bluuebunny/crossref_metadata_2025 huggingface dataset:
Gadoxetic acid‑enhanced MRI for evaluation of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma
Authors: Qu Qi , Zhang Tao , Zhang Xueqin , Lu Mengtian , Liu Zixin , Zhao Xiance
Published: November 2024

Motivation: VETC and MVI have a synergistic effect on prognosis assessment and treatment selection of HCC. Preoperative noninvasive evaluation of VETC and MVI is important. Goal(s): To explore the diagnosis value of nomograms based on preoperative gadoxetic acid (GA)-enhanced MRI features for MVI, VETC, and RFS in patients with HCC. Approach: Retrospective study explored the relationship between the clinical and imaging features of MVI, VETC, and MVI+/VETC+ using the same dataset. Results: Nomograms incorporating independent indicators showed good performance in the training and internal validation cohorts. Significant differences in recurrence rates between the nomogram-evaluated high- and low-risk stratification were found. Impact: Through the in-depth exploration of the combination of MVI and VETC, we can deepen the understanding of tumor metastasis heterogeneity, and the diagnosis and treatment plan, prognosis prediction and risk stratification of tumor patients can be formulated.
Multi-shot DWI has a high sensitivity in hepatic NET metastasis detection, outperforming multiplanar T2w imaging
Authors: Raichandani Surbhi , Brunsing Ryan
Published: November 2024

Motivation: Liver metastasis detection without intravenous contrast has potential applications in MRI surveillance and whole-body imaging. Goal(s): To compare multishot DWI (msDWI) and T2w imaging with fat suppression (T2w+FS) in detecting neuroendocrine tumor (NET) liver metastases. Approach: We retrospectively determined the visibility of 114 lesions (35 patients) on msDWI and multiplanar T2w+FS. Sensitivities were calculated. Results: msDWI outperforms T2w+FS, with sensitivity approaching 90% for lesions <=5mm. Impact: HBP (Gadoxetate) imaging is the gold standard for detecting liver metastases, however rapid MRI without intravenous contrast is attractive from a cost and patient perspective. msDWI alone may prove sufficient for NET metastasis detection.
Risk prediction and postoperative recurrence of Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma based on LI-RADS v2018
Authors: Yan Zuyi , Zhang Xueqin , Zhang Tao , Zhao Xiance
Published: November 2024

Motivation: Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma (MTM-HCC) has been confirmed to possess higher invasiveness and malignancy. Goal(s): This study aims to compare the clinical parameters and imaging characteristics between MTM-HCC and non-MTM-HCC patients, construct a predictive model for the MTM-HCC subtype, and evaluate the model's value in risk stratification. Approach: This study is a retrospective research. Results: The ≥ 50% AP hypovascular component and peritumoral hypointensity on HBP images are independent predictive factors for diagnosing MTM-HCC, and the predictive model exhibits a significant difference in overall recurrence-free survival (RFS) and early recurrence (P < 0.05). Impact: This study, based on the significant imaging features of LI-RADS v2018 and other imaging signs, offers clinical value for the diagnosis and prognosis prediction of the MTM-HCC pathological subtype, providing new insights for the personalized treatment of HCC patients.
Microvascular invasion-negative hepatocellular carcinoma: prognostic value of qualitative and quantitative Gd-EOB-DTPA MRI analysis
Authors: Wang Leyao , Wang Sicong , Ma Xiaohong , Zhao Xinming
Published: November 2024

Motivation: To explore the prognostic parameters related to microvascular invasion (MVI)-negative HCC patients. Goal(s): The aim of this study was to establish a model for predicting the prognosis of patients with MVI-negative HCC based on qualitative and quantitative analyses of Gd-EOB-DTPA MRI. Approach: Totally, 122 patients were retrospectively enrolled. Multivariate logistic regression analysis was performed to identify significant clinical parameters, and quantitative and qualitative MRI parameters associated with prognosis. Results: Multivariate analysis showed that lower RER, HBP hypointensity without APHE, LI-RADS category, mild-moderate T2 hyperintensity, and higher AST levels were risk factors associated with prognosis. The most appropriate threshold value for RER was 1.197. Impact: The nomogram incorporated preoperative qualitative and quantitative parameters and laboratory indices, namely LI-RADS category, HBP hypointensity without APHE, lower RER, mild-moderate T2 hyperintensity, and higher AST levels, can be a reliable tool for predicting prognosis in patients with MVI-negative HCC.
MRI-based Prediction of the Need for Wide Resection Margins in Patients With Single Hepatocellular Carcinomas
Authors: Wang Yanshu , Qu Yali , Yang Chongtu , Song Bin , Jiang Hanyu
Published: November 2024

Motivation: Individualized selection between wide (≥1 cm) and narrow (<1 cm) resection margins may help improve the postoperative survivals of hepatocellular carcinoma (HCC). Goal(s): To develop an MRI-based model to support the selection between narrow and wide resection margins based on the prediction of postoperative early recurrence-free survival (RFS, ≤2 years) in patients with single HCC. Approach: In patients with narrow resection margins, a predictive score (named “MARGIN”) was developed for early RFS rate with Cox regression analyses. Results: Improved early RFS rate was observed for wide resection margins for the MARGIN-predicted high-risk patients. Impact: In patients with single HCC, the MARGIN score may serve as a promising decision-making too to inform the need for wide resection margins.
Phase contrast magnetic resonance imaging for diagnosis of inferior vena cava obstruction in Budd-Chiari syndrome
Authors: Xu Peng , Xu Hanbo , Lyu Lulu , Hu Chunfeng , Xu Kai
Published: November 2024

Motivation: It is critical to determine whether the inferior vena cava (IVC) is occluded in Budd-Chiari syndrome (BCS) patients before interventional treatment. Magnetic resonance venography (MRV) has high sensitivity, but the specificity needs to be further improved. Goal(s): This prospective study aims to compare the accuracy of IVC occlusion assessment using phase contrast MRI (PC-MRI) and MRV. Approach: The Kappa coefficient test was used to evaluate the agreement, using digital subtraction angiography (DSA) as the gold standard. Results: Intergroup agreement was excellent between PC-MR and DSA (κ=0.904), which was higher than that between MRV and DSA (κ=0.716). Impact: Our results suggest that PC-MR is a more accurate method for assessing IVC obstruction. However, reproducibility tests still need to be performed. In addition, the hepatic vein was not evaluated, which could be investigated in future studies.
Application of MR Magnetization Transfer Imaging in patients with liver fibrosis
Authors: Jiang Yanli , wang Shaoyu , Zhang Jing
Published: November 2024

Motivation: Liver fibrosis is likely to progress into cirrhosis, which affect the patient’s quality of life. Magnetization transfer (MT) imaging can indirectly reflect the content of structural macromolecular substances. Goal(s): To evaluate the diagnostic accuracy of MTR imaging in staging of liver fibrosis and probe the sarcopenia in liver fibrosis stage. Approach: 74 chronic liver diseases patients and 24 health control underwent MR examinations, MTR and all the patients’ MTR value, APRI and FIB-4 value were recorded. Results: MTR -Muscle was higher than MTR-Liver in any fibrosis stages except S1. And as the liver fibrosis stages increased, the MTR-Musle decreased accordingly. Impact: MTR appeared may not serve as a specific marker for detecting the different liver fibrosis stages.
Age and Gender-Related Variations in Hepatic Vascular Phenotypes - a Population Study
Authors: Hu Xumei , Xia Xueqin , Liu Meng , Sun Longyu , Li Qing , Chen Weibo , Zhang Xinyu , Wang Chengyan
Published: November 2024

Motivation: Research on hepatic vascular phenotypes in human populations is currently limited. Investigating thevariations in hepatic vascular tissues across groups of diverse ages holds significant potential for identifying innovative biomarkers associated with aging and various diseases. Goal(s): To establish a comprehensive understanding of how hepatic vascular phenotypes evolve in relation to the aging process and differ between genders. Approach: First, skeletonize the branches of the vessel network via a fast-marching approach. And then Organization and Morphology phenotypes were extracted from mDixon imaging. Results: The study revealed 21 phenotypes exhibited a declining trend as individuals age, while 15 phenotypes showed a positive correlation with age. Impact: These findings highlight the complex link between vascular traits and aging, revealing subtle vascular changes over time. The study's implications are transformative, identifying novel aging-related biomarkers with potential applications in liver diseases.
UPsampling by Subspace Informed ZEro-padded Reconstruction (UPSIZER) in Diffusion Tensor Imaging
Authors: Wiley Neale , Balaji Sharada , Dvorak Adam , Vavasour Irene , Kolind Shannon
Published: November 2024

Motivation: Diffusion tensor imaging (DTI) is inherently resolution limited by MRI gradient performance and human tolerance of gradient slew rates but could benefit greatly from finer detail for tissue mapping. Goal(s): To increase the resolution of DTI images by using the joint information between different encoding directions to improve efficiency of upsampling. Approach: Using a compressed sensing subspace-based reconstruction algorithm on zero-padded k-space to estimate a higher resolution image with finer detail than current interpolation strategies. Results: Smoother diffusion encoded images and reduced spatial blurring in calculated metrics compared to standard cubic interpolation was achieved. Impact: A new method for upsampling diffusion images using subspace-based compressed sensing reconstruction is introduced that includes fine detail and reduces noise. Potential for improving on standard cubic interpolation is demonstrated, which will benefit DTI analysis including tractography.
Fast Multi-contrast MRI using Deep Factor Model
Authors: Chen Yan , Kecskemeti Steven , Holmes James , Corum Curtis , Magnotta Vincent , Jacob Mathews
Published: November 2024

Motivation: 3D MR fingerprinting (MRF) and MPnRAGE acquire a large number (400-1000+) of non-steady state images with different encodings to estimate quantitative relaxometry parameters. The large number of volumetric images presents serious computational and memory issues for many advanced image reconstruction techniques, especially those utilizing deep learning. Goal(s): This work develops a memory efficient, pretrained deep factor model (DFM) for high quality, high temporal images. Approach: We apply a progressive training and pre-training strategy to accelerate the convergence for a self-supervised DFM. Results: DFM recovers 384 3D brain images (1mm isotropic resolution) from a 2.3 minutes MPnRAGE scan within 30 minutes of reconstruction time. Impact: The proposed pretrained deep factor model enables fast MRF reconstruction from accelerated acquisition in a 3D+time setting. It has the potential to significantly shorten the acquisition time for quantitative MRI, while providing high quality weighted MRI results.
A comparative study of subspace based EPTI reconstructions using different temporal basis variants
Authors: Bai Haoran , Dai Ke , Qiu Yueqi , Chen Hao , Bao Jianfeng , Zhang Zhiyong
Published: November 2024

Motivation: Subspace reconstruction is widely used in MRI reconstruction, but the selection and impact of bases need further analysis. Goal(s): We want to evaluate the influence of bases obtained by different methods on subspace reconstruction. Approach: We generated different bases from Bloch simulation and calibration scan on healthy subjects and brain tumor subjects and evaluate the subspace reconstruction results with GESE-EPTI data. Results: Subspace bases from calibration scan can optimize the reconstructed results without increasing scan time. The bases from brain tumor subjects and healthy subjects are evaluated and indicate consistent results. Besides, linear transformations can optimize results without the need for reconstruction. Impact: We evaluate the subspace bases from Bloch simulation and calibration scan in MRI subspace reconstruction and demonstrate bases from calibration scan can optimize the reconstructed results. Subspace reconstruction results were consistent by bases from brain tumor and healthy subjects.
K-space Low-rankness Enabled Additive NoisE Removal (KLEANER) to Denoise Multi-Coil Multi-Contrast Low-Field MRI
Authors: Shih Shu-Fu , Zhang Zhaohuan , Tasdelen Bilal , Yagiz Ecrin , Cui Sophia , Zhong Xiaodong , Nayak Krishna , Wu Holden
Published: November 2024

Motivation: Low-field MRI is limited by the low signal-to-noise ratio (SNR). Multiple scan averages increase SNR but also increase the acquisition time. In applications that acquire multiple contrasts, such as quantitative imaging, acquisition time can be further prolonged. Goal(s): To develop a multi-coil multi-contrast k-space denoising technique that can also be compatible with parallel imaging-accelerated datasets. Approach: A low-rank block-Hankel matrix was constructed from the multi-dimensional k-space data, followed by optimal singular value shrinkage to suppress Gaussian noise. Results: In a pilot cohort, the proposed method improved SNR by 1.6-fold and reduced standard deviations in quantitative maps in the liver. Impact: The proposed k-space denoising technique effectively suppresses noise in multi-coil multi-contrast k-space data from low-field MRI and is compatible with parallel imaging-accelerated datasets. It can improve image quality and/or shorten the acquisition time for multi-contrast low-field MRI.
Zero-FRESCO: Zero-Shot Fast REconstruction for Multi-Shot Sensitivity EnCOded Diffusion MRI
Authors: Vurankaya Ismail , Cho Jaejin , Jun Yohan , Bilgic Berkin
Published: November 2024

Motivation: Self-supervised neural network reconstruction improves multi-shot diffusion MRI (dMRI), yet suffers from prohibitively long computation times. Goal(s): To develop a zero-shot self-supervised learning method for fast multi-shot dMRI reconstruction. Approach: We propose a physics-guided neural network that operates in both k- and image-spaces to combine information from different EPI shots. We show that reconstruction quality can be improved with a novel sampling mask strategy, and that faster training is possible with a new training strategy. Finally, we extend our results to SMS acquisitions. Results: Our results show that the proposed method provides improved and fast reconstructions compared to 2-shot LORAKS and 2-shot ZS-SSL. Impact: The proposed physics-guided self-supervised learning method provides fast and high-quality reconstruction of multi-shot diffusion MRI volumes, while also eliminating the need for external training datasets.
A Self-Consistency Guided Multi-Prior Deep Learning Framework for Reconstruction of Fast Spatiotemporal MRI and Its Applications in Cardiac MRI
Authors: Zhang Liping , Chen Weitian
Published: November 2024

Motivation: Cardiac MRI (CMR) is widely used for assessment of cardiac diseases, but long acquisition time can cause patient discomfort and motion artifacts. Existing methods face challenges in reconstructing detailed information from highly undersampled spatiotemporal CMR acquisitions. Goal(s): We propose a self-consistency guided multi-prior deep learning framework termed $$$k$$$-$$$t$$$ CLAIR to address this challenge. Approach: This method exploits spatiotemporal correlations in data and incorporates calibration information to learn complementary priors across the $$$x$$$-$$$t$$$, $$$x$$$-$$$f$$$, and $$$k$$$-$$$t$$$ domains. Results: Evaluation performed on publicly available cardiac cine and T1/T2 mapping datasets demonstrated that the proposed method can effectively reconstruct detailed information from highly undersampled CMR data. Impact: The proposed method achieves high-quality reconstruction of highly undersampled CMR datasets including both cine imaging and T1/T2 mapping. This method has potential to improve CMR in clinical use.
Deep Learning enhanced joint reconstruction and Nyquist ghost correction in multiband diffusion imaging
Authors: Sundaresan Rajagopalan , Abad Nastaren , Lee Seung-Kyun , Yang Baolian , In Myung-Ho , Kelley Douglas , Mckinnon Graeme , Kerr Adam , Foo Thomas , Venkatesan Ramesh
Published: November 2024

Motivation: Multiband imaging in EPI Diffusion sequences can suffer from Nyquist ghosting artifacts and poor slice separation. This affects evaluation of ADC, FA, and kurtosis maps in high performance gradient systems. Goal(s): Reduce ghosting and improve SNR in multiband images so that ADC, FA, and kurtosis maps deviate minimally from single-band imaging. Approach: EPI data is split into odd and even echoes and independently reconstructed with ARC algorithm. Virtual channel combination with phase correction along with a Deep Learning algorithm provides SNR enhancement. Results: There was minimal error in the ADC, FA, and kurtosis maps with the proposed approach compared to single-band images. Impact: Our reconstruction algorithm helps multiband imaging achieve minimal deviation in ADC, FA, orthogonal and parallel kurtoses as in single-band imaging but in a shorter acquisition time.
Genetic mechanisms underlying gray matter atrophy in Parkinson’s disease: a combined transcriptome and neuroimaging study
Authors: Ji Yi , Fang Xiangming
Published: November 2024

Motivation: Extensive research has shown prominent gray matter atrophy in patients with Parkinson's disease, yet its genetic mechanisms are largely unknown. Goal(s): We aimed to investigate the genetic mechanisms underlying gray matter atrophy in PD. Approach: We performed a comprehensive neuroimaging meta-analysis along with an independent dataset analysis. Utilizing the Allen Human Brain Atlas, we performed spatial association analyses linking transcriptome data to neuroimaging findings, along with gene functional feature analyses for the identified genes. Results: Our findings suggest that prominent gray matter atrophy in PD may be a consequence of intricate interactions among a diverse set of genes with various functional features. Impact: Our findings may offer unique insights into the genetic mechanisms underlying brain gray matter atrophy in Parkinson’s Disease through bridging the gap between microscale molecular function and macroscale brain architecture.
Cerebral metabolic changes in healthy aging: A cross-sectional 1H-MRS study
Authors: Nisar Sabah , Gibney Kyla , Chakraborty Kasturee , Sanchez Laura , Brinkman Tara , Hudson Melissa , Ness Kirsten , Jacola Lisa , Krull Kevin , Bagga Puneet
Published: November 2024

Motivation: Understanding age-associated changes in cerebral metabolism may provide mechanistic insights into the physiology of cognitive decline. Goal(s): The goal of the study was to explore the effect of normal aging on neurometabolite levels in different brain regions using proton magnetic resonance spectroscopy (1H-MRS). Approach: We performed 1H-MRS in brain regions associated with executive function, memory, and motor coordination in individuals without known disease recruited from the community and ranging from 19-57 years of age. Results: We found a steady decline in markers of neuronal health and neurotransmission and an increase in markers of gliosis and/or neuroinflammation in the dorsolateral prefrontal cortex with age. Impact: This study will inform future studies using 1H-MRS based normative brain metabolic data by mapping metabolic levels in healthy brain regions involved with executive function, memory, and motor coordination.
Combining brain MRI and MRS, neuropsychological testing and brain-derived cell-free DNA level for the early detection of Alzheimer’s Disease
Authors: Sighinolfi Giovanni , Cantoni Elena , Bacalini Maria Giulia , Mitolo Micaela , Sambati Luisa , Guidi Lucia , Manners David Neil , Ferrari Susy , Santoro Rossella , Morandi Luca , Pollarini Virginia , Pellegrini Camilla , Francesco Ravaioli , Pirazzini Chiara , Cortelli Pietro , Lodi Raffaele , Tonon Caterina
Published: November 2024

Motivation: There is a lack of biomarkers in elderly people with ongoing subjective or clinical cognitive decline that indicate evolution to Alzheimer’s disease, necessary for the timely identification of candidate for pharmacological treatment or cognitive rehabilitation. Goal(s): Our goal was to determine whether combining MR, neuropsychological and biological data could provide robust biomarkers for this purpose. Approach: We evaluated patients with subjective cognitive decline, mild cognitive impairment, Alzheimer’s disease and healthy individuals, using a multimodal approach. Results: We identified candidate biomarkers of condition worsening at early stages of the disease among alterations possibly explained by ongoing neurodegeneration. Impact: Our results support the combined investigation of imaging, biological and neuropsychological data to detect patients in the early stages of the Alzheimer’s disease spectrum, who may undergo active neurodegeneration and worsening of the condition.
Assessing Hippocampal Energy Metabolism with Alzheimer’s Disease Progression
Authors: DASHORA CHAYNITA , AGRAWAL PRANSHI , PATEL ANANT
Published: November 2024

Motivation: Alzheimer’s Disease (AD) is the major form of dementia but there is no biomarker(s) and treatment available. Goal(s): This study aimed to investigate how decline in cognitive function is manifested in neurometabolic activity with the progression of AD. Approach: 5xFAD mice (6- and 12-month) were subjected to memory assessment using the Morris Water Maze test followed by neurometabolic analysis using 1H-[13C]-NMR spectroscopy in conjunction with an infusion of [1,6-13C2]glucose or [2-13C]acetate. Results: The findings of the study suggest enhanced neuroinflammation, and reduced glutamatergic and GABAergic neurotransmission in 5xFAD mice. Impact: 13C NMR based assessment of neuronal and astroglial metabolic activity may help get an insight into AD pathology, thus providing biomarker(s) for the diagnosis of AD.
Volume Measurement of the STN Using High-Spatial-Resolution QSM: A Diagnostic and Disease Monitoring Marker for PSP
Authors: Kim Eung Yeop , Lim Chae Young
Published: November 2024

Motivation: The subthalamic nucleus (STN) volumes have not been fully assessed in progressive supranuclear palsy (PSP) and Parkinson’s disease (PD). Goal(s): Our goal was to explore diagnostic accuracy of STN volumes in PSP and PD. Approach: We measured the volumes of the STN, brainstem, and superior cerebellar peduncle in PSP and PD patients. Results: The STN volumes in patients with PSP were significantly reduced compared to those with PD patients. Furthermore, there was a significant negative correlation between the STN volume and disease duration in PSP patients. Impact: The use of high-spatial-resolution QSM to measure the STN volume has potential as a diagnostic marker for PSP, as well as for monitoring the progression of the disease.
1H-MRSI of the Globus Pallidus and White Matter in Patients With Amyotrophic Lateral Sclerosis
Authors: Torlak Meryem , Hatay Gökçe Hale , İşak Barış , Kaya Dilaver , Dinçer Alp , Özcan Alpay , Ozturk-Isik Esin
Published: November 2024

Motivation: There is a need to understand the metabolic effects of Amyotrophic lateral sclerosis(ALS) in more brain regions, including globus pallidus that has a role in controlling conscious and proprioceptive movement. Goal(s): To evaluate metabolic differences of ALS patients and healthy controls(HC) at the white matter(WM) and globus pallidus(GP). Approach: LCModel was used to quantify multivoxel MR spectral data of ALS patients and HC. Metabolite peak ratios of these groups were compared at WM and GP regions. Results: Lower Glu/tCr ratio at left WM than right side, lower GSH/tCr ratio at GP, and lower tNAA/tCr ratio at WM of ALS patients were observed. Impact: Glu/tCr, GSH/tCr and tNAA/tCr ratio differences were identified at globus pallidus and white matter as possible biomarkers of ALS disease, which might affect treatment planning in ALS patients in the future.
Quantitative Susceptibility Mapping and Non-contrast Spin-labeling for CSF Outflow: A Dual Biomarker Approach for Neurodegenerative disease
Authors: Malis Vadim , Mesa Anya , Vucevic Diana , Bae Won , Kassai Yoshimori , Miyazaki Mitsue
Published: November 2024

Motivation: Recognizing the crucial role of iron accumulation and CSF clearance in neurodegenerative diseases, this study aimed to address gaps in measuring these factors simultaneously, which may enhance our understanding of neurodegenerative diseases. Goal(s): To employ a multiparametric approach integrating Quantitative Susceptibility Mapping (QSM) with non-contrast CSF outflow measurements in a single study. Approach: Utilize QSM and Time-SLIP techniques on a small cohort to measure and correlate iron levels in the hippocampus and CSF outflow dynamics. Results: We observed a correlation between iron deposition and age, though no significant age-related changes in CSF outflow were detected, underscoring the need for larger-scale studies. Impact: This study offers researchers a new perspective on the relationship between iron accumulation CSF dynamics and aging, potentially opening pathways for deeper investigation into the mechanisms of neurodegenerative diseases.
Validation of deep-learning accelerated quantitative susceptibility imaging for application in deep brain nuclei
Authors: Zhou Ying , Xu Shan , Liu Lingyun , Ye Yongquan , Sun Jianzhong , Huang Peiyu
Published: November 2024

Motivation: Quantitative susceptibility imaging (QSM) has demonstrated its potential in clinical applications. In patients with Parkinson’s disease, stroke, etc., a shorter acquisition time is desired. Goal(s): Here we aim to validate the accuracy of a deep learning (DL) based method for accelerating QSM in human volunteers. Approach: We enrolled 59 participants from communities and acquired both routine QSM and DL-QSM images. We measured iron deposition in deep brain nucleus and studied the influence of different acceleration factors (3,4, and 5). Results: Results showed that susceptibility values from DL-QSM are highly consistent with routine parallel imaging accelerated images, and they also correlated well with age. Impact: As we validated the reliability and accuracy of deep-learning accelerated quantitative susceptibility imaging, future clinical studies can use this method on patients who cannot tolerate long scan time.
Auditory GABA levels and FC between Heschl's gyrus and ACC mediate the relationship between hearing loss and cognitive impairment in presbycusis
Authors: Liu Yuxi , Li Ning , Zhao Min , Hu Xin , Chen Weibo , Edden Richard , Ren Fuxin , Gao Fei
Published: November 2024

Motivation: The increasing evidence suggests a connection between presbycusis and cognitive impairment, yet the neurochemical mechanisms remain largely unexplored. Goal(s): To compare auditory cortex metabolite levels and brain functional connectivity strength between presbycusis patients and healthy controls, and explore their potential associations with cognitive function and hearing. Approach: We used Magnetic Resonance Spectroscopy (MRS) and resting-state functional Magnetic Resonance Imaging (rs-fMRI). Results: We found reduced GABA levels in the auditory cortex (AC), and decreased connectivity between the AC and the ACC in presbycusis. Furthermore, the changes jointly mediated the association between hearing loss and declines in verbal memory and information processing speed. Impact: These findings support the sensory deprivation hypothesis and highlight the role of GABA-induced inhibitory reduction and the dysconnectivity of AC and ACC in presbycusis-related cognitive impairment, providing new insights into the neurochemical mechanisms of hearing loss-associated cognitive impairment.
Functional Connectivity Mediates the Impact of Iron Content on Cognition in Women with Suspected Coronary Microvascular Dysfunction
Authors: Silemek Arzu Has , Wertheimer Jeffrey , Wei Janet , Dumitrascu Oana , Kremen Sarah , Xie Yibin , Li Debiao , Nelson Michael , Tan Zaldy , Merz Noel Bairey , Sati Pascal , Gao Wei
Published: November 2024

Motivation: Women with ischemia and non-obstructive coronary arteries (INOCA) may experience cognitive decline due to non-heme iron accumulation causing oxidative stress and cell death, but underlying mechanism is still unknown. Goal(s): This study aims to understand how iron affects brain function and cognitive performance in women with suspected INOCA. Approach: By combining high-resolution Quantitative Susceptibility Mapping and resting-state fMRI, the research focused on thalamic iron and its association with brain connectivity and cognitive metrics. Results: Results indicated that thalamic iron impacts cognitive outcomes, particularly executive functions and processing speed, in women with suspected INOCA, with these effects partly mediated by changes in functional connectivity. Impact: This study's insights into iron's cognitive effects may guide early interventions, influence therapeutic strategies for INOCA patients, and prompt further research into the systemic impact of iron on brain connectivity and cognitive health.
Patterns of iron accumulation in patients with Multiple System Atrophy evaluated using Quantitative Susceptibility Mapping
Authors: Trujillo Paula , Hett Kilian , Wynn Amy , Donahue Manus , Bradbury Margaret , Wong Cynthia , Stamler David , Claassen Daniel
Published: November 2024

Motivation: Iron accumulation is central to multiple system atrophy (MSA) pathogenesis, and promising compounds targeting iron dysregulation are being developed. Biomarkers for quantifying iron deposition are needed to improve diagnosis and demonstrate target engagement. Goal(s): To assess the utility of iron quantification for early MSA diagnosis and progression monitoring. Approach: We enrolled patients with MSA and employed quantitative susceptibility mapping (QSM) and a novel histogram analysis to quantify the topography of subcortical iron accumulation. Results: Our study revealed distinctive iron accumulation patterns in early MSA stages, highlighting the significance of iron quantification for differential diagnosis and progression assessment in MSA patients. Impact: Quantitative susceptibility mapping (QSM) provides valuable insights into the pathological alterations associated with iron dysregulation in patients with multiple system atrophy (MSA) and could serve as a valuable tool for improving early diagnosis and evaluating novel treatments.
Susceptibility alteration in deep gray matter after Gadobutrol administration assessed by quantitative susceptibility mapping
Authors: Tian Chuanshuai , Yang Meng , Liang Xue , Zhu Zhengyang , Zhou Jia nan , Lin Zengping , Tang Ran , Yuan Jianmin , Zhang Xin , Zhang Bing
Published: November 2024

Motivation: To evaluate the effect of gadolinium-based contrast agents (GBCAs) injection on the deep cerebral cortical nuclei using a non-invasive method. Goal(s): To evaluate the magnetic susceptibility alteration in deep brain nuclei before and after Gadobutrol injection based on quantitative susceptibility mapping (QSM). Approach: Magnetic susceptibility values of the brain stem and 14 subcortical nuclei were extracted from QSM image, and then compared before and after enhancement. Results: Magnetic susceptibility in the left amygdala, right hippocampus and pallidum differed between pre- and post-enhancement. QSM is an effective and accurate technique to evaluate the magnetic susceptibility of deep brain nuclei before and after enhancement. Impact: Susceptibility in specific deep gray matter altered after a single administration of Gadobutrol, which reflects differential sensitivity to gadolinium-based contrast agents. QSM acquired by multi-parametric sequence may be an effective method to detect small quantities of Gadobutrol deposition.
Investigation of hippocampal subfield volumes in long COVID and ME/CFS using 7T MRI
Authors: Thapaliya Kiran , Marshall-Gradisnik Sonya , Eaton-Fitch Natalie , Barth Markus , Inderyas Maira , Barnden Leighton
Published: November 2024

Motivation: To study hippocampal atrophy in long COVID and Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS patients). Goal(s): Our goal was to investigate hippocampal atrophy in long COVID and ME/CFS patients. Approach: We used T1-weighted MP2RAGE data from 7T MRI and used FreeSurfer to estimate hippocampal subfield volumes in long COVID and ME/CFS patients. Results: We found that hippocampal subfield volumes were larger in long COVID and ME/CFS patients compared to HC. Impact: Our study showed atypical hippocampal atrophy in long COVID and ME/CFS patients indicating that existing brain research findings in ME/CFS patients may help better understand the neurological dysfunction in long COVID patients.
Relationship between changes in hypothalamic subfield volume and kidney damage in diabetic kidney disease
Authors: Tian Chong , Zhang Yan , Xu Zi , Da Jingjing , Yang Yuqi , Chen Meining , Wang Rongpin , Zha Yan
Published: November 2024

Motivation: The hypothalamus plays an important role in the progression of renal damage in diabetic kidney disease (DKD) patients, but the relationship between hypothalamic structure and renal function is currently unclear. Goal(s): To investigate the relationship between hypothalamic structure and impaired renal function in DKD patients. Approach: The hypothalamic subregion volume in DKD patients was quantitatively analyzed using T1-weighted magnetization prepared rapid gradient-echo MRI. The relationship between renal function and hypothalamic subregion volume was also explored. Results: During DKD progression, reduced volume in the anterior-superior area of the hypothalamus, especially the paraventricular nucleus, was associated with worsening renal function. Impact: The volume of the anterior-superior area of the hypothalamus may provide useful neuroimaging evidence of renal function damage in DKD patients, supporting the kidney-brain axis theory of DKD.
qMRI based investigation of neuronal brain changes following mindfulness practice in a population with sleep disorders
Authors: Haker Rona , Tarrasch Ricardo , Benhamou Salomon , Mircus Gal , Radunsky Dvir , Blumenfeld-Katzir Tamar , Ben-Eliezer Noam
Published: November 2024

Motivation: Provide evidence for the positive effect of Mindfulness on sleep quality, and investigate the corresponding neuronal changes in the white matter. Goal(s): Quantify the improvement in sleep quality following Mindfulness meditation in subjects suffering from sleep disorders, and correlate these to change in quantitative T1, T2 and proton-density values. Approach: Meditation naïve subjects underwent MRI scans and filled sleep-quality questionnaires before and after participating in an 8-week MBSR course. Waitlist control group was scanned at the same time-points without intervention. Results: Sleep quality improved significantly in the MBSR group, compared to controls. No changes were observed in qMRI values in the white matter. Impact: Discover the psychological-physiological mechanism underlying the improvement of sleep following mindfulness practice; introduce new ways to study the effects of mindfulness; advance better and more personalized treatment plans for insomnia/sleep disorders.
Structural and functional changes in obstructive sleep apnea and their associated gene expression profiles
Authors: Huang Yijie , Zhao Wei , Ju Chao , Liu Jun
Published: November 2024

Motivation: The genetic mechanisms underlying the structural and functional changes in the brain of patients with obstructive sleep apnea (OSA) are largely unknown. Goal(s): This study investigates the gene expression patterns related to brain structure and function in patients with OSA. Approach: We explore the gene expression profiles associated with changes in brain structure and function in OSA based on transcriptome-neuroimaging spatial correlation analysis. Results: The brain regions associated with cognition, emotion, and sleep regulation have undergone changes in the OSA group. Genes associated with altered brain structure and function are predominantly enriched in activities related to gated channels and synaptic communication. Impact: Our study suggests that complex polygenic genetic mechanisms play a role in brain morphological and functional abnormalities in OSA, providing a new perspective on the relationship between genes, and brain function in patients with obstructive sleep apnea.
Effects of sleep quality on static functional connectivity between white and gray matter in human brain
Authors: Hu Shuang , Zhou Yuhan , Qin yuanyuan , Zhu Wenzhen
Published: November 2024

Motivation: Sleep, accounting for one-third of human life, is a universal biological function, and sleep disorders may adversely affect health and well-being. Goal(s): Sleep disorder affect functional structure of brain and the effect on white matter functional connectivity is unclear. Approach: we divided white and gray matter into 48 and 82 regions and calculated Pearson's correlation coefficients between the regions to explore the functional connectivity of white and gray matter affected by sleep quality. Results: The left secondary visual cortex and the dorsal anterior cingulate cortex were significantly correlated with the sleep quality. Impact: Which suggests that V2 and dACC, as well as the white matter fiber tracts connected to these two brain regions, need more attention in the diagnosis and treatment of sleep disorders.
Interpretable Deep Learning Model for Age Prediction Using Human Brain Cortico-Hippocampal Functional Connectivity
Authors: Sun Yifei , Dalton Marshall , Calamante Fernando , Lv Jinglei
Published: November 2024

Motivation: Normal aging involves human brain changes. Recognizing healthy aging patterns can advance age care, help understand unhealthy aging, and guide interventions. Goal(s): We aim to characterise age-related functional changes and explore how they can be predicted and interpreted. Approach: Using deep learning, we predicted age with cortical functional connectivity of the whole, anterior, and posterior hippocampus. LayerCAM generates the whole brain saliency map. Results: Models yielded a mean prediction error of 6.9 years. Personalised saliency maps revealed highly contributing regions to age prediction, such as the precuneus. Models also capture the prediction and saliency difference between anterior and posterior hippocampal-cortical functional connectivity. Impact: We introduced an interpretable deep learning approach to explore age-related brain functional changes. Our work generates new knowledge that could lead to early detection and better management of aging-related disorders.
Sodium Quantification in the Abdomen at 3 T
Authors: Birchall Jonathan , Horvat-Menih Ines , Kaggie Joshua , Riemer Frank , Benjamin Arnold , Graves Martin , Wilkinson Ian , Gallagher Ferdia , McLean Mary
Published: November 2024

Motivation: Sodium homeostasis is altered in abdominal pathologies such as cancer and cardiovascular disease, but established quantification techniques require an invasive biopsy. Goal(s): Quantification of abdominal sodium content and T2* relaxation in abdominal organs and fluid-filled structures using 23Na-MRI. Approach: Proton and sodium imaging of the abdomen was performed in 18 healthy volunteers using a 3D cones sequence and a sodium-tuned body coil on a clinical 3 T system. Results: Mean total sodium concentration and long T2* relaxation times were measured in 8 organs and fluid-filled structures; results agreed with previously published work in human and animal models. Impact: Quantitative 23Na-MRI measurements in healthy volunteers will serve as a baseline to evaluate pathological changes in hypertension and kidney tumour characterisation. This may assist in the assessment of treatment efficacy, thereby reducing the need for invasive biopsy techniques.
GAGA: Gapped Arrangement of Golden Angles for sliding window reconstruction of hyperpolarized dynamic 13C MRSI data acquired with 3D radial EPSI
Authors: Rincon Gino , Awenius Marcel , Abeln Helen , Biegger Philipp , Müller Melanie , Franke Vanessa , Schmidt Andreas , Ladd Mark , Bachert Peter , Korzowski Andreas
Published: November 2024

Motivation: Hyperpolarized 13C magnetic resonance spectroscopic imaging (13C-MRSI) provides real-time metabolic insights but requires extremely fast and efficient imaging sequences, especially for dynamic measurements. Goal(s): This work aims to enhance three-dimensional 13C radially-sampled echo-planar spectroscopic imaging (rEPSI) to enable dynamic acquisitions. Approach: A novel algorithm for a 3D Gapped Arrangement of Golden Angles (GAGA) is introduced, combined with sliding window (SW) reconstruction, and tested in simulation and phantom studies. Results: The algorithm developed for GAGA creates homogeneous k-space sampling distributions for individual temporal subframes, and SW reconstruction successfully recovers the temporal dynamics while displaying only minor temporal flickering and incoherent undersampling artifacts. Impact: The novel algorithm for a Gapped Arrangement of Golden Angles (GAGA) enables dynamic measurements of volumetric hyperpolarized 13C MRSI with a high temporal resolution. Further studies will exploit its potential for the investigation of metabolic processes using dynamic hyperpolarized 13C-MRSI.
MORSE-PI – Flexible and artefact-free image reconstruction for structural and functional QSM and other phase-critical imaging applications.
Authors: Dymerska Barbara , Josephs Oliver , Graedel Nadine , Malekian Vahid , Callaghan Martina
Published: November 2024

Motivation: We address the issue of phase singularities and artefacts in phase-critical imaging applications, such as QSM, especially in more complicated scenarios: 7T, under-sampled, or single-echo scans. Goal(s): To develop an image reconstruction method, “MORSE-PI”, that provides high SNR, artefact-free and singularity-free phase for structural and functional brain imaging. Approach: MORSE-PI extends our previous approach, MORSE-CODE, by adding a Virtual Reference Coil computation that is used to correct phase offsets in the MORSE-CODE sensitivity estimates. Results: MORSE-PI reconstructs structural and functional brain images at 3T and 7T free from artefacts and phase singularities yielding high SNR QSM. Impact: MORSE-PI flexibly provides high SNR, fold-over-free and singularity-free phase images for single-echo and multi-echo structural GRE and functional EPI scans with real-time reconstruction. MORSE-PI naturally lends itself to phase-based imaging techniques such as structural and functional QSM.
Unsupervised MRI Super-Resolution Reconstruction Using a Hybrid Regularizer Powered Deep Image Prior
Authors: Zhong Yuxiang , Zou Lixian , Chen Futao , Li Qian , Zhang Bing , Li Ye , Liang Dong , Liu Xin , Zheng Hairong , Zhang Na
Published: November 2024

Motivation: While deep networks have shown great effectiveness for post-acquisition MRI resolution enhancement, their training requires an enormous of datasets. Deep Image Prior (DIP) is a novel approach that leverages the inductive bias of deep convolutional architecture, allowing for MRI super-resolution without the need for training. Goal(s): We aim to improve the capabilities of DIP and thus achieve resolution enhancement. Approach: We introduced a hybrid regularizer that integrates total variation with a neural network denoiser into the DIP framework. Results: Validated on 5T MR datasets, our method further improved on DIP and generated high-resolution MRI with realistic details, outshining several competing methods. Impact: The proposed unsupervised method offered a robust framework for MRI super-resolution reconstruction that leverages intrinsic image structure to ensure resolution enhancement without the need for training data, thus boosting the efficiency of medical imaging and potentially benefiting clinical diagnostics.
Efficient spatial regularisation of dictionary matching using discrete Markov random fields
Authors: Tripp Donovan , Kunze Karl , Prieto Claudia , Botnar René , Neji Radhouene
Published: November 2024

Motivation: Dictionary matching, used extensively in quantitative MRI, is resistant to standard approaches to spatial regularisation due to its discrete nature. Goal(s): Demonstrate the feasibilty of efficient spatial regularisation of parameter maps produced via dictionary matching, in this work using total variation (TV) regularisation. Approach: Spatially regularised dictionary matching was formulated as an optimisation on a discrete Markov random field, and the result optimisation problem solved using a primal-dual strategy, with the efficient iterative solver FastPD. Results: TV regularisation improved apparent quality of parameter maps in phantoms and in vivo.Impact: The proposed technique offers a means to improve the quality of parameter maps from any quantitative framework employing dictionary matching, covering a wide range of possible anatomies and clinical applications.
Pixel SNR, pixel density, detection, estimation, and the advantages of red noise
Authors: Pipe James
Published: November 2024

Motivation: Pixel SNR is a common metric for sequence design and scan implementation, however it is not ideal for detectability and estimation. Goal(s): To take and modify an existing noise measure from the literature and test it against pixel SNR for both white and red (high spatial frequency) noise. Approach: Ten individuals performed a detection task on 100 synthetic low SNR images with white or red noise. Results: The new metric was more predictive of detection than pixel SNR or circle radius. Images with red noise had worse pixel SNR but better detection rates. Impact: A proposed SNR metric is more relevant than pixel SNR for both detection and estimation, and consistent with our findings of better detectability in red noise over white noise. This is important for the design of clinical pulse sequences.
Automatically Quantitative Intratumoral Susceptibility Signal in Predicting the Expression of Ki-67 in Ovarian cancer
Authors: Hao Li , Liu Ailian , Li Ye , Song Qingling , Shi Yuting , Song Qingwei , Wang Hongkai , Zhuang Mingrui
Published: November 2024

Motivation: The high expression of Ki-67 in ovarian cancer was associated with lower survival rate, but its expression was only obtained after postoperative immunohistochemical staining analysisGoal(s): Automatically quantitative Intratumoral Susceptibility Signal (ITSS) can reflect the new growth inside the tumor vascular and bleeding conditions,ITSS can obtain the expression of Ki-67 before operation, which can help clinical provide treatment planApproach: Automatically quantitative ITSS in predicting the expression of Ki-67 in ovarian cancer,by using AS (AnatomySketch 1.0) software (Dalian University of Technology)Results: The rate of ITSS in patients with high Ki-67 expression was significantly higher than that in patients with low Ki-67 expressionImpact: Automatic quantitative ITSS is expected to be applied to the study of ovarian tumors and more sites in the future. ITSS can effectively predict the expression of Ki-67 in ovarian cancer and provide valuable information for making treatment plan