bluuebunny/crossref_metadata_2025 huggingface dataset:
Hyperpolarize imaging of fructolysis in fed vs fasted mice liver
Authors: de la Torre Celia Martinez , Cullen Quinlan , Ruan Thomas , Figlioli Grace , Keshari Kayvan
Published: November 2024

Motivation: Fructose intake has increased 80-times over the past century, although the effect of this increase in the body remains unclear. Goal(s): To assess the metabolic flux of fructose at different metabolic states (fed vs fasted) for future human studies. Approach: Fed or fasted mice were injected with hyperpolarized fructose to observe metabolic flux. Results: Fed mice showed the formation of glucose (gluconeogenesis) by F1P, while fasted mice metabolized the fructose to obtain energy. Impact: The results pressented have significant implications for the understanding of fructoylsis and provides a translational metabolic imaging strategy for future human studies.
Imaging the metabolic profile of the normal appearing brain in patients with brain metastases using hyperpolarized [1-13C]-pyruvate MRI
Authors: Cappelletto Nicole , Soliman Hany , Bragagnolo Nadia , Uthayakumar Biranavan , Sahgal Arjun , Chen Albert , Endre Ruby , Ma Nathan , Perks William , Detsky Jay , Heyn Chris , Cunningham Charles
Published: November 2024

Motivation: The metabolic profile of normal appearing brain tissue in patients with brain metastases may be related to the course of disease. Goal(s): To test whether patients with brain metastases exhibit differential metabolism in normal appearing brain parenchyma compared to healthy control participants. Approach: Hyperpolarized [1-13C]-pyruvate and T1w MRI were used to compare the metabolism and volumes of normal appearing brain regions in patients and healthy control participants. Results: The lactate-to-bicarbonate (p=0.0004) and lactate-to-pyruvate (p=0.04) ratios were significantly increased in the normal appearing brain parenchyma of patients compared to controls. Impact: The metabolic profile of normal appearing brain parenchyma in patients with brain metastases exhibits significantly increased glycolytic metabolism compared to healthy control brains when imaged using hyperpolarized [1-13C]-pyruvate MRI and may be related to the course of disease.
Measuring lipid turnover in human subjects using 2H magnetic resonance and heavy water loading
Authors: Cocking Daniel , Damion Robin , Brook Matthew , Auer Dorothee , Bowtell Richard
Published: November 2024

Motivation: During heavy water loading, deuterium is incorporated into newly synthesised lipids; measurement of deuterium content thus provides a measure of lipid turnover. Currently this involves in vitro analysis of biopsy samples. Goal(s): We investigated whether deuterium magnetic resonance can detect increased deuteration of subcutaneous fat following heavy water loading. Approach: Deuterium signals from calf and abdomen from three participants were monitored during/after a 28-day period of loading with heavy water to ~100x natural abundance. Results: Fat signal was increased relative to natural abundance in 5 of the 6 measurements (average at times > 50 days), reaching statistical significance (P<0.05) in three measurements. Impact: A non-invasive technique for monitoring lipid turnover anywhere in the human body would be a powerful tool, allowing investigation of fat metabolism in health and disease. Deuterium magnetic resonance during heavy water loading could form this tool.
Evaluating the Immunotherapy Efficacy of Lung Cancer by 129Xe MRI
Authors: Qiu Maosong , Wang Ruifang , Zhang Lei , Chen shizhen , Zhou Xin
Published: November 2024

Motivation: The evaluation of lung cancer immunotherapy progress using non-invasive methods is challenging. Also, multiple scans of CT during the treatment period expose patients to more radiation. Goal(s): To confirm 129Xe MRI is a potentially robust technology for monitoring immunotherapy effects. Approach: Fe3O4-based nanoprobe that could induce ferroptosis of tumor cells was prepared for immunotherapy of lung metastatic cancer combined with α-PD-L1, finally the immunotherapy effect was evaluated via 129Xe MRI. Results: The 129Xe MRI displayed a complete ventilatory image of the lung in the probe plus α-PD-L1 group while severe ventilation deficiency was observed in the control group. Impact: The 129Xe MRI results of the lung showed significant differences in ventilation defects among different treatment groups, revealing the excellent tumor immunotherapy efficiency of nanoprobe-mediated immunotherapy, which represents a potential protocol for the evaluation of immunotherapy against tumors.
VesselVoyager: An Interactive 3D Intracranial Vessel Tracing Software
Authors: Zhang Kaiyu , Guan Ted , Wang Xin , Kerwin William , Guo Yin , Canton Gador , Hatsukami Thomas , Balu Niranjan , Mossa-Basha Mahmud , Yuan Chun
Published: November 2024

Motivation: Aiming to unravel the complexities of intracranial arterial structures, we recognized the need for advanced 3D annotation capabilities to improve upon conventional 2D methodologies. Goal(s): Our goal was to develop VesselVoyager, a tool that facilitates detailed 3D mapping and analysis of cerebral vasculature, filling a critical gap in neurovascular diagnostic technology. Approach: We utilized real-time 3D rendering and a 3D virtual camera system in VesselVoyager, enabling precise, interactive annotation and analysis within a user-friendly, gamified environment. Results: VesselVoyager not only improved accuracy but also created new approaches for analyzing understudied diseases, enhancing our understanding of complex neurovascular conditions. Impact: VesselVoyager enhances centerline tracing precision and triples processing efficiency, thus lightening the workload for clinicians and researchers. This advancement broadens its applicability to intricate vascular pathologies, including moyamoya disease.
NeoAudi Tract: An Automated Tool for Identifying Auditory Fiber Bundles in Infants
Authors: Liu Feihong , Wang Yaoxuan , Gu Jinchen , Huang Jiawei , Liu Jiameng , Hua Rui , Zhu Yuting , Jiang Mengda , Shi Feng , Zhang Han , Wang Zhaoyan , Feng Jun , Wu Hao , Shen Dinggang
Published: November 2024

Motivation: Charting the development of infant auditory system is vital for understanding language acquisition and hearing disorders. Goal(s): Extracting auditory fiber bundles from diffusion MRI data and overcoming the processing difficulties due to tiny and complex structures, as well as very low tissue contrast in the structural MRI data. Approach: We propose an NAT framework with three core processes: 1) constructing a high-resolution atlas, 2) segmenting tissues and regions of interest (ROIs), and 3) applying a hierarchical registration framework. Results: A high-resolution auditory fiber bundle template is constructed, and 12 auditory fiber bundles are successfully extracted. Impact: Our approach is the first toolbox to identify individual auditory fiber bundles in infants, thus effectively mitigating the processing challenges caused by spatial-temporal asynchrony during the development of the first two postnatal years.
A Repository-Integrated Quantitative Imaging Data Analysis Pipeline for Enabling Multi-Centre Clinical Biomarker Studies
Authors: Birchall Jonathan , Berks Michael , Keaveney Sam , Gill Andrew , Gallagher Edith , Markus Julia , Doran Simon , Little Ross , Dubec Michael , O'Connor James , Cristinacce Penny
Published: November 2024

Motivation: Robust evaluation of novel quantitative imaging biomarkers in multi-centre imaging trials requires harmonised workflow for storage, quality control and analysis of imaging data to facilitate clinical translation. Goal(s): To establish a standardised, repository-integrated framework for quantitative MR data analysis to aid reproducibility. Approach: A software container for quantitative MR data analysis was created using Docker, integrated with the XNAT imaging repository and demonstrated using example data. Results: Repository-integrated software was used to generate quantitative maps of T1, ADC and hypoxia, as well as DCE and IVIM modelling parameters in primary and nodal tumours from a patient with head-and-neck cancer. Impact: Quantitative MR biomarker translation can be accelerated by standardisation of analysis protocols across multi-centre trials. Integration of containerised, user-configurable data analysis software within imaging repositories will improve repeatability and lower the barrier for entry to clinical trial involvement.
Brain Asymmetry Suite: a framework for the comprehensive examination of the brain’s asymmetry
Authors: Boshkovski Tommy , Peña-Nogales Óscar , Neylon Evie , Ramos Marc , Rodrigues Paulo , Prchkovska Vesna , Trivodaliev Kire
Published: November 2024

Motivation: Hemispheric asymmetries have shown a great potential for early detection of a variety of neurological disorders such as traumatic brain injury. However, the lack of consistency in study outcomes is a common issue, largely stemming from the restricted sample sizes and methodological variations. Goal(s): To develop a unified framework for the processing of multimodal imaging data for a comprehensive evaluation of brain asymmetry. Approach: Combination of state-of-the-art algorithms for multimodal imaging data to estimate different metrics of brain asymmetry. Results: By providing comprehensive measurements of brain asymmetry, this framework has the potential for early detection and monitoring of neurological disease. Impact: The study aims to address inconsistencies in hemispheric asymmetry research by developing a unified framework for the estimation of brain asymmetry and offers a promising avenue for early detection and monitoring of neurological diseases.
simDRIFT – An open-source software package for massively parallel simulation of DWI experiments on biophysically accurate tissue systems.
Authors: Blum Jacob , Utt Kainen , Rim Donsub , Song Sheng-Kwei
Published: November 2024

Motivation: The software encompassed by this abstract, which we call simDRIFT, fulfills a presently unmet need by allowing for mesh-free Monte Carlo simulations of DWI that unify researchers' needs for computational performance and biophysical realism with easy-to-use and configurable open-source software. Goal(s): simDRIFT aims to provides for rapid and flexible Monte-Carlo simulations of Pulsed Gradient Spin Echo (PGSE) Diffusion-Weighted Magnetic Resonance Imaging (DWI) experiments. Approach: We compared the performance of simDRIFT against other open-source DWI simulators on identical voxel geometries. Results: Our results show that simDRIFT achieves orders of magnitude improved performance relative to other simulators, especially for large resident spin ensemble sizes. Impact: The performance gains achieved by simDRIFT support its aim to provide a customizable tool for the rapid prototyping of diffusion models, ground-truth model validation, and in silico phantom production.
ProFit-1D for quantifying J-difference edited data at 3T
Authors: Chan Kimberly , Borbath Tamas , Sherlock Sydney , Maher Elizabeth , Patel Toral , Henning Anke
Published: November 2024

Motivation: Reproducible and accurate fitting of the MR spectrum is critical in estimating metabolite concentrations. We have previously developed a fitting software called ProFit-1D which was shown to fit 9.4T sLASER data from the human brain with high accuracy and precision. Goal(s): Here, ProFit-1D was optimized for fitting 3T J-difference edited data. Approach: ProFit-1D was evaluated for accuracy and precision in simulated and in vivo 2HG-edited and GABA-edited data and compared to that of Gannet and LCModelResults: ProFit-1D was found to be more accurate than LCModel in fitting the GABA-edited and 2HG-edited data and more precise than Gannet in fitting the GABA-edited spectra. Impact: Here, we show that ProFit-1D produces more accurate measurements than LCModel and more precise measurements than Gannet in simulated and in vivo spectra from tumors and healthy participants. ProFit-1D is a promising fitting software with high clinical applicability.
In Vivo Detection of Lactate and its T2 in the Resting Human Brain by Transverse Relaxation Encoding with Narrowband Decoupling
Authors: An Li , Araneta Maria Ferraris , Turon Tara , Johnson Christopher , Hong Sungtak , Derbyshire John , Shen Jun
Published: November 2024

Motivation: The published T2 relaxation times in healthy brains are highly inconsistent. Goal(s): To reliably measure T2 relaxation times of lactate and glutamate. Approach: A new editing pulse was crafted and incorporated into the TREND technique for simultaneous homonuclear decoupling of lactate and glutamate at 7 Tesla. Results: The concentrations and T2 relaxation times of lactate and glutamate were measured in vivo with low CVs and CRLBs. Impact: As lactate and glutamate are the markers of glycolysis and oxidative metabolism, respectively, this technique can be used for clinical MRS studies of the biophysical aspects of cerebral metabolic alterations or abnormalities.
Predicting Pathogenic DNA Damage Repair Gene Mutations in Prostate Cancer Patients: A Multi-Center MRI Radiomics Study
Authors: Yuan Enyu , Chen Yuntian , Ye Lei , Yao Jin , Song Bin
Published: November 2024

Motivation: Pathogenic DDR gene alterations are associated with aggressive disease and poor outcomes among prostate cancer (PCa) patients. Goal(s): To develop a radiomics-based pre-testing model for identifying DDR mutation carriers among PCa patients. Approach: A total of 225 patients from three centers with both multiparameter MRI and genetic DDR mutations testing were included. Radiomic models were established based on T2WI and ADC sequences of MRI images. The predictive values were validated in both internal and external validation cohorts. Results: The radiomics-based model exhibited an AUC of 0.835 in the training dataset, 0.824 in the internal validation dataset, and 0.836 in the external validation dataset. Impact: In the current study, we introduced a noninvasive radiomics feature-based tool designed to predict pDDRg mutations in prostate cancer patients. External validation of the novel tool by datasets from other medical centers revealed a high predictive accuracy for pDDRg mutations.
Investigating Quantitative Perfusion Characteristics of Prostate Cancer for African American and White Men
Authors: Naim Sohaib , Ramirez Raymi , Zabihollahy Fatemeh , Miao Qi , Kim Harrison , Reiter Robert , Raman Steven , Sung Kyunghyun
Published: November 2024

Motivation: Race and ethnicity strongly impact the risk of prostate cancer, and understanding the impact of biological heterogeneity in patients from different racial/ethnic backgrounds is crucial for reducing the observed gaps in clinical outcomes. Goal(s): To investigate potential differences in quantitative perfusion characteristics of prostate cancer between African American and White men. Approach: After matching commonly known clinical risk factors, the quantitative DCE-MRI analysis was performed to assess differences in perfusion parameters of the pathology- and MRI-based lesions African American and White men. Results: Notable differences between the two cohorts were observed in the Ktrans of tumors with csPCa. Impact: Considering race-specific perfusion characteristics can help in understanding biological factors of health disparity in prostate cancer.
Liver T1, T2 and ADC Magnetic Resonance Fingerprinting in a single breath-hold
Authors: Velasco Carlos , Castillo-Passi Carlos , Chaher Nadia , Phinikaridou Alkystis , Botnar René , Prieto Claudia
Published: November 2024

Motivation: Single breath-hold simultaneous T1, T2 and ADC abdominal Magnetic Resonance Fingerprinting (MRF) in liver would allow for fast and comprehensive liver tissue characterization. Goal(s): To develop a fast MRF sequence for T1, T2 and ADC quantification on liver tissue in a single breath-hold at 3TApproach: Radial spoiled-GRE ~16-second acquisition with magnetization preparation modules for T1, T2 and ADC encoding was proposed, with an optimized diffusion-preparation module. Sequence was evaluated in phantoms and in 11 healthy subjects. Results: T1, T2 and ADC quantification shows good correlation with reference maps in phantoms and good agreement in vivo against clinical scans. Impact: Simultaneous quantification of T1, T2 and ADC in liver tissue in a single-scan is now possible with this proposed MRF sequence, allowing for a more comprehensive evaluation of hepatic disease through co-registered multiparametric imaging.
Robust water-only liver T1 mapping with Look-Locker spiral out-in-out-in imaging at 0.55T
Authors: Tian Ye , Tasdelen Bilal , Yuan Liyun , Nayak Krishna
Published: November 2024

Motivation: Evaluation of patients with fatty liver disease can benefit from low-field scanners due to their larger bore size and reduced cost. However, fat is a cofounding factor for liver T1 mapping, and there is currently no reliable water-only T1 mapping method for low B0 field strengths. Goal(s): To develop a practical water-only liver T1 mapping method at 0.55T. Approach: Inversion recovery preparation combined with a 3-echo bSSFP spiral out-in-out-in (OIOI) readout was used to obtain water/fat separated images and quantify water-only T1. Results: The proposed water-only T1 mapping method is insensitive to liver PDFF, compared to Cartesian MOLLI and water-fat in-phase T1 values. Impact: For patients with fatty liver disease, this new method provides reliable water-only T1 mapping at low field strength (0.55T). This method may be incorporated into clinical protocols as an indicator of liver inflammation, fibrosis, and stiffness.
Robust measurement of T2 relaxation time in the developing fetal brain using ultrafast MOLED technique
Authors: Ge Nuowei , Yang Qinqin , Bao Jianfeng , Wu Zhigang , Zhong Jianhui , Cai Congbo , Cai Shuhui
Published: November 2024

Motivation: Unpredictable and intense motion poses serious challenges to quantitative imaging of the fetal brain. Goal(s): To evaluate the value of single-shot multiple overlapping-echo detachment acquisition (MOLED) imaging technique in accurately T2 mapping the developing fetal brain. Approach: Single-shot MOLED imaging was utilized for motion-robust T2 mapping in the developing fetal brain. The method was tested on phantom and in vivo, and a scan-rescan assessment was performed on nine fetuses. Results: MOLED T2 mapping strongly agreed with single-echo spin echo T2 (r = 0.996 in phantom). The median intra-subject coefficient of variation of T2 values between scan-rescan tests across the nine subjects is 1.385%. Impact: MOLED is a motion-robust, accurate, and repeatable method for T2 mapping of the whole developing fetal brain in only a few seconds. This method makes it feasible to use T2 maps to quantify early myelination in the fetal brain.
Multiorgan quantitative abdominal MRI in paediatric patients with Cystic Fibrosis
Authors: Bradley Chris , Yule Alex , Dey Nayan , Ng Christabella , Palaniyappan Naaventhan , Peggs Zachary , Brooke Jonathan , Dellschaft Neele , Marciani Luca , Spiller Robin , Aithal Guruprasad , Hoad Caroline , Gowland Penny , Hall Ian , Smyth Alan , Francis Susan , Prayle Andrew
Published: November 2024

Motivation: Cystic fibrosis (CF) is a multi-system, life-limiting genetic disorder. There is a need for multiorgan assessment in children with CF and the response to modulator therapies. Goal(s): To perform multiorgan MRI in children to quantify lung, liver, pancreas, spleen, and gut function. Approach: 8 children with CF and 3 healthy volunteers (HV) performed the protocol including quantitative assessment (T1, T2, T2*, fat fraction (FF), stiffness) of liver, pancreas and spleen. Results: Increased pancreas FF, T1 and T2, spleen T1 and hepatic arterial velocity in CF compared to HVs. Increased liver T1, T2 and stiffness in a CF child independently diagnosed with liver disease. Impact: This protocol will be used to study the effect of the Cystic Fibrosis Transmembrane Regulator Modulator therapy in children on liver toxicity, pancreas and gut function, and lung health, by performing MRI before and 12 months after commencement of Kaftrio.
Abbreviated Whole-Body MRI as a Novel Imaging Modality for Pediatric Lymphoma Follow-Up: A Multicenter study
Authors: Shan Dongqiu , Zheng Bingjie , Wu Yue
Published: November 2024

Motivation: Our study is motivated by the need to assess abbreviated whole-body MR for evaluating treatment response in pediatric patients with lymphoma. Goal(s): To evaluate the diagnostic efficacy and reproducibility of a novel abbreviated whole-body MRI (WB-MRI) for response assessment in pediatric patients with lymphoma. Approach: We conducted a multicenter prospective study included pediatric patients underwent both PET/CT and abbreviated WB-MRI at baseline and during follow-up. Results: The image quality of abbreviated WB-MRI was rated good or excellent, with high sensitivity and accuracy. Impact: Abbreviated WB-MRI has the advantage of being non-invasive and without radiation exposure, making it an alternative to PET/CT for response surveillance in pediatric patients.
The Rugby Connectome: A Longitudinal Analysis of Structural Connectivity in an Adolescent Cohort with Repeated Head Impacts
Authors: Clarkson Edward , Tayebi Maryam , Schierding William , Condron Paul , Potter Leigh , Maller Jerome , Qiao Miao , Fernandez Justin , Holdsworth Samantha , Kwon Eryn , McGeown Joshua , Shim Vickie
Published: November 2024

Motivation: Evidence suggests that repeated head impacts which do not produce conscious changes in cognition, may have detrimental effects on neurological function and brain micro-structure. Goal(s): Our study aims to quantify longitudinal changes in structural connectivity within a cohort of young rugby players throughout a rugby season. Approach: Using head impact data and advanced MRI techniques including whole brain tractography from multi-shell diffusion MRI, structural connectivity adjacency matrices were derived from tractograms and analyzed using graph theory. Results: Global clustering coefficient increased significantly from preseason to mid-season and post-season. These changes correlated with measures of cumulative head impact exposure. Impact: Data from our adolescent rugby cohort offers a rare opportunity to document the longitudinal effect of repeated head impact exposure on structural connectivity. The structural connectivity changes we observed may not be indicative of clinically relevant brain injury.
Association of white matter brain diffusivity properties with football exposure in former professional American-style football players
Authors: Wu Ona , Grashow Rachel , Weisskopf Marc , Miller Karen , Iverson Grant , Dodelson Jacob , Kulpanowski Annelise , Hancock Brandon , Doyle Michael , Copen William , Baggish Aaron , Zafonte Ross
Published: November 2024

Motivation: The possible long-term effects of repetitive head impacts experienced by American-style professional football players are poorly understood. White matter injury is a known sequela of head trauma. Peak-width skeletonized mean diffusivity measurements have been associated with cerebrovascular disease. Goal(s): Our goal is to evaluate the association of peak-width skeletonized diffusion values with football exposure. Approach: We measured peak-width skeletonized diffusion values in 103 retired professional football players who underwent multi-shell diffusion imaging. Results: Age, hypertension, body-mass index, concussion signs and symptom history score, total years of non-professional play, and episodes of loss of consciousness were significantly associated with peak-width skeletonized diffusion values. Impact: Measured peak-width skeletonized diffusion values in white matter may provide an improved understanding of the association between football exposure and later-in-life brain microstructural integrity.
Acute impact of soccer ball heading on brain tissue electrical conductivity
Authors: Cao Jun , Delang Nathan , Henderson Luke , Robertson Rebecca , Mendoza Fernando , Desbrow Ben , Irwin Christopher , Cairns Elizabeth , Austin Paul , Ball Shane , Buckland Michael , McGregor Iain , McCartney Danielle , Rae Caroline
Published: November 2024

Motivation: The effects of sub-concussive head impacts are not well understood. New biomarkers are needed to detect sub-concussion. Goal(s): Our goal was to investigate the acute effects of sub-concussive impacts using MREPT. Approach: Fourteen soccer players were scanned with MREPT in two separate sessions after performing the task of either heading or kicking soccer balls for 20 minutes. Results: Electrical conductivity measured in multiple brain regions such as inferior fronto-occipital fasciculus after the heading session was significantly less than that measured following kicking, indicating that MREPT could be a useful tool for detecting sub-concussive injury. Impact: The finding that heading soccer balls for a short period can cause significant acute decreases in brain electrical conductivity suggests that this activity may have detrimental short term effects on brain function.
Quantifying changes of axonal shape in traumatic brain injury with time-dependent diffusion
Authors: Abdollahzadeh Ali , Coronado-Leija Ricardo , Lee Hong-Hsi , Sierra Alejandra , Fieremans Els , Novikov Dmitry
Published: November 2024

Motivation: Interpreting diffusion MRI (dMRI) in terms of brain tissue micro-geometry. Goal(s): To identify cellular features that govern diffusion measurements among myriads of parameters specifying tissue microstructure. Approach: We found how axonal micro-geometry is manifested in a dMRI measurement by analytically solving the diffusion equation in a tube with a randomly varying cross-section. Results: We identify a specific power-law approach of the time-dependent diffusion coefficient along the axon to its long-time limit. The average inverse cross-section and the variance of long-range cross-sectional fluctuations govern the diffusive dynamics. We quantify changes in these non-trivial geometrical features, associated with axonal beading, in a rat TBI model. Impact: Beading is a characteristic feature of numerous neurodegenerative diseases triggered by different pathological conditions and injuries. Here, we detect geometrical changes in the axonal micro-geometry, accessible via the along-tract diffusivity from the diffusion tensor using clinically feasible diffusion weightings.
Brain iron accumulation kinetics in Parkinson’s disease revealed by relaxometry network and susceptibility-weighted imaging
Authors: Lu Weizhao , Song Tianbin , Lu Jie
Published: November 2024

Motivation: Iron deposition is implicated in the pathogenesis of Parkinson's disease (PD). However, most of the previous studies failed to report progressive iron accumulation with disease progression. Goal(s): This study aimed to explore the kinetics of iron accumulation in the PD brain using a novel relaxometry covariance network (RCN) approach. Approach: The RCN approach consisted of three steps, the identification of brain regions as propagators of iron, construction of causal RCN and individual differential RCN. Results: The left substantia nigra pars reticulata, left substantia nigra pars compacta, and lobule VII of cerebellum vermis were identified as propagators of iron. Impact: The application of our novel relaxometry covariance network on susceptibility-weighted imaging revealed iron accumulation kinetics in Parkinson's disease, which were closely related to the pathophysiological aspects of the disease. The current findings deserved further exploration to elucidate the underlying mechanisms.
Characterizing iron deposits in subcortical grey matter from in vivo gradient-echo data
Authors: Oliveira Rita , Raynaud Quentin , Kiselev Valerij , Jelescu Ileana , Lutti Antoine
Published: November 2024

Motivation: Transverse relaxation rate and magnetic susceptibility are MRI measures of iron concentration in subcortical grey matter. However, their relation to the microscopic distribution of iron deposits within the tissue remains elusive. Goal(s): Our goal is to characterize the distribution of iron deposits in subcortical grey matter from in vivo gradient-echo data. Approach: We characterize iron-rich deposits from the combination of transverse relaxation parameters of the magnitude and phase of gradient-echo data, under two limiting regimes. Results: The estimates of iron distribution are consistent with ex vivo studies. Data suggest that an intermediate regime might be applicable in subcortical tissue. Impact: The characterization of the microscopic distribution of iron deposits in subcortical grey matter, from in vivo gradient-echo data, brings the opportunity to study iron-related brain changes in neurodegenerative diseases with improved specificity.
Multi-parametric radiomics of T1and susceptibility-weighted imaging for differentiating Parkinson’s disease and multiple system atrophy.
Authors: Bu Shuting , Jiang Yueluan , Fan Guoguang
Published: November 2024

Motivation: Aim to differentiate PD from MSA in the early stage. Goal(s): To build a radiomic model based on features derived from basal ganglia regions by using commonly applied sequences in clinical settings, to distinguish between PD and MSA. Approach: This study constructed three machine learning models- logistic regression, support vector machine and light gradient boosting method to differentiate PD motor subtypes. Results: The light gradient boosting machine trained by features extracted from SWI and T1 sequences achieved a great classification performance between PD and MSA (AUC=0.881). Impact: This study has developed an effective classification model using commonly utilized clinical MRI sequences, which provides a valuable tool for distinguishing between PD and MSA in clinical practice.
Improving laminar fMRI specificity by reducing macrovascular bias caused by respiration effects
Authors: Chai Yuhui , Morgan A. Tyler , Handwerker Daniel , Li Linqing , Huber Laurentius , Sutton Bradley , Bandettini Peter
Published: November 2024

Motivation: Although fMRI has achieved sub-millimeter spatial resolution especially with ultra-high field (≥7T) scanners, its spatial specificity has not kept pace. Goal(s): This study aims to map and validate the influences of natural respiratory variations on fMRI signals and use it to improve laminar fMRI specificity. Approach: We compare the influences of natural respiratory variations with the patterns induced by deep breath and breath hold tasks and probe their spatial correlation with vascular density. Results: This respiratory variation revealed information can be used to remove macrovascular-dominated voxels, thereby enhance laminar fMRI specificity. Impact: We highlight the significance of natural respiratory variations for improving laminar fMRI specificity. By understanding these variations and their link with vascular density, we can better identify and exclude macrovascular-dominated voxels, marking a notable advancement in high-resolution fMRI specificity.
A novel biophysical simulation framework for intravascular MRI signals using 3D Vascular Anatomical Networks applied to VASO-fMRI
Authors: Hartung Grant , Gomez Daniel , Berman Avery , Polimeni Jonathan
Published: November 2024

Motivation: The emerging fMRI method VASO provides improved neuronal specificity compared to BOLD, however the precise interpretation of its origins and principled means to optimize this sequence is not straightforward. Goal(s): To use biophysical models to investigate the origins of the VASO signal and compare it with direct estimates of CBV. Approach: We extend our 3D biophysical Vascular Anatomical Network framework to incorporate intravascular signals undergoing inversion recovery to model the VASO sequence. Results: The VASO signal appears sensitive to slab thickness, and activation biases occur if the slab is too thin. Simulated profiles of VASO differ from measurements, possibly due to model simplifications. Impact: Our new methodology enables biophysical simulations of fMRI based on inverting blood. Our findings may provide a deeper understanding of the hemodynamic origins of VASO and provide guidance for optimizing SS-SI VASO protocols to yield veridical representation of neural activity.
Understanding signal specificity in fMRI: bSSFP vs. GRE-EPI signal dependence on cortical orientation to B0 at 9.4 Tesla
Authors: Ramadan Dana , Bause Jonas , Mueller Sebastian , Bosch Dario , Stirnberg Ruediger , Ehses Philipp , Scheffler Klaus
Published: November 2024

Motivation: GRE-EPI, the most widely used sequence for BOLD fMRI, is highly biased towards large draining veins that follow the cortical curvature and influence the surrounding magnetic field in an orientation-dependent manner increasing with field strength. Goal(s): This work aims to investigate large vein biases resulting in cortical orientation-dependent signal variations in GRE-EPI and bSSFP resting-state fMRI signals. Approach: We compared 2D and 3D GRE-EPI with 3D bSSFP rs-fMRI signal fluctuations in their dependence on the cortical orientation to B0 in five subjects at 9.4 Tesla. Results: Unlike GRE-EPI, intra- and inter-subject comparisons revealed no dependence of bSSFP on the cortical orientation to B0. Impact: Fluctuations in the GRE-EPI signal are highly dependent on the cortical orientation and depth. This was not observed with bSSFP, demonstrating the potentially higher specificity of bSSFP for smaller veins, closer to brain activation at field strengths ≥ 7 Tesla.
Improved estimates of cerebral circulation time from BOLD fMRI data using putamen and sagittal sinus signals
Authors: Zvolanek Kristina , Shen Claire , Moia Stefano , Moum Sarah , Bright Molly
Published: November 2024

Motivation: Cerebral circulation time (CCT) is a metric that provides insight into cerebrovascular health. However, conventional CCT measurements typically require injection of contrast agents, or demonstrate high variability. Goal(s): We propose an improved, contrast-free method to calculate CCT by cross-correlating fMRI signals from the putamen and sagittal sinus. Approach: n 16 healthy adult datasets (8 subjects, 2 sessions), we compared CCT estimates using the internal carotid artery (as proposed in the literature) or putamen as “arterial” references in breath-hold and resting-state data. Results: The putamen ROI provides more reliable CCT estimates, consistent with values from bolus-tracking methods. Impact: A modified analysis of fMRI data provides a robust method to measure cerebral circulation time on a single-subject level. This method may offer an accessible, contrast-free metric of cerebrovascular health for future application in patient populations.
Structural and functional connectivity patterns of brainstem nuclei in living humans by 7 Tesla MRI
Authors: Koley Subhranil , Singh Kavita , García-Gomar María Guadalupe , Cauzzo Simone , Hannanu Firdaus Fabrice , Bianciardi Marta
Published: November 2024

Motivation: A definitive baseline connectome of brainstem nuclei is missing. Goal(s): To improve brainstem hodology in living humans by using the similarity between functional and structural connectomes of brainstem nuclei as ground truth. Approach: In healthy subjects, we mapped 58 Brainstem Navigator atlas labels to high spatial resolution functional and diffusion-weighted 7 Tesla MRI, and computed their functional and structural connectivity, the latter computed using three probabilistic tractography methods proposed in the literature (seed-, ACT-, ACT-SIFT-based), with 148 cortical and 21 subcortical areas. Results: ACT-SIFT outperformed the other methods within the brainstem and the cortex by reducing large fiber bias. Impact: Comparison of structural and functional connectomes achieved with different methodology can improve the understanding and mapping of brainstem nuclei connections in living humans and establish a baseline connectome useful to evaluate a broad set of diseases including movement/sleep disorders.
Differential control of nonlinear functional dynamics by cerebro-cerebellar interactions during action execution and observation
Authors: Lorenzi Roberta , Korkmaz Gökçe , Alahmadi Adnan , Monteverdi Anita , Casiraghi Letizia , D'Angelo Egidio , Palesi Fulvia , Kingshott Claudia Gandini Wheeler
Published: November 2024

Motivation: Task-driven BOLD signal nonlinearities in visuomotor areas have been reported both during execution and observation of tasks. Goal(s): We aim to study how cerebral and cerebellar regions of a visuomotor network influence each other and drive nonlinear BOLD responses. Approach: Dynamic Causal Modeling was used to estimate causal influences as effective connectivity to assess how the activity of each region modulated BOLD signal nonlinearities in a visuomotor task. Results: Execution and observation networks showed the same fixed (0th order) effective connectivity, while BOLD signal nonlinearities were modulated in the motor planning loop during execution only and were driven by the cerebellum. Impact: Dynamic causal modeling elucidates the central role of the cerebellum as a forward controller in regulating input-driven modulation differentially in execution and observation. These mechanisms may be affected by pathologies and could have an important role in visuomotor disability.
The Role of Inhibitory Thalamic Reticular Nucleus (TRN) in Brain-wide Resting-state Functional MRI (rsfMRI) Connectivity
Authors: Leong Alex , Wang Xunda , Zhang Yankai , Xie Linshan , Wu Ed
Published: November 2024

Motivation: Despite the enormous potential inherent in rsfMRI, the neural basis underlying rsfMRI connectivity remains unclear. Goal(s): We aim to dissect the role of the TRN inhibitory neural population in rsfMRI connectivity given its prominent role in maintaining/regulating thalamo-cortical oscillations. Approach: We examined brain-wide activity and rsfMRI connectivity changes after optogenetically manipulating neural activity in rodent TRN. Results: We demonstrate that somatosensory-specific TRN inhibitory networks play a role in modulating rsfMRI connectivity of sensorimotor and default mode networks. Impact: Present studies examining neural basis of rsfMRI have primarily focused on excitatory networks. Here, we investigated the role of a major inhibitory thalamic nucleus to advance our understanding of the contributions of inhibitory inputs in regulating brain-wide rsfMRI networks.
Deep Complex Neural Network for Undersampling Spiral Artefact Removal in Diffusion Tensor Cardiovascular Magnetic Resonance with In-vivo Study
Authors: Luo Yaqing , Ferreira Pedro , Pennell Dudley , Yang Guang , Nielles-Vallespin Sonia , Scott Andrew
Published: November 2024

Motivation: Diffusion Tensor Cardiovascular Magnetic Resonance (DT-CMR) is hindered by low resolution and long acquisitions. Spiral trajectories could be efficient with effective removal of artefacts from undersampled images. Goal(s): To remove artefacts from highly accelerated spiral in-vivo DT-CMR acquisitions using a novel deep learning method. Approach: We proposed a Residual U-Net based Complex-valued Edge Attention Network (CEAN) to remove undersampling artefacts. Training with and without transfer learning were explored. Results: CEAN with transfer learning outperformed other networks, achieving the lowest Mean Absolute Error (MAE) for DT-CMR parameters and preserving diffusion encoding information, suggesting future potentials in accelerating clinical DT-CMR studies. Impact: This work will allow the acquisition and reconstruction of highly accelerated STEAM spiral DT-CMR, aided by the proposed deep Complex-valued Edge Attention Network. Further developments will allow increases in spatial resolution to facilitate clinical translation of DT-CMR.
Joint sequence optimization beats pure neural network approaches for super-resolution TSE
Authors: Dang Hoai Nam , Golkov Vladimir , Endres Jonathan , Weinmüller Simon , Glang Felix , Wimmer Thomas , Cremers Daniel , Dörfler Arnd , Maier Andreas , Zaiss Moritz
Published: November 2024

Motivation: TSE flip angle trains can have a strong influence on the actual resolution of the acquired image and have consequently a considerable impact on the performance of a super-resolution task. Goal(s): We demonstrate the advantage of end-to-end optimization of sequence and neural network parameter compared to pure network training approaches. Approach: This MR-physics-informed training procedure jointly optimizes radiofrequency pulse trains of a PD- and T2-weighted TSE and subsequently applied CNN to predict corresponding PDw and T2w super-resolution TSE images. Results: The method generalizes from simulation-based optimization to in vivo measurements and acquired super-resolution images show higher accuracy compared to pure network training approaches. Impact: Acquired super-resolution image may improve evaluation of the data. Reduction of acquisition time compared to direct high-resolution acquisition leads to increase in patient comfort and minimization of motion artifacts.
Unsupervised 4D-Flow MRI reconstruction with Deep Image Prior and Graph Convolution Neural-Network
Authors: Li Zhongsen , Sun Aiqi , Chen Wenxuan , Liu Xiancong , Wei Haining , Liu Chuyu , Li Rui
Published: November 2024

Motivation: Deep learning reconstruction algorithms offer significant advantages for accelerating 4D-Flow MRI acquisition. However, a large high-quality fully-sampled dataset is usually unavailable for network training. Goal(s): To propose an unsupervised algorithm for 4D-Flow MRI reconstruction, without the need for any fully-sampled data. Approach: We use branched CNNs and a Graph-Convolution-Network as the generator. Additionally, we devise an ADMM algorithm to alternately optimize the images and the network parameters. Experiments are conducted on aortic and intracranial 4D-Flow data. Results: The proposed algorithm demonstrates superior reconstruction results, outperforming even supervised deep-learning method. Moreover, it exhibits good generalization capability when applied to another imaging target. Impact: The proposed method is a promising algorithm for accelerating MR blood-flow imaging, owing to its exceptional performance and generalization capacity. Furthermore, the algorithm introduces a new model for 4D-flow MRI reconstruction which is valuable for further research.
Generalizable and Accurate Federated learning for Fast MR imaging Equipped with Laplacian Attention Mechanism
Authors: Wu Ruoyou , Li Cheng , Zou Juan , Zheng Hairong , Wang Shanshan
Published: November 2024

Motivation: Federated MR image reconstruction can make full use of data from multiple institutions while protecting patient privacy, but the images obtained by existing methods still need improvement in terms of fine structures. Goal(s): Our goal is to improve the quality of clinical diagnosis by achieving accurate MR image reconstruction. Approach: A Laplacian attention mechanism is proposed to capture fine structure and details for accurate MR image reconstruction from undersampled data. Results: Qualitative and quantitative experimental results on an in-house and two public datasets validate the effectiveness of our method. Impact: Federated MR image reconstruction promotes collaboration across multiple institutions and effectively leverages data from different organizations to enhance model performance, while mitigating privacy concerns.
Coil Geometry Effects on Deep-learning-based MR Image Reconstruction
Authors: Dubljevic Natalia , Moore Stephen , Lauzon Michel , Souza Roberto , Frayne Richard
Published: November 2024

Motivation: Parallel imaging coil constraints can make it difficult to design comfortable coil arrays. Goal(s): To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method. Approach: We synthesized an eight-channel head coil configuration and gradually increased coil overlap making the coils less ideal for parallel imaging. A DL reconstruction method was compared to a traditional non-DL method. Results: As coil overlap increased, a smaller decrease in reconstruction performance was seen when using a DL method versus a non-DL method. Impact: Our works suggests parallel imaging geometric coil constraints may be relaxed when using a deep learning reconstruction method. This flexibility would lead to an increased range of coil configurations that allow for improved patient comfort while decreasing scan times.
Physics-informed Synthetic Data Learning Boosts Multi-Scenario Fast MRI Reconstruction
Authors: Wang Zi , Yu Xiaotong , Wang Chengyan , Chen Weibo , Wang Jiazheng , Chu Ying-Hua , Sun Hongwei , Li Rushuai , Li Peiyong , Yang Fan , Han Haiwei , Kang Taishan , Lin Jianzhong , Yang Chen , Chang Shufu , Shi Zhang , Hua Sha , Li Yan , Hu Juan , Zhu Liuhong , Zhou Jianjun , Lin Meijing , Guo Jiefeng , Cai Congbo , Chen Zhong , Guo Di , Qu Xiaobo
Published: November 2024

Motivation: Deep learning (DL) is powerful for fast MRI reconstruction, but remains largely untapped in multiple clinical imaging scenarios. Goal(s): To provide a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications. Approach: In this work, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. Results: PISF trained on synthetic data enables high-quality, ultra-fast, and robust MRI reconstruction from different 4contrasts, 5 anatomies, 5 vendors and centers, and 2 pathologies, without further re-training. Impact: Physics-informed synthetic data learning (DL) provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.
SISMIK: Search In Segmented Motion Input in K-space
Authors: Dabrowski Oscar , Falcone Jean-Luc , Klauser Antoine , Songeon Julien , Kocher Michel , Chopard Bastien , Lazeyras Francois , Courvoisier Sebastien
Published: November 2024

Motivation: Motion correction in MRI predominantly relies on image-based methods and continues to be a challenge. Innovative approaches could harness better motion information latent in k-space (i.e., the measurement space). Goal(s): Developing a reference-less motion correction pipeline in k-space using deep learning. Approach: Our k-space motion correction pipeline combines deep learning for motion parameter estimation with model-based image reconstruction. Large datasets were generated through physics-based simulations on 2D brain MRI acquisitions to enhance model training and performance. Results: Our deep-learning model performs well in motion parameter estimation, even for successive motion events, effectively removing substantial motion artifacts when combined with model-based reconstruction. Impact: SISMIK, our deep learning model successfully estimates motion parameters in the acquisition space of multi-slice 2D brain MRI. It allows substantial motion artifact removal through a model-based reconstruction approach, which is, by design, free of hallucination artifacts.
In vivo metabolism of glucose and acetate in human meningiomas: A 13C NMR-based metabolic flux analysis
Authors: Ijare Omkar B. , Baskin David S. , Powell Suzanne Z. , Pichumani Kumar
Published: November 2024

Motivation: Metabolism plays a key role in the growth and proliferation of brain tumors including aggressive meningiomas. We previously reported that meningiomas preferentially utilize acetate as a bioenergetic substrate. However, the metabolism of acetate in the presence of glucose is not well understood. Goal(s): To investigate the simultaneous in vivo metabolism of acetate and glucose in meningiomas. Approach: We infused [2-13C]acetate and [U-13C]glucose as metabolic tracers in meningioma patients to determine the relative utilization of both nutrients by meningiomas. Results: Grade-II meningiomas utilize relatively less amount of glucose (grade-II: 0.6% vs. grade-I: ~5.4%) and more acetate than grade-I meningiomas (grade-II: 45.8% vs. grade-I: ~31.24%). Impact: No chemotherapy is available for the treatment of meningiomas. The findings from this study will be helpful in designing targeted metabolic therapy for aggressive meningiomas using small molecule inhibitors (e.g., ACSS2 inhibitor) involved in acetate metabolism.