bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated VOI analysis of amyloid PET-MRI
Authors: Fushimi Yasutaka , Nakajima Satoshi , Okuchi Sachi , Sakata Akihiko , Otani Sayo , Sakurama Azusa , Tagawa Hiroshi , Wang Yang , Ikeda Satoshi , Ito Shuichi , Umehana Masaaki , Ma Yongping , Mitsumoto Katsuhiko , Kubota Manabu , Shima Atsushi , Sawamoto Nobukatsu , Nakamoto Yuji
Published: November 2024

Motivation: Automated analysis of amyloid PET becomes available, however, these results have not been well analyzed on PET/MRI. Goal(s): To compare the reference regions and VOIs associated with amyloid positivity between the software and Centiloid project on Amyloid PET/MRI. Approach: We analyzed Amyloid PET/MRI data of 84 subjects automatically and values, SUVr, Centiloid scale were compared. Results: The reference VOIs and VOIs associated with amyloid positivity showed good correlation between the software and Centiloid project. Impact: The values obtained with the automated software on amyloid PET/MRI can be utilized with the other quantitative MR data, which may lead to comprehensive analysis of amyloid deposition.
Joint Embolization: Feasibility of Targeted Treatment and Response Assessment in the Shoulder, Hip and Knee Using Dynamic Contrast-Enhanced MRI
Authors: Bultman Eric , Mandl Lisa , Kishore Sirish
Published: November 2024

Motivation: Embolization, a new treatment for chronic joint pain, may be more clinically effective when treatment is targeted using pre-procedural dynamic contrast-enhanced (DCE)-MRI. Goal(s): Assess presence of synovitis and identify culprit arterial vessels using pre-procedural DCE-MRI. Approach: Representative patients with mild-moderate osteoarthritis of the shoulder, hip and knee underwent fat-suppressed DCE-MRI using CDT-VIBE. Imaging was performed with high temporal (6-7 sec/frame) and spatial (0.6 mm isotropic interpolated) resolution with 3-minute acquisition and 5cc/sec contrast injection. Results: DCE-MRI readily identifies regions of synovitis and its contributory arterial vessels. By exploiting contrast kinetics whole-joint synovial volumes can be semi-automatically calculated. Impact: MRI-targeted joint embolization therapy has the potential to result in improved pain and functional outcomes with reduced side effects. Rapid calculation of whole-joint synovial volumes may enable their use as a biomarker of arthritis severity and embolization treatment response.
Dynamic contrast-enhanced MRI of the synovium and synovial subregions in knee osteoarthritis
Authors: Mostert Jacob , van Zadelhoff Tijmen , Poot Dirk , Oei Edwin , van der Heijden Rianne
Published: November 2024

Motivation: A precise method to quantify the degree of synovial inflammation in patients with knee osteoarthritis (OA) is needed to evaluate response to new disease modifying treatments in clinical trials. Goal(s): To evaluate the repeatability of quantitative dynamic contrast enhanced (DCE) MRI derived biomarkers for synovitis quantification in the whole synovium and synovial subregions. Approach: Test-retest study comprising DCE-MRI at baseline and 1 month follow-up in knee OA patients. Vessel mapping was used to determine synovial subregions. Results: DCE-MRI biomarkers have good test-retest repeatability in both the whole synovium and synovial subregions, with Ktrans showing the best performance. Impact: Quantitative DCE-MRI can provide precise biomarkers for synovitis quantification in knee osteoarthritis. Subregional evaluation using vessel mapping is an important tool for more precise treatment response evaluation in clinical trials with targeted local interventions, such as selective arterial embolization.
The Application of FunctIonal Renal MRI to improve assessment of Chronic Kidney Disease (AFiRM) Study
Authors: Francis Susan , Buchanan Charlotte , Daniel Alexander , Morris David , Priest Andrew , Teh Kevin , Thomas David , Gilthorpe Mark , Kalra Philip , Mendichovszky Iosif , Taal Maarten , Sourbron Steven , Selby Nicholas , investigators On behalf of the AFiRM
Published: November 2024

Motivation: The need for better biomarkers to assess progression of Chronic Kidney Disease (CKD). Goal(s): To determine if multiparametric MRI can detect changes in structure and function in CKD. Approach: In the Application of Functional Renal MRI (AFiRM) study, multiparametric MRI is to be collected on 400 CKD participants at baseline and Year 2. The MRI protocol comprises T2- and T1-weighted scans, B0 and B1 mapping, T1, T2 and T2* relaxometry, DWI, MTR, PC-MRI and ASL perfusion. Results: To date, 387 have been scanned, with preliminary MRI analyses on 300 datasets for B0 and B1, T1, T2*, MTR, and Total Kidney Volume (TKV). Impact: Application of the UKRIN-MAPS multiparametric renal MRI protocol to study changes in renal structure and function in CKD progression. This will provide definitive evidence on the question of whether MRI is better at tracking disease progression than conventional biomarkers.
Deep Learning-based Fully-Automated Detection and Segmentation of Small Renal Masses on Multi-sequences MRI: A Multi-center Study
Authors: cui Mengqiu , Zeng Zilong , Wang He , Jiang Jiahui , Zhao Jian , Bai Xu , Hao Yuwei , Ye Huiyi , Wang Haiyi
Published: November 2024

Motivation: Automated detection and segmentation method could serve as a fundamental step for diagnosis of small renal mass (SRM)Goal(s): To develop and assess automated segmentation method for SRM using a deep learning method based on multi-sequences MRIApproach: A total of 913 SRM patients from three institutions was used in deep learning model training and testing for five sequences (T2WI, T1WI, CP, NP, DP). The model was evaluated on internal and external test set using DSC (dice similarity coefficient)Results: The overall median DSC of five sequences (T2WI, T1WI, CP, NP, and DP) yield 0.824, 0.769, 0.845, 0.847, 0.855 on whole test set. Impact: The value of radiomics in preoperative diagnosis of benign and malignant SRM had been proven. However, manual segmentation impeded the conduction of radiomics. Automated segmentation models could help efficiently build radiomics model and reduce radiologists’ workloads.
MRI-conditional deep brain stimulation (DBS) implants: a new engineered design for enhanced safety
Authors: Marturano Francesca , Zaidi Tayeb Anderson , Tummala Aditya , Rad Laleh Golestani , Bonmassar Giorgio
Published: November 2024

Motivation: Currently, patients with Deep Brain stimulation (DBS) implants cannot leverage the diagnostic potential of Magnetic Resonance Imaging (MRI) as traditional metal-based leads pose several safety concerns. Goal(s): We propose a new technology for manufacturing DBS microwires that ensures their safe operation with MRI up to 3T. Approach: Through the development of a metamaterial-based design, we have engineered microwires that effectively partially reflect RF-induced current, thereby reducing Specific Absorption Rate (SAR), tip heating, and associated artifacts. Results: Our manufactured microwires demonstrated minimal tip heating in both 1.5T and 3T MRI scanners when compared to standard wires. Impact: This innovative design facilitates safe MRI imaging for individuals with DBS implants, marking a pivotal advancement in the study of neural mechanisms involved in medically refractory pathological conditions, such as Parkinson’s disease.
Pediatric neuroimaging at 7 Tesla: towards building and validating VOPs for local SAR management in pTX
Authors: Dudysheva Natalia , Luong Michel , Amadon Alexis , Boulant Nicolas , Gras Vincent
Published: November 2024

Motivation: Pediatric MRI neuroimaging has become common practice at 1.5 and 3 Tesla and we see the emergence of 7T and parallel transmission (pTX). However, today relevant local SAR prediction models are based on adult standards. Goal(s): In this work, we study the local SAR at 7T with pTX for children population (6 to 14 years old) using RF electric field simulations. Approach: We exploit a vast simulation database to build a pediatric local SAR prediction model and propose a methodology for its initial validation using convex optimization. Results: A large simulation database (above 25) is desirable to combine RF safety and pTX performance. Impact: This work aims to push pediatric MRI to ultra-high fields by developing safe SAR control for the children population. It also introduces a tool to determine appropriate safety margins to avoid excessive penalizing in the pTX pulse design.
3T Parallel-Transmit for Simultaneous Stereoelectroencephalography and MRI for Realistic Electrode Placement in Epilepsy Patients
Authors: Shegurova Elizaveta , Sparks Rachel , Ipek Özlem
Published: November 2024

Motivation: Investigating optimised RF heating and B1+ field disturbance with sEEG electrodes placement, covering epileptogenic zones in five configurations, using electromagnetic simulations with 16-channel parallel-transmit coil array at 3T for simultaneous sEEG-MRI. Goal(s): The challenge is reducing RF heating from implanted electrodes. Approach: We investigated five electrode placements covering epileptogenic zones to control RF heating and local signal increases using computational field simulations on a realistic computational human model. Results: Implanting electrodes in the left hemisphere improved RF uniformity. Shimming optimisation, integrating B1+ and 0.1g SAR, showed negligible SAR differences from electrode-free setups. Lower SAR correlated with reduced RF strength, suggesting a necessary trade-off. Impact: Our study advances simultaneous EEG/fMRI for drug-resistant epilepsy, optimising electrode safety to better localise seizure onset zones and refine surgical approaches, while ensuring patient safety. It allows for more precise epileptogenic zone resection, potentially advancing therapeutic outcomes.
Aberrant interstitial fluids may be associated with cognitive impairment in patients on chronic kidney disease
Authors: Yang Wenbo , Song Lijun , Li Mingan , Xu Boyan , Yang Zhenghan , Wang Hao , Wang Zhen-chang
Published: November 2024

Motivation: Chronic kidney disease (CKD) leads to cognitive impairment; however, the pathophysiology remains unclear. Goal(s): This study aims to evaluate differences in interstitial fluids (ISF) between patients with CKD and healthy controls, and investigate correlation between ISF circulation with cognitive impairment. Approach: Spectral diffusion analysis was applied to measure the ISF fraction (fint). Results: A significant difference in fint was detected between the HD and HC groups in the right basa gangliaand biliteral centrum semiovale(CSO). In the CKD group, MoCA scores were negatively correlated with fint in the biliteral CSO. Impact: A novel approach to measure ISF exhibits the potential for detecting brain glymphatic dysfunction in patients with CKD, which provides unique insights into the pathological mechanisms of patients on CKD with cognitive impairment.
GSURE Denoising enables training of higher quality generative priors for accelerated Multi-Coil MRI Reconstruction
Authors: Aali Asad , Arvinte Marius , Kumar Sidharth , Arefeen Yamin , Tamir Jonathan
Published: November 2024

Motivation: Publicly available k-space data used for training are inherently noisy with no available ground truth. Goal(s): To denoise k-space data in an unsupervised manner for downstream applications. Approach: We use Generalized Stein’s Unbiased Risk Estimate (GSURE) applied to multi-coil MRI to denoise images without access to ground truth. Subsequently, we train a generative model to show improved accelerated MRI reconstruction. Results: We demonstrate: (1) GSURE can successfully remove noise from k-space; (2) generative priors learned on GSURE-denoised samples produce realistic synthetic samples; and (3) reconstruction performance on subsampled MRI improves using priors trained on denoised images in comparison to training on noisy samples. Impact: This abstract shows that we can denoise multi-coil data without ground truth and train deep generative models directly on noisy k-space in an unsupervised manner, for improved accelerated reconstruction.
CAMP-Net: The Application of Consistency-Aware Multi-Prior in Deep Learning for Rapid MRI
Authors: Zhang Liping , Chen Weitian
Published: November 2024

Motivation: Accelerated MRI acquisitions offer reduced imaging scan times but pose challenges in image reconstructions. Tremendous progress has been made to reconstruct accelerated MRI, but it remains challenging to restore high-frequency image details in highly undersampled data. Goal(s): Our goal is to develop a solution that can restore subtle structures even for highly accelerated MRI. Approach: We propose CAMP-Net, a consistency-aware multi-prior framework, that leverages scan-specific features with both image and $$$k$$$-space domain knowledge for MRI reconstruction. Results: Results on a publicly available brain dataset demonstrated that CAMP-Net can achieve high-quality reconstructions with fine brain anatomical structures even at an acceleration factor of 10X. Impact: The successful restoration of subtle structures for MRI with high acceleration factors can significantly reduce MRI scan time in clinical routines, benefiting patients, increasing the access to MRI, and significantly reducing healthcare cost of MRI.
High-resolution Susceptibility Weighted Fetal Brain using ZOOMit EPI and Slice to Volume reconstruction
Authors: Wang Xiaoqing , Velasco-Annis Clemente , Calixto Camilo , Gholipour Ali , Jaimes Camilo
Published: November 2024

Motivation: Susceptibility weighted imaging is of great interest in fetal MRI. Conventional single-shot EPI often suffers from field distortion and motion artifacts due to long readout. Goal(s): To develop a susceptibility-weighted isotropic fetal brain imaging technique with reduced artifacts. Approach: ZOOMit EPI was used for data acquisition. A slice-to-volume reconstruction was further employed to correct the motion between slices and reconstruct an isotropic high-resolution volume of the fetal brain. Results: Zoomit EPI produces images with reduced artifacts due to reduced acquisition time. The slice-to-volume reconstruction further corrects the motion between slices and reconstruct an isotropic high-resolution SWI-weighted volume of the fetal brain. Impact: Zoomit EPI offers faster scan times and produces images with reduced artifacts. With Zoomit EPI, high-resolution SWI-weighted isotropic fetal brain imaging has been achieved using slice-to-volume reconstruction.
Hepatic inflammation grading in diffuse liver disease using three-dimensional multifrequency MR elastography
Authors: Cai Shan , Simonsson Christian , Tellman Jens , Dahlström Nils , Ignatova Simone , Kechagias Stergios , Nasr Patrik , Ekstedt Mattias , Sinkus Ralph , Lundberg Peter
Published: November 2024

Motivation: Additional viscoelastic parameters measured using 3D multifrequency MRE have been reported to detect inflammation in the liver. However, a clinical cohort with multiple etiologies has not been well-studied. Goal(s): Our goal was to determine the role of 3D multifrequency MRE for staging hepatic inflammation in diffuse liver disease with various etiologies. Approach: Viscoelastic parameters were derived from 3D MRE with low, medium and high vibration frequencies, and compared with histological assessment. Results: The moduli |G*|, G’ and G” and the exponent of power law γ were found to be associated with inflammation. Impact: Three-dimensional multifrequency MRE, with its ability to provide additional viscoelastic parameters, could be a powerful tool for the non-invasive detection of inflammation activity in patients with diffuse liver disease.
Hybrid Multidimensional MRI: Demonstrating Repeatability in Non-Invasive Prostate Cancer Tissue Characterization
Authors: Campos Abel Lorente , Chatterjee Aritrick , Yousuf Ambereen , Antic Tatjana , Oto Aytekin , Karczmar Gregory
Published: November 2024

Motivation: Validate the consistency and reliability of Hybrid Multidimensional MRI (HM-MRI) through repeatability assessments. Goal(s): Evaluate the repeatability of HM-MRI in prostate tissue characterization and its diagnostic accuracy for prostate cancer. Approach: Our approach consists on performing dual HM-MRI scans with a one-week interval to assess measurement consistency and diagnostic accuracy of HM-MRI biomarkers. Results: The results indicate high reproducibility of HM-MRI metrics, evidenced by strong inter-class correlation coefficients, with consistent tissue composition measurements across initial and repeat scans, and robust diagnostic performance of HM-MRI parameters in prostate cancer detection. Impact: The findings of this study highlights the potential of HM-MRI as a reliable tool for prostate cancer diagnosis, paving the way for non-invasive clinical applications and inspiring further research into its utility in other diagnostic imaging techniques.
Identification of intrinsic imaging phenotype for endometrial carcinoma based on multi-modality MRI using multi-Omics clustering algorithm
Authors: Jiang Xiaoting , Duan Shaofeng , Song Jiacheng , Liu Xisheng , Chen Ting
Published: November 2024

Motivation: Heterogeneity of endometrial carcinoma (EC) leads to differences in prognosis among different patients. The method of unsupervised machine learning can classify tumors into different subtypes by identifying heterogeneity and similarity in radiomics features, which may have the ability of preoperative risk stratification. Goal(s): To identify the intrinsic imaging phenotype for EC using multi-modality MR-based radiomics features. Approach: Ten multi-omics clustering methods were used for imaging phenotypes identification and reached a consensus. Results: Among the three identified imaging phenotypes, multiple pathological features and disease-free survival time showed significant differences. Impact: Based on multi-modality MRI using an unsupervised machine learning approach to classify EC into different imaging phenotypes, which were associated with clinicopathological features and prognosis, and can be used for preoperative risk stratification.
Pathology Without a Knife: MRI-based Non-invasive Determination of Prostate Cancer Grade with Physics-Informed Deep Learning
Authors: Gundogdu Batuhan , Chatterjee Aritrick , Kalidoss Senthooran , Karczmar Gregory , Oto Aytekin
Published: November 2024

Motivation: Millions of prostate biopsies are being ordered each year, a great majority of which yield negative results. A reliable and non-invasive method for detecting prostate cancer grade is critical. Goal(s): To develop a robust and efficient MRI-based non-invasive model to detect the Gleason score of the lesions without the need for a biopsy. Approach: We propose a physics-informed autoencoder that integrates the strengths of model-based and deep learning-based methods, while overcoming their respective weaknesses. Results: Physically-interpretable biomarkers that our model yields correlate strongly with Gleason score, providing important new diagnostic markers, and laying the groundwork for a potential new quantitative MRI method. Impact: The proposed model offers for many potential usages in diagnostic radiology, by presenting a non-invasive method for diagnosing and staging prostate cancer, potentially affecting about a million patients annually by reducing unnecessary biopsies and saving millions in healthcare costs.
Self-supervised representational learning for automated risk assessment in longitudinal imaging
Authors: Umapathy Lavanya , Tibrewala Radhika , Feng Li , Chandarana Hersh , Sodickson Daniel
Published: November 2024

Motivation: Techniques that allow automated evaluation of the evolution of disease risk over time can be of great value for active surveillance and other imaging-based monitoring. Goal(s): We introduce a novel self-supervised framework to learn representations that can identify increases in risk over time. Approach: We propose a contrastive learning model to first learn subject-specific representations from low-slice-resolution images followed by learning a risk axis in the representational space to provide information on global changes in risk over time. Results: The developed framework was used to assess risk of new metastases in a cohort of subjects from the NYU-Mets longitudinal imaging dataset. Impact: A key question when moving to lower field strengths in MRI is if we can get comparable information from lower-quality images as we can from the current standard of high-quality, high-resolution images. Self-supervised contrastive learning approaches can hold the key.
Characterisation of cortical depth dependent hemodynamics in early human development using high-resolution BOLD fMRI at 7 Tesla.
Authors: Moore Jucha Willers , Pickles Elisabeth , Bridgen Philippa , Uus Alena , Tomazinho Ines , Bonse Beya , Deprez Maria , Giles Sharon , Edwards A. David , Hajnal Jo , Malik Shaihan , Arichi Tomoki , Polimeni Jonathan
Published: November 2024

Motivation: Hemodynamic responses in adults vary across cortical depths, partly due to specific differences in vascular anatomy and physiology. It is unknown how these differences relate to the responses seen in neonates, when the cortex and neurovasculature are rapidly maturing. Goal(s): To characterize the amplitude and timing of the hemodynamic response across cortical depths during the neonatal period. Approach: Cortical depth-dependent hemodynamic responses to sensorimotor stimulation were delineated using GRE-BOLD fMRI in term-aged neonates. Results: A cortical depth-specific profile of key hemodynamic response parameters, including onset and undershoot, was observed in the neonatal cortex that differs from those seen in the adult. Impact: We demonstrate for the first time how developing vascular network may alter hemodynamic response across cortical depths. It illuminates what underlying neurobiology may generate the neonatal specific BOLD signal profile and what components are altered relative to the adult response.
T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data
Authors: Kan Chung , Stirnberg Rüdiger , Montequin Marcela , Gulban Omer , Morgan A , Marrett Sean , Bandettini Peter , Huber Renzo
Published: November 2024

Motivation: High-resolution fMRI at 7T is limited by misregistration of functional data with structural scans. Goal(s): We aim to provide a fast acquisition method that provides distortion matched, artifact mitigated structural reference data. Approach: T1234: T1-weighted 2-inversion 3D-EPI with 4 directions for high-resolution fMRI. A forward Bloch model is implemented for T1 quantification and protocol optimization. Results: Our protocol is fast (3:40 min) and provides whole-brain segmentations in EPI-space. It is robust across sessions, participants, and scanners. Impact: This structural mapping approach allows precise registration with fMRI data. T1234 is implemented, validated, and tested to serve users of our sequence (locally and 43 centers worldwide).
Motor unit magnetic resonance imaging to assess muscle twitch dynamics in mitochondrial disease after an exercise programme.
Authors: Birkbeck Matthew , Elameer Mathew , Heskamp Linda , Newman Jane , Stefanetti Renae , Barrow Isabel , Gorman Gráinne , Schofield Ian , Hall Julie , Blamire Andrew , Whittaker Roger
Published: November 2024

Motivation: Changes to muscle twitch dynamics are overlooked in trials assessing resistance exercise in primary mitochondrial myopathies (PMM). Goal(s): Motor unit MRI to measure twitch dynamics in PMM participants before and after a 12-week exercise programme. Approach: Voxel-wise measurements of rise time (Trise), contraction time (Tcontract) and half-relaxation time (Thalf-relax) in the tibialis anterior in 10 controls and 9 PMM participants. PMM participants scanned twice, before and after a 12-week exercise programme. Results: Tcontract of the tibialis anterior was significantly longer in PMM participants post exercise; Trise, Thalf-relax demonstrated no change. In participants who had the highest adherence to exercise Tcontract increased the most. Impact: Motor unit MRI (MUMRI) detected slower muscle contraction times in primary mitochondrial myopathies post resistance exercise programme. This may evidence increased numbers of type-I fibres post-exercise. MUMRI could be used to measure changes in muscle twitch dynamics in neuromuscular diseases.
Unlocking Muscle Fatigue: Insights from Multi-Parametric 1H, 23Na & 31P MRI in Exercise-Induced Muscle Damage and Inflammation
Authors: Zambolin Fabio , Pinner Susan , McStravick James , Kennerley Aneurin , McPhee Jamie
Published: November 2024

Motivation: Exercise Induced Muscle Damage (EIMD) presents as a viable model to study the muscle inflammation and fatigue which may present across various age-related pathologies. Goal(s): Our model presents as a useful tool to test therapies aimed at improved patient rehabilitation. Approach: Here we use multi-parametric 31P, 23Na and 1H based MRI to show EMID is charactered by reduced muscle oxygenation with an increase in phosphate metabolism, sodium perturbation and overall perception of effort. Interestingly the cardiovascular response to exercise remained unchanged. Results: Our results suggest a discrepancy between cardiovascular and muscle metabolic demand leads to altered oxygen delivery during exercise and increased fatigability. Impact: This study highlights the importance of an integrative multi-parametric MRI approach to study the physiological consequences underlying muscle inflammation. This is crucial for advance our understanding on the abnormal responses present in age-related/health conditions characterised by muscle weakness and fatigue.
Combined Diffusion-Relaxation MRI to Assess Muscle Microstructure and Composition
Authors: Figini Matteo , Slator Paddy , Rizzo Giovanna , Mastropietro Alfonso
Published: November 2024

Motivation: Quantifying muscle tissue properties is crucial for understanding physio-pathological changes occurring in skeletal muscle (SM). However, current methods measure T2 and diffusion separately, and hence conflate them. Goal(s): Demonstrate a combined diffusion-relaxation MRI approach for disentangling T2 and diffusivity properties in SM. Approach: We devise and implement a combined T2-diffusion sequence in the leg muscles in five healthy volunteers after exercise. DTI and an advanced diffusion model were implemented and compared. Results: We calculated disentangled T2 and diffusion-related parameter maps. Our maps capture muscle tissue differences in specific muscle groups highlighting differences related to muscle involvement during exercise. Impact: Combined diffusion-relaxation MRI can provide detailed non-invasive estimation of muscle tissue properties by mitigating T2 effects on diffusion parameters. These approaches could reduce the need for invasive biopsies for evaluating muscle changes related to neuromuscular diseases, exercise, and rehabilitation.
Tensor encoded diffusion weighting improves model parameter estimation of SMEX/NEXI
Authors: Ghazi Nayereh , Coelho Santiago , Shemesh Noam , Jespersen Sune
Published: November 2024

Motivation: SMEX is a model of diffusion which may enable gray matter (GM) microstructural mapping in-vivo. However, clinical translation necessitates reliable parameter estimation with short scan time and limited gradient strength. Goal(s): Resolve degeneracies for SMEX parameter estimation and enable more accurate and precise GM microstructural mappingApproach: We complement the Single Diffusion Encoding measurement with Double Diffusion Encoding. We analyze the signal theoretically for low b-values in terms of the cumulant expansion, and for high b-values with numerical simulations. Results: Planar diffusion encoding resolves an intrinsic degeneracy in SMEX at low b, and generally provides higher accuracy and precision in model parameter estimation. Impact: The improvement in parameter estimation afforded by tensor encoded diffusion may enable shorter sequences and lower gradient strengths, thereby facilitating clinical translation of SMEX.
A deep dive into hippocampus growth: Unveiling neurite and soma development with in vivo diffusion MRI
Authors: Karat Bradley , Genc Sila , Raven Erika , Palombo Marco , Khan Ali , Jones Derek
Published: November 2024

Motivation: The hippocampus serves multiple cognitive functions, yet little is known about its microstructural development. Goal(s): To leverage recent MRI hardware and microstructure modeling advances to capture hippocampal cell-body (soma) and projection (neurite) development during late-childhood and adolescence. Approach: Diffusion MRI data was acquired in 88 participants aged 8-18 years using a 3T Connectom scanner (with 300mT/m gradients), and analyzed using the Soma and Neurite Density Imaging model. Results: For the first time, we identified distinct developmental patterns of hippocampal microstructural subcomponents. Specifically, we found an age-related increase in neurite fraction and concurrent decrease in extracellular fraction and soma radius. Impact: We report, for the first time, distinct neurite and soma developmental profiles in the hippocampus during late childhood/adolescence. This forms a crucial baseline for understanding developmental disorders, and opens new avenues for corroborating in vivo diffusion with histology.
Open-Source Console Software for the MRI4ALL Hackathon Scanner
Authors: Block Kai Tobias , Wiggins Roy , Saimbhi Amanpreet , Dutt Tarun , Sanchez Antonio Verdone , Geethanath Sairam
Published: November 2024

Motivation: Commercial MRI scanners have limited accessibility due to high costs and the proprietary nature of the hardware and software platforms. Goal(s): To develop a comprehensive console software for community-built low-field MRI systems that is solely based on open-source components and provides a user interface similar to commercial systems. Approach: The console software was programmed in the Python programming language. The platform is divided into three decoupled services that 1) provide the interface for scan planning and visualization, 2) run the scanner control, and 3) perform the image reconstruction. Results: The architecture is described, providing a starting point for utilizing it in other projects. Impact: The development of open-source MRI software and hardware will help to disseminate fundamental knowledge about the construction of MRI scanners. The software platform described in this work may serve as foundation for future community initiatives on building open-source scanners.
Spatial Imaging-Transcriptomic analysis reveals the Molecular Basis of diffusion MRI signature in mouse model of Alzheimer’s Disease
Authors: Shen Yao , Wang Menglei , Shen Yiqi , Zhu Qinfeng , Zhao Zhiyong , Cao Zuozhen , Xu Guojun , Li Sihui , Wu Dan
Published: November 2024

Motivation: While numerous studies noted the reduction of fractional anisotropy (FA) from diffusion MRI as a sensitive marker in Alzheimer's disease, the underlying biology remained elusive. Goal(s): We aimed to explore biological basis of DTI metrics by integrating diffusion MRI with spatial transcriptomic data from the same individual. Approach: We performed voxelwise correlation between the co-registered transcriptomic and MRI data and downstream enrichment analysis. Results: We revealed links to myelin, oligodendrocytes, and Alzheimer's-associated biological processes.These findings enhanced our understanding of changes of diffusion MRI in Alzheimer's disease. Impact: Spatial imaging-transcriptomic provides a certain level of biological evidence for the molecular processes underlying DTI signatures of Alzheimer’s disease. Similar approach can be applied to other types of MRI markers in different neurodegenerative diseases.
Fatigue, smell and cognitive functions: multimodal MRI can explain the long-COVID syndrome
Authors: Grosso Elena , Ricciardi Antonio , Shatila Madiha , Zandi Michael , Yannakas Marios , Prados Ferran , Kanber Baris , Wingrove Jed , Rolandi Nicolò , Shmueli Karin , Grussu Francesco , Battiston Marco , Samson Rebecca , Ciccarelli Olga , Battheram Rachel , Makaronidis Janine , D'Angelo Egidio , Palesi Fulvia , Tur Carmen , Wheeler-Kingshott Claudia Gandini
Published: November 2024

Motivation: Long-COVID is a disabling health problem caused by SARS-COV-2 syndrome, whose underlying biological mechanisms are still debated. Goal(s): This study aimed at finding the set of quantitative MRI (qMRI) metrics that best correlate with fatigue, smell (i.e. anosmia),and cognitive dysfunction, common in this condition. Approach: People with COVID19 history with and without long-COVID were assessed through a multimodal one-hour-long qMRI protocol and underwent clinical evaluation. Results: Correlation analyses between qMRI metrics and clinical scores showed that neurite density index changes explain both fatigue and smell function (also affected by changes in brain stem volume),while mean diffusivity and magnetic susceptibility changes explain cognitive function. Impact: This work sheds light on the underlying biological mechanisms of long-COVID (anosmia, fatigue, and cognitive impairment). Metrics sensitive to microstructure, inflammation and possible iron accumulation best explain persistent symptoms, emphasizing the role of multimodal qMRI in the clinic.
Non-contrast MRI assessment of CSF drainage into superior sagittal sinus
Authors: Jiang Dengrong , Song Jie , Gou Yifan , Hu Zhiyi , Shi Wen , Lin Zixuan , Moghekar Abhay , Lu Hanzhang
Published: November 2024

Motivation: The absorption of CSF into the superior-sagittal-sinus (SSS) is an important CSF drainage pathway and has been implicated in brain disorders like Alzheimer’s disease and idiopathic-intracranial-hypertension. However, we still lack non-invasive and non-contrast techniques to evaluate this CSF-venous drainage system. Goal(s): To develop a novel non-contrast MRI technique to assess the CSF drainage into the SSS. Approach: We propose to magnetically label the CSF spins and use a control-label subtraction to isolate signals from the CSF spins that have been absorbed into SSS. Results: In all subjects, we observed considerable signals from absorbed CSF spins in frontal SSS. Test-retest experiments demonstrated good reproducibility. Impact: We have proposed a novel non-contrast MRI technique to assess the drainage of CSF into the superior-sagittal-sinus, which may address an important technical gap in evaluating the circulation of CSF.
Advancing Estimation of Microvascular Perfusion in the Choroid Plexus through Inversion Recovery Prepared IVIM MRI
Authors: Li Chenyang , Sun Zhe , Sigmund Eric , Zhang Jiangyang , Ge Yulin
Published: November 2024

Motivation: Measuring vascular perfusion in the Choroid Plexus (ChP) is challenging due to its smaller size and the pronounced partial volume effects from CSF. Goal(s): To improve blood perfusion assessment of ChP utilizing Inversion recovery (IR) prepared IVIM imaging. Approach: Higher in-plane resolution (1.5x1.5mm2) IVIM diffusion MRI data with and without the IR preparation were acquired. The signal was analyzed using inverse Laplace transform (ILT) to estimate diffusion compartments within ChP for IVIM-derived perfusion fraction analysis. Results: ILT analysis of IR-prepared IVIM revealed three diffusion compartments within the ChP and reported higher perfusion fraction observed in IR-prepared IVIM versus IVIM without IR-preparation. Impact: This study provides a novel approach to investigate the microvascular perfusion in Choroid Plexus (ChP) with CSF signal removed. IR preparation does not completely remove the free-diffusion compartment but increases the microvascular perfusion fraction estimation in ChP.
Segmentation of Brain Lesions Using Posterior Distributions Learned by Subspace-assisted Generative Model
Authors: Zhuang Huixiang , Guan Yue , Ding Yi , Xu Chang , Ma Yuhao , Meng Ziyu , Liu Ruihao , Liang Zhi-Pei , Li Yao
Published: November 2024

Motivation: Deep learning shows great potential for brain lesion segmentation but poor generalization (due to limited training data) could lead to false positives. Goal(s): Our goal was to improve the segmentation accuracy by learning target-specific posterior distributions. Approach: We proposed a new Bayesian brain lesion segmentation method, leveraging posterior distributions learning, including both posterior normal and lesion distributions, through a subspace-assisted deep generative model. Results: The proposed method achieved significantly improved segmentation performance across multiple public datasets with stroke, tumor, and multiple sclerosis lesions, in comparison with the state-of-the-art methods. Impact: The proposed method significantly improved accuracy and robustness of lesions segmentation in brain MR images, which may provide a useful tool for brain lesion delineation in image processing and clinical applications.
Brain Tissue Segmentation robust to motion artifacts using Deformation-Aware Network
Authors: Jung Sunyoung , Choi Yoonseok , Al-masni Mohammed A. , Choeng Yongjeon , Lee Seonkyoung , Bae Jihyun , Jung Min-Young , Kim Dong-Hyun
Published: November 2024

Motivation: Motion artifacts in MRI scans present challenges by causing blurred images with tissue-like appearances, significantly complicating the tissue segmentation process. Goal(s): Our goal is to achieve accurate brain tissue segmentation even in the presence of motion artifacts. Approach: We propose a brain tissue segmentation method robust to motion artifacts, that generates a motion deformation map and a prediction mask for brain tissue segmentation. The motion deformation map serves as an indicator within the segmentation network, aiding in the identification of regions impacted by motion artifacts. Results: Our method demonstrates superior performance compared to other segmentation models, especially when dealing with motion-corrupted data. Impact: We propose a motion-robust segmentation network that incorporates prior motion knowledge via a motion estimation network. By employing a multi-task learning approach involving joint motion estimation and segmentation networks, we improve brain tissue segmentation by recovering incorrectly segmented structures.
First experimental results using RF Elements with Switching Transmit Sensitivities at ultrahigh field MRI
Authors: Bosch Dario , Solomakha Georgiy , Freudensprung Martin , Glang Felix , Avdievich Nikolai , Scheffler Klaus
Published: November 2024

Motivation: Dipole RF elements with electronically switchable B1 field patterns have been shown to improve receive performance. The question whether they are beneficial for transmission is still open. Goal(s): Improve flip angle homogeneity by modulating the Tx sensitivity of a dipole during excitation. Approach: An RF coil built from eight dipoles with electronically switchable sensitivities was constructed. Achievable flip angle homogeneity with 2-kT-points pulses was evaluated in simulations and in experiments. Results: Flip angle homogeneity could be increased by electronically switching the Tx sensitivity during the course of the RF pulse. Impact: RF elements with switchable transmit sensitivities offer a novel degree of freedom for excitation that promises improved flip angle homogeneity. This addresses one of the most pressing problems in ultra-high field MRI.
Cortical Microhemorrhage Presentation of Small Vessel Primary Angiitis of the Central Nervous System
Authors: Guo Ai , Zhang Zhe , Dong Ge-Hong , Li Yuan , Su Lei , Gao Chenyang , Zhang Mengting , Shi Xiaoyu , Wang Huabing , Zhang Xinghu , Lu De-Hong , Fu Ying , Jing Jing , Shi Fu-Dong , Tian De-cai
Published: November 2024

Motivation: PACNS entails a biopsy for diagnosis, but only with an intermediate sensitivity. It is necessary to revisit PACNS with advanced imaging technique to provide a non-invasive diagnostic standard. Goal(s): We aim to find more pathological details with enough sensitivity and specificity to provide potential biomarkers for PACNS. Approach: 21 patients with small-vessel PACNS were included in this study. T1-MPRAGE, T2 T2*W, and SWI images were collected. Results: Our study highlighted the image features of patients with small-vessel PACNS with coral-like signs through 7T MRI. Due to the small patient cohort, no specific clinical differences between hemorrhagic and non-hemorrhagic patients were found. Impact: The signal characteristics of the coral-like sign represent cerebral cortical microhemorrhages with atrophy, which could be an important MRI pattern of small-vessel PACNS.
Correcting Susceptibility artifacts with Unified RF/Shim Coil (UNIC) in Cervical Spine MRI for Subjects with and without Orthodontic Implants
Authors: Malagi Archana , Binesh Nader , Serry Fardad , Zepeda Jeremy , Long Ziyang , Yang Chia-Chi , Huang Li-Ting , Zhang Yi , Shim Hyunsuk , Li Debiao , Yang Hsin-Jung , Han Hui
Published: November 2024

Motivation: Metallic orthodontic devices can introduce strong susceptibility artifacts that hinder clinical interpretation of cervical spine MRI. Goal(s): Using novel unified RF/shim coils (UNIC) to reduce field variation caused by metal susceptibility artifact. Approach: Total of eighteen subjects (5 with braces) were scanned with a UNIC coil research prototype. Results: The integrated UNIC shim array significantly increased the voxel percentage from 28% with scanner-shimming to 46% in DWI, reducing distortion by 61% with braces and 15% without braces in DWI, and by 41% in DTI. It also decreased B0 field variation by 63% with braces and 31% without. Impact: Unified RF/shim coil delivered improved quality and corrected image distortions in cervical spine scans by mitigating magnetic field variation, including those from orthodontic braces, ensuring more accurate diffusion measurements and consistently reducing susceptibility artifacts.
Implicit Neural Representations of GRAPPA Kernels for Rapid Non-Cartesian and Time-Segmented Reconstructions
Authors: Abraham Daniel , Nishimura Mark , Cao Xiaozhi , Liao Congyu , Setsompop Kawin
Published: November 2024

Motivation: Using non-Cartesian trajectories allows for motion robustness, and a more efficient encoding. However, these non-Cartesian acquisitions necessitate the use of NUFFTs and field correction techniques, leading to costly reconstruction times. Goal(s): We aim to remove the need for NUFFTs in non-Cartesian MRI, and drastically reduce the computational footprint of field correction. Approach: Our approach is to correct the raw k-space data of phase due to field imperfections and off-grid sampling using an implicit representation of GRAPPA kernels. Results: We show an order of magnitude increase in comparison to current standard techniques with near identical reconstructions quality. Impact: This work aims to significantly reduce the computational requirement for reconstructing non-Cartesian data. This will help with the adoption of long readout non-Cartesian acquisitions, which naturally accelerate MRI exams.
A Multi-Resolution Approach to Estimate Cardiac and Respiratory Motion Fields for 5D Whole-Heart MR Image Reconstruction – a Proof of Concept
Authors: Yerly Jérôme , Ogier Augustin , Roy Christopher , van Heeswijk Ruud , Stuber Matthias
Published: November 2024

Motivation: Estimating cardiac and respiratory inter-bin deformation fields from 5D motion-resolved free-running data is particularly challenging due to a high level of undersampling. Goal(s): To address this challenge through an innovative multi-resolution approach to estimate the deformation fields and reconstruct 5D motion-resolved images. Approach: The approach consists of a sequence of compressed-sensing image reconstructions that iteratively progresses from low to high spatial resolutions, where one lower-resolution iteration’s output is exploited as input for the next higher resolution until target resolution is reached. Results: Using optimized regularization weights, the proposed approach achieved left-ventricular ejection fraction within a 4% error margin compared the 2D cine. Impact: This study presents a multi-resolution framework for estimating cardiac and respiratory inter-bin deformation fields aimed at improved motion-resolved whole-heart 5D-imaging. This multi-resolution compressed sensing framework has the potential to accurately estimate deformation fields and reduce compression artefacts.
Fast Quantitative T1, T2, PD, B1 and QSM Mapping Using A Single MR Fingerprinting Acquisition And A Phase-Sensitive Deep Reconstruction Network.
Authors: Martinez Jessica A. , Otazo Ricardo , Cohen Ouri
Published: November 2024

Motivation: Incorporating MR phase into the MRF scheme can provide further diagnostic information, such as QSM. However, the MRF dictionary exponentially grows with the number of parameters to estimate. Goal(s): To validate QSM and B1 mapping using MRF and PS-DRONE reconstruction network against conventional reference maps. Approach: Data were acquired at 3T with an EPI-MRF, a multi-echo GRE sequence for QSM and a Bloch Siegert sequence for B1 mapping. Results: PS-DRONE enabled simultaneous quantification of T1, T2, PD, B1 and maps in 2 minutes. Tissue parameter maps were reconstructed in 1 second. Strong correlations were observed to reference B1 and QSM maps. Impact: The ability of PS-DRONE to quantitatively image T1, T2, PD, B1 and QSM with similar accuracy to conventional techniques, but in a fraction of the time, would promote the use of multiparametric quantitative MRI in clinical practice.
Development and validation of a rapid robust 3D-MRF with fast online recon suitable for large-scale neuroscientific and clinical applications
Authors: Zhou Zihan , Cao Xiaozhi , Liao Congyu , Nishimura Mark , Schauman Sophie , Gao Mengze , Yurt Mahmut , Wang Nan , Yablonski Maya , Li Zhitao , Soares Bruno , Syed Ali , Kerr Adam , Yeatman Jason , Setsompop Kawin
Published: November 2024

Motivation: A number of promising multiparameter mapping approaches have been developed but are not yet in routine-use in clinical and neuroscientific settings. Goal(s): To create a rapid and robust 3D-MRF acquisition/reconstruction package, suitable for large-scale neuroscientific and clinical applications. Approach: 3D-MRF acquisition is developed for 1-mm whole-brain-mapping in 2.5-minutes, with build-in robustness to motion, and quantification bias from B0&B1 inhomogeneities. Highly-efficient reconstruction package is created for online generation of quantitative maps and synthesized-contrasts within 4 minutes of scan-completion, benchmarked using a consumer-grade GPU. Results: The pipeline has been validated on over 100-scans performed across clinical and neuroscientific settings with plan for open-source distribution soon. Impact: The distribution of such an acquisition/reconstruction package should help facilitate wide-spread deployment of multiparameter mapping.
POCS-Transformer for MR Image Reconstruction
Authors: Nazir Anam , Cheema Muhammad Nadeem , Li Yiran , Chang Yulin , Detre John A , Wang Ze
Published: November 2024

Motivation: Transformers excel in medical image processing but require many parameters and training data. We mitigated this issue with the POCS-Transformer method. Goal(s): POCS-Transformer goal was to enhances MR image reconstruction along with preserving image quality with various under-sampling masks. Approach: The POCS-Transformer, built on Swin-T using FastMRI data, employed binary undersampling, POCS augmentation, data consistency penalties, and was compared to VN and POCS-CycleGAN on test data. Results: POCS-Transformer outperformed POCS-CycleGAN with superior image quality and less blurring. POCS-Transformer achieved higher mean PSNR and SSIM compared to both VN and POCS-CycleGAN in knee and brain image datasets. Impact: The POCS-Transformer improves MR image reconstruction in terms of reducing blurring even under diverse under-sampling conditions. Its impact extends to healthcare and research. New questions involve its applications in medical imaging, merging traditional and modern methods to inspire further innovations.