bluuebunny/crossref_metadata_2025 huggingface dataset:
Mesoscale myelin-water fraction and T1/T2/PD mapping through optimized 3D ViSTa-MRF and stochastic reconstruction with preconditioning
Authors: Liao Congyu , Cao Xiaozhi , Iyer Siddharth , Zhou Zihan , Liu Yunsong , Haldar Justin , Yurt Mahmut , Gong Ting , Wu Zhe , He Hongjian , Zhong Jianhui , Kerr Adam , Setsompop Kawin
Published: August 2023

In this work, we developed ViSTa-MRF, which combined Visualization of Short Transverse relaxation time component (ViSTa) technique with MR Fingerprinting (MRF), to achieve high-fidelity whole-brain myelin-water fraction (MWF) and T1/T2/PD mapping at sub-millimeter isotropic resolution on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling (TGAS) spiral-projection acquisition and stochastic subspace reconstruction with optimized k-space diagonal preconditioning. With the proposed ViSTa-MRF method, high-fidelity direct MWF mapping was achieved without a need for multi-compartment fitting.
Multicenter reproducibility of quantitative MRI using vendor-neutral sequences (VENUS)
Authors: Karakuzu Agah , Cohen-Adad Julien , Stikov Nikola
Published: August 2023

Vendor-native implementations can lead to unaccounted variance in quantitative MRI (qMRI). The purpose of this study is to show that fully transparent qMRI workflows coupled with VEndor-NeUtral Sequences (VENUS) improve multicenter reproducibility of quantitative MRI maps across three scanners by two vendors, in phantoms and in-vivo. We developed and deployed a vendor-neutral 3D spoiled gradient-echo (SPGR) sequence on three commercial scanners and showed that vendor-neutral qMRI maps of T1, MTR and MTsat outperform their vendor-native counterparts in terms of reproducibility.
Microscopic neuropathology can be seen using accelerated combined diffusion-relaxation MRI
Authors: Benjamini Dan , Bouhrara Mustapha , Komlosh Michal , Iacono Diego , Perl Daniel , Brody David , Basser Peter
Published: August 2023

Here we present MRI acquisitions that maximize the amount of information in an image by using a combination of magnetic field profiles to probe relaxation and diffusion mechanisms simultaneously, thus resolving sub-voxel information from ex vivo brain segments of human subjects that sustained traumatic brain injury. We then propose a strategy to refine the ensuing abundance of high-dimensional data, and to sort and recover biologically relevant information into actionable and quantitative injury biomarker images that capture subtle neuropathology.
Quantitative atlas of T1 relaxation times in the cerebral cortex for personalized characterization of pathology
Authors: Piredda Gian Franco , Claudet Marion , Hilbert Tom , Vaneckova Manuela , Krasensky Jan , Andelova Michaela , Uher Tomas , Havrdova Eva , Vodehnalova Karolina , Horakova Dana , Egger Karl , Yang Shan , Ludovichetti Riccardo , Crowe Lindsey , Delattre Bénédicte , Vargas Maria Isabel , Ravano Veronica , Maréchal Bénédicte , Thiran Jean-Philippe , Kober Tobias
Published: August 2023

Comparing single-patient data to atlases of normative relaxation times in the brain enables personalized characterization of pathological tissue. However, an accurate comparison is challenging in the cortex due to the gyrification of gray matter and resulting registration problems. This work addresses this problem for parametric T1 maps that were acquired from 285 healthy subjects. A method aligning inter-subject brain cortices was developed to enable the comparison of cortical T1 values. We thus established a normative atlas accounting for healthy T1 values in brain cortical tissues, to be used to detect and characterize pathology-induced T1 alterations in patients.
Denoising and Uncertainty Estimation in parameter mapping With Bayesian Deep Image Prior
Authors: Hellström Max , Garpebring Anders
Published: August 2023

Tissue parameter estimation often gives noisy parameter maps due to noise in the signal data. In this work, we improve parameter mapping by incorporating noise reduction and uncertainty estimation with Bayesian Deep Image Prior. We implement task-specific loss functions for different applications. We test our method by estimating T1, T2, and ADC with synthetic- and in-vivo MRI data. Our method results in denoised tissue parameter maps, with associated error estimates. Our method is easy to implement, does not require any training data, and is easy to customize for different applications in parameter mapping.
Feasibility of magnetization transfer measurement in distinct white matter connections
Authors: Bontempi Pietro , Leppert Ilana , Schiavi Simona , Campbell Jennifer , Nelson Mark , Pike Bruce , Tardif Christine , Daducci Alessandro
Published: August 2023

Magnetization Transfer (MT) imaging is a MR technique that sensitizes image contrast to protons bound to macromolecules such as protein and lipids. Using an innovative sequence that combines MT and diffusion- imaging, and Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT), a method that allows estimation of bundle tissue properties, we investigate bundle specific MT ratio (MTR) values.
Deciphering the compartmental origin of glucoCEST signal using diffusion-weighted CEST-MRS
Authors: Mathieu-Daudé Yohann , Vincent Mélissa , Valette Julien , Flament Julien
Published: August 2023

Compartmental origin of glucoCEST signal is still an ongoing debate. To address this crucial question, we propose in this study to use diffusion-weighted CEST-MRS in order to disentangle extracellular/extravascular and intracellular contributions to the glucoCEST signal. D-glucose and 2-deoxy-D-glucose were injected intravenously and kinetics of glucoCEST signal were measured over time. The comparison of kinetics acquired with and without diffusion grandients showed that 2-deoxy-D-glucose glucoCEST signal was dominated by intracellular contribution, contrarily of D-glucose. This study could constitute a major step toward glucoCEST quantification and could be beneficial for several applications, especially to study energy metabolism defects in neurodegenerative disorders.
Magnetic resonance imaging of cationic compounds using their proton exchange modulating effect
Authors: Han Zheng , Aafreen Safiya , Zhou Yang , Bie Chongxue , Xu Jiadi , Zheng Lei , van Zijl Peter , Liu Guanshu
Published: August 2023

Here we report a novel approach to detect non-labeled cationic compounds, named modulated saturation transfer (MOST)-MRI, based on their effect of modulating the exchange rate of surrounding exchangeable protons. MOST-MRI was able to detect polyethylenimine (PEI600, M.W. = 600 Da), at a high sensitivity (μM or μg/mL range) through its quenching effects on surrounding fast-exchanging protons. In animals, MOST-MRI detected the tumor uptake of PEI600 that was injected intratumorally at a low dose (50 μg/kg b.w.), indicating the potential of MOST-MRI for label-free imaging guidance that is needed for the development and clinical translation of cationic gene delivery systems.
Molecular-enriched functional connectivity in the human brain using multi-band multi-echo simultaneous ASL/BOLD fMRI
Authors: Dipasquale Ottavia , Cohen Alexander , Martins Daniel , Zelaya Fernando , Turkheimer Federico , Veronese Mattia , Williams Steve , Yang Baolian , Banarjee Suchandrima , Wang Yang
Published: August 2023

This work is a proof of concept for the integration of ASL fMRI and molecular imaging using REACT, a novel analytical strategy that enriches functional data with the molecular information on the neurotransmitter distribution density. We applied REACT to high-resolution, whole-brain simultaneous ASL/BOLD data to estimate BOLD- and perfusion-weighted (PW)-based functional connectivity maps related to specific molecular systems and compared the results from the two fMRI modalities, showing very similar patterns of molecular-enriched functional connectivity. Our findings show that the PW signal is as informative as BOLD in terms of detection of functional circuits associated to specific molecular pathways.
Resting state fluctuations in BOLD fMRI might not systematically reflect measures of cerebrovascular physiology between or within subjects
Authors: Moia Stefano , Chen Gang , Uruñuela Eneko , Stickland Rachael , Termenon Maite , Caballero-Gaudes César , Bright Molly
Published: August 2023

Resting State Fluctuations (RSF) metrics are frequently associated with vascular and physiological factors, to the point of being suggested as alternative estimates of cerebrovascular reactivity (CVR) and as tools for fMRI data calibration. Using a densely sampled fMRI dataset, we demonstrate high individual variability in the inter-session relationships between RSF metrics and CVR. Moreover, while physiological factors such as blood pressure show a significant relationship with CVR, they do not with common RSF metrics. These results indicate that RSF parameters might not be suitable alternatives for CVR, and may not properly account for inter-individual physiological variations in BOLD fMRI data.
Exploratory study of relationships between [18F] Sodium fluoride PET metabolic bone measures and MRI bone porosity index in the tibial tuberosity
Authors: Barbieri* Marco , Watkins* Lauren , Haddock Bryan , Gold Garry , Kogan Feliks
Published: August 2023

Molecular information derived from dynamic [18F]NaF PET imaging holds promise to study bone remodeling in bone and joint disorders. The porosity index (PI), based on ultra-short echo-time (uTE) MRI, has been proposed to study bone porosity in clinically valuable acquisition times. We explored the association between bone metabolic and porosity information in the tibial tuberosity in a cohort of 10 subjects with knee OA using hybrid PET-MRI imaging. We found a moderate negative correlation between the metabolic bone remodeling (Ki) and the porosity index. These promising results may highlight a new tool to unveil unknown pathways in musculoskeletal disease pathophysiology.
Translational exploration of dynamic functional connectivity changes in Alzheimer’s disease patients and Thy-Tau22 mice at the prodromal stage
Authors: Degiorgis Laetitia , Chabran Elena , Sourty Marion , Blanc Frédéric , Harsan Laura-Adela
Published: August 2023

Between-species comparisons become essential to validate findings from animal models. In case of Alzheimer’s disease (AD) especially important is to evaluate their predictive potential for the sporadic form and to assess their relevance for the early stages of pathology. Therefore, we used dynamic resting-state functional connectivity to investigate and comparatively assess changes in early AD patients and prodromal Thy-Tau22 mice, a mouse model of tauopathy. We highlighted between-species similarities showing less dynamic communication of brain areas in diseased groups than in controls, and comparable modifications of memory and default mode-like networks in early AD and prodromal Thy-Tau22 mice.
Whole brain mapping of transcranial electrical stimulation-induced effects by BOLD-fMRI in rats
Authors: Voroslakos Mihaly , Arefin Tanzil , Zhang Jiangyang , Alon Leeor , Buzsaki Gyorgy
Published: August 2023

Transcranial Electrical Stimulation (TES) is a noninvasive method that can modulate neuronal activity. Despite 20 years of intensive research, the basic mechanisms, and the extent of TES influence on brain functions is still not well understood. To gain a better understanding of the TES effects, we combined neurostimulation with BOLD-fMRI in rats. We have designed an MR-compatible TES setup, including a deuterium-based stimulation electrode system. Our results revealed BOLD responses to TES and altered cortical and cortico-subcortical network responses induced by a variety of stimulation patterns and intensities.
Differential information of perfusion and diffusion MRI following traumatic spinal cord injury in the rat.
Authors: Meyer Briana , Lee Seung-Yi , Budde Matthew
Published: August 2023

Spinal cord tissue perfusion plays a central role in the acute care of spinal cord injury (SCI). We previously optimized pseudo-continuous arterial spin labeling (pCASL) MRI for the rat spinal cord. This work represents the first longitudinal study combining pCASL and filtered diffusion weighted MRI to examine the relationship between spinal cord blood flow and filtered axial diffusivity following a rat model of SCI. This work reveals unique and differing spatiotemporal dynamics between the two contrasts.
Evidence of a cortical hypoperfusion spreading following local ultrasound Blood Brain Barrier opening in the rat brain.
Authors: Labriji Wafae , Clauzel Julien , Cirillo Carla , Lafond Maxime , Lafon Cyril , Hirschler Lydiane , Warnking Jan , Barbier Emmanuel L. , Loubinoux Isabelle , Desmoulin Franck
Published: August 2023

The use of ultrasound combined with microbubbles to open the blood-brain barrier is an increasingly common method in clinical and preclinical research. This work describes the effect of this BBB opening method on brain perfusion in rats, using pCASL sequence. Surprisingly, through a dynamic analysis, we observed a long time hypoperfusion spreading in the brain cortex following sonication. A similar phenomenon has been described in studies where cortical spreading depression (CSD) has been induced.
Towards an ultra-high resolution structural connectivity atlas of the parrot brain using extreme high-field 17.2T diffusion MRI
Authors: Uszynski Ivy , Ciobanu Luisa , Bardin Solène , Estienne Pierre , Yamamoto Kei , Poupon Cyril
Published: August 2023

The cognitive abilities of parrots are known to be highly developed to the extent of matching those exhibited by crows and primates. However, little is known about the anatomy and structural connectivity of the parrot’s brain. In this work, we propose to use the power of diffusion MRI along with the strong gradients of a unique 17.2T preclinical MRI system to deliver a first white matter atlas of the parrot brain.
Laminar Myeloarchitectonic Mapping using T1- and T2-weighted MRI in Macaque Monkeys
Authors: Autio Joonas , Ose Takayuki , Uematsu Akiko , Ikeda Takuro , Ohno Masahiro , Masamoto Yuki , Kennedy Henry , Van Essen David , Glasser Matthew , Hayashi Takuya
Published: August 2023

Classically, many cortical areas have been defined by their distinctive myeloarchitectonic laminar profiles. Here, we explore laminar transitions by adjusting the structural MRI resolution according to the average width of standard six cortical layers and by attenuating Gibbs’ ringing artefact using T1w/T2w-FLAIR ratio in anesthetized macaque monkeys. We demonstrate that laminar similarity measures, together with the ‘Human Connectome Project’-style midthickness weighted gradients, reveal sharp transitions between several cortical areas in heavily and lightly myelinated brain areas providing mutually supportive information of cortical organization.
Improving myelin water mapping using a new data-driven paradigm for multicomponent analysis of T2 relaxation times
Authors: Omer Noam , Stern Neta , Blumenfeld-Katzir Tamar , Solomon Chen , Galun Meirav , Ben-Eliezer Noam
Published: August 2023

The common approach to myelin mapping relies on multi-T2 component (mcT2) analysis, where the signal from a single voxel is separated into its underlying distribution of T2 values. This approach is highly challenging due to the ill posedness of extracting multiple free parameters from a single voxel data. We present a new data-driven paradigm, where the white matter is first analyzed to identify a finite set of multi-T2 distributions, which are then used to locally analyze the signal in each voxel. Application on white matter tissue produced improved myelin quantifications, without a priory fixing the number of sub-voxel compartments.
Connectivity matrix from a fODF weighted graph: An alternative to probabilistic tractography
Authors: Paquette Michael , Eichner Cornelius , Anwander Alfred
Published: August 2023

We introduce a computationally efficient fODF-weighted graph structure where shortest-paths through white matter compute the probability of connection while naturally limiting the angle of propagation between steps. Connectivity matrices obtained from this structure maintain many properties of probabilistic streamline count connectomes while avoiding the sampling bias of tractography.
Retrospective Pharmacokinetic Quantification of Clinical Abdominal DCE-MRI using Deep Learning
Authors: Wu Chaowei , Wang Nan , Gaddam Srinivas , Han Hui , Pandol Stephen , Christodoulou Anthony , Xie Yibin , Li Debiao
Published: August 2023

Quantitative dynamic contrast-enhanced (DCE) MRI has the potential for early detection, accurate staging, and therapy monitoring of cancers. However, clinical abdominal DCE-MRI has limited temporal resolution and can only provide qualitative or semi-quantitative assessments of tissue vascularity. In this study, we investigated the feasibility of retrospective quantification of multi-phasic abdominal DCE-MRI by improving the temporal resolution via deep learning. Simulated multi-phasic DCE data was generated using 2-sec temporal-resolution Multitasking DCE images. Results show that DCE kinetic parameters retrospectively estimated by deep learning agree with the ground truth, and are capable of differentiating abnormal tissues.
Diffusion MRI data analysis using brain segmentation from anatomical images synthesized from diffusion data by deep learning (DeepAnat)
Authors: Li Ziyu , Fan Qiuyun , Bilgic Berkin , Wang Guangzhi , Polimeni Jonathan , Huang Susie , Tian Qiyuan
Published: August 2023

The analysis of diffusion MRI data requires brain segmentation from separate anatomical images, which may be unavailable or cannot be accurately co-registered to diffusion images due to image distortions in diffusion data. Two state-of-the-art convolutional neural networks, U-Net and generative adversarial network (GAN), are employed to synthesize high-quality, distortion-matched T1w images directly from diffusion data, suitable for generating accurate cerebral cortical surfaces and volumetric segmentation for surface-based analysis of DTI metrics and tractography. The accuracy is quantitatively evaluated, and the systematical comparison shows that GAN-synthesized images are more visually appealing while U-Net-synthesized images achieve higher data consistency and segmentation accuracy.
Gauging tumour pressure and vasculature organization via Magnetic Resonance Elastography to grade brain tumours.
Authors: Annio Giacomo , Bugge Robin , Svensson Siri , Darwish Omar , Seano Giorgio , Biernat Donata , Skogen Karoline , Ramm-Pettersen Jon , Nordsletten David , Vik-Mo Einar , Schregel Katharina , Emblem Kyrre , Sinkus Ralph
Published: August 2023

Distinctive traits of malignant tumours are abnormal angiogenesis and high pressure. Conventional magnetic resonance imaging (MRI) plays a critical role in radiological evaluation of patients and tumour grading, but challenges remain. Pressure and vasculature have a strong impact on the tissue rheology and therefore they can be quantified by Magnetic Resonance Elastography (MRE). We show that MRE allows to quantify non-invasively tumour grade using pressure and tumour vasculature through wave scattering. We believe that MRE could play a central role in tumour grading and diagnosis as well as in therapy planning and dosage, especially in multidrug treatments scenarios.
Quantitative assessment of cerebral oxygen extraction fraction (OEF) in the medial temporal lobe
Authors: Jiang Dengrong , Liu Peiying , Lin Zixuan , Moghekar Abhay , Pillai Jay , Lu Hanzhang
Published: August 2023

The medial temporal lobe (MTL), including the hippocampus, is a key area implicated in many brain diseases, such as Alzheimer’s disease. Since neural activity is tightly coupled to the brain’s oxygen consumption, oxygen-extraction-fraction (OEF) in the MTL (MTL-OEF) may serve as a functional biomarker for this critical region. In this work, we developed a novel non-invasive MRI technique, AS-aTRUPC, to specifically measure the MTL-OEF in the human brain. We found that in healthy volunteers, the MTL-OEF is lower than the cortical OEF. The sensitivity of our technique in detecting changes in MTL-OEF was demonstrated in caffeine challenge experiments.
Time-lapse MRI: in vivo single-cell tracking of patrolling immune cells
Authors: Wilken Enrica , Fredrich Ina , Havlas Asli , Freppon Felix , Masthoff Max , Faber Cornelius
Published: August 2023

We validate that time-lapse MRI is able to resolve and follow single immune cells patrolling the brain vasculature. For that reason, we simulate motion-dependent contrast based on artificial k-space, imitate migrating cells with a rotating phantom system, and track iron-labeled monocytes in vivo with repeated T2* weighted imaging. Furthermore, we show that time-lapse MRI allows to differentiate between different motion patterns non-invasively and with whole-brain coverage, and to study altered motion behavior upon inflammatory stimulus and the onset of immune responses distant from the site of inflammation.
Fat suppression using dynamic parallel
transmission at 7T: application to whole-brain CEST MRI
Authors: Lévy Simon , Herrler Jürgen , Liebert Andrzej , Tkotz Katharina , Fabian Moritz , Eisen Christian , Uder Michael , Grodzki David , Zaiss Moritz , Nagel Armin
Published: August 2023

Fat signal can cause strong artifacts in MR images and/or bias measurements such as in Chemical Exchange Saturation Transfer (CEST) where metabolites peaks are reduced. At 7T, the signal-to-noise ratio and the peak separation are increased but the Specific Absorption Rate (SAR) constraints too, limiting the use of fat suppression pulses. Parallel transmit dynamic pulses for fat saturation (using kT-points) and water-selection (using a Spiral Non-Selective trajectory) were implemented for a whole-brain CEST protocol and optimized to reduce SAR compared to the standard circularly-polarized approach. Results showed high improvements in flip angle homogeneity and fat suppression efficiency, with reduced SAR.
Simultaneous Multislice pTx for Readout-Segmented Diffusion Imaging at 7 T
Authors: Williams Sydney , Dragonu Iulius , Ding Belinda , Liebig Patrick , Porter David
Published: August 2023

This abstract addresses the challenges of 7T diffusion MRI associated with shorter T2/T2* and inhomogeneous B1+ field by implementing simultaneous multislice (SMS) parallel-transmit (pTx) slice-by-slice shimming in a readout-segmented diffusion-weighted sequence, RESOLVE. The benefits of pTx were compared to conventional single-transmit circularly polarized (CP) mode both with and without SMS using a matched high-resolution diffusion protocol in the same healthy volunteer. Slice-by-slice shimming significantly improves signal homogeneity while SMS opens the door for improved scan efficiency. The simplicity of slice-by-slice shimming and sequence integration makes this a practical solution for routine 7T imaging.
White matter changes in myelin, iron and R2* in Alzheimer's disease
Authors: Munroe Lindsay , Ashraf Azhaar , Ekonomou Antigoni , Parkes Harold , Troakes Claire , So Po-Wah
Published: August 2023

Brain iron dyshomeostasis/iron overload has been observed in Alzheimer's disease (AD). Being sensitive to the presence of iron, magnetic resonance imaging (MRI), is often used to study the role of iron in disease. In this study, we determined group differences in iron, R2* and myelin, acquired from the same ex-vivo mid-temporal gyri samples from AD and cognitively normal (CN) subjects. We observed decreased myelin and iron in white matter AD compared to CN. These white matter changes are likely due to a loss of iron-rich oligodendrocytes and demyelination during AD pathogenesis.
Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases
Authors: Monteverdi Anita , Palesi Fulvia , Wheeler-Kingshott Claudia Gandini , D'Angelo Egidio
Published: August 2023

The Virtual Brain (TVB) is a novel modeling platform to simulate whole-brain dynamics starting from individual structural and functional connectivity. In this work, we performed TVB simulations in neurodegenerative diseases (i.e. Alzheimer’s disease, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis) providing a unique description of the excitatory/inhibitory balance at single-subject level. TVB-derived biophysical parameters were different among clinical phenotypes, presented an association with neuropsychological domains and improved discriminative power between clinical conditions. Overall, this work suggests a novel TVB-based approach for future personalized diagnosis and therapy in neurodegenerative diseases, providing a subject-specific description of excitation/inhibition profiles.
MR Fingerprinting Features in Patients with MRI-negative Pharmacoresistant Focal Epilepsy
Authors: Su Ting-Yu , Tang Yingying , Choi Joon Yul , Hu Siyuan , Sakaie Ken , Murakami Hiroatsu , Jones Stephen , Najm Imad , Ma Dan , Wang Zhong
Published: August 2023

MR fingerprinting (MRF) is an advanced quantitative MR technique that allows for efficient acquisition of multiparametric tissue maps. We applied high-resolution 3D MRF to examine T1 and T2 changes in 30 pharmacoresistant focal epilepsy patients with negative MRI, using a voxel-wise group-analysis approach. Forty age-and-gender-matched healthy controls were also included for comparison. Significant T1 increase was detected in the temporal pole, mesial temporal, and superior temporal regions, as well as the orbitofrontal cortex, ipsilateral to the side of the epilepsy. These MRF-detected subtle tissue property changes suggest potential structural damage in the ipsilateral limbic network in MRI-negative pharmacoresistant focal epilepsy.
Detection of kidney cysts using a Convolutional Neural Network on 11,000 MRI data sets from the German National Cohort
Authors: Reichardt Wilfried , Lipovsek Jan , Reisert Marco , Horbach Harald , Schlett Christopher , Bamberg Fabian , Sekula Peggy , Köttgen Anna , Kellner Elias , Büchert Martin
Published: August 2023

We implemented a processing pipeline for kidney cyst segmentation using a hierarchical patch-based stack of U-nets and applied it to abdominal MRI images of the German National Cohort (GNC) study. The training data set included 300 cases, and the final net was applied to the dataset of 11,207 MRIs. Kidney cysts could be segmented with 98% sensitivity above a lower detection threshold of 1ml. The relation of first parameters based on the cyst segmentation with age and sex are presented. This result presents an optimal starting point to identify more advanced bimarkaers and their correlates, especially with kidney function parameters.
A Travelling Kidney study using a harmonised multiparametric renal MRI protocol
Authors: Buchanan Charlotte , Li Hao , Morris David , Daniel Alexander , Sousa João , Sourbron Steven , Thomas David , Priest Andrew , Francis Susan
Published: August 2023

Standardisation and multicentre evaluation of renal MRI measures is crucial for clinical translation. Here we present results of a Travelling kidney study on GE, Philips and Siemens using a harmonised multiparametric renal MRI protocol, and show results of B0 and B1 mapping, T1, and T2* mapping. The mode B0 within the kidneys did not vary across vendors, however the FWHM was narrower for Vendor 1. No significant differences were seen in MOLLI T1 values of the renal cortex and medulla after this data was corrected for the offset in T1 seen in the NIST phantom.
Noninvasive assessment of unilateral ureter obstruction using dynamic contrast-enhanced MR-CEST urography
Authors: Stabinska Julia , Singh Aruna , Sedaghat Farzad , Kates Max , Li Yuguo , McMahon Michael
Published: August 2023

The extent of recovery of renal function in patients with urinary tract obstructions depends on early diagnosis and prompt intervention. In the present study, we employed dynamic contrast-enhanced MR-CEST urography to noninvasively assess kidney function in mice with unilateral ureter obstruction (UUO) at two time points (day 1 and 2) after UUO surgery. Using a dynamic CEST MRI protocol, we were able to detect a slight increase in pH values (pH 6.74 ± 0.08 and 6.62 ± 0.04 in the UUO and contralateral kidney, respectively) as well as iopamidol retention in the UUO kidney.
MRI-based cortical shear strain measurement in healthy volunteers: repeatability study and its implications for sub-concussive trauma
Authors: Shan Xiang , Murphy Matthew , Sui Yi , Zheng Keni , Manduca Armando , Ehman Richard , Huston John , Yin Ziying
Published: August 2023

Repetitive head impact (RHI) increases the risk of concussion, probably due to alterations of the brain-skull mechanical coupling. It would be of clinical value to have a reliable biomarker capable of assessing the status of the brain-skull coupling condition. MRE-based normalized octahedral shear strain (NOSS) has been proposed as a method for such brain membrane system evaluation. To verify the test-retest reliability of NOSS, datasets from 14 healthy volunteers were acquired with different vibration directions and within- and between-day scanning. This study demonstrated the excellent repeatability of the proposed method, showing the NOSS distributions were robust to loading variations.
Plug-and-play Advanced Magnetic Resonance Spectroscopy
Authors: Deelchand Dinesh , Henry Pierre-Gilles , Joers James , Auerbach Edward , Park Young Woo , Kara Firat , Ratai Eva , Kantarci Kejal , Öz Gülin
Published: August 2023

We developed an automated advanced-single-voxel MRS acquisition protocol at 3T to facilitate acquisition of high-quality spectroscopic data without local MRS expertise. Voxel-based B0 and B1 calibrations were incorporated into the consensus-recommended semi-LASER sequence and combined with automated voxel prescription. Automated B0 and B1 calibrations saved ~4.5min per voxel vs. manual calibrations. All spectra acquired with the automated protocol by rotating MR technologists were usable, while only 83% of those collected with the manual protocol were usable and spectral quality was more variable. The protocol allows automated acquisition of high-quality MRS data with high success rate on a clinical 3T platform.
Smaller MRgFUS lesions that overlap patient-fit normative VIM—Precentral tracts improve Quality-of-Life outcomes in Essential Tremor
Authors: Chodakiewitz Yosef , Purger David , Wang Alan Rehn , Barbosa Daniel , Tov Lior Lev , Datta Anjali , Bitton Rachelle , McNab Jennifer , Buch Vivek , Ghanouni Pejman
Published: August 2023

While Focused-Ultrasound thalamotomy has proven effective at reducing tremor, traditional targeting methods can be suboptimal at balancing primary tremor-reduction outcomes against undesired side-effects. The traditional “canonical” technique involves an indirect method which applies a non-individualized stereotactic coordinate atlas towards identifying the presumed approximate location of VIM thalamus, the ablation target; the canonical lesion is empirically grown in size based-on dynamic intraoperative feedback from an awake patient, until the surgeon judges that an appropriate balance of tremor-reduction and side-effect risk has been achieved. We propose optimized methods to define and monitor the ideal anatomical ablation for optimized tremor-reduction/Quality-of-Life balancing.
Ablative Efficacy of MR-guided Focused Ultrasound in Pediatric Patients: A Retrospective Study
Authors: Syed Ali , Ackroyd Suzanna , Duex Daniel , Young Victoria , Vasanawala Shreyas , Ghanouni Pejman , Thakor Avnesh
Published: August 2023

Performance of MRgFUS in pediatric patients has not been well characterized. Retrospective review of 21 MRgFUS treatments in 9 patients below the age of 18 shows significant reduction in viable tumor volume in a variety of indications including desmoid tumors, vascular malformations, and osteoid osteoma. MRgFUS is an effective ablative therapy in children that warrants further investigation.
EigenMRI: Data-driven shallow-learning reconstruction of undersampled MRI data
Authors: Otazo Ricardo , Jafari Ramin , Zarepisheh Masoud
Published: August 2023

Machine learning, and specifically deep learning, has recently demonstrated high-performance reconstruction of undersampled k-space data. However, training of deep learning reconstruction methods does not take advantage of the thousands of previously acquired images that are stored in clinical databases. Inspired by the eigenfaces method from computer vision, where an image model derived from thousands of pre-existing images is used to identify new faces, this work presents an image reconstruction method named EigenMRI that learns a data-driven regularization approach using thousands of images extracted from a clinical database to reconstruct 3D brain images with 2D acceleration
Separating Tissue Components in Ex-vivo Mouse Brain with Joint Diffusion-Relaxation MRI and InSpect
Authors: Slator Paddy , Shemesh Noam , Ianus Andrada
Published: August 2023

We identify and map intra-voxel tissue components in the mouse brain by applying InSpect, a data-driven approach for quantitative MRI analysis, to high resolution ex-vivo T2-diffusion MRI. Our approach reveals distinct tissue microenvironments with minimal model assumptions, revealing features that cannot be seen in diffusion kurtosis maps.
Phase-based 3D diffusion mapping using RF phase-modulated gradient echo imaging
Authors: Tamada Daiki , Reeder Scott
Published: August 2023

A novel 3D diffusion mapping method using RF phase-modulated gradient echo imaging was developed by encoding diffusion weighting into signal phase. We developed closed form signal equations based on configuration theory. A four-pass acquisition and lookup table-based reconstruction is performed to estimate diffusion and T2 simultaneously. Simulation results revealed excellent agreement of signal magnitude and phase between Bloch equation and the developed equations. Phantom and in vivo studies demonstrated the feasibility of the proposed approach. These results demonstrate that the proposed method can enable qualitative and quantitative diffusion imaging with smaller gradient amplitude compared to the conventional diffusion-weighted imaging.
DEXSY can measure water exchange linked to cellular homeostasis and active states in central nervous system tissue
Authors: Williamson Nathan , Ravin Rea , Cai Teddy , Falgairolle Melanie , O'Donovan Michael , Basser Peter
Published: August 2023

We propose a new functional MR method based on steady-state transmembrane water exchange rate measurements with diffusion exchange spectroscopy (DEXSY). Rapid DEXSY methods are implemented on a low-field MR system with a strong static gradient to test for an active component of water exchange in ex vivo neonatal mouse spinal cords. Temperature-dependent Arrhenius activation energies for water exchange are significantly greater in viable “live” samples than in the same samples after fixation, suggesting a connection to ATP-driven enzymatic processes. Moreover, exchange rates in live samples significantly decrease after blocking Na+/K+-ATPase pump activity, revealing an active component of water exchange.
qModeL: A Model-based Deep Learning Framework for the Recovery of Diffusion-based Microstructural Parameters from Healthy and Lesion Data
Authors: Mani Merry , Yang Baolian , Bathla Girish , Magnotta Vincent , Jacob Mathews
Published: August 2023

A flexible deep learning based framework is presented for the recovery of microstructural parameter maps from advanced diffusion models. The method is shown to work well across field strengths, and healthy and diseased tissue without needing separate training of the DL network. The DL framework is embedded in a model-based reconstruction, which enables the framework to handle variations in data acquisition settings such as various acceleration factors and noise levels, without having to change the network. k-q accelerations in the range of 12-18 fold is demonstrated for single and multi-shell diffusion data.
Automated Superstructure-based Segmentation of Ascending Arousal Network Nuclei for Diffusion Tractography
Authors: Olchanyi Mark , Edlow Brian , Brown Emery , Iglesias Juan
Published: August 2023

Traumatic brain injury that causes sheering of white matter pathways that connect ascending arousal network (AAN) nuclei in the rostral brainstem to cortical and subcortical targets leads to disorders of consciousness. Connectivity studies involving these nuclei are limited due to their small size, vague boundaries, and lack of reliable annotations. We present a method to automatically segment AAN nuclei from diffusion MR volumes using image registration constrained by brainstem structures with definable contrast boundaries. We test AAN segmentation robustness with probabilistic tractography and provide new insights into connectivity between AAN nuclei and the thalamus.
First templates of human brain kurtosis sources: relevance of microscopic kurtosis from Correlation Tensor MRI at 3 Tesla
Authors: Novello Lisa , Henriques Rafael Neto , Ianuş Andrada , Feiweier Thorsten , Shemesh Noam , Jovicich Jorge
Published: August 2023

The Correlation Tensor MRI (CTI) framework has been recently formulated to disentangle anisotropic, isotropic, and microscopic kurtosis sources without a priori assumptions from Double-Diffusion-Encoding (DDE) data. In this work, group average maps (templates) are presented for the first time, thereby mapping the contrasts for anisotropic and isotropic kurtosis, and non-vanishing positive microscopic kurtosis in grey and white matter. The relative weight of the microscopic kurtosis within the total kurtosis, and the bias associated with neglecting this component under the Multiple Gaussian Component assumption are investigated and suggest that this component significantly contributes to the total diffusional kurtosis.
Accounting for covariance in studies of multimodal microstructure - a model for multivariate quantification in health and disease
Authors: Tremblay Stefanie , Pirhadi Amir , Alasmar Zaki , Carbonell Felix , Iturria-Medina Yasser , Gauthier Claudine , Steele Christopher
Published: August 2023

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, approaches commonly used are biased by the covariance between variables. Here, we propose computing the voxel-wise Mahalanobis distance (MhD), as a measure of deviation from normality that accounts for covariance between metrics. We show that this measure can be linked to behavior and to potential physiological underpinnings by extracting metrics contributing most to the MhD. Integrative multivariate models are crucial to expand our understanding of the multiple factors underlying disease development and progression.
Negative BOLD closely follows neuronal suppression in superior colliculus
Authors: Gil Rita , Valente Mafalda , Renart Alfonso , Shemesh Noam
Published: August 2023

The underlying sources of negative BOLD responses (NBRs) are still debated. Here, we show rat superior colliculus (SC) NBRs associated with visual stimulation at short inter-stimulus intervals (ISIs) along with decreases in power of local field potentials and multi-unit activity signals measured in this region. This hints to neuronal suppression, possibly due to impossibility of complete excitability recovery upon short ISIs, associated with NBRs. Moreover, both NBRs and electrophysiological power time profiles reveal one peak after stimulus started and another when it ceased, highlighting the SC nature of detecting "brightness changes" when individual flashes are no longer perceivable.
The relationship between cytochrome oxidase redox state and CBF is not constant: A multimodal 9.4T NIRS-MRI study on animal model
Authors: Hashem Mada , Wu Ying , Dunn Jeff
Published: August 2023

Non-invasive imaging of cerebral oxygen delivery and consumption is crucial to understand neurovascular coupling and oxidative metabolism. We combine NIRS and MRI to determine the correlation between mitochondrial status and perfusion in the cortex of mice, when exposed to hypercapnia or varying oxygen levels. There was no correlation between perfusion and redox state under hypercapnia, while a strong correlation was observed under varying oxygen levels. This proves that the relationship between perfusion and mitochondrial redox can be quantified non-invasively in vivo. Such simultaneous measurements, in neurovascular diseases, will help determine whether there is abnormal neurovascular coupling or abnormal oxidative metabolism.
Quantitative Assessment of Whole Knee Joint Using a New Phase Modulated Ultrashort Echo Time Adiabatic T1rho (PM-UTE-AdiabT1rho) Sequence
Authors: Ma Yajun , Carl Michael , Bao Alan , Jang Hyungseok , Jerban Saeed , Lombardi Alecio , Chung Christine , Chang Eric , Du Jiang
Published: August 2023

Adiabatic T1rho (AdiabT1rho) is much less sensitive to the magic angle effect compared to the regular continuous wave T1rho (CW-T1rho). In this study we developed a novel phase modulated ultrashort echo time adiabatic T1rho (PM-UTE-AdiabT1rho) sequence for quantitative assessment of both short and long T2 tissue components in the knee joint, including cartilage, meniscus, ligaments, tendons, and muscle, on a clinical 3T scanner. Our results showed excellent single exponential fitting for all the major tissue components in both ex vivo and in vivo normal knee joints.
Large-scale Analysis of Meniscus Morphology as Risk Factor to Incidence of Osteoarthritis
Authors: Gao Kenneth , Xie Emily , Chen Vincent , Caliva Francesco , Iriondo Claudia , Souza Richard , Majumdar Sharmila , Pedoia Valentina
Published: August 2023

While meniscal morphology is central to its capacity for weight distribution and stabilization in the knee joint, the relationship between the geometric shape of the meniscus and osteoarthritis is relatively unexplored. With 4,791 subjects from the Osteoarthritis Initiative, this study utilizes an automatic statistical shape modeling technique to identify variations in meniscus morphology as precursors to future development of osteoarthritis. Seven of the 20 shape features extracted were found to be significantly associated with osteoarthritis incidence. These features were further characterized with local changes in cartilage thickness across 8 years.
Accelerated 2D Cardiac MRF Using a Self-Supervised Deep Image Prior Reconstruction
Authors: Hamilton Jesse
Published: August 2023

A physics-based deep learning reconstruction is proposed for cardiac MRF that is based on the deep image prior framework, which employs self-supervised training and does not require additional in vivo training data. Calculation of the forward model is accelerated using a neural network to rapidly output MRF signal timecourses (in place of a Bloch simulation) and low-rank modeling to reduce the number of NUFFT operations. The proposed reconstruction enables cardiac T1/T2 mapping during a shortened 5-heartbeat breathhold and 150ms acquisition window.
ResoNet: Physics Informed Deep Learning based Off-Resonance Correction Trained on Synthetic Data
Authors: De Goyeneche Alfredo , Ramachandran Shreya , Wang Ke , Karasan Ekin , Yu Stella , Lustig Michael
Published: August 2023

We propose a physics-inspired, unrolled-deep-learning framework for off-resonance correction. Our forward model includes coil sensitivities, multi-frequency bins, and non-uniform Fourier transforms hence compatible with fat/water imaging and parallel imaging acceleration. The network, which includes data-consistency terms and CNN modules serving as proximal operators, is trained end-to-end using only synthetic random field maps, coil sensitivities, and noise-like images with statistics (smoothness) mimicking natural signals. Our aim is to train the network to reverse off-resonance irrespective of the type of imaging, and hence generalizable to any anatomy and contrast without retraining. We demonstrate initial results in simulations, phantom, and in-vivo data.
Registration and quantification net (RQnet) for IVIM-DKI analysis
Authors: Lee Wonil , Choi Giyong , Lee Jongyeon , Park HyunWook
Published: August 2023

Accurate alignment of multiple diffusion-weighted images must be preceded to predict accurate diffusion parameters. A number of registration approaches have been studied (1,2). However, most of them minimize the dissimilarity between diffusion weighted image and a reference, which can cause errors because the characteristics of the images are different. In order to accurately investigate diffusion, perfusion, and kurtosis parameters using hybrid IVIM-DKI model, a deep learning network is proposed as an end-to-end fashion. This method is entirely unsupervised learning, which does not require reference image for registration and the labeled IVIM-DKI parameters for registration and quantification.
Fast multi-compartment microstructure fingerprinting using deep neural networks
Authors: Dessain Quentin , Delinte Nicolas , Macq Benoit , Rensonnet Gaëtan
Published: August 2023

We estimate microstructural features of crossing fascicles in the white matter by using a fast multi-compartment fingerprinting, an extension of MR fingerprinting to diffusion MRI. The acceleration uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation. The accuracy of the results and the speedup factors obtained on in vivo brain data suggest the potential of our method for a fast quantitative estimation of microstructural features in complex white matter configurations.
Fast high-resolution Electric Properties Mapping using three-dimensional MR Fingerprinting based water fraction estimation (MRF-EPT)
Authors: Cencini Matteo , Lancione Marta , Biagi Laura , Pasquariello Rossella , Peretti Luca , Pirkl Carolin , Schulte Rolf , Buonincontri Guido , Arduino Alessandro , Zilberti Luca , Tosetti Michela
Published: August 2023

MR-EPT aims to non-invasively assess the electrical properties (EP) of the tissues. However, conventional methods based on second-order derivatives of the transmit field are hampered by high noise amplification and boundary errors. Here, we propose to rely on the correlation between electrical properties and water content, which in turn is correlated to tissue T1, in order to extract EP maps from MR Fingerprinting based T1 estimation. We demonstrate whole-brain high-resolution EP mapping of healthy human volunteers and consistent results with both theoretical predictions and standard MR-EPT estimations.
Methodological advances in serial hyperpolarized carbon-13 (HP-13C) metabolic imaging of patients with glioma
Authors: Autry Adam , Vaziri Sana , Gordon Jeremy , LaFontaine Marisa , Chen Hsin-Yu , Kim Yaewon , Villanueva-Meyer Javier , Chang Susan , Clarke Jennifer , Oberheim-Bush Nancy , Xu Duan , Lupo Janine , Larson Peder , Vigneron Daniel , Li Yan
Published: August 2023

This study aimed to improve hyperpolarized carbon-13 (HP-13C) metabolic imaging methodologies for serial monitoring of patients with malignant brain tumors. Several analyses were pursued among 29 patients diagnosed with gliomas and 7 volunteers. Implementation of atlas-based prescription of echo-planar imaging (EPI) demonstrated consistent volumetric coverage (dice:0.967-0.978) and improved hemispheric symmetry in modeled kinetics(p=0.006), which helped highlight tumor. A 2s versus 5s acquisition delay post injection of tracer increased the capture of [1-13C]pyruvate inflow dynamics(p=0.04), while reducing simulated kinetic modeling error. Post-acquisition FID-CSI spectra demonstrated improved referencing of [1-13C]pyruvate f0 using water versus a coil-embedded urea phantom(p=0.00003), thereby maximizing EPI excitation.