bluuebunny/crossref_metadata_2025 huggingface dataset:
Accuracy of Measuring Opening and Closing Characteristics of the Aortic Valve with SPEEDI MRI
Authors: Luo Qingfei , Dhar Yosman , Scotti Alessandro , Zhou Xiaohong
Published: November 2024

Motivation: get-SPEEDI, a recently published pulse sequence, offers a promising MRI technique for imaging the rapid dynamics of the aortic valve (AV) opening and closing with sub-millisecond temporal resolution. Goal(s): This study aims to assess the accuracy of measuring the AV dynamics with get-SPEEDI by comparing its performance with transthoracic ultrasound, which is the gold standard clinically. Approach: Ultrasound echocardiograms and get-SPEEDI MR images of AV were acquired in healthy human subjects with 0.6- and 0.8-ms temporal resolution, respectively. Results: There were no statistically significant differences between get-SPEEDI and ultrasound in the measurements of AV opening and closing dynamics and the maximum AV area. Impact: The dynamic characteristics of the aortic valve measured with get-SPEEDI MRI agree well with the ultrasound measurements. get-SPEEDI MRI provides a new imaging tool for diagnosis of aortic valve diseases.
bSSFP Phase Contrast (PC-SSFP) at 0.55T for Aortic Flow
Authors: Xiang Jie , Ramasawmy Rajiv , Seemann Felicia , Peters Dana , Campbell-Washburn Adrienne
Published: November 2024

Motivation: Aortic flow imaging at 0.55T has limited SNR, especially in diastolic phases, which can limit the accuracy of flow and regurgitant fraction measurements. Goal(s): Our goal was to improve the SNR of phase contrast images at 0.55T using a bSSFP readout and maintain accurate flow quantification. Approach: We developed a through-plane phase contrast sequence with bSSFP readout and tested it in phantoms and healthy volunteers. Results: Significantly improved SNR (25.5±9.6 vs 8.2±2.9, p<0.001) and accuracy of velocities measurement were demonstrated in phantom. Accurate flow and constant high SNR were measured using our method in 8 subjects, for both breath-hold and free-breathing cases. Impact: Our proposed phase contrast with bSSFP readout can reliably evaluate aortic flow at 0.55T with higher SNR and VNR leading to more accurate quantification compared to conventional GRE based phase contrast method.
Accelerated free-breathing 3D simultaneous Bright and black-blood Whole-Heart Imaging at 0.55T: Comparison to 1.5T.
Authors: Fotaki Anastasia , Castillo-Passi Carlos , Crabb Michael , Kunze Karl K , Pushparajah Kuberan , Botnar Rene
Published: November 2024

Motivation: Bright-blood and black-blood MRI is fundamental for the comprehensive assessment of cardiac disease.Low field MRI promises high quality imaging at lower cost and improved patient accessibility.3D bright-blood and black-blood whole-heart imaging applications at low-field are limited. Goal(s): To implement and evaluate the diagnostic quality of a novel framework for simultaneous 3D whole-heart bright-and black-blood(BOOST) imaging at 0.55T. Approach: 3D whole-heart BOOST including image-based navigation, motion corrected reconstruction and patch-based denoising was evaluated at 0.55T.Diagnostic capabilities of 3D BOOST was evaluated versus its counterpart at 1.5T. Results: 3D whole-heart BOOST at 0.55T offers good quality imaging, that is comparable to its counterpart at 1.5T. Impact: This study demonstrates that high-quality bright- and black-blood 3D whole-heart imaging can be achieved at 0.55T CMR, showing comparable image and diagnostic quality to the current clinical standard in ­both healthy subjects and clinical patients.
Initial demonstration of free-running whole-heart radial 4D flow using ultra-short echo times (UTE)
Authors: Akporeha Efena , Ferincz Robin , Rutz Tobias , Rossi Giulia , Falcao Mariana , Quesada Isabel , Yerly Jérôme , van Heeswijk Ruud , Prsa Milan , Markl Michael , Stuber Matthias , Roy Christopher
Published: November 2024

Motivation: Free-running phase-contrast whole-heart MRI, despite its ease of use and efficiency may suffer from artifacts and inaccuracy in the presence of non-laminar flow and high velocities due to prolonged echo times. Goal(s): Our goal was to reduce the echo time in free-running radial 4D flow (radial 4D flow). Approach: We incorporated ultra-short echo time (UTE) imaging techniques into our radial 4D flow framework. Results: Net flow and peak flow measurements that correlate well with reference values were obtained in a cohort of healthy volunteers with the modified sequence and enabled qualitative evaluation of turbulent flow in patients with congenital heart disease. Impact: The combination of UTE and radial 4D flow MRI marks a step towards a more precise and robust quantification of hemodynamics under complex conditions, in an effort to improve both diagnosis and patient outcomes.
Multiparametric imaging of dopaminergic, cholinergic and noradrenergic contribution in olfactory dysfunction in Parkinson’s disease
Authors: Perot Jean-Baptiste , Fraysse Audrey , Ouarab Salim , Rebbah Sana , Lejeune François-Xavier , Massy Emma , Gaurav Rahul , Arnulf Isabelle , Corvol Jean-Christophe , Vidailhet Marie , Pyatigorskaya Nadya , Gallea Cécile , Lehéricy Stéphane
Published: November 2024

Motivation: Olfactory dysfunction is an early symptom of Parkinson’s disease (PD), involving impairments of cholinergic, dopaminergic, and noradrenergic networks. Patients with idiopathic REM sleep behavior disorder (iRBD), a prodromal phase of PD, are associated with anosmia and severe noradrenergic defects. Goal(s): This study aimed to decipher the contribution of cholinergic, dopaminergic, and noradrenergic alterations in olfactory impairments in the presence or absence of RBD. Approach: Multiparametric imaging highlighted specific alterations of the Locus Coeruleus (LC) and Nucleus Basalis of Meynert that correlated with olfactory scoreResults: Alterations were modulated by the presence of RBD, suggesting specific progression pattern. Impact: Our results show that olfactory dysfunction originates from different altered subcortical nodes in Parkinson’s disease patients depending on the presence of sleep disorder. This suggests that patients with sleep disorder display different progression pattern of Parkinson’s disease.
Effect of iron content on GSH levels in the basal ganglia and midbrain in young healthy controls
Authors: Zhang Youmin , He Naying , Wu Peng , Yu Qiurong , Yao Yefeng , Haacke Ewart , Yan Fuhua
Published: November 2024

Motivation: It is important to characterize the relationship between oxidative stress and iron deposition in neurodegenerative diseases. Goal(s): To investigate if there is a correlation between the glutathione (GSH) levels and brain iron in young healthy controls. Approach: Taking advantage of proton magnetic resonance spectroscopy (1H-MRS) with MEscher-Garwood Point RESolved Spectroscopy (MEGA-PRESS) and quantitative susceptibility mapping to detect GSH and iron levels. Results: An intriguing phenomenon was found that as the iron content increased in the putamen, substantia nigra, and red nucleus, the GSH level showed an increasing trend in the basal ganglia and midbrain region respectively. Impact: The relationship between oxidative stress and excessive iron deposition is complicated. This preliminary study offers new insight to investigate the time sequence in iron homeostasis and oxidative stress.
SimulScan and Partial Least Squares: Linking speech and swallowing dynamics to brain function
Authors: Bosshardt Anthony , Malandraki Georgia , Sutton Bradley
Published: November 2024

Motivation: SimulScan enables the joint imaging of dynamic oropharyngeal movements during speech and swallowing along with their central control from functional MRI. Goal(s): The large, complex dataset requires memory-efficient analysis to find the uncover the underlying relationships between the dynamic and functional imaging data. Approach: Here we develop a memory-efficient implementation of partial least square (PLS) and apply it to a blocked tongue tapping task. Results: The PLS method separates out correlated motions and brain function for different components of the task. Impact: SimulScan with PLS analysis can enable the visualization of central control of complex processes such as speech and swallowing. This approach will enable the in-depth study of healthy and disordered speech and swallowing mechanisms in age and disease.
Underlying mechanism of hemodynamic and fMRI response to optogenetic stimulation of somatostatin neurons.
Authors: Vo Thanh , Jin Tong , Kim Seong-Gi
Published: November 2024

Motivation: SST neurons, 30% of cortical interneurons, are crucial in interpreting fMRI data and understanding neurovascular coupling within the cortex. Goal(s): In this study we want to investigate the the SST-induced hemodynamic response Approach: we used several methods such as neural recording, BOLD-fMRI, and optical intrinsic signaling (OIS) with pharmacological applications. Results: We observed SST neuron activation causing local neural inhibition, resulting in negative BOLD-fMRI at projection sites. Additionally, it triggered initial NO-induced fast vasodilation, followed by astrocyte-mediated slow vasodilation. Impact: BOLD-fMRI reflects neural activity changes, yet certain interneurons induce hemodynamic responses without altering neural activity. Studying SST-induced responses is vital for interpreting fMRI.
Investigating hippocampal-cortical and cortical-cortical connectivity changes during pattern separation: a 7T fMRI study
Authors: Zhuang Xiaowei , Yang Zhengshi , Koenig Katherine , Leverenz James , Curran Tim , Lowe Mark , Cordes Dietmar
Published: November 2024

Motivation: Studies have extensively demonstrated roles of hippocampus and its subdivisions during pattern separation, but cortical involvement has not yet been elucidated. Goal(s): Our goal is to evaluate whole-brain functional connectivity changes during pattern separation. Approach: We compared cortical-hippocampus and cortical-cortical FNCs during a total of 258 correct and incorrect lure discrimination trials, using high-resolution and high-quality 7T fMRI data. Results: Cortical-CA3DG FNCs and cortical-CA1 FNCs were significantly involved during pattern separation and completion, respectively. Around 83.35% cortical-cortical connections were with higher FNCs during lure discriminations, indicating their potential involvement during pattern separation. Impact: Besides hippocampus and its subdivisions, cortical regions and its connections to hippocampus might be extensively involved in pattern separation process.
Joint learning of optimal acquisition and high quality ADC mapping for low field diffusion-weighted PROPELLER MRI
Authors: Wang Jiechao , Wang Lu , Zhang Chunguang , Lin Liangjie , Cai Congbo , Cai Shuhui
Published: November 2024

Motivation: Adequate image signal-to-noise ratio (SNR) and resolution within a reasonable scan time is challenging for low-field diffusion quantitative MRI. Goal(s): To present a PROPELLER-acquisition and ADC mapping joint learning neural network to accelerate DWI with improved image SNR and resolution. Approach: Considering the similar anatomical structure between diffusion-weighted MR images, this work achieved DWI PROPELLER-acquisition optimization and reconstructed high quality ADC maps from data acquired by optimized acquisition using U-net. Results: In vivo and simulation results demonstrate that our proposed method can generate high SNR and resolution ADC maps within 2 minutes scan time under 0.23T human scanner. Impact: Joint learning acquisition and quantitative reconstruction based on PROPELLER sampling trajectory using neural network has successfully achieved fast ADC mapping, offering great possibility for quantitative analysis in low-field diffusion MRI.
Deep learning radiomic nomogram can distinguish intracranial solitary fibrous tumor from angiomatous meningioma: a multicenter study
Authors: Liang Xiaohong , Ke Xaioai , Zhou Junlin , Zhao Liqin
Published: November 2024

Motivation: A novel and noninvasive method for distinguishing intracranial solitary fibrous tumor (ISFT) from angiomatous meningioma (AM) and predicting patient outcomes is urgent. Goal(s): To evaluate the value of a MRI-based deep learning radiomic nomogram (DLRN) in distinguishing ISFT from AM and predicting patient outcomes. Approach: A MRI-based DLRN was developed on training cohort (TC). We then validated it's performance on external validation cohort (EVC). Moreover, we investigated the value of the DLRN in survival analysis. Results: The performance of DLRN was excellent (0.86 [0.84–0.88]) on EVC. Besides, DLRN was significantly associated with the overall survival (OS) of patients (n=273). Impact: The proposed DLRN can potentially provide a noninvasive method for neurosurgeon to offer decision support for developing personalized treatment plans and predicting patient outcomes.
Assessment of structural-functional integration impact on connectivity abnormalities in glioma patients
Authors: Colpo Maria , Silvestri Erica , Cecchin Diego , Corbetta Maurizio , Bertoldo Alessandra
Published: November 2024

Motivation: Brain networks glioma’s disruption was often explored through separate examinations of structural and functional connectivity. However, there were limited efforts in glioma research to investigate the interplay between structure-function and how this connection might influence our comprehension. Goal(s): Can integrating structural and functional connectivity aid understanding the alterations’ neurobiological foundation in brain networks caused by glioma?Approach: The study design involves standard diffusion MR and rs-fMRI preprocessing, statistical methods including Pearson and Spearman correlation and Euclidean distance computation. Results: This study underscores the significance of examining structure-function integration, where both microstructure and function play crucial roles in relation to white matter integrity. Impact: Glioma, the primary brain tumor, affects both structural and functional connectivity. Understanding alterations in structure-function integration and connection with single-modalities, may be of highest significance for a more comprehensive explanation of compensatory mechanisms induced by glioma and its clinical progression.
Radiomics for predicting Grades, IDH mutation and MGMT promoter methylation of Adult Diffuse Gliomas: Combination of structural MRI, ADC and SWI
Authors: Zhu Zhengyang , Zhou Jianan , Yang Huiquan , Liang Xue , Zhang Xin , Zhang Bing
Published: November 2024

Motivation: WHO Grade, IDH mutation and MGMT promoter methylation are important for precise diagnosis and treatment plans for diffuse glioma patients. Goal(s): This study aimed to investigate the predictive value of radiomics features extracted from Structural MRI, ADC and SWI. Approach: Radiomic features were extracted from T1WI, T2WI, T1CE, FLAIR, ADC and SWI. Analysis of variance F-test were used for feature selection. 11 classifiers were utilized for model establishment. Results: For WHO Grade task, the highest AUC was 0.990; for IDH mutation task, the highest AUC was 0.947. All the constructed models failed to predict MGMT promoter methylation status efficiently. Impact: This work will help neuro-oncologists better understand the radiological manifestation of gliomas.
Using a Video Diffusion Model-prior for reconstructing undersampled dynamic MR-data – An application to real-time cardiac MRI
Authors: Schad Oliver , Heidenreich Julius , Petri Nils-Christian , Petritsch Bernhard , Wech Tobias
Published: November 2024

Motivation: MR-based “real-time” imaging of dynamic processes, as the beating heart, often depends on fast (undersampled) scans, which are subsequently reconstructed by algorithms exploiting prior knowledge. Spatio-temporal models describing the data in suboptimal manner can thereby lead to residual artifacts. Goal(s): A high-quality model to regularize the reconstruction of real-time cardiac MRI based on undersampled spiral data acquisitions. Approach: A video diffusion model was trained using cine videos in magnitude reconstruction and subsequently applied as a prior in a plug-and-play FISTA approach. Results: Reconstructions of undersampled real-time frames with higher image quality than a low rank plus sparse approach. Impact: We show the potential of probabilistic video diffusion models as a promising prior in iterative reconstructions of undersampled dynamic MR data. In our example, the approach enabled high quality real-time cardiac functional MRI in patients with arrhythmia.
Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI
Authors: Qiu Shihan , Pan Shaoyan , Liu Yikang , Zhao Lin , Xu Jian , Liu Qi , Chen Terrence , Chen Eric , Chen Xiao , Sun Shanhui
Published: November 2024

Motivation: Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring. Goal(s): To improve image sharpness and motion delineation for cine MRI under high undersampling rates. Approach: A combined non-generative reconstruction and diffusion enhancement model along with a novel paired sampling strategy was developed. Results: The proposed combined method provided sharper tissue boundaries and clearer motion than the original reconstruction in experts’ evaluation on clinical data. The innovative paired sampling strategy substantially reduced artificial noises in the generative results. Impact: The approach has the potential to improve reconstruction quality in highly accelerated cardiac cine imaging. The novel paired sampling for diffusion generation may be applied to other conditional tasks to reduce the artificial noises stemming from noisy training data.
Unified Diffusion model for Multi-contrast Ensembling Synthesis
Authors: Lee Yeeun , Shin Yejee , Park Doohyun , Son Geonhui , Eo Taejoon , Hwang Dosik
Published: November 2024

Motivation: Scanning for multi-contrast MR images is time-consuming. To reduce scan time, it is beneficial to explore methods for efficiently synthesizing target contrast images from existing contrast scans. Goal(s): To address the stability issues encountered when dealing with multi-contrast MR image domains individually, we propose a methodology for effectively synthesizing images while incorporating multi-contrast domains. Approach: Our model is a novel unified diffusion model (UDM) that improves the synthesis of detailed anatomical structures in target contrast images through an ensemble method. Results: UDM demonstrates effectiveness across multiple domains, outperforming existing methodologies in synthesizing images for each contrast domain. Impact: By reducing scan times and costs for multi-contrast imaging, UDM facilitates prognosis prediction and treatment planning. This method is not only usable for image synthesis but also extendable to various applications such as reconstruction.
Real-time motion correction and multicoil shim array B0 update for whole-brain MR spectroscopic imaging
Authors: Andronesi Ovidiu , Frost Robert , Arango Nicolas , Jayadev Nutandev Bikkamane , Chang Yulin , Wighton Paul , Hoffmann Malte , Stockmann Jason , van der Kouwe Andre
Published: November 2024

Motivation: Very high quality of MR spectroscopic imaging (MRSI) data is needed for robust and reproducible metabolite quantification. This critically depends on the B0 shimming and scan stability. Integrated RF-receive/B0-shim arrays significantly improve spectral quality. Goal(s): Real-time motion correction and multicoil shimming update with an integrated RF-receive/B0-shim array for robust whole-brain MRSI. Approach: We developed a rapid navigator for head tracking and B0 fieldmapping in combination with rapid processing for real-time update of multicoil shim currents and MRSI localization. Results: Real-time motion correction and multicoil shimming provides significantly narrower linewidth, higher signal-to-noise, reduced quantification errors and reproducible metabolic imaging. Impact: Whole-brain MRSI is a unique method for non-invasive mapping of brain neurochemistry, and in combination with real-time motion correction and multicoil shim array update provides robust and reproducible quantitative metabolic imaging for clinical use.
A targeted Fe-based MR contrast agent for glioma imaging
Authors: Zhu Yue , Zhang Lei , Chen Shizhen , Zhou Xin
Published: November 2024

Motivation: Neurological adverse effects caused by the deposition of gadolinium-based contrast agents in the brain remain uncertain, and it is necessary to develop a biocompatible alternative molecule for evaluation of glioma malignancy and site occupancy. Goal(s): To demonstrate that Fe-based contrast agents have superior safety, stability, and longevity in glioma imaging compared to gadolinium-based contrast agents. Approach: The T7-Fe-PyC3A was synthesized, and its stability was validated through in vitro kinetic thermodynamic experiments. Furthermore, A in situ glioma model mice was choosen for investigating the imaging ability. Results: A targeted Fe-based MR contrast agent provides clear glioma contours with a 15-60 minutes post-dose imaging window. Impact: Although gadolinium-based contrast agents are widely used in the clinic, iron-based contrast agents targeting gliomas show the enhanced safety and stability profile. The extended imaging window allows them as preferable alternative for gliomas imaging.
Longitudinal Changes of Hepatic Mechanical Properties in Patients with Early-Stage HCV Infection Treated with Direct-Acting Antiviral Agents
Authors: qiu Caixin , Owusu Nana , Glaser Kevin , Li Jiahui , Wu Hao , Venkatesh Sudhakar , Simonetto Douglas , Richard Ehman , Yin Meng
Published: November 2024

Motivation: While DAAs have significantly improved the treatment of HCV patients, some individuals still face relapse or progression. There is currently a deficiency in non-invasive and simple methods for effectively monitoring the longitudinal treatment effects. Goal(s): To develop a reliable biomarker for detecting inflammatory response to DAA treatment in the early stages of HCV infection. Approach: Longitudinal monitoring of changes in early-stage HCV patients after DAA treatment using multiparametric MRE. Results: Multi-parameter MRE can detect HCV at the early stage and can monitor both short-term inflammatory response and long-term treatment effect of the liver in HCV patients after DAA treatment. Impact: Developed a non-invasive biomarker for monitoring the efficacy of DAA treatment in early-stage HCV patients.
Magnetic Resonance Cholangiopancreatography at 5 T: Quantitative and Qualitative Comparison with 3 T
Authors: Yin Liang , Li Zhongchang , Tang Bowen , Yu Dan , Gan Jie
Published: November 2024

Motivation: To evaluate the 5 T MRI’s effectiveness for MRCP imaging. Goal(s): To demonstrate 5 T imaging's comparability in visualization, SNR, and image quality to 3 T. Approach: MRCP was performed on subjects using both 3 T and 5 T MRI, assessing the bile duct tree visualization and SNR, with radiologists evaluating image quality and artifacts. Results: 5 T revealed superior bile duct tree visualization, with comparable SNR, image quality, and artifact management to 3 T. Impact: The study highlights 5 T MRI's potential in MRCP, suggesting improved biliary visualization which could lead to better clinical outcomes and guide further technological advancements in non-invasive diagnostics.
Brain functional connectome phenotype relates to psychopathology in middle-aged and older adults
Authors: Nguyen Thuan Tinh , Ng Kwun Kei , Koi Janice Jue Xin , Zhou Juan Helen
Published: November 2024

Motivation: To better understand brain network vulnerability in mental health disorders in the understudied middle-aged and older adults and see if it fits the hierarchical model of psychopathology. Goal(s): We sought to unveil the structure of psychopathology through investigating the brain phenotypes underlying various mental outcomes using the UK Biobank cohort. Approach: We investigated how brain functional connectivity relates to 36 mental outcome items using a multivariate partial least squares correlation approach. Results: Across middle-aged and older adults, we identified a general disease factor and another reflecting the divergence between alcohol addiction and depression/PTSD related symptoms, consistent with the hierarchical model of psychopathology. Impact: These findings highlighted the importance of characterizing mental disorders in terms of transdiagnostic dimensions instead of separate disorders. It also shed light on how imaging biomarkers can be used to characterize population suffering from poor mental health.
Explainable depression classification: a machine learning approach based on brain network size and functional connectivity
Authors: Pilmeyer Jesper , Koolen Lisa , Breeuwer Marcel , Jansen Jacobus , Zinger Svitlana
Published: November 2024

Motivation: Major depressive disorder (MDD) affects ~6% of adults annually worldwide, but a lack of understanding of the pathology and heterogeneity may underlie its low treatment effectiveness. Goal(s): This study aimed to identify explainable functional MRI biomarkers of MDD on an individual level. Approach: Classification models were run to predict MDD for three functional measures. Results: In two datasets, >70% MDD accuracy was achieved for each measure. Highest performance was obtained with region-based functional connectivity but spatial extent provided novel perspectives on abnormal brain functioning, such as decreased cerebellum involvement in the frontoparietal network, potentially reflecting decreased emotion regulation or control during cognitive processes. Impact: This MRI research contributes to the identification of robust depression biomarkers, enhancing our understanding of the abnormal brain functioning. The explainability of the spatial extent feature provides additional insights into its pathology which may be utilized for diagnostic tools.
Distinct functional connectivity of anterior cingulate cortex subregional networks in first-episode and recurrent major depressive disorder
Authors: Zhou Zilin , Cao Lingxiao , Gao Yingxue , Bao Weijie , Tang Mengyue , Li Hailong , Zhang Lianqing , Sun Huaiqiang , Gong Qiyong , Huang Xiaoqi
Published: November 2024

Motivation: The fine-grained anterior cingulate cortex (ACC) subregional functional connectivity alterations in first-episode and recurrent major depressive disorder (MDD) remained unclear. Goal(s): To obtain optimal functional ACC subdivisions and explore alterations in intrinsic functional connectivity of ACC subregional networks in first-episode and recurrent MDD. Approach: We utilized a data-driven connectivity-based parcellation to obtain optimal ACC subdivisions, calculated ACC subregional functional connectivity, and compared among first-episode, recurrent MDD patients and healthy controls. Results: Ventral and dorsal ACC per hemisphere were identified as optimal parcellation. The ACC subregional connectivity was reduced in all MDD patients, while dorsal ACC connectivity was significantly reduced only in recurrent patients. Impact: Our discovery of impaired functional architectures of ACC subdivisions in MDD, with a more prominent disrupted connectivity of dorsal ACC in relapsed patients, emphasize a potential role of ACC subregional connectivity in distinguishing MDD at different episodes and predicting relapse.
Abnormal Topology and Connectivity of Structural Covariance Network Related to Diagnosis and Phenotyping in Major Depressive Disorder
Authors: Qin Kun , Long Jing-Yi , Pan Nanfang , Kong Cunqing , Liu Weiyin Vivian , Chen Wen , Li Yi
Published: November 2024

Motivation: Findings on brain network abnormalties in major depressive disorder (MDD) were mixed owing to small-scale and single-site designs. The diagnostic value of network topology and connectivity remain unclear. Goal(s): To identify robust structural network abnormalities in MDD and relevant clinical phenotypes and to discern the diagnostic value of network topology and connectivity. Approach: Group-level comparsion and individual-level machine learning classification was performed based on structural covariance network connectivity and topological metrics. Results: Different patterns of network topology and connectivity abnormalities were observed between first-episode drug-naive and recurrent patients with MDD. Topological metrics enabled more accurate classification performance on MDD diagnosis and phenotyping. Impact: Our findings advance the current understanding of network-level neurobiological mechanisms of MDD, providing a solid basis for future development of network topology-based diagnosis models.
Toward Hypoxia Imaging for Adaptive Dose Painting: Optimization of MP2RAGE for T1 Mapping in a Low-Field 0.35T MR-Linac (MRL)
Authors: Park Claire , Warner Noah , Kaza Evangelia , Sudhyadhom Atchar
Published: November 2024

Motivation: Stereotactic MR-guided Adaptive Radiation Therapy (SMART) ‘dose painting’ for hypoxia can improve treatment outcomes, but implementation on MR-Linac (MRL) faces low-field SNR challenges. Optimization of T1-to-noise ratio at 0.35T will enable clinical implementation of oxygen-enhanced (OE)-MRI. Goal(s): Develop and validate an optimized MP2RAGE sequence for low-field T1 mapping, establish feasibility and evaluate reproducibility in phantoms and healthy subjects. Approach: We optimized and validated an MP2RAGE sequence with simulations and a ground-truth phantom. T1 mapping feasibility was established in healthy subjects, variability and reproducibility was assessed. Results: A clinically feasible optimized low-field MP2RAGE protocol was developed, yielding accurate and reproducible T1 mapping. Impact: This work builds a foundation towards clinically feasible hypoxia imaging for low-field MRL. This would facilitate a paradigm shift toward MR-guided biological adaptation and dose painting, leveraging the spatial distribution of hypoxia, and improving patient outcomes in conventionally challenging-to-treat cancers.
T1 Contrast-Augmented Single-Spoke Real-Time 4D MRI
Authors: Feng Li , Chen Jingjia , Xia Ding , Chandarana Hersh , Sodickson Daniel
Published: November 2024

Motivation: 4D MRI is a powerful technique for free-breathing volumetric imaging, holding great potential for application in MRI-guided radiotherapy. Traditional 4D MRI typically requires explicit motion detection and data binning for respiratory-resolved reconstruction and commonly employs fast steady-state MRI acquisition, which may not yield optimal contrast. Goal(s): This work proposes a novel T contrast-augmented, free-breathing, real-time 4D MRI technique. Approach: The proposed technique improves image contrast through highly-accelerated inversion recovery-prepared acquisition and reconstruction of real-time 4D images at a sub-second temporal resolution without requiring explicit motion compensation. Results: T1 contrast-augmented real-time 4D MRI demonstrated improved image contrast over conventional 4D MRI with steady-state acquisition. Impact: The contrast-augmented real-time 4D MRI technique proposed in this work can improve image contrast for free-breathing imaging without requiring explicit motion detection, data binning and respiratory motion compensation. It holds great potential for various applications, such as MRI-guided radiotherapy.
SPARTA-3D: 3D Real-time Tracking Solution for Interventional MRI
Authors: Hu Yichen , Hu Junpu , Zhong Zheng , Padua Abraham , Liu Qi , Ye Yongquan , Xu Jian
Published: November 2024

Motivation: MRI excels in exceptional soft tissue delineation and its radiation-free nature in interventional contexts. However, achieving effective 3D real-time tracking with MRI poses a significant challenge. Goal(s): To facilitate 3D real-time monitoring and navigation, achieving a high temporal resolution and enhanced spatial resolution that precisely captures the movement of small interventional instruments. Approach: We utilized a manually controlled device and employed highly accelerated golden-angle rotated spiral-in/out sequence with randomized variable density kz encoding, along with a specifically tailored iterative reconstruction algorithm. Results: High temporal (150 ms/phase) and spatial (0.7×0.7×1.5 mm3) resolutions were achieved, smoothly visualizing minute movements in 3D within our phantom setup. Impact: The SPARTA-3D approach holds the promise of revolutionizing interventional MRI by meeting the crucial demand for 3D real-time tracking and navigation. It could facilitate more rapid and less invasive procedures, opening up novel avenues in the domain of MR-guided interventions.
Tissue detail of pregnant body models needed to estimate SAR for fetal MRI
Authors: Yetisir Filiz , Turk Esra Abaci , Adalsteinsson Elfar , Grant P.
Published: November 2024

Motivation: Due to the large variation of the body shape and size in the pregnant population, several pregnant body models are needed in fetal MRI safety studies. However, creating detailed pregnant body models is challenging. Goal(s): Our goal is to investigate the effect of reducing maternal tissue detail on maternal and fetal SAR. Approach: We simplified >18 maternal tissues into 3 (muscle, lung and fat) in 7 pregnant body models and quantified the change in maternal and fetal SAR. Results: The value and location of maternal and fetal SAR changes by up to 13% and 34 mm with maternal tissue detail reduction. Impact: Simplification of maternal tissues into muscle, lung and fat changes maternal and fetal SAR (<14%) significantly less than the variation of SAR between pregnant subjects (>x2) and might provide a good trade-off between accuracy and practicality in RF safety studies.
Advances in Musculoskeletal Imaging: The Potential of Wireless Metasurface Coils.
Authors: Düx Daniel , Kowal Robert , Schröer Simon , Maune Holger , Speck Oliver , Wacker Frank , Gutberlet Marcel , Hensen Bennet
Published: November 2024

Motivation: Musculoskeletal imaging (MSK) traditionally employs specific coil designs for distinct anatomical areas. Metasurface coils (MC) are innovative, light-weighted, and wireless coils promising for MSK imaging. Goal(s): Primary objective of the MC was similar image quality but better patient comfort compared to standard coils. Approach: Signal-to-noise ratios (SNRs) in elbow, knee, foot and hand imaging of conventional coils were compared with the MC. Results: The MC showed improvement in SNR values for hand and foot. Imaging of knee and elbow was improved as well, but with less SNR near the spine coil. The MC's design streamlined positioning, maneuverability and can improve patient comfort. Impact: Metasurface coils promise transformative benefits for future musculoskeletal imaging, matching the image quality of standard coils with increased flexibility and patient comfort. Their versatility allows compatibility across MRI vendors, only depending on the field strength of the designated coil.
Fast spin-echo triple-echo dixon (fTED) for fat suppression at 0.5T in the presence on knee implant.
Authors: Rajamani Sajith , Agarwal Harsh , Narula Arjun , Patil Uday , Venkatesan Ramesh
Published: November 2024

Motivation: Fat saturation in musculoskeletal MRI at 0.5T in the presence of knee implants. Goal(s): Echo spacing requirement of Fast spin-echo triple-echo Dixon (fTED) renders it as a preferred option for fat suppression even in the presence of implants without significant scan time penalty. Approach: Commercially available fTED MRI was used to acquire 3 echoes (intra echo spacing of 6.71msec) between two consecutive refocusing pulses of FSE data acquisition at 0.5T research MRI system. Results: Good quality fat suppression was observed in PD fat saturation knee MRI over 3 patients with knee implants. Impact: Low and mid field MRI can benefit from fTED algorithm for fat suppression where water and fat are resonating in close proximity (in Hz) of each other without significant scan time penalty.
Single point Dixon for water-fat separation in musculoskeletal TSE imaging
Authors: Van Anh , Weiss Kilian , Argudo Mateo , Boehm Christof , Feuerriegel Georg , Gersing Alexandra , Karampinos Dimitrios
Published: November 2024

Motivation: TSE Dixon imaging is highly desired in clinical musculoskeletal imaging for its versatile contrasts. However, conventional Dixon imaging needs at least two different echo times, resulting in prolonged scan time. Goal(s): To develop a single point Dixon imaging technique for TSE acquisition (sTSE-Dixon) that provide fat-separated, water-separated and in phase images from a single echo time acquisition. Approach: Shifted single echo TSE acquisition was combined with smoothness-constrained non-linear inverse water fat problem. Results: High quality water-separated, fat-separated and in phase images comparable to those obtained with conventional two-point Dixon were achieved from a single echo time shifted TSE acquisition. Impact: In this work, a single echo time TSE sequence can deliver three image sets that are of great clinical need, including fat-separated images (comparable to standard T1-weighted), water-separated images (comparable to STIR), in-phase images (comparable to standard T2-weighted)
Classifiers for ADHD Based on Gray-White Matter Structural Connectivity Couplings and Corresponding Transcriptional Signatures
Authors: Pan Nanfang , Long Yajing , Chen Ying , Gong Qiyong
Published: November 2024

Motivation: The research aims to uncover intricate gray-white matter structural connectivity (GWSC) patterns and associated gene expression profiles in ADHD. Goal(s): Develop machine-learning classifiers based on GWSC to distinguish ADHD from controls, bridging its gap with gene expression to unveil neurobiological mechanisms. Approach: Utilize T1-weighted and diffusion-weighted MRI data to construct GWSC networks. Employed four machine-learning classifiers for classification. Analyzed transcriptomes from the Allen Human Brain Atlas to link with gene expression. Results: Classifiers achieved over 75% accuracy, with Gaussian-kernel SVM leading at 82.6%. Ventromedial prefrontal cortex emerged as a key contributor. Transcriptome analysis identified enrichment in "neuron projection development."Impact: These findings empower clinicians with accurate ADHD classifiers and pinpoint the ventromedial prefrontal cortex as a hub. The revelation of gene expression nuances in neuron projection development advances targeted interventions, fostering a shift towards more personalized and effective ADHD treatments.
MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas
Authors: Zheng Fei , Yin Ping , Wang Yujian , Hao Wenhan , Hao Qi , Chen Xuzhu , Hong Nan
Published: November 2024

Motivation: Encephalitis and glioma can appear very similar in atypical cases. However, their treatment protocols differ significantly. As such, distinguishing between these two diseases is crucial. Goal(s): Our objective is to assess and compare the performance of various machine learning (ML) techniques in discriminating between encephalitis and glioma in atypical cases. Approach: We compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and assess the effectiveness of utilizing radiomics features extracted from both CML and DL in distinguishing encephalitis from glioma in atypical cases. Results: ML models can distinguish between encephalitis and glioma in atypical cases. Impact: Surgery is commonly considered as the initial treatment for glioma, while non-operative therapy is the primary approach for managing encephalitis. Precise identification of glioma and encephalitis facilitates physicians in avoiding misdiagnosis and delays in treatment.
Platelet Rich Plasma Effects on Knee Cartilage and Osteoarthritis Symptoms: T2, UTE-T2* and Patient Reported Outcomes Over 6-months Follow-Up
Authors: Williams Ashley , Asare Daniella , Torres Holly , Chu Constance
Published: November 2024

Motivation: Clinical evidence of structural benefits to cartilage from Platelet Rich Plasma (PRP) injections to treat knee osteoarthritis is lacking. Goal(s): Our goal was to use T2 and UTE-T2* relaxation times to assess cartilage structural improvements following PRP therapy. Approach: Patient reported outcomes, T2 and UTE-T2* maps were acquired before and 6 months after completion of PRP treatment in 50 patients with symptomatic knee osteoarthritis. Results: On average, participants reported symptomatic improvements following PRP. Significant changes to cartilage T2 and UTE-T2* were also observed. Improvements in patient-reported knee function and stiffness correlated to concurrent decreases in both T2 and UTE-T2*. Impact: qMRI evaluation of the clinical efficacy of PRP treatment of symptomatic knee OA shows that changes to patient reported knee function associate with concurrent changes to cartilage structure assessed with T2 and UTE-T2*.
The application of multi-modal fMRI to investigate coma induction induced by Endothelin-driven brainstem injury
Authors: Man Weitao , Liu Xiaochen , Xie Zeping , Gomez-Cid Lidia , Jiang Yuanyuan , Yu Xin
Published: November 2024

Motivation: This study aimed to create a reproducible coma animal model and develop neuroimaging techniques to illustrate underlying mechanisms during coma induction. Goal(s): To investigate brain dynamic changes during coma induction and to identify the key regulatory brain regions involved in the process. Approach: We induced a brainstem coma in rats, optimizing surgical procedures and utilizing multi-modal fMRI techniques. We recorded Glu signals and BOLD fMRI simultaneously during coma induction. Results: The study revealed specific Glu-oscillations before coma induction and identified certain subcortical nuclei as potential key regulatory brain regions for inducing coma. These findings could enhance our understanding of coma-related brain state changes. Impact: The optimized coma model and multi-modal imaging techniques in this study offer a deeper understanding of coma dynamics. The identification of specific regulatory brain regions and Glutamate oscillations may pave the way for improved clinical strategies and patient outcomes.
Measure the synchrony of fMRI signals on group-averaged fiber architectures
Authors: Chen Yuhao , Li Luying , Liang Huilou , Zhang Miaoqi , Zhang Wenjing , Lui Su , Yang Zhipeng , Zhao Yu
Published: November 2024

Motivation: Fiber architecture-informed synchrony mapping (FAISM) has been proposed to capture task-evoked nonlinear brain activations, which involves image acquisitions of fMRI and high angular resolution diffusion image MRI (HARDI) with a long scan time and thus limits its applications. Goal(s): The aim of this study is to modify the FAISM approach to mapping brain activation without time-consuming acquisitions of the HARDI data. Approach: Hence, group-averaged fiber structures were used to replace the individual data to perform a fast FAISM. Results: This approach demonstrates a high reproducibility at an individual level, which suggests that it can be used for reliable detections of nonlinear brain activation. Impact: This approach is expected to serve as a standardized tool to measure neural activations with nonlinear hemodynamic responses.
PhyCHarm : Physics-Constrained Deep Neural Networks for Multi-Scanner Harmonization
Authors: Lee Gawon , Lee Junhyeok , Ye Dong Hye , Oh Se-Hong
Published: November 2024

Motivation: The MR scanner effect in a multi-site dataset can affect bias in statistical analysis or reduce generality in deep neural networks. Goal(s): We aim to suggest a MR physics-informed harmonization framework (PhyCHarm) that generates consistent quantitative maps and harmonized T1w images. Approach: We introduce a Quantitative Maps Generator and a Harmonization Network to be trained with a constraint loss based on a signal equation. Results: PhyCHarm shows the highest evaluation scores in both networks and consistent segmentation accuracy in the downstream task (FSL FAST GM and WM segmentation). Impact: PhyCHarm works based on the Bloch equation. PhyCHarm enables us to reduce scanner effects efficiently in the dataset before conducting test/retest, longitudinal, or multi-site studies. It can be helpful to ensure deep neural networks' generality.
A Newly Designed Hyperpolarized Aminopeptidase N Probe Sensitively Detects Early Therapeutic Responses Heterogeneously on Pancreatic Tumors
Authors: Koyasu Norikazu , Yatabe Hiroyuki , Takakusagi Yoichi , Saito Yutaro , Sando Shinske , Krishna Murali C. , Yamamoto Kazutoshi
Published: November 2024

Motivation: Molecular imaging is a promising methodology for diagnosing cancer and monitoring treatments by noninvasively visualizing the alternations of cancer metabolisms. Goal(s): A framework for developing novel dissolution Dynamic Nuclear Polarization(dDNP) probes is needed to overcome their limited availabilities for in vivo and clinical applications. Approach: dDNP-metabolic MRI successfully monitors therapeutic responses in spatiotemporal enzymatic activities particularly at earlier stages before the volumetric changes can be observed. Results: In this presentation, we will demonstrate a model case for a rationally designed novel dDNP probe, aminopeptidase-N(CD13), which allows us to detect heterogenetic treatment responses with an anti-angiogenic/antitumor drug, sunitinib, at the earlier stages in tumors. Impact: This work exhibits a framework that a rationally designed hyperpolarized MR probe targeted to a highly-selective enzymatic activity, aminopeptidase-N, leads to monitor early therapeutic responses on cancer tissues in vivo and to observe tumor heterogeneity in their treatment responses non-invasively.
Assessing pathological prognostic factors of resectable rectal cancer: comparison of a monoexponential model and multiple advanced DWI models
Authors: Zhou Mi , Chen Meining , Zhang Qin , Huang Hongyun
Published: November 2024

Motivation: Diffusion-weighted imaging (DWI) can help to predicti rectal cancer prognosis; however, inconsistencies in its effectiveness necessitate exploring advanced DWI models to improve diagnostic accuracy. Goal(s): We investigated the value of the stretched exponential (SEM), fractional-order calculus (FROC), and continuous-time random-walk (CTRW) models in assessing prognostic factors for rectal cancer. Approach: This study included 181 rectal cancer patients with using traditional and advanced DWI models to explore correlations between DWI parameters and histopathological indicators. Results: The parameters of advanced DWI models were significantly correlated with histopathological prognostic factors. CTRW-α was the superior predictor for histological type and pT stage. Impact: Our study demonstrates the value of CTRW model in predicting prognostic factors for rectal cancer. Utilizing these noninvasive and accurate advanced DWI models before surgery can help physicians to make appropriate surgical plan to help patients achieve better prognosis.
Viscoelastic properties of pancreatic ductal adenocarcinoma: ex vivo biomechanical validation of in vivo MR elastography
Authors: van Schelt Anne-Sophie , Wassenaar Nienke , Schrauben Eric , Nelissen Jules , Runge Jurgen , van Turnhout Marc , Driessen Rob , Stoker Jaap , Nederveen Aart
Published: November 2024

Motivation: There are discrepancies in pancreatic MRE outcomes. Biomechanical validation of MRE in pancreatic ductal adenocarcinoma (PDAC) poses substantial challenges. Goal(s): This study aims to conduct biomechanical analysis on the surgically removed specimen and correlate this to preoperative MRE outcomes. Approach: Six PDAC patients underwent MRE prior to surgical intervention, resulting in shear wave speed (SWS) and phase angle (ф) for whole-tumor and tumor-center. Compression testing was performed on specimen at 5%-strain resulting in Young’s moduli (E). Correlation analysis was done with MRE outcomes and E. Results: Pearson’s correlation revealed a significant correlation between SWStumor-whole and E (r(6)=.857, p=.029) and фtumor-center (r(6)=.844, p=.035). Impact: Validation using ex vivo compression mechanical testing of pancreatic MRE outcomes gives more insight in the accuracy of the measured elastic properties in complex and inhomogeneous tumorous tissue, which is imperative for clinical application of MRE as a predictive biomarker.