bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiparametric MRI Radiomic Features Improve Patient Selection for Active Surveillance in Prostate Cancer
Authors: Wallaengen Veronica , Zacharaki Evangelia , Alhusseini Mohammed , Kimbel Isabella , Prakash Nachiketh Soodana , Algohary Ahmad , Breto Adrian , Gaston Sandra , Acosta Rosa Castillo , Kryvenko Oleksandr , Nahar Bruno , Parekh Dipen , Pollack Alan , Punnen Sanoj , Stoyanova Radka
Published: November 2024

Motivation: Accurate selection of prostate cancer patients to undergo active surveillance (AS) is crucial to ensure suitable treatment. Goal(s): To develop an automated framework for mpMRI analysis to assist clinical decision making about whether a patient should remain on AS. Approach: We developed a progression risk stratification model using mpMRI data from an AS trial, and incorporating clinical biomarkers and radiomic features from lesions identified by a deep neural network. Results: The lesion segmentation network achieved a median DSC of 60.7%, and the progression prediction model an AUC of 81.1% in determining likelihood of progression within 12 months. Impact: We present a fully automated methodology to assess prostate cancer progression risk for AS patients within the timeframe between their follow-up visits, thereby providing essential data for clinicians that can prospectively improve AS patient selection.
Prediction of pathological complete response in breast cancer by histogram signatures from multi-phase contrast enhanced MRI
Authors: Zhu Hai-Tao , Qu Yu-Hong , Cao Kun , Li Xiao-Ting , Sun Ying-Shi
Published: November 2024

Motivation: Accurate prediction of pathological complete response (pCR) after neoadjuvant chemotherapy enables individualized treatment options to avoid unnecessary breast excision and improve patients’ life quality. Goal(s): To improve the prediction accuracy by simultaneously extracting temporal and spatial features of MRI signal during contrast enhancement. Approach: A histogram signature is designed by concatenating histograms at different enhancing phases into a 2D picture and classified by convolutional neural network into pCR or non-pCR. Results: The AUC, sensitivity, specificity of the histogram signature for pCR prediction is 0.833 in the test group (n=132). The model combining histogram signature with ER and HER2 increases AUC to 0.842. Impact: Histogram signatures from multi-phase MRI can be used as a new marker to measure tumor heterogeneity, estimate drug uptake, evaluate treatment response and predict prognosis for breast cancer or other cancers.
An MRI-based nomogram predicts brain metastasis response to targeted therapy in lung cancer patients: A multi-center study
Authors: Chen Junwei , Mao Jiaji , Li Junhao , Li Baoxun , Li Haojiang , Lin Daiying , Fang Xuewen , Xiao Fang , Huang Zehe , Wang Wensheng , Chen Shaoxian , Cai Zonghuan , Liang Manqiu , Pan Shengzhang , Deng Dabiao , Wu Zhiyuan , Shen Jun
Published: November 2024

Motivation: To determine an effective individualized treatment decision for lung cancer patients with brain metastasis (BrM) to receive targeted therapy. Goal(s): we developed an MRI-based nomogram that can predict the response of lung cancer BrM to targeted therapy using multi-center data. Approach: Clinical predictors, radiomics and deep learning features extracted from BrM baseline MR images were incorporated to establish the nomogram using the LASSO logistics coefficients. Results: The nomogram can accurately predict the 6-month and 12-month responses of BrM to targeted therapy across the training cohort, internal validation cohort, and external test set, outperforming all other models. Impact: The MRI-based nomogram can be used as a pretreatment and personalized tool to predict response to targeted therapy in lung cancer patients with BrMs and thus assist in optimizing treatment for lung cancer patients who suffer from BrMs.
Preoperative Prediction of Her2-zero, -low and -overexpression Breast Cancers Using Multiparametric MRI and Machine Learning Modeling
Authors: Zhou Jiejie , Zhang Yang , Wang Jinhao , Lin Yezhi , Yoon Ga , Liu Yan-lin , Chen Jeon-Hor , Wang Hailing , Wang Meihao , Su Min-ying
Published: November 2024

Motivation: Her2-low breast cancers could benefit from new anti-HER2 therapies. Goal(s): To construct a preoperative prediction model of HER2 expression levels using multiparametric MRI and machine learning (ML) algorithms. Approach: 621 patients were investigated. Four ML methods were used to build models based on MRI features to predict HER2 expression levels. Results: MRI features of multiple lesions, spiculated margin, peritumoral edema and largest diameter were selected to build the models. ML models performed better for predicting HER2-zero vs. HER2-low/-overexpression than HER2-low vs. HER2-overexpression. The best model was KNN of AUC 0.86, sensitivity of 76%, specificity of 73%, and accuracy of 75%. Impact: MRI features of breast cancer are associated with different HER2 expression levels. MRI-based ML models have the potential to preoperatively predict the HER2 expression status.
MRI-Based Habitat, Radiomics, and Deep Learning for Assessing Response of Platinum-Based Chemotherapy in HGSOC Patients
Authors: Bi Qiu , Qiang Jinwei , Song Yang , Wu Yunzhu
Published: November 2024

Motivation: High-grade serous ovarian carcinoma (HGSOC) poses a significant challenge due to platinum resistance and the inherent difficulty in its prediction. Goal(s): We aimed to explore MRI-based habitat model for predicting response of platinum-based chemotherapy in HGSOC patients, and compared with radiomics and deep learning models. Approach: We leveraged the K-means algorithm for clustering on multiparameter MRI data. Then the radiomics, habitat, and deep learning models were constructed. Results: Habitat model had the potential to predict platinum resistence, with a superior performance to radiomics and deep learning models. The nomogram integrating habitat with neoadjuvant chemotherapy yielded a better performance compared to others. Impact: This study holds substantial clinical significance as it establishes a foundational framework for the customization of treatment strategies for patients afflicted with HGSOC.
An artificial intelligence decision tree diagnostic platform helps neuroradiologists reclassify adult-type diffuse gliomas
Authors: Zhang Liqiang , Xu Xinyi , Pan Hongyu , Gao Jueni , Wang Linling , Liu Zhi , Cao Xu , Li Yongmei
Published: November 2024

Motivation: Deep learning networks offers an opportunity for diffuse gliomas classification, which may be help for therapeutic decision making and selection of patient groups suitable for targeted genetic analysis. Goal(s): The purpose of this study is to develop an artificial intelligence method to reclassify adult-type diffuse gliomas based on the new WHO CNS tumor classification. Approach: An artificial intelligence decision tree diagnostic platform(DTDP) based on MRI and deep learning networks was developed by combined 6 individualized CNNs models in series and parallel Results: The DTDP performed well with accuracy of 86.67%. Impact: The DTDP achieved automatic classification and comprehensive diagnosis of adult‑type diffuse gliomas by combining genetic biomarkers and histological grading, and effectively helped neuroradiologists to reclassify adult-type diffuse gliomas.
MIROR: The Clinical Decision Support System with Functional Imaging and Machine Learning
Authors: Zhao Dadi , Burling Sara , MacPherson Lesley , Worthington Lara , Arvanitis Theodoros , Apps John , Peet Andrew
Published: November 2024

Motivation: Radiomics has potential to bring added values to cancer diagnosis and prognosis, whilst a practical tool that is accessible for clinicians and radiologists has not been available. Goal(s): To design MIROR, a clinical decision support system, that can aid tumour diagnosis with real-time image and spectroscopy analysis. Approach: The project keeps collecting childhood brain tumour proton MR images and spectroscopy in England and has built a multi-centre database that includes 377 cases. Results: MIROR supports key features including visualisation, image analysis, feature extraction, spectroscopy quantification, and tumour type and subtype prediction through machine learning. Impact: A practical solution that translates radiomics and machine learning into clinical scenarios can aid tumour diagnosis and treatment planning, bring benefits to patient healthcare, and improve clinical outcomes.
Metal artifact synthesis: Enabling inclusive Deep learning for patients with implants
Authors: Singhal Vanika , Anand Deepa , C Florintina , Dubey Harshit , Madhavan RAdhika , Bhushan Chitresh , Shanbhag Dattesh
Published: November 2024

Motivation: AI medical imaging solutions are impacted by the presence metal implants and a design of appropriate synthesis method can improve robustness of DL models. Goal(s): Simulation of patient medical condition like metal artifacts in MRI medical images. Approach: The proposed method blends regions from template images containing metal artifacts into target images by using metal segmentation mask for selection, blending this region into a chosen target image RoI . Results: Improvement in knee classification accuracy of 8% and decrease in spine plane distance error by 25-40% and plane angle error by 4-30% using the proposed approach. Impact: A data adaptive metal simulation method in semantically relevant regions in anatomy ensures robust of DL models in patients with metal implants who hitherto would not have benefitted from AI driven tasks .
Laminar profile of hippocampal subregions during spatial navigation
Authors: Ahmadi Khazar , Stawarczyk David , Pfaffenrot Viktor , Gomez Carlos , Patai Zita , Norris David , Axmacher Nikolai
Published: November 2024

Motivation: Despite substantial progress in understanding the role of the hippocampus in spatial navigation, the layer-specific microcircuits underlying distinct navigation processes are yet to be determined. Goal(s): We aimed to investigate the laminar organization of hippocampal subregions during spatial navigation including its relationship to specific strategies. Approach: Leveraging submillimeter-resolution fMRI at 7T, we quantified BOLD signal changes across hippocampal depths and applied mixed-effect models to probe the relationship between subregional laminar activity and specific strategies reflected by straight paths and deviation towards environmental boundaries. Results: We show that laminar profiles in hippocampal subregions are differentially associated with navigation strategies. Impact: Our results demonstrate the promise of laminar fMRI for mapping complex cognitive functions in the hippocampus at mesoscale. Given the vulnerability of this region to Alzheimer’s disease pathology, these findings may have clinical implications for early diagnosis.
Decoding directionality of information in cortical networks using layer-based connective field model
Authors: Carvalho Joana , Fernandes Francisca , Haak Koen , Shemesh Noam
Published: November 2024

Motivation: To disentangle feedback and feedforward signals in cortical circuits. Goal(s): To unravel the intricate neural connections within cortical layers. Approach: We implemented a layer connective field (lCF) model and applied it to ultrafast RS data and RS dfMRI data. Results: 1.Intracortical lCF shows two lCF size profiles: feedforward with inverse U shape with the larger lCF sizes at layer 5 and feedback with U shape and larger CF sizes at superficial and deeper layers. 2.In the absence of visual input the functional connectivity reflects visuotopic organization. 3.lCF estimates obtained from dfMRI(ADC) are more layer specific than the ones estimated from BOLD. Impact: This study showcases the ability of high spatio-temporal resolution MRI techniques (ultrafast BOLD and dfMRI) when coupled with biologically grounded connectivity models (lCF) to unveil the intricacies of information directionality within topographically organized cortices.
Quantitative assessment of tongue tissue structure with 3D partially spoiled gradient echo
Authors: VERMEULEN Eléonore , Baudin Pierre-Yves , Lapert Marc , Marty Benjamin
Published: November 2024

Motivation: Fat fraction and water T2 have been identified as biomarkers of muscle tissue alterations in neuromuscular diseases. Tongue muscles are involved in several muscular disorder, but due to swallowing motion, quantitative MRI is almost never applied at this level. Goal(s): Assess the feasibility of a water T2 mapping in the tongue. Approach: In this feasibility study, we proposed an RF phase-modulated gradient-echo acquisition with 3D radial encoding to obtain FF and water T2 maps of the tongue. Results: The proposed method enables 3D mapping. The values found in the tongue are consistent with those found with the same methods in the leg muscles. Impact: Performing quantitative imaging in the tongue is challenging due to swallowing motion. Applying a 3D radial sequence appears the appropriate strategy for water T2 imaging.
Connectome 2.0: Performance evaluation and initial in vivo human brain diffusion MRI results
Authors: Ramos-Llordén Gabriel , Dietz Peter , Davids Mathias , Lee Hong-Hsi , Ma Yixin , Mahmutovic Mirsad , Scholz Alina , Lee Hansol , Maffei Chiara , Yendiki Anastasia , Bilgic Berkin , Kirsch John E. , Park Daniel J. , Clifford Bryan , Lo Wei-Ching , Stocker Stefan , Fischer Jasmine , Rummert Elmar , Krug Andreas , Potthast Andreas , Benner Thomas , Ramb Rebecca , Basser Peter J. , Witzel Thomas , Wald Lawrence L. , Rosen Bruce R. , Keil Boris , Huang Susie Y.
Published: November 2024

Motivation: Current human MR scanners cannot resolve the full range of length scales needed to study the brain's microscopic and mesoscopic structure. Goal(s): To construct and validate the next-generation human connectomics and microstructure MRI scanner known as Connectome 2.0. Approach: The 3T Connectome 2.0 scanner incorporates a peripheral nerve stimulation-optimized asymmetric head gradient driven by dual gradient power amplifiers. Custom-built high-sensitivity 72-channel (in vivo imaging) and 64-channel (ex vivo imaging) receive coils were integrated. Results: The Connectome 2.0 scanner achieves Gmax=500 mT/m and SRmax=600 T/m/s, demonstrates 2x improved SNR for diffusion MRI over Connectome 1.0, and enables high-resolution tractography. Impact: The Connectome 2.0 scanner will allow the exploration of new microstructure properties and connectional anatomy in the living human brain with unprecedented spatial and diffusion resolution.
Impact of third order shim coils on gradient-magnet interactions and gradient waveform fidelity
Authors: Boulant Nicolas , Le Ster Caroline , Amadon Alexis , Aubert Guy , Beckett Alexander , Belorgey Jean , Bonnelye Cédric , Bosch Dario , Brunner David , Dilasser Guillaume , Dubois Olivier , Ehses Philipp , Feinberg David , Feizollah Sajjad , Gras Vincent , Gross Simon , Guihard Quentin , Lannou Hervé , Le Bihan Denis , Mauconduit Franck , Molinié Frédéric , Nunio François , Pruessmann Klaas , Quettier Lionel , Scheffler Klaus , Stöcker Tony , Tardif Christine , Ugurbil Kamil , Vignaud Alexandre , Vu An , Wu Xiaoping
Published: November 2024

Motivation: Third order shim coils can impact gradient-magnet interactions with consequences on image quality and magnet safety. Goal(s): To demonstrate the influence of third order shim coils on the SC72 gradient coil using field and vibration measurements. Approach: The gradient transfer function was measured at 11.7T (Iseult) and at 7T (Terra) with and without connection of the 3rd order shim coils. Vibration measurements were carried out on Iseult from 0 to 11.7T in the two configurations as well. Results: The data demonstrate a drastic influence of the 3rd order shim coils and their circuits. Impact: The work suggests caution when using third order shim coils at 7T and above. It also paves the way for further investigations to improve gradient waveform fidelity.
Enhancing Hepatocellular Carcinoma (HCC) Diagnosis Through TWIST MRI Sequence
Authors: Elboraey Mohamed , LeGout Jordon
Published: November 2024

Motivation: Our report targets precise timing in hepatocellular carcinoma (HCC) diagnosis via MRI. Timely recognition of the late hepatic arterial phase is crucial to avoid unnecessary invasive biopsies and minimize patient risks. Goal(s): Our report evaluates the TWIST technique's effectiveness in HCC diagnosis, aiming to reduce the need for percutaneous biopsies. Approach: We integrated TWIST into our liver MRI protocols, capturing images at preset intervals, enhancing temporal resolution for HCC diagnosis. Results: Our report successfully employs the TWIST technique to diagnose HCC by capturing arterial hyperenhancement. This innovation improves diagnostic accuracy and diminishes the necessity for invasive procedures, benefiting patients. Impact: The implementation of the TWIST technique promises safer and more accurate hepatocellular carcinoma (HCC) diagnosis. This technique minimizes invasive biopsies, reshaping clinical practice and offering a new perspective on non-invasive diagnostic techniques.
Dynamic Mode Decomposition reveals 23Na Multi-Quantum Coherences and allows incomplete RF Phase-Cycling
Authors: Licht Christian , Ilicak Efe , Reichert Simon , Schad Lothar R , Rapacchi Stanislas
Published: November 2024

Motivation: Sodium (23Na) Multi-Quantum Coherences (MQC) MRI potentially provides richer tissue information. However, separation of the single (SQ) and triple (TQ) quantum coherences is challenging and is done by computing the Fourier transform (FT). Unfortunately, the FT is susceptible to noise and phase-cycle imperfections. Goal(s): To enable reliable frequency separation of the superimposed 23Na MQC signal even with undersampling phase-cycling. Approach: Dynamic Mode Decomposition (DMD) was used to separate the signal components and was tested on numerical simulations, phantom and in vivo brain data acquired at 3T. Results: DMD reliably separated SQ and TQ signal components from 23Na MQC MRI despite missing phase-cycling steps. Impact: DMD reliably separates SQ and TQ signal components and has the potential to enable phase-cycle undersampling below the TQ Nyquist limit to accelerate 23Na MQC MRI. Despite 23Na MQC MRI, every MRI experiment involving phase-cycling could benefit from this approach.
NEXI for the quantification of human gray matter microstructure on a clinical MRI scanner
Authors: Uhl Quentin , Pavan Tommaso , Feiweier Thorsten , Piredda Gian Franco , Jespersen Sune , Jelescu Ileana
Published: November 2024

Motivation: This study assesses the Neurite Exchange Imaging (NEXI) microstructure model in human cortex using clinical MRI data, addressing the model's clinical applicability. Goal(s): How meaningful, robust, and reproducible are microstructural properties in the human cortex estimated using the NEXI diffusion model on clinical data?Approach: Scan-rescan clinical data from six volunteers were collected and processed. NEXI was estimated and compared to expected cortical distributions and to the myelin water fraction distribution. Results: NEXI provided robust, biologically plausible results and maintained inter-subject sensitivity and intra-subject reproducibility. A significant correlation between exchange time and myelin water fraction supports the relationship between membrane permeability and myelination. Impact: We successfully estimate the Neurite Exchange Imaging (NEXI) model on clinical MRI data and report a strong correlation between the estimated exchange time, a proxy for membrane permeability, and the Myelin Water Fraction in the human cortex.
Self-gated 2D lung imaging using single petal rosette trajectory
Authors: Frantz Hanna , Rasche Volker
Published: November 2024

Motivation: Major limitation of lung MRI is respiratory motion which can be overcome by retrospective self-gating approaches. Goal(s): Achieving sufficient SNR values in the parenchyma is crucial for clinical evaluations and the assessment of physiological parameters. Approach: This abstract presents a single-petal rosette UTE trajectory that is evaluated for k-space-, as well as image-based, retrospective self-gated lung imaging in comparison to the radial UTE trajectory. Results: Higher SNR values and sharpness are obtained when using the SPR trajectory, compared to radial UTE sampling approaches at constant temporal resolution. Impact: This abstract presents a single-petal rosette UTE trajectory (SPR) for 2D self-gated lung imaging, yielding higher SNR and sharpness in comparison to radial UTE sampling approaches.
A preliminary investigation for a Localized Quadratic Encoded 3D bSSFP cardiac cine MRI
Authors: Chao Tzu Cheng , Wang Dinghui , Waddle Spencer , Leiner Tim
Published: November 2024

Motivation: A gated 3D cardiac cine MRI will be useful to evaluate cardiac structure and function from different viewing angles, unlike conventional 2D scans covering only specific orientations. Goal(s): To develop a retrospectively cardiac gated 3D imaging sequence for the cine scan with reasonable breath-hold time and reconstruction speed. Approach: A bSSFP sequence was combined with Localized Quadratic Encoding RF pulses for the cardiac gated 3D imaging. Results: The 3D cardiac cine imaging requires around 20 breath-holds and each breath-hold takes around 25 seconds. The images feature similar soft tissue contrast to the conventional 2D bSSFP scanning with isotropic resolution along different viewing angles. Impact: The proposed method enables a 3D cardiac cine scan with reasonable breath-hold duration and much faster reconstruction speed.
Multi-slab whole-brain in vivo 0.35 mm human brain at 7 T with low undersampling to validate future acceleration & denoising
Authors: Gulban Omer Faruk , Dowdle Logan , Tse Desmond Ho Yan , Bollmann Saskia , Goebel Rainer , Poser Benedikt A. , Ivanov Dimo
Published: November 2024

Motivation: Our previous work provided 0.35×0.35×0.35 mm3 voxel resolution T2* dataset where the intracortical angioarchitecture details were captured and analyzed (Gulban et al. 2022). However, this work only covered a third of the brain while requiring two scanning sessions. Goal(s): Our aim here is to explore reducing the scanning time while expanding the brain coverage to get similar quality data for vascular analyses. Approach: Our approach consisted of exploring further acceleration for T2* imaging and boosting the SNR of T1 images though denoising instead of multi-run averaging. Results: Our results suggest that we can reduce the scanning time five-fold while accomplishing whole brain overage. Impact: We provide a low undersampling 0.35 mm in vivo human brain dataset and a scanning protocol (including 7 T T2*, T1 contrasts) for cortical angioarchitecture studies while delivering a reference dataset to test further acceleration and denoising.
Dynamic Causal Modeling Reveals Distinct Network-Specific Effective Connectivity within Olfactory Pathway between Healthy and Aged Rats
Authors: Ma Teng , Lin Xuehong , Wang Xunda , Lyu Qiuyi , Zhang Zhangjin , Cao Peng , Wu Ed , Leong Alex
Published: November 2024

Motivation: Presently, olfactory dysfunctions such as with aging, neurodegenerative diseases and COVID-19 remain poorly understood at the systems level despite extensive knowledge of the microcircuit changes at the olfactory bulb (OB). Goal(s): We aim to reveal the systematic abnormalities of downstream olfactory information processing from the OB in prematurely aged rats. Approach: We examined the effective connectivity of olfactory networks in both healthy and aged rat models with optogenetic fMRI and dynamic causal modeling. Results: We demonstrate that network-specific dynamics in the olfactory system between aged and healthy rats could be attributed to altered effective connectivity driven by primary olfactory regions downstream from OB. Impact: The ability to stimulate olfactory bulb excitatory neurons and model the downstream neural activity dynamics at the system level in healthy and aged animals has revealed key regions that are involved in olfactory dysfunctions, which can guide future therapeutic interventions.
Common patterns of gray matter volume reduction and the genetic association in multiple sclerosis and Alzheimer's disease
Authors: Zhao Yunfei , Sun Jie , Zhao Wenjin , Yu Zeyang , Zhang Che , Zhang Han , Zhang Chen , Sun Chunyang , Zhang Ningnannan
Published: November 2024

Motivation: Both Alzheimer’s disease (AD) and multiple sclerosis (MS) patients exhibit brain atrophy driven cognitive impairment. Goal(s): To identify the specific and common regions in GMV reduction in AD and MS and genetic basis associated with volume changes. Approach: VBM meta-analyses and conjunction analyses were performed for comparison. GMV associated gene expression data were extracted from Allen Human Brain Atlas by cross-sample partial least squares regression. Results: MS patients have reduced thalamic volume, while AD have hippocampal atrophy. Both MS and AD patients exhibit medial temporal lobe atrophy patterns, which were associated with 843 genes in functioning at biological processes, neurons, and immune cells. Impact: MS and AD patients have specific and common patterns of gray matter volume reduction, given a neuroimage clue that the ageing population present with similar symptoms of cognitive impairment.
High-resolution diffusion tensor imaging shows cortical microstructure changes in multiple sclerosis across the lifespan
Authors: Acosta-Franco J Alejandro , Weber Carly , Cabrera Diana Valdés , Smyth Penny , Blevins Gregg , Wilbur Colin , Little Graham , Beaulieu Christian
Published: November 2024

Motivation: Patterns of cortical microstructural damage in multiple sclerosis (MS) can be examined in vivo with high-resolution diffusion tensor imaging (DTI). Goal(s): To assess cortical diffusion changes in MS across the lifespan. Approach: High-resolution DTI from controls (5-74 years) and MS participants (13-72 years) were segmented using an only-DTI-based method. Thickness, standard DTI metrics and radiality were evaluated in the entire cortex in MS against normative development/aging. Results: Cortical changes were observed in ~1/3 of MS participants versus controls over the entire lifespan, such as thinning, higher mean (MD), axial (AD) and radial (RD) diffusivities, and lower radiality. Impact: This study highlights microstructural abnormalities in the cortex of multiple sclerosis (MS) patients throughout the lifespan. These findings will help to understand in vivo cortical pathology in MS that might precede atrophy and that could be linked with disease progression/phenotypes.
Neuronal and Cerebrovascular Response to tDCS in Multiple Sclerosis: A Simultaneous tDCS-MRI Study
Authors: Muccio Marco , Pilloni Giuseppina , Krupp Lauren , Datta Abhishek , Bikson Marom , Charvet Leigh , Ge Yulin
Published: November 2024

Motivation: The cerebral metabolic underpinnings of tDCS, both during the stimulation itself and as result of repeated sessions are still not fully understood. Goal(s): To quantify the immediate tDCS effects (simultaneous) using real-time tDCS-MRI and treatment-related effects (cumulative after repeated sessions) in multiple sclerosis (MS) patients. Approach: MS patients had tDCS-MRI performed at baseline and after 20 tDCS treatment sessions. Imaging measurements were acquired pre-, during- (2.0mA left frontal anodal) and post-tDCS. Results: During tDCS, at baseline, we observed a 7.6% increase in cerebral metabolic rate of oxygen (CMRO2). tDCS-treatment induced a 9.6% increase of the pre-tDCS CMRO2 levels. Impact: The significant increase in neuronal metabolism following both real-time and repeated tDCS treatment in MS patients offers valuable insights into the biophysiological mechanisms regarding acute and cumulative tDCS effects, informing future clinical applications in MS and other neurodegenerative diseases.
Dynamic measurement of concurrent BOLD and brain tissue displacement quantification in vivo at 7T using motion-encoded stimulated-echo EPI
Authors: Strom Amelia , Berman Avery , Reese Timothy , Dong Zijing , Scheffler Klaus , Lewis Laura , Polimeni Jonathan
Published: November 2024

Motivation: Understanding the spatiotemporal relationships between blood volume changes, tissue displacement, and CSF flow is important for elucidating brain waste clearance mechanisms, and measuring these compartments concurrently would enable effective analysis. Goal(s): To demonstrate the feasibility of leveraging both magnitude-valued and phase-valued data to measure BOLD fMRI and tissue motion simultaneously. Approach: We apply a combination of computer simulations and in vivo imaging with visual stimulation using the Displacement Encoding with Stimulated Echoes (DENSE) pulse sequence. Results: DENSE magnitude-valued data show significant response to visual stimulation in the visual cortex, while the phase-valued data show typical cardiac-gated motion in both cortex and brainstem. Impact: BOLD fMRI can be acquired simultaneously with brain tissue displacement quantification using the DENSE pulse sequence, enabling future spatiotemporal analyses of concurrent blood volume changes, tissue displacement, and CSF flow for understanding waste clearance mechanisms.
Effect of visual stimulation on cerebrospinal fluid flow is impaired in glaucoma patients
Authors: Bang Ji Won , Parra Carlos , Yu Kevin , Wollstein Gadi , Schuman Joel , Chan Kevin
Published: November 2024

Motivation: Glaucoma is an age-related neurodegenerative disease of the visual system. Recent studies suggested that glaucoma may lead to changes in cerebrospinal fluid (CSF) dynamics, which can be influenced by neural activity. However, it remains unclear how the CSF dynamics is altered in glaucoma. Goal(s): To test whether the impact of visual stimulation on CSF inflow is impaired in glaucoma. Approach: We used visually-evoked BOLD fMRI responses to compare the coupling between BOLD activity and CSF inflow across healthy individuals and glaucoma patients. Results: Our findings indicate that the influence of visual stimulation on CSF inflow decreases with glaucoma severity. Impact: Our findings suggest that CSF inflow is altered in glaucoma. Future studies should investigate whether this reduced impact of visual stimulation on CSF inflow is due to impaired neural activity or impaired interaction between cerebrovascular activity and CSF dynamics.
Imaging GBCA enhancement in the periosteal and meningeal layers of dura mater using high resolution MRI at 7T
Authors: Li Yinghao , Sun Yuanqi , Paez Adrian , Knutsson Linda , Van Zijl Peter , Hua Jun
Published: November 2024

Motivation: The GBCA enhancement detected in the parasagittal dura (PSD) is hypothesized to originate from the dura mater blood vessels lacking a blood-brain-barrier. The dura mater consists of two layers: periosteal and meningeal dura mater. No prior study has examined the pattern of GBCA enhancement within these layers. Goal(s): To visualize GBCA enhancement in the periosteal and meningeal layers of dura mater in the human brain. Approach: High-resolution black-blood and FLAIR MRI were performed at 7T. Results: The two layers and their GBCA-enhancement could be visualized. T1 and T2* values in each layer were measured. Impact: This work allows us investigate the relationship between GBCA-enhancement in the dura mater and GBCA-induced-signal-changes in the PSD and meningeal-lymphatic-vessels. This is of importance given that GBCA-enhanced MRI has been a gold-standard technique for investigating human brain clearance.
A 32-Channel Cap for Temporal Lobes Exploration at 11.7 T
Authors: Gapais Paul-François , Luong Michel , Giacomini Eric , Guillot Jules , Gunamony Shajan , Chu Son , Hosseinnezhadian Sajad , Amadon Alexis
Published: November 2024

Motivation: Our newly operational 11.7 T machine provides an improved signal-to-noise ratio (SNR) in the human brain. This gain in SNR can be even enhanced by designing region-focused receive arrays. Goal(s): Our main goal was to maximize the SNR in the temporal lobes and provide high-acceleration capabilities for fMRI studies. Approach: We developed a modular 32-channel receive array made of non-overlapped hexagonal loops placed on a flexible cap, using high-impedance coils (HIC). We compared our coil to a 32-channel whole-brain receive array at 11.7 T. Results: The cap receive array provides a significant SNR boost in the targeted region. Impact: Our cap receive array should ease sub-millimeter resolution fMRI with high SNR in the temporal lobes. Moreover, the detachable hexagonal modules could easily be re-arranged to target any other brain region, with no need for retuning.
A novel mathematical model to quantify tumor fluid properties on longitudinal breast DCE-MRI for neoadjuvant chemotherapy response assessment
Authors: Chen Xinan , Huang Wei , Shukla-Dave Amita , Paudyal Ramesh , Tannenbaum Allen , Deasy Joseph
Published: November 2024

Motivation: To advance the field of pharmacokinetic analysis of breast DCE-MRI by developing a model accounting for inter-fluid transport within tumor tissueGoal(s): To develop a novel DCE-MRI pharmacokinetic method to quantify and visualize fluid flows in tumors and identify predictive imaging biomarkers of therapeutic response to neoadjuvant chemotherapy (NACT) in breast cancer. Approach: We developed a mathematical model in computational fluid dynamics termed the unbalanced regularized optimal mass transport (urOMT)Results: Our urOMT model provides fluid transport properties of the tumor using breast DCE-MRI; the urOMT-derived quantitative metrics may be future predictive imaging biomarkers to measure treatment effectiveness in patients treated with NACT. Impact: We developed a novel mathematical model to quantify, track, and visualize fluid flows in tumors with breast DCE-MRI data. The proposed quantitative metrics after validation may serve as predictive imaging biomarkers for breast cancer patients treated with neoadjuvant chemotherapy.
Postherpetic Neuralgia and Morphological Alterations in the Human Dorsal Root Ganglia: A Study Using 3T MR Neurography
Authors: She Dejun , Yan Yalan , Zhao Xiance , Cao Dairong
Published: November 2024

Motivation: Despite the crucial role of imaging in localizing lesions, excluding causes, and guiding surgery, there remains a paucity of reports pertaining to the radiological manifestations in patients with herpetic neuralgia. Goal(s): To investigate the correlation between the volume and T2 signal intensity (SI) of the ganglia in 3T MRN with clinical and serological parameters in herpetic neuralgia patients. Approach: With 18 patients examined by MRN. Volume, T2 signal measurements of the T1-T12 ganglia were performed manually for each patient. Results: The changes in ganglion volume observed on MRN may reflect disease progression. Among all serological indicators, ESR was correlated with the volume ratio. Impact: This study is the first to quantify ganglia in herpetic neuralgia patients, and the first to investigate the correlation between ganglia volume and serological data. In herpetic neuralgia patients, these MRN findings will contribute to the diagnosis and management.
Increased brain iron deposition in the basial ganglia is associated with cognitive and motor dysfunction in type 2 diabetes
Authors: Sui Chaofan , Li Meng , Zhang Qihao , Li Jing , Gao Yian , Zhang Xinyue , Wang Na , Liang Changhu , Guo Lingfei
Published: November 2024

Motivation: Diabetes is thought to be related to an imbalance in iron homeostasis and abnormal iron accumulation. Goal(s): To explore the changing mode of brain iron metabolism in basal ganglia in type 2 diabetes (T2DM) patients with diabetic peripheral neuropathy (DPN) and diabetes without DPN (NDPN) using quantitative susceptibility mapping (QSM). Approach: Brain iron of T2DM was assessed using QSM. Results: Susceptibilities in the putamen and the caudate nucleus were higher in T2DM than in healthy controls, while there was no significant difference between the DPN and NDPN groups. Susceptibility of the putamen negatively correlated with moto- and cognitive function in T2DM. Impact: Iron-based susceptibility in the putamen, measured by QSM, can reflect the motor function in patients with type 2 diabetes, and might hint micro pathological changes in brain tissue in patients with type 2 diabetes.
High Resolution R1rho Dispersion Imaging in Swine Spinal Cord: A Specimen Study
Authors: Wang David , Li Katherine , Christiansen Mitchell , Rivera-Garcia Alan , Wang Ping
Published: November 2024

Motivation: R1rho imaging can provide novel information on dynamic processes within tissues, allowing for a more comprehensive analysis of the parameters of chemical exchange and/or intrinsic microstructure. Goal(s): To examine the feasibility of R1rho imaging in spinal cord. Approach: We performed high resolution R1rho imaging in the swine spinal cord of swine spine specimens. Results: The results showed that the dispersion is measurable in both spinal white matter and gray matter, suggesting that R1rho dispersion may have potential to characterize the de/remyelination and nerve injuries/repairs in neurological disorders. Impact: The study suggested that R1rho dispersion is measurable in spinal cord, which may have potential to characterize the de/remyelination and nerve injuries/repairs in spinal cord disorders.
Parkinson’s disease in the spinal cord: an exploratory study to establish T2*w, MTR and diffusion-weighted imaging metric values
Authors: St-Onge Samuelle , Coustaury Camille , Landelle Caroline , Dahlberg Linda , Lungu Ovidiu , Doyon Julien , De Leener Benjamin
Published: November 2024

Motivation: Much remains unknown about the impact of Parkinson's disease on the central nervous system, particularly how it affects the spinal cord microstructure. Goal(s): This exploratory study aims to establish DWI, MTR and T2*w metric values to explore potential morphometric alterations in the spinal cord related to Parkinson’s disease. Approach: DWI, MTR and T2*w metrics were extracted from both Parkinson’s disease patients and healthy controls. Results: Significant correlations were found in several regions of the spinal cord, particularly for ODI, FICVF and FA, suggesting the relevance of further studying these metrics in the spinal cord of Parkinson's disease patients. Impact: This study is the first to establish values for MTR, T2* and DWI metrics in the spinal cord of a population with Parkinson’s disease, which could contribute to a better understanding of this disease in the central nervous system.
Investigating Respiration Induced B0 Field Variations in the Thoracic and Lumbar Spinal Cord at 7T
Authors: Aigner Christoph , D'Astous Alexandre , Dietrich Sebastian , Lutz Max , Alonso-Ortiz Eva , Cohen-Adad Julien , Schmitter Sebastian
Published: November 2024

Motivation: Respiration-related B0 variations and their modulation in the thoracolumbar spinal cord (SC) at 7T have not been thoroughly investigated. Knowing B0 variations is crucial for assessing the potential of SC imaging. Goal(s): Quantification of B0 variations in the thoracolumbar SC across different respiratory states and evaluation of the effectiveness of tailored B0 shims in mitigating these variations. Approach: Non-Cartesian respiration-resolved 3D B0 field maps were acquired during free-breathing and employed for B0 shimming using the SC Shimming Toolbox. Results: Tailored B0 shims reduce the B0 variations to approximately 50Hz (standard deviation) across the thoracolumbar SC. Respiration introduces up to 40Hz of additional field-offset. Impact: Our results quantify B0 fluctuations in the thoracolumbar SC and show that customized B0 shims effectively reduce these fluctuations to a standard deviation of around 50Hz. Further investigation of deep breathing is crucial for optimizing shim strategies in future uses.
Accelerating Longitudinal Dynamic MRI by Exploiting Multi-Session Temporal Correlations
Authors: Chen Jingjia , Sodickson Daniel , Feng Li
Published: November 2024

Motivation: Longitudinal MRI scans performed on the same patient offer valuable temporal redundancy that can be exploited for image reconstruction. However, this wealth of information is usually ignored in current clinical practice, with data from different sessions typically reconstructed separately. Goal(s): This study introduces a longitudinal dynamic MRI framework that leverages temporal correlations across multiple imaging sessions to improve image reconstruction. Approach: Our reconstruction approach aims to reconstruct multi-session data jointly as a dynamic image series employing a combination of low-rank subspace and spatiotemporal constraints. Results: The initial results demonstrate that joint longitudinal reconstruction outperforms standard separate reconstructions, which may allow for additional acceleration. Impact: By exploiting image correlations across multiple sessions, our longitudinal dynamic MRI framework can improve image reconstruction and enable higher acceleration compared to standard separate reconstruction.
High-Resolution Free-breathing Perfusion MRI with High Slice Coverage via SAM in Self-Supervised Learning with Self-Regularization
Authors: Sun Changyu , Kumar Senthil , Altes Talissa
Published: November 2024

Motivation: Enhancing myocardial perfusion MRI with self-supervised learning is key to achieving higher image quality and fidelity, especially in patients with varying image matrix sizes and asymmetric echo. Goal(s): To enhance perfusion MRI by increasing resolution and slice coverage using self-supervised learning, self-regularization, and spatial attention, tailored for varied image sizes and asymmetric echo. Approach: Implemented an accelerated perfusion MRI sequence with asymmetric echo; collected data from 20 patients; developed self-LR with SAM to enhance image quality. Results: Self-LR with SAM yielded superior image quality and fewer artifacts in varied sizes and asymmetric echo, outperforming other methods, confirmed by expert evaluations. Impact: The integration of Spatial Attention Module (SAM) with Self-Supervised Learning and Self-Regularization significantly enhances myocardial perfusion MRI, enriching spatial resolution and slice coverage. This development could potentially improve diagnostic accuracy, facilitating non-invasive whole-heart assessments with improved image quality.
Rotating-view super-resolution (ROVER)-MRI reconstruction using tailored Implicit Neural Network
Authors: Lyu Jun , Ning Lipeng , Consagra William , Liu Qiang , Rathi Yogesh
Published: November 2024

Motivation: Direct acquisition of high resolution data is time-consuming and degrades SNR. Super-resolution reconstruction (SRR) is widely used to address these challenges. However, existing reconstruction tools use algorithms that are sensitive to noise and motion. Goal(s): Our study aims to develop a training-free deep learning-based SRR method that integrates multi-view thick-slice data to reconstruct images with enhanced spatial resolution and high SNR. Approach: We used an implicit neural representation (INR) network, leveraging data from scans at various views, to achieve high isotropic SRR. Results: Our technique exhibited 30% better SNR and significant motion-robustness compared to existing techniques. Impact: Implicit neural representations allow continuous functional representation of MRI images thereby being a natural candidate for performing SRR in low SNR regimes. Our study validates the feasibility of employing INRs to reduce scan time, motion artifacts, and achieve high-quality SRR.
Subspace dual-domain-loss for self-supervised deep learning reconstruction of dynamic MRI: Method and Application to CMR Multitasking
Authors: Chen Zihao , Xie Yibin , Li Debiao , Christodoulou Anthony
Published: November 2024

Motivation: Supervised deep learning (DL) can reduce reconstruction time for CMR Multitasking, but the lack of ground truth limits the quality of supervised DL to that of iteratively reconstructed labels. Goal(s): Our goal was to develop a self-supervised learning (SSL) reconstruction method, whose performance is not limited by the iterative reconstruction. Approach: We developed a dual-domain subspace SSL reconstruction method for non-Cartesian dynamic MRI, applying it to CMR Multitasking. Results: The proposed method can perform image reconstruction without reference images and shows better interscan consistency than supervised DL. Impact: With the proposed method, image quality of DL reconstruction for CMR Multitasking can potentially surpass iterative reconstruction. We applied subspace constraints to SSL reconstruction, showing an efficient way to relieve the computational burden of dynamic MRI SSL reconstruction.
Spatiotemporal atlas driven reconstruction of dynamic speech imaging
Authors: Jin Riwei , Xing Fangxu , Gilbert Imani , Perry Jamie , Woo Jonghye , Shosted Ryan , Liang Zhi-Pei , Sutton Brad
Published: November 2024

Motivation: Individuals across a population typically exhibit similar articulatory movements when performing speech tasks with specific speech samples. From an imaging experiment, we are interested in representing how an individual’s speech behavior is different from the ‘standard’ motion, which assists the preoperative planning of velopharyngeal surgery. Goal(s): We expected to visualize velopharyngeal variations between individual subjects and the average population. Approach: We have integrated an atlas into a low-rank residual reconstruction framework to capture the distinctive motion variations unique to each subject. Results: We demonstrated the ability of the method to visualize velopharyngeal variations as well as enhancing the quality of the reconstruction process. Impact: By applying a spatio-temporal atlas-driven reconstruction method, we were able to visualize and analysis velopharyngeal variations between individuals and the average population which will specifically benefit the surgical planning of individual cleft palate patients.
Optimization of sparse saturation transfer MR fingerprinting (ST-MRF) by ranking the importance of saturation transfer contrast features
Authors: Singh Munendra , Mahmud Sultan Zaman , van Zjil Peter , Heo Hye-Young
Published: November 2024

Motivation: Optimizing ST-MRF sequence design is critical to accelerate image acquisition and improve reconstruction accuracy. Goal(s): To develop a deep-learning framework that can optimize MRF acquisition for tissue parameter determination with a minimal number of scan parameter settings. Approach: An interpretable neural network was designed to optimize MRF sequences by ranking the importance of saturation contrast features and evaluated using numerical phantoms and in vivo experiments at 3T. Results: Importance-ranking network-based sequence optimization demonstrated its ability to improve the choice of scan parameter values for quantification of tissue parameters. Sequence optimization achieved 1.7-fold acquisition acceleration without compromising the fidelity of the tissue parameter quantification. Impact: Ranking the importance of saturation transfer contrast features facilitates choosing the best combination of sequence parameters for tissue quantification with fingerprinting (ST-MRF). An interpretable network based on importance ranking can significantly accelerate data acquisition for ST-MRF and conventional Z-spectral acquisition.