lymph node metastasis in patients with infiltrating ductal carcinoma. We found that relative peak enhancement can be used to predict lymph node metastasis.
Evaluation of AI Based Reconstruction to Improve image Quality of T2w Images of the Breast
Authors: Walker Christopher , Kapoor Megha , Mayo Ray , Sun Jia , Stafford R , Le-Petross Huong
Published: November 2024

Motivation: Breast MRI has exceptional sensitivity but is limited by image quality and several artifacts. Advancements in AI-based reconstruction hold promise for improved image quality and efficiency. Goal(s): This study assesses the impact of applying a vendor AI-based reconstruction on breast MRI at 3T. Approach: This study retroactively reconstructed 45 series using a commercially available AI-based reconstruction. Three board-certified radiologists scored traditional and reconstructed sequences for quality and improvement. Results: AI reconstruction showed more conspicuous margins as well as an enhanced noise texture. 54 cases showed improvement, 63 showed no change, and 15 exhibited degraded quality. Higher-quality exams were associated with the greatest improvement. Impact: A retrospective analysis of a recent FDA approved AI based reconstruction method to improve MRI image quality for breast studies.
Multiplexed sensitivity-encoding (MUSE) DWI with deep learning-based reconstruction in breast MR imaging: A comparison with conventional DWI
Authors: Xiao Yitian , Yang Fan , Sun Jiayu , Zhang Bo , Liang Huilou
Published: November 2024

Motivation: Conventional DWI has limitations due to low spatial resolution and geometry distortion. Multiplexed sensitivity-encoding (MUSE) DWI can obtain images with higher resolution and less distortion but require longer acquisition time. Goal(s): Our aim was to apply deep-learning based reconstruction (DLR) in MUSE DWI for breast imaging, and to investigate if DLR can shorten the scan time while maintaining image quality of MUSE. Approach: We compared quantitative parameters and subjective image quality of MUSE, MUSE-DLR, and conventional DWI. Results: MUSE-DLR showed improved image quality than MUSE with slightly longer acquisition time compared to conventional DWI. Impact: MUSE DWI with deep-learning based reconstruction can enhance the accuracy of clinical breast imaging while maintaining an acceptable scanning time, and also has the potential to improve diffusion imaging in other parts of the human body.
The detrimental lifelong impacts of early life adversity on brain
Authors: Uematsu Akiko , Yokoyama Chihiro , Kawasaki Akihiro , Takeda Chiho , Ishibuchi Tomoko , Hayashi Takuya
Published: November 2024

Motivation: Early life adversities (ELAs) have long-lasting detrimental impacts on physical and mental health across life course, insisting the experience of ELAs disturb healthy neuronal brain development. Goal(s): Our goal is to investigate on the underlying mechanism of the long-lasting ELAs impacts on brain. Approach: We examined the brains of non-human primate experienced with the ELAs of social separation from conspecific caregivers from our non-human primate MRI database in retrospective manner. Results: Non-human primate brain with caregiver adversities (i.e. ELAs) exerts underdevelopment of multiple brain regions in both cortical and subcortical regions. Impact: Our study demonstrates the lifelong detrimental impact of the early life adversities on brains, proving that the early life experiences, especially social ones, are the important factor for structuring brain during development.
Medial temporal lobe grey and white matter differences in late-life depression: a combined structural and multi-shell diffusion MRI analysis
Authors: Takamiya Akihiro , Radwan Ahmed , Christiaens Daan , Van Cauwenberge Margot , Casteele Thomas Vande , Laroy Maarten , Sunaert Stefan , Van den Stock Jan , Bouckaert Filip , Vandenbulcke Mathieu , Emsell Louise
Published: November 2024

Motivation: Multi-tissue characterization of structural alterations in late-life depression (LLD) is mainly based on structural MRI and diffusion tensor imaging. Goal(s): To investigate medial temporal lobe (MTL)-related structural alterations using advanced diffusion MRI (dMRI) models and their association with MTL grey matter (GM) volume. Approach: Multi-shell dMRI using constrained spherical deconvolution probabilistic tractography, diffusion kurtosis imaging and fixel-based analysis to investigate alterations in the uncinate fasciculus, cingulum bundle, and fornix. Results: Compared to controls, LLD patients showed differences in multiple dMRI metrics, with lower mean kurtosis in the uncinate fasciculus and fornix being correlated with lower MTL GM volume. Impact: Advanced multi-shell diffusion MRI modelling can identify subtle white matter microstructural differences in late-life depression. Using multimodal MRI to investigate grey and white matter differences holistically in the same population can further our understanding of the neurobiology of psychiatric disorders.
Effects of intermittent Theta Burst Stimulation and adjunctive D-Cycloserine on GABA levels in the medial Prefrontal Cortex
Authors: DeMayo Marilena , Cole Jaeden , Bray Signe , Harris Ashley , McGirr Alexander
Published: November 2024

Motivation: Non-invasive brain stimulation is an evidenced based treatment for major depression. Change in GABA in the medial prefrontal cortex (mPFC) has been suggested as a mechanism of antidepressant effect of brain stimulation, including intermittent Theta Burst Stimulation (iTBS). Goal(s): Investigate the effect of iTBS combined with adjunctive D-Cycloserine on magnetic resonance spectroscopy measured GABA levels in the mPFC. Approach: Participants were randomized to receive adjunctive D-Cycloserine or placebo in addition to two weeks of iTBS treatment, with assessments at baseline and after two weeks of intervention. Results: There was no effect of iTBS, adjunctive D-Cycloserine or responder status on GABA levels. Impact: This study provides evidence of dissociation between GABA change and clinical response following the delivery of intermittent Theta Burst Stimulation (iTBS) in major depression. The antidepressant effects of iTBS may not rely on change in GABA within the mPFC.
Pretreatment brain magnetic resonance imaging outperforms clinical ratings in predicting major depressive disorder treatment outcomes
Authors: Long Fenghua , Chen Yufei , Zhang Qian , Wang Yaxuan , Wang Yitian , Li Qian , Zhao Youjin , Li Fei
Published: November 2024

Motivation: It is clinically needed to explore predictive potential of pretreatment features in major depressive disorder (MDD) treatment outcomes to guide personalized medicine approaches. Goal(s): To evaluate and compare overall predictive performance of clinical and brain magnetic resonance imaging (MRI) features for treatment outcomes and assess performance of distinct modalities and interventions in MRI studies. Approach: We conducted separate meta-analyses on clinical and MRI studies followed with subgroup meta-analyses and meta-regression. Results: MRI studies outperformed clinical studies in sensitivity. Within MRI group, resting-state functional MRI exhibited higher specificity than task-based functional MRI . No significant differences were found among remaining modality and intervention subgroups. Impact: Our findings highlight the potential of utilizing pretreatment brain MRI data to predict treatment outcomes for MDD. This is valuable for using MRI features to early identify patients' treatment outcomes, potentially assisting clinicians in considering alternative treatment options.
White Matter Microstructure Alterations and Their Link to Symptomatology in Early Psychosis and Schizophrenia
Authors: Pavan Tommaso , Alemán-Gómez Yasser , Steullet Pascal , Schilliger Zoe , Dwir Daniella , Jenni Raoul , Cleusix Martine , Alameda Luis , Do Kim , Conus Philippe , Klauser Paul , Hagmann Patric , Jelescu Ileana
Published: November 2024

Motivation: Schizophrenia features complex symptomatology. Increased dMRI measures specificity is the key to capture the relation of white matter microstructure alterations with patients psychopathology. Goal(s): We aim to better characterize WM pathology and, thus, understand its relation with the symptomatology of early-psychosis and schizophrenia. Approach: Diffusion Kurtosis Imaging and White Matter Tract Integrity–Watson were estimated in 275 individuals. Whole-brain WM estimates were compared between patients and controls, and associated with patients psychopathology. Results: dMRI patterns suggest that WM alterations are already present and widespread in EP. Two trends of WM deterioration with concomitant demyelination vs neuroinflammation were found associated with clinical scales regression analysis. Impact: Our findings possibly provide a missing link between specific symptoms and underlying pathology. The association between patients' psychopathology and advanced dMRI metrics may spark further interest in linking specific symptom in psychiatry diseases to microstructure alterations.
Evaluation of bone marrow fat content and fatty acid composition using quantitative MRI combined with metabonomics in diabetic rabbits
Authors: Zhang Tian , Hu Lei , Fei Ziyan , Gao Yufan , Wang Yan , Yan Yuchen , Liu Weiyin , Zha Yunfei
Published: November 2024

Motivation: Bone marrow is a key target for diabetes mellitus. Goal(s): Quantitative assessment of fat content in lumbar bone marrow and trabecular microstructural changes in alloxan-induced diabetics rabbits. Approach: Iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation sequence (IDEAL-IQ), micro computed tomography (Micro-CT), and gas chromatography-mass spectrometry (GC-MS) were employed. Results: IDEAL-IQ and GC-MS techniques are capable of detecting changes in fat content and variations in fatty acid composition during the early stages of diabetic bone marrow. Impact: The fat content and fatty acid composition in the bone marrow plays an important role in bone metabolism in diabetic patients.
T2 relaxometry of foot muscles in young patients with Charcot-Marie-Tooth disease: comparison between centres and scanner vendors
Authors: Zafeiropoulos Nick , Doherty Carolynne , Paige Howard , Wastling Stephen , Zuccarino Riccardo , Milev Evelin , Banks Tina , Shah Sachit , Stephens Katherine , Grider Tiffany , Feely Shawna , Nopoulos Peggy , Skorupinska Mariola , Pipis Menelaos , Nicolaisen Emma , McDowell Amy , O'Donnell Luke , Rossor Alex , Matilde Laura , Muntoni Francesco , Yousry Tarek , Thedens Daniel , Morrow Jasper , Shy Michael , Reilly Mary , Thomas David , Thornton John
Published: November 2024

Motivation: Current clinical use of quantitative MRI biomarkers for the assessment of neuromuscular disease is limited by accuracy and reliabilityGoal(s): To develop a robust processing strategy for multi-vendor muscle-water T2 mapping (T2m) in foot muscleApproach: A multi-component CPMG extended phase graph signal model was used to determine T2m and apparent fat fraction (ffa) in fat-infiltrated foot muscle using maximum likelihood estimationResults: Stable estimates of foot muscle T2m and ffa were obtained in patients with Charcot-Marie-Tooth disease and healthy controls, at baseline and 1 year follow up. In patients, T2m and ffa were both elevated at each time pointImpact: T2m and ffa obtained from CPMG images using extended phase graph modelling and maximum likelihood estimation may be sensitive measures of neuromuscular pathology
The MOTION study – First quantitative muscle MRI results in a large cross-sectional cohort of healthy subjects
Authors: Heskamp Linda , Brenninkmeijer Roosmarijn , Schlaffke Lara , Bartels Bart , Froeling Martijn
Published: November 2024

Motivation: The overall aim of our ongoing study MOTION is to connect whole leg quantitative MRI (qMRI) and muscle structure to function and lifestyle in 162 healthy volunteers. Goal(s): Preliminary analysis of 31 subjects to identify potential covariates that may impact qMRI measurements. Approach: Muscle fat fraction (FF), mean diffusivity (MD), fractional anisotropy (FA) and T2water were determined in 20 muscles from tendon to tendon bilaterally, and related to age, gender, and body composition. Results: Muscle FF is higher in heavier subjects with more body fat. FA is lower and MD and volume are higher in larger subjects. T2water depends on age and gender. Impact: Accounting for covariates describing body composition (BMI, lean body mass and body fat mass) on top of gender and age is crucial for proper interpretation of quantitative muscle MRI in clinical studies on neuromuscular disease patients and healthy controls.
3D high-resolution isotropic multi-contrast knee MRI using 3D mDixon TSE with deep learning constrained Compressed SENSE
Authors: Morita Yasuyuki , Nagao Michinobu , Yoneyama Masami , Kato Mana , Ogawa Takumi , Kodaira Kazuo , Hamatani Yutaka , Shiina Isao , Goto Yasuhiro , Sakai Shuji
Published: November 2024

Motivation: Knee joint MRI requires m sequences involving multiple planes and with- and without fat suppression, which is time-consuming. Goal(s): Provides detailed cartilage and ligament information in a short period of time and allows multiplanar reconstruction in any plane of any thickness from a single high-resolution isotropic imaging. Approach: CS-AI 3D mDixon TSE images were compared with conventional SENSE and CS images. Results: It indicated that 3D mDixon CS-AI can provide more accurate image information of cartilage and ligaments with high reproducibility and robustness. Impact: This technique may be helpful to further assess knee pathology.
AIR recon deep learning on oZTEo technique in cortical bone MRI: an ex vivo ankle joint of adolescent pig and a knee joint of adolescent child
Authors: Bai Wanjing , Xiong Xian , Liang Huilou
Published: November 2024

Motivation: Exploring oZTEo sequence with NEX=2 after AIR recon deep learning (DL) alternatives to 3D-ZTE for diagnosing bone diseases in children is clinically desirable. Goal(s): To compare the image quality among different kinds of oZTEo sequences. Approach: A specimen and a child were subjected to different oZTEo MRI with and witout DL. The specimen was subjected to CT examination. The thicknesses of bone cortex and SNR of neighboring muscles were measured. Results: The image quality of oZTEo CT-like with NEX=2 after AIR recon DL was superior than that with NEX=6 before AIR recon DL. Impact: The oZTEo technique with NEX=2 after AIR recon DL, with shorter scanning time and witout ionizing radiation, is a safer choice for diagnosing children bone diseases.
A prospective cohort study of Nusinersen in the treatment of spinal muscular atrophy in children with type 2 and 3 on quantitative muscle MRI
Authors: Huang Yang , Hu Ying-Yi , Chen Taiya , Deng Kan , Chan Queenie
Published: November 2024

Motivation: A biomarker to assess the efficacy of Nusinersen is needed as it enhances motor function and prolongs survival in SMA patients. Goal(s): We investigated whether qMRI could monitor the efficacy of Nusinersen in the treatment of SMA. Approach: Patients underwent measurements of thigh qMRI (mDixon-Quant, T2 mapping and DTI) and Hammersmith Functional Motor Scale Expanded (HFMSE) before and after nusinersen treatment, X-ray exams to obtain Cobb angle were performed at baseline. Results: After treatment, the mean thigh FF and FA decreased over time, and the HFMSE increased, while T2 values and ADC remain stable. Impact: qMRI can evaluate the treatment outcomes of SMA patients. The younger patients with less scoliosis more likely to have minimumclinically significant difference at 6 months after treatment.
Application of Cellular Microstructural Mapping (Cell size imaging) by Time-Dependent Diffusion MRI in rectal lesions: a preliminary study
Authors: Kou Peisi , Lin Liangjie , Li Ying , Zhang Kun , Zhang Wenhua , Li Juan , Zhang Yong , Cheng Jingliang
Published: November 2024

Motivation: Preoperative evaluation of rectal lesions remains challenging, we need better methods to evaluate malignant and benign lesions, and to estimate prognostic factors for rectal cancer. Goal(s): To explore the application value of cellular microstructural mapping by td-dMRI in evaluating the histological type and prognostic factors of rectal lesions. Approach: Sixty-six patients with rectal lesions were enrolled in this study. MRI was performed on a 3T scanner (Ingenia Elition, Philips Healthcare, Best, the Netherlands). All subjects underwent conventional MRI, oscillating gradient spin-echo (OGSE) and pulse gradient spin-echo (PGSE). Results: Our preliminary results illustrated that td-dMRI are useful in preoperative evaluation of rectal lesions. Impact: Cellular microstructural mapping by time-dependent diffusion MRI has great potential in preoperative evaluation of rectal lesions. The results illustrated that it could be helpful in differentiating malignant and benign lesions, distinguishing AC from MC, and in predicting the TB status.
How to best match voxels – evaluating different sequential co-registration strategies for ultra-high b-value DWI in multiparametric breast MRI
Authors: Brock Luise , Liebert Andrzej , Schreiter Hannes , Ehring Chris , Eberle Jessica , Laun Frederik , Uder Michael , Kapsner Lorenz , Ohlmeyer Sabine , Knoll Florian , Bickelhaupt Sebastian
Published: November 2024

Motivation: Breast MRI increasingly includes ultra-high b-value DWI, but technical and patient-related challenges can cause mismatches to morphologic sequences. Co-registration strategies are needed to reliably apply AI-algorithms to the data. Goal(s): The study examines how different co-registration orders for ultra-high b-value acquisitions in multiparametric breast MRI impact co-localization to morphologic sequences, both quantitatively and qualitatively. Approach: This IRB-approved retrospective study of 144 multiparametric breast MRI exams with ultra-high b-value DWI, assessed different co-registration algorithms using ANTs library methods quantitatively and qualitatively. Results: The sequential arrangement of contrasts used during co-registration significantly affects cross-sequence congruence of lesions in multiparametric breast MRI incorporating ultra-high b-value DWI. Impact: The study highlights the relevance of considering different sequential arrangements used during co-registration in ultra-high b-value DWI.