bluuebunny/crossref_metadata_2025 huggingface dataset:
A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain
Authors: Nishimaki Kei , Onda Kengo , Ikuta Kumpei , Chotiyanonta Jill , Uchida Yuto , Mori Susumu , Iyatomi Hitoshi , Oishi Kenichi
Published: November 2024

Motivation: Whole-brain MRI parcellation serves as a feature extraction technique, allowing for the condensation of over a million pixels of information into a few hundred neuroanatomically defined elements. Goal(s): The multi-atlas label-fusion (MALF) method is known for accurate parcellation but typically necessitates several hours to process a single image. Our goal was to develop a faster parcellation tool with an accuracy comparable to that of MALF. Approach: We introduce open-source multiple anatomical parcellation T1 (OpenMAP-T1), based on deep learning and multi-processing. Results: The OpenMAP achieves an equivalent parcellation performance to MALF and is 40 times faster. Impact: OpenMAP significantly accelerates processing speed, allowing for large-scale data analysis using volumetric information derived from detailed parcellation of the whole brain, including both gray and white matter regions.
Capturing Central uiSNR at Ultrahigh Field: Number and Size of the Receive Elements Matter
Authors: Sadeghi-Tarakameh Alireza , Grant Andrea , Giannakopoulos Ilias , Waks Matt , Lagore Russell , DelaBarre Lance , Auerbach Edward , Lattanzi Riccardo , Adriany Gregor , Ugurbil Kamil , Eryaman Yigitcan
Published: November 2024

Motivation: In addition to peripheral SNR gain, the promise of a quadratic increase of SNR at the center of a human head with field strength draws significant attention to many ultrahigh field head MRI applications. Goal(s): Assess the performance of state-of-the-art RF receive array coils in capturing the theoretical upper limit of central head SNR across different field strengths. Approach: We experimentally investigated the impact of combining transceiver elements with highly-dense conventional loop arrays to capture the ultimate intrinsic SNR in head applications. Results: We demonstrated that achieving central SNR gains at UHF requires an increased number of receive elements and larger transceiver elements. Impact: Capability of conventional loop technology to capture the SNR's upper-limit in human head is investigated across different field strengths, which can pave the way for the RF technology developments focused on capturing the SNR gain in ultrahigh field head applications.
Combining transceiver loops with a conventional receive array increases central SNR in brain imaging at 7T
Authors: Ding Belinda , Yan Jiaruo , Woodward Rosemary , Allwood-Spiers Sarah , Williams Sydney , Keith Graeme , McElhinney Paul , Fullerton Natasha , Porter David , Gunamony Shajan
Published: November 2024

Motivation: Previous studies have reported central SNR improvements at 7T with dipole transceivers, but not with loops-based arrays. Goal(s): Assess the performance of an 8TxRx56Rx loop-based transceiver array against three conventional 8Tx32/64Rx arrays. Approach: SNR and g-factor maps were acquired from phantom and healthy volunteers for four head coils (1Tx32Rx, 8Tx32Rx, 8Tx64Rx, 8TxRx56Rx) at 7TResults: The modified 8TxRx56Rx coil showed a 12.6% increase in central SNR for in vivo scans. The peripheral SNR and g-factor maps remain comparable to their 8Tx64Rx counterpart, and both 64Rx coils performed significantly better than 32Rx coils at high acceleration factors. Impact: A 56-channel receive 8-channel loop-based transceiver array can improve central image SNR at 7T without compromising g-factor compared to a conventional 64-channel receive 8-channel transmit coil.
A Novel PET/MRI Hybrid Probe enables Non-invasive Imaging of Perfusion and Excretion in vivo
Authors: Chiaffarelli Remy , Kretschmer Jan , Cotton Jonathan , Polasek Miloslav , Martins André
Published: November 2024

Motivation: Our work is driven by the conviction that the integration of PET and MRI in molecular imaging has the potential to revolutionize diagnostic precision and patient care. Goal(s): Enhance precision molecular imaging through the development of a unique hybrid PET/MRI perfusion probe, [18F][Gd(FL1­­)]. Approach: Develop a PET/MRI probe, [18F][Gd(FL1­­)], that is exceptionally stable, rapidly synthesized, efficiently radiolabeled, and allows simultaneous PET/MR imaging, focusing on tissue perfusion and renal filtration in preclinical models. Results: After achieving high-yield radiosynthesis with a unique approach, our dual-modality probe showed consistent signals in both imaging modalities and was reliably quantified thanks to the combination of PET and MRI. Impact: The first-of-its-kind hybrid PET/MR probe [18F][Gd(FL1­­)] can be readily and efficiently radio-synthetized and allows simultaneous PET/MR quantitative measurement of perfusion and excretion. This probe opens the path for quantifying molecular imaging probes in research and clinical practice.
Distinct virtual histology of gray matter atrophy in neuroinflammatory diseases
Authors: Sun Jun , Lizhu Yuerong , Guo Min , Xu Siyao , Zhang Xianchang , Zhuo Zhizheng , Liu Yaou
Published: November 2024

Motivation: Gray matter (GM) atrophied early in multiple sclerosis (MS), anti-aquaporin-4 antibody-positive [AQP4+] / -negative [AQP4-] neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Their neurobiological underpinnings have not been clarified. Goal(s): The purpose was to explore their GM atrophy-associated histology using a multicenter cohort. Approach: 324 MS, 197 AQP4+ NMOSD, 75 AQP4- NMOSD, 47 MOGAD, and 2,169 healthy controls (HCs) were examined by virtual histology method. Results: The unique virtual histology was glial cells for MS, astrocytes for AQP4+ NMOSD and oligodendrocytes for MOGAD. The neuronal and endothelial cells were shared potential targets. Impact: It might help to optimize therapy.
COVID-19-related anosmia is driven by inflammation and myelin alterations as shown by Voxel Based Analysis
Authors: Lupi Eleonora , Gaviraghi Marta , Grosso Elena , Monteverdi Anita , Battiston Marco , Grussu Francesco , Kanber Baris , Carrasco Ferran , Makaronidis Janine , Samson Rebecca , Yiannakas Marios , D’Angelo Egidio , Palesi Fulvia , Wheeler-Kingshott Claudia Gandini
Published: November 2024

Motivation: Voxel Based Analysis (VBA) can be a powerful tool to detect localized alterations. Goal(s): Here VBA was used to understand the underpinnings of COVID-19-related anosmia. Approach: Quantitative magnetization transfer and diffusion-weighted imaging derived maps were used to detect pathological changes affecting white matter structures. Results: Microstructural differences were detected between healthy controls and subjects experiencing anosmia or those who recovered from it. Results highlighted the presence of widespread inflammation in persistent anosmia subjects, with myelin damage and possible repair in those who recovered. Myelin alterations involved the olfactory circuit, as well as other brain regions, providing insights into possible mechanisms of COVID-19-related anosmia. Impact: Voxel Based Analysis is a powerful tool to highlight local tissue disruption linked to neuroinflammatory processes. Here VBA provided an insight into microstructure and myelin changes associated to COVID-19-related persistent or recovered anosmia symptoms.
Mapping spinal cord vascular reactivity and vascular territories using fMRI in highly sampled individuals
Authors: Hemmerling Kimberly , Bright Molly
Published: November 2024

Motivation: Impaired vascular function in the spinal cord contributes to numerous neurological pathologies and it is important to be able to image those changes in individuals. Goal(s): Map spinal cord vascular reactivity (SCVR) amplitude and hemodynamic delay at the individual level in two highly sampled adults. Approach: Participants performed 18 spinal cord fMRI runs of a breath-holding task. SCVR amplitude was mapped using an end-tidal CO2 regressor and hemodynamic delay was mapped by temporally shifting that regressor. Results: Individual SCVR amplitude and hemodynamic delay maps reveal an earlier ventral response and later dorsal response, aligning with vascular territories of the cervical spinal cord. Impact: Individual maps of spinal cord vascular reactivity (SCVR) hemodynamic delay align with arterial territories. These are the first non-invasive maps of vascular territories in the human spinal cord. Future work will map altered SCVR in patients with neurological conditions.
Self-navigated Subspace Reconstruction for Real-time MRI Speech Tracking
Authors: Cao Peng , Jiang Wenting , Chen Changhe , Wang Yiang , Havenhill Jonathan
Published: November 2024

Motivation: Real-time MRI offers a continuous and dynamic view of the object being imaged. Researchers have applied real-time MRI to speech tracking, which allows for the visualization of the vocal tract during speech production. Goal(s): In this study, we propose applying self-navigated subspace reconstruction to real-time MRI for speech tracking. Approach: During reconstruction, 1000 frames were compressed to a few principal components, and iterative low-rank approximation was performed on compressed k-space, greatly reducing computation costs. Results: The proposed method allows for the joint reconstruction of all time frames and provides the dynamic motion pattern of the vocal tract at a high frame rate. Impact: Our study presented a subspace reconstruction technique that does not require a navigator echo, which can be used for real-time MRI, particularly in speech tracking applications.
Accelerated CEST Imaging with Deep Learning Priors and Synthetic Brain Tumor Datasets
Authors: Wang Yuyan , Xu Jianping , Dai Zhechuan , Hsu Yi-Cheng , Zhang Yi
Published: November 2024

Motivation: The clinical application of CEST MRI is constrained by its relatively long scan time. Goal(s): We aim to develop a deep learning reconstruction method for accelerating CEST imaging in the absence of true experimental data. Approach: Here, we propose a model-based deep learning framework, in conjunction with the Channel-wise Attention mechanism and Total variation regularization, dubbed as MoDL-CAT. Moreover, we propose a new workflow to synthesize CEST data from the BraTS and fastMRI repositories. Results: We demonstrate that the BraTS-CEST dataset can improve the performance of all deep learning networks tested, and the MoDL-CAT method achieves superior reconstruction quality to the state-of-the-art methods. Impact: The proposed deep learning framework with channel-wise attention may offer a better prior for reconstruction. And our novel workflow to synthesize high-quality brain tumor CEST datasets might help researchers with limited data to explore various methods for accelerating CEST imaging.
Rapid and simplified post-processing for B0 and B1 mapping with WASABI-RADISH in the application of CEST at 7T
Authors: Quach Mara , Strik Myrte , Glarin Rebecca , Moffat Bradford , Wright David , Johnston Leigh
Published: November 2024

Motivation: WASABI provides high fidelity B0 and B1 maps necessary for CEST correction yet suffers from prolonged post-processing incompatible with clinical use. Goal(s): Our goal was to design an optimisation-free method to expedite map estimations. Approach: A direct relationship was derived between B0 and B1 and information in WASABI Z-spectra. Results: The proposed approach accelerated post-processing by a factor of 80, with improved estimation in brain regions that are noisy and/or have unpredictable initial magnetisation. Impact: Improvement in speed and accuracy provided by RADISH has the ability to make WASABI, and quantitative CEST at ultra-high-field in general, more reliable and clinically feasible.
Fast, motion-robust CEST imaging with inherent B0 correction using rosette trajectories
Authors: Mahmud Sultan Zaman , Singh Munendra , van Zijl Peter , Heo Hye-Young
Published: November 2024

Motivation: CEST-MRI typically requires a long scan time and an additional B0 map scan for inhomogeneity correction. Goal(s): To implement a rosette readout for fast CEST imaging with improved robustness to bulk-motion and inherent correction of B0 inhomogeneity. Approach: Rosette trajectories which sample more densely near the k-space center provided faster and more motion-robust CEST imaging than Cartesian trajectories. B0 inhomogeneities were estimated using the phase difference between two images from two halves of the rosette lobe and corrected subsequently. Results: Rosette trajectories significantly reduced the CEST imaging time. No extra scans were needed for B0 correction due to the inherent B0 mapping capability. Impact: Fast, motion-robust, and inherent B0-corrected CEST imaging with rosette trajectories can help improve patient comfort and compliance. The work is expected to significantly accelerate the translation of CEST-MRI into a robust clinically viable approach.
Orientation Dependence of Magnetization Transfer and “Inhomogeneous Magnetization Transfer” in Spinal Cord
Authors: Wallstein Niklas , Pampel André , Jäger Carsten , Müller Roland , Stieler Jens , Martin Sven , Morawski Markus , Möller Harald
Published: November 2024

Motivation: Magnetization transfer (MT) and inhomogeneous MT (ihMT) are assumed to report on myelin content. Recently, anisotropy of MT and ihMT have been demonstrated in model systems and in white matter. Goal(s): Our goal was to quantify orientation effects in (ih)MT, as they closely relate to the microstructure and serve to confirm assumptions about the relaxation mechanism. Approach: Comprehensive (ih)MT investigations were performed in fixed spinal cord with variation of the fiber-to-field angle (θFB). Results: Unambiguous orientation dependence was observed for (ih)MT. The variation depends strongly on the offset frequency, which was quantitatively predicted in simulations of the BSBM with a realistic fiber model. Impact: This study investigates the orientation dependency of magnetization transfer and related model parameters. Some subtle orientation effects are observed for the first time and are quantitatively explained by using a reasonable model for the RF saturation lineshape.
Cortico-cerebellar effective connectivity of visual attention areas is altered in developmental dyslexia compared to typical readers
Authors: Korkmaz Gökçe , Lorenzi Roberta , Mascheretti Sara , Peruzzo Denis , Arrigoni Filippo , D’Angelo Egidio , Palesi Fulvia , Wheeler-Kingshott Claudia Gandini
Published: November 2024

Motivation: Alterations in functional connectivity between regions involved in reading and visuo-attention networks have been associated with developmental dyslexia. However, the causal relationship between regional activity remains unknown. Goal(s): We aimed to investigate the causal relationship between regions of the visuo-attention network in developmental dyslexia and typical readers during a coherent motion detection task. Approach: Using Dynamic Causal Modeling, the causal connectivity between regions in the cortex and cerebellum was estimated to understand aberrant network function. Results: Children with developmental dyslexia showed remarkable differences in patterns of excitatory and inhibitory communication between cerebellum and visuo-attention regions compared to typical reader children. Impact: Dynamic Causal Modeling can evaluate cortico-cerebellar causal relationship (i.e., effective connectivity) in healthy subjects and in neurodevelopmental conditions such as developmental dyslexia. New evidence points toward a critical role of the cerebellum in reading impairment, with potential consequences for intervention.
Diffusion tractography and functional connectivity profiles of the dorsal raphe nucleus in Parkinson’s Disease with sleep symptoms
Authors: Hsu Wei-Jing , Bhasin Srijan , Seow Poh Choo , Welton Thomas , Hartono Septian , Chen Celeste , Lee Weiling , Wong Wilson , Tan Louis , Tan Eng King , Chan Ling Ling
Published: November 2024

Motivation: The role of the dorsal raphe nucleus (DRN) in sleep related pathologies in Parkinson’s disease (PD) remains under investigated. Goal(s): To characterize functional connectivity patterns and correlational structural tractography changes specific to the DRN in PD and sleep-related symptoms. Approach: Resting-state functional MRI and diffusion spectrum MRI metrics were compared across PD patients and healthy controls experiencing severe sleep disturbances. Results: We found changes in functional connectivity profiles of the DRN and findings suggesting axonal damage that showed stronger correlation with sleep symptoms in healthy controls compared to patients, implying potentially distinct pathophysiological mechanisms in symptom development. Impact: Clarifying involvement of the dorsal raphe nucleus and serotonergic pathways in the pathogenesis of sleep symptoms in Parkinson’s Disease may contribute to development of novel therapies targeted toward specific dysfunctional pathways involved in this quality-of-life disturbing condition.
Instant modulatory effects of transcutaneous vagus nerve stimulation in patients with Parkinson disease.
Authors: Fu Chengwei , Zhang Yue , Deng Kan , Zhao Xiance , Liu Bo
Published: November 2024

Motivation: A growing body of evidence suggests that taVNS may improve the motor function of PD patients whereas little is known about the neuropathologic mechanism. Goal(s): To explore the potential mechanism of taVNS in treating PD by rs-fMRI. Approach: Fifty patients with PD underwent three times fMRI scanning. And the difference in ALFF among the baeline state,real taVNS and sham taVNS state were investigated. Results: Compared with baseline and sham taVNS state, the ALFF value showed a significant decrease in 5 clusters. Pearson correlation analysis indicated ALFF of SPL_r in real taVNS condition was negatively correlated with the total UPDRS score, UPDRS-Ⅲscore and PDQ. Impact: The taVNS may produce treatment effects by modulating the abnormal ALFF of sensorimotor network,salience network and visual network. This may shed light on the neural mechanisms underlying taVNS treatment of PD.
Integration of myelin-sensitive biophysical features in virtual brain models: towards healthy and pathological Brain Digital Twins
Authors: Lupi Eleonora , Monteverdi Anita , Gaviraghi Marta , Grosso Elena , Marinelli Alessandro , Battiston Marco , Grussu Francesco , Kanber Baris , Carrasco Ferran , Ricciardi Antonio , Rolandi Nicolò , Samson Rebecca , Shatila Madiha , Wingrove Jed , Yiannakas Marios , Casellato Claudia , D’Angelo Egidio , Wheeler-Kingshott Claudia Gandini , Palesi Fulvia
Published: November 2024

Motivation: The Virtual Brain (TVB) is a neuroinformatic platform used to perform brain dynamic simulations integrating subject-specific imaging data. In standard TVB the input conduction velocity is fixed, making it insensitive to local effective measures of myelin content. Goal(s): Here we parameterized signal conduction velocity for TVB simulations. Approach: Considering myelin role in efficient neural conduction, myelin measures were integrated into TVB. Results: Making TVB sensitive to myelin content highlights variations in simulation outcomes with potential improvements in capturing spatiotemporal dynamics of brain activity. This advancement opens perspectives for realizing more accurate subject-specific simulations, representing a new step towards brain digital twinning. Impact: Brain Digital Twin technologies will transform personalized medicine, providing a better understanding of pathophysiological underpinnings of diseases. Our study demonstrates how simulating brain activity with The Virtual Brain model improves when integrating subject-specific neural conduction values, calculated from myelin measures.
Decreased global signal topography in recurrent major depressive disorder
Authors: Gao Huaijin , Qian Rui , Zhu Wen , Liao Chengjiaao , Wu Dan , Zhao Zhiyong
Published: November 2024

Motivation: Global signal (GS) distribution changes remain unclear in major depressive disorder (MDD). Goal(s): This study aimed to explore abnormal GS topography in MDD, and its underlying structural mechanism and relationship with clinical assessments. Approach: We used resting-state fMRI and T1-weighted data from the REST-meta-MDD consortium, and calculated the GS correlation (GSCORR) and gray matter volume (GMV). Results: We found decreased GS topography in sensorimotor networks in recurrent MDD, and altered GMV-GSCORR coupling in cingulo-opercular and frontoparietal/occipital networks in first-episode and recurrent MDD, respectively. The alterations of GS topography in temporal lobe and cerebellum correlated with HAMD/HAMA scores, which were partially mediated by GMV. Impact: Our findings demonstrated that first-episode and recurrent MDD showed different alterations in GS topography, which were associated with cortical GMV and clinical symptoms of patients, contributing to the understanding of relationship between global and local neuronal activities in MDD.
Investigating the Effect of Central Thalamic Deep Brain Stimulation on Sleep in Alzheimer’s Disease Model
Authors: Chang Ching-Wen , Wang Mu-Hua , Lin Yi-Chen , Wang Chih-Yu , Li Ssu-Ju , Chen Ting-Chieh , Liang Yao-Wen , Chen Ching-Te , Chen You-Yin , Lin Sheng-Huang
Published: November 2024

Motivation: In Alzheimer's disease (AD), the neuropsychiatric inventory is strongly affected by sleep disorders, and vice versa. Central thalamic deep brain stimulation (CT-iTBS) has improved memory and altered the hypothalamic function which may impact the orexinergic system and sleep. Goal(s): Investigating the therapeutic effect of CT-iTBS on sleep, orexinergic system, and cognitive function in AD. Approach: Applied functional magnetic resonance imaging, electrocorticogram-electromyogram, behavioral tasks, and ELISA to assess the impact of CT-iTBS in 3xTgAD mouse model. Results: CT-iTBS significantly improved sleep fragmentation, functional connectivity, cognitive function, and orexin receptors and concentrations in 3xTgAD mice. Impact: We discovered that CT-iTBS may play an important role in modulating sleep, the orexinergic system, and cognitive function in AD. Improved outcomes pave the future direction of treating sleep disorders in Alzheimer's disease.
Analysis of time varying energy period profiles using Hilbert Huang Transform in resting state fMRI for Alzheimer’s disease
Authors: Giriprakash Pavithran Pattiam , Cieri Filippo , Yang Zhengshi , Zhuang Xiaowei , Cordes Dietmar
Published: November 2024

Motivation: The time frequency analysis of brain networks in resting state fMRI has largely been based on linear decompositions. Goal(s): The primary goal of this study is to analyze the temporal dynamics of these networks using an adaptive nonlinear approach devoid of any apriori assumptions or basis functions. Approach: Empirical Mode Decomposition (EMD), a data driven technique is utilized to investigate the energy period relationship differences in brain networks across cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Results: The AD group operates at a higher frequency with reduced energy in typical resting state networks compared to both CN and MCI. Impact: The time varying energy and period profiles obtained from EMD could serve as a potential neuromarker for disease progression from MCI to AD, resulting in timely and early clinical intervention.
Pupil-fMRI correlation-based Explainable AI to classify Alzheimer’s Disease
Authors: Liu Xiaochen , Xu William , Hike David , Xie Zeping , Liu Andy , Choi Sangcheon , Zhu Biyue , Ran Chongzhao , Jiang Yuanyuan , Yu Xin
Published: November 2024

Motivation: The pupil-fMRI correlation analysis reveals that erroneous pupillary light responses in AD mice are highly correlated to specific neuromodulatory systems. Goal(s): This study applied an explainable AI method with a pre-trained deep convolutional neural network to process pupil-fMRI interactive measurements of awake mice to verify AD biomarkers. Approach: Using the GradCAM method, we produced the saliency heatmap, which can be used to verify the underlying responsible functional nuclei for classification that could be impaired due to AD degeneration. Results: This study applied a novel GradCAM-based machine learning scheme to elucidate AD-specific pupillary responses based on impaired neuromodulatory dysfunction as a non-invasive AD biomarker. Impact: The GradCAM-based saliency map obtained with an XAI method could be used to verify the statistical differential maps of PLR-based fMRI correlation between AD and WT mice, providing a novel non-invasive AD bioimaging marker.
High resolution pCASL mapping of perfusion in the mouse brain
Authors: Monteiro Sara , Hirschler Lydiane , Barbier Emmanuel L. , Figueiredo Patrícia , Shemesh Noam
Published: November 2024

Motivation: pCASL perfusion mapping has many potential applications in preclinical imaging, but its use is still challenging particularly in mice and at higher fields due to limited sensitivity and constraints on labelling arising from the mouse’s anatomy. Goal(s): Here, we set to push the spatial resolution limitations of pCASL in mice by over an order of magnitude. Approach: For this, we leverage SNR increases provided by cryogenic coils and develop a novel experimental setup optimizing and stabilizing the positioning of the mice. Results: We then show x11 higher spatial resolution CBF maps compared to the previous state-of-the-art and higher stability and reproducibility of findings. Impact: We developed a setup optimizing carotid positioning for mice, thereby enabling efficient pCASL labeling. When combined with a cryogenic coil, perfusion images of the mouse brain were enhanced x11 in spatial resolution and were highly reproducible compared to current state-of-the-art.
The resolution dependence of MRI vessel size index varies across brain regions.
Authors: Lee Dongkyu , Gong Yelim , Han Sohyun , Cho HyungJoon
Published: November 2024

Motivation: Vessel size index (VSI) MRI of the rodent brain is measured at various in-plane resolutions, but the resolution dependence of VSI has not yet been explored. Goal(s): Here, we investigated the differences in rat brain VSI at mostly measured in-plane resolutions of 125μm2 and 250μm2. Approach: Resolution-dependent differences in VSI across brain regions were investigated in in-vivo rat experiments via a steady-state susceptibility contrast method by injection of monocrystalline iron oxide nanoparticles and validated through Monte Carlo simulations. Results: In the white matter and hippocampus regions, the VSI was measured to be 12% larger as the resolution was lowered from 125μm2 to 250μm2. Impact: Because the resolution dependence of VSI quantification varies across brain regions depending on the vascular configuration within MRI voxels, caution is required when comparing and analyzing brain VSI MRI obtained at different resolutions.
Hierarchical-µGUIDE: fast and robust Bayesian hierarchical modelling using deep learning simulation-based inference
Authors: Rouillard Louis , Wassermann Demian , Palombo Marco , Jallais Maëliss
Published: November 2024

Motivation: In-vivo brain microstructure can be estimated using diffusion MRI. However, most approaches do not quantify estimates reliability, although crucial for interpreting the results, and consider every voxel independently, leading to high uncertainties. Goal(s): Our goal is to develop a new framework to efficiently estimate tissue microstructure and improve data fitting quality. Approach: We propose Hierarchical-µGUIDE, a Bayesian method that estimates posterior distributions, by combining simulation-based inference with a hierarchical structure. Results: Hierarchical-µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches. Sharper microstructure parameter maps that preserve tissue heterogeneity are obtained, along with a tissue parcellation that segments an epileptic lesion. Impact: The proposed Bayesian framework improves single-subject inference for clinical diagnosis, by efficiently estimating posterior distributions, reducing estimates uncertainty, and learning a tissue parcellation. This works unlocks the possibility to apply hierarchical Bayesian methods taylored for microstructure estimation to large datasets.
Ultra-Short Echo-Time Based Accelerated Four-Dimensional Rosette J-resolved Spectroscopic Imaging (4D UTE-ROS-JRESI): A pilot study
Authors: Joy Ajin , Emir Uzay , Macey Paul , Thomas M. Albert
Published: November 2024

Motivation: Two-dimensional spectrum resolves information-coupled metabolites along an additional spectral dimension. However, acquisition after adding the 2nd spectral encoding can increase the total acquisition time significantly. Goal(s): To achieve clinically feasible runtimes for J-resolved Spectroscopic Imaging (JRESI) using Ultra-Short Echo-Time (UTE) based sequence implementation. Approach: Implement a UTE based 4D JRESI sequence with rosette readout, which would enable shorter repetition times and use of higher undersampling factors. Results: This study demonstrated in-vivo 4D UTE-ROSE-JRESI in less than 10 minutes as compared to a semi-laser sequence with a TR of 1.5 seconds which will take 17 minutes for the same sequence. Impact: With higher incoherence level of sampling patterns rosette based spectroscopic imaging sequence showed the potential for highly accelerated acquisitions. Using UTE based rosette 4D J-resolved Spectroscopic Imaging sequence allowed further reduction in scan time with the help of shorter TR.
Abnormal Cerebral Blood Flow, Brain Volume, Microstructural Tissue Changes in Pediatric Obstructive Sleep Apnea
Authors: Roy Bhaswati , Carrier Megan , West Alisha , Kumar Rajesh
Published: November 2024

Motivation: OSA Children show cognitive and behavioral impairments along with brain tissue changes. The status of CBF, which may contribute to brain changes impacting cognitive and behavioral issues in pediatric OSA, is unknown. Goal(s): Our goal was to examine CBF, brain volume, and microstructural changes in pediatric OSA and evaluate associations between CBF, cognition, and behavioral issues. Approach: We used ANCOVA to compare CBF and brain tissue changes between OSA and controls, and partial correlations for associations. Results: We observed reduced CBF in pediatric-OSA and associations of CBF with behavioral and cognitive issues, which may contribute to OSA pathogenesis. Impact: This study showed altered cerebral blood flow and its associations with abnormal behavioral and cognitive functions in children with OSA, which have never been studied before. The reduced regional flow may lead to further neural damage in the condition.
Multi-parametric MRI for Response Assessment in Soft-Tissue Sarcoma; Post-Treatment EF and ADC Correlate with Viable Tumour Percentage
Authors: Thrussell Imogen , Winfield Jessica , Thway Khin , Usman Sadiq , Newman Jennifer , Hopkinson Georgina , Wong Amy , Hayes Andrew , Zaidi Shane , Miah Aisha , Messiou Christina , Blackledge Matthew
Published: November 2024

Motivation: New biomarkers are needed for response assessment of soft-tissue sarcoma (STS) that reflect underlying biology. Goal(s): To (i) describe changes in six quantitative MRI biomarkers following radiotherapy treatment, (ii) assess correlation between changes in these markers, and (iii) evaluate correlation of post-treatment values with viable tumour percentage (VTP) after resection. Approach: We evaluate the Pearson correlation between changes in all six biomarkers in a cohort of 23 patients treated with pre-operative radiotherapy for limb sarcoma. Results: Large correlations are observed in changes of T2, ADC, fractional-anisotropy, fat-fraction and magnetization-transfer-ratio. Post-treatment values of tumour enhancement and ADC reflect VTP. Impact: Multiparametric quantitative MR protocols capture heterogeneous changes in soft-tissue sarcomas following treatment. Changes in derived quantitative biomarkers following treatment are correlated, and post-treatment values may reflect viable tumour percentage determined through histopathology.
High Multiband Factor Highly Accelerated Whole Heart SMILE Perfusion
Authors: Zhao Shen , Wang Junyu , Salerno Michael
Published: November 2024

Motivation: Clinical cardiac perfusion captures a limited number of slices sequentially within each heartbeat, causing incomplete left ventricle coverage and potential quantification variability due to different cardiac phases. Goal(s): To image multiple slices simultaneously thereby reducing quantification variability and improve heart coverage. Approach: We apply highly accelerated Simultaneous Multi-slice Imaging via Linear phase modulated Extended field of view (SMILE) acquisition and reconstruction framework to cardiac perfusion. Results: With a high multiband factor and accelerated rate, SMILE perfusion achieve whole heart coverage and allows for more slices to be in the same cardiac phase. Retrospective and prospective experiments validated its good quality. Impact: The proposed methods can significantly enhance the heart coverage of clinical cardiac perfusion and potentially benefit reducing variability in quantitative perfusion assessment.
Pre-surgical assessments of CSF flow and brain motion are indicative of improved cerebral dynamics following surgery in Chiari Malformation I
Authors: McIlvain Grace , Mohsenian Saeed , Samman Mohamad Motaz Al , Barrow Daniel , Loth Francis , Oshinski John
Published: November 2024

Motivation: Chiari Malformation I (CM-I) is a condition characterized by cerebellar tonsil herniation, leading to reduced cerebrospinal fluid (CSF) flow and various neurological symptoms. Posterior fossa decompression (PFD) surgery can relieve symptoms, but surgical decision making is often unclear. Goal(s): Tonsillar descent poorly correlates with symptoms and surgical outcomes. We seek to better characterize the cerebral dynamic effects of CM-I and PFD surgery. Approach: Measure CSF flow using PCMR, and brain motion using DENSE, before and after PFD surgery. Results: Surgery showed best improvement in patients with significantly restricted pre-surgical CSF flow or severely increased pre-surgical brain motion, regardless of amount of tonsillar descent. Impact: Presurgical indicators of an individual’s likelihood of surgical improvement are critical in developing informed care plans. We find that direct measures of cerebral dynamics outperform standard measures of tonsillar decent at predicting improvement from posterior fossa decompression surgery in CM-I.
Monitoring pulsatile CSF motion in the subarachnoid space using MRI
Authors: Hu Zhiyi , Jiang Dengrong , Cao Yimei , Fan Hongli , Shi Wen , Lu Hanzhang
Published: November 2024

Motivation: The characterization of pulsatile cerebrospinal fluid (CSF) flow within the subarachnoid space remains insufficiently understood, presenting a challenge in distinguishing CSF flow from blood flow. Goal(s): Our goal was to develop a time-efficient approach to monitor pulsatile CSF motion, independent of the pulsatile blood signal. Approach: We introduced a cardiac-gated BOLD sequence with flow-sensitive bipolar gradients to characterize CSF motion, and evaluated blood contamination using cardiac-gated arterial-spin-labeling. Results: We demonstrated that the main cause of the signal fluctuation is pulsatile CSF. The fluctuation patterns could be characterized by two components (hump and trough), which were consistently observe across subjects. Impact: The pulsatile CSF motion in the subarachnoid space can now be efficiently monitored with our method. This technique complements the ventricular CSF motion methods and together they may provide a better understanding of CSF and glymphatic circulation in the brain.
Enhancing MRI Resolution with a Lightweight Network and Reverse Residual Attention Fusion
Authors: Li Xia , Zhang Hui , Yang Hao , Li Tie-Qiang
Published: November 2024

Motivation: In MRI reconstruction, deep-learning methods often increase network complexity for improved super-resolution, leading to longer reconstruction times and training difficulties. Goal(s): Our solution introduces an enhanced lightweight network that maintains high-quality performance. Approach: We accomplish this by stacking Reverse Residual Attention Fusion (RRAF) with PCA and Enhanced Spatial Attention (ESA) for precise feature extraction, utilizing Transformers with depth-wise dilated convolution for better context information, and employing High-Frequency Image Refinement (HFIR) for detailed information recovery. Results: Our experiments confirm the effectiveness of our approach. Impact: Introducing the lightweight network represents an important improvement in MRI SR reconstruction. By integrating Reverse Residual Attention Fusion, it upholds exceptional image quality, streamlines network complexity, reduces reconstruction time, and simplifies training for SR MRI image reconstruction.
Synthetic CT generation using focal frequency loss improves image sharpness
Authors: Ravano Veronica , Elwakil Adham , Yu Thomas , Hilbert Tom , Maréchal Bénédicte , Richiardi Jonas , Thiran Jean-Philippe , Mourad Charbel , Margain Paul , Favre Julien , Kober Tobias , Omoumi Patrick , Sommer Stefan
Published: November 2024

Motivation: Standard intensity-based voxel-wise losses, generally used in image-to-image translation techniques, are typically biased towards the estimation of the low frequency content in image spectra. For the generation of synthetic CT (sCT) contrast, this results in limited image sharpness, and consequently a limited clinical utility. Goal(s): To improve sharpness in synthetic contrasts. Approach: We trained a model using a combination of intensity- and frequency-based losses for the generation of sCT images from MRI. Results: Compared to a baseline model, sCT images generated using the focal-frequency loss resulted in an enhanced level of details in knee images. Impact: Our results suggest that the use of frequency-based losses, in conjunction with an intensity-based L1 loss, improves image sharpness in synthetic contrasts, and thereby shows the potential to increase their clinical usefulness.
A deep-learning model for effective ringing artifact removal by developing a novel multi-frequency Gibbs generator algorithm
Authors: Dai Lisong , Miao Zhenzhuang , Lu Lei , Ling Yuting , Guo Hongyu , Liang Xiaoyun , Xu Qin , Li Yuehua
Published: November 2024

Motivation: Gibbs artifact generated by zero-padding k-space data for model training poses a huge challenge for the model to learn different severity and manifestation of Gibbs artifact in the image domain. Goal(s): Our goal was to effectively remove ringing artifact with a deep-learning model by developing a novel multi-frequency Gibbs generator algorithm. Approach: We introduced Gibbs artifact generator (GAG) algorithm to create Gibbs artifacts with different truncation ratios as the input and tested the performance with a proposed deep-learning model. Results: The images processed using the proposed approach demonstrated higher image quality score than the original images (all P < 0.05). Impact: The images generated by our new GAG algorithm with pronounced multi-frequency Gibbs artifacts could be used as a reliable training set for deep-learning model training, enabling the model to effectively identify and eliminate Gibbs artifacts in spinal MR imaging.
SegFormer for Precise Quantification of Lung Ventilation Defects in Hyperpolarized Gas Lung MRI
Authors: Babaeipour Ramtin , Zhu Ryan , Patel Harsh , Fox Matthew , Ouriadov Alexei
Published: November 2024

Motivation: Current methods for quantifying lung ventilation defects using hyperpolarized gas MRI are effective but time-consuming. Deep Learning offers potential enhancements in image segmentation, with Vision Transformers (ViTs) emerging as notable alternatives to traditional CNNs. Goal(s): The study aims to assess SegFormer's capability for automating the segmentation and quantification of ventilation defects in hyperpolarized gas MRI, comparing its efficiency and accuracy against traditional methods. Approach: Utilizing a dataset from 56 study participants, the study adopted the SegFormer architecture for segmenting MRI slices. Results: SegFormer, especially with ImageNet pretraining, surpassed CNN-based techniques in segmentation. Specifically, the MiT-B2 configuration of SegFormer showcased exceptional efficacy and efficiency. Impact: SegFormer's efficiency in hyperpolarized gas MRI enhances future clinical decision-making with swift and precise segmentation. Its superiority may inspire broader adoption and further exploration into Vision Transformers' potential in medical imaging.
Reduction of Radiofrequency Induced Heating around Passive Implants via Flexible Metasurface Shielding at 7T
Authors: Jacobs Paul , Wilson Neil , Brink Wyger , Swain Anshuman , Hanumapur Aniketh , Panchal Neeraj , Mehta Samir , Elliott Mark , Reddy Ravinder
Published: November 2024

Motivation: Metallic implant compatibility at ultra-high field strengths (≥7T) continues to be often contraindicated as RF induced heating can result in surrounding tissue damage. Metasurface technology has been shown in the past to locally null the B1+ field, thereby providing a potential solution for shielding implants. Goal(s): To demonstrate a metasurface based method for shielding metallic implants to reduce RF heating. Approach: Eight implants were tested in a polyacrylic acid (PAA) phantom using a high-SAR sequence with and without a prototype metasurface. Results: On average the metasurface design reduced RF induced implant heating in the phantom by 41.6%. Impact: Patient undergoing invasive brain or trauma surgeries typically have passive metallic devices placed, making them unable to receive MRI scans at ultra-high field strengths due to RF induced heating. This work benefits this patient population by reducing RF heating.
Computer-assisted surgical modification of lead trajectory can substantially reduce RF heating of epicardial leads in children during 1.5T MRI
Authors: Jiang Fuchang , Bhusal Bhumi , Sanpitak Pia , Webster Gregory , Monge Michael , Bonmassar Giorgio , Kim Daniel , Golestanirad Laleh
Published: November 2024

Motivation: Once epicardial CIEDs are implanted in pediatric patients, they pose a relative contraindication for MRI scans due to the increased risk of RF heating. Goal(s): Enhance MRI safety by reducing RF heating through surgical modification of the lead configuration. Approach: Mathematical models of the CIED with different lead configurations were developed and validated using the transfer function approach. Low heating trajectories were identified and implemented in a patient. Results: Placing the excess lead length on the heart’s inferior surface resulted in an average 4-fold reduction in RF heating compared to anterior placement. Impact: Implementing physics-based surgical modifications to the trajectory of epicardial leads can consistently and significantly reduce RF heating in children undergoing MRI at 1.5T.
Feasibility of boosting SNR using cooled RF receive coils for low field human brain imaging at 72mT
Authors: Sliwiak Monika , Purchase Aaron , Wald Lawrence , Cooley Clarissa , Stockmann Jason
Published: November 2024

Motivation: Cooled coils could provide an boost SNR for portable low field MRI as an alternative to increasing the field strength using larger, heavier magnets. Goal(s): Test whether cooling RF receive coils with liquid nitrogen could provide an appreciable boost to signal-to-noise ratio (SNR) on low-field portable MRI scanners, where noise is dominated by copper losses in the coil. Approach: A 3.04 MHz multi-turn surface coil was cooled to ~77K using liquid nitrogen and performance was assessed using bench Q measurements and imaging data. Results: The unloaded Q increases from 220 at 293K to 541 at 77K. SNR for the cooled coil increased 4.15x. Impact: Liquid nitrogen cooled radiofrequency receive coils could improve image quality for low field portable MRI scanners, broadening the impact of these systems.
Tailored dielectric shimming in MRI using machine learning - a feasibility study
Authors: Zhang Mengying , Panjwani Nawal , Motovilova Elizaveta , Dyke Jonathan , Robb Fraser , Winkler Simone
Published: November 2024

Motivation: Inhomogeneities of the MRI transmit field cause image shading and hinder diagnosis. In dielectric shimming, pads of high permittivity are used to recover signal in low intensity areas, but full-wave calculation of the resulting fields is too slow for real-time use at the scanner. Goal(s): We study feasibility of using AI to rapidly predict the transmit field with dielectric pads. Approach: An AI pipeline is trained using a small simulated data set for proof of concept. Results: We obtain a structural similarity of 97% with a mean squared error of 0.02%, demonstrating feasibility and the potential for a real-time implementation in the future. Impact: This work improves image shading and diagnostics. Dielectric shimming in particular and rapid calculation of electromagnetic fields in general especially apply to ultra-high field strengths such as 7T, 9.4T and 10.5T, where significant inhomogeneity is hindering proper evaluation.
Integrated Multi-label 3D Deep Learning Multi-task Model for Intelligent MR Spine Scan Planning
Authors: Saxena Ashish , Bhushan Chitresh , Ghose Saumya , Patil Uday , Shanbhag Dattesh
Published: November 2024

Motivation: Obtaining consistent spine MRI images irrespective of patient posture, spine deformities, and technologists’ skills, with minimal disruption in the existing workflow. Goal(s): To develop an intelligent scan plane prescription for spine MRI using deep learning on regular 3-plane localizer images. Approach: We adopted a multi-resolution CNN network for multiple segmentation tasks - spine vertebrae, intervertebral disc (IVD), and saturation band (SB) across all the spine stations (cervical, thoracic, and lumbar) and orientations (sagittal and coronal). Results: We reported good segmentation of vertebrae and IVD, along with consistent SB placement with angle error of less than 5 degree and no overlap with the spine region. Impact: We present a first-of-its-kind integrated multi-label 3D DL model that operates on 2D 3-plane regular localizers to aid consistent MRI scan planning. This model combines MRI localizer images across orientation, across spine stations, and across multiple imaging tasks.
Deep learning-based automated scan planning for brain MRI
Authors: Zhu Gaojie , Shen Xiongjie , Guo Hua
Published: November 2024

Motivation: Manual scan planning in clinical MRI is inaccurate, inconsistent and time-consuming. Goal(s): A deep learning-based end-to-end automated scan planning framework has been developed for MRI head scans. Approach: We propose a two-stage end-to-end 3D cascaded convolutional network framework, called 3D CFP-UNet, which localizes the positions of five key anatomical landmarks and achieves a coarse-to-fine result. We also propose loss functions PRL and DRL with physical meaning in automatic scan planning. Results: Our approach yields satisfactory scan planning results on 229 test subjects, with PAE and PRE reaching 0.872mm and 0.10%, respectively. Impact: MRI automated scan planning can help improve scan efficiency. Also, it improves scan consistency for follow-up comparisons.