bluuebunny/crossref_metadata_2025 huggingface dataset:
Radiation-induced White Matter Abnormalities in Nasopharyngeal Carcinoma: Evaluation by DKI and NODDI
Authors: Lin Yuhao , Lv Xiaofei , Liang Jiahui , Feng Yanqiu , Zhang Xinyuan
Published: August 2023

Based on the multi-shell diffusion MRI data acquisition and tract-based spatial statistics (TBSS), we conducted a longitudinal study to explore the changing patterns of DKI and NODDI metrics after radiotherapy for nasopharyngeal carcinoma (NPC) patients. We found most of the significant regions showed a “self-recovery” mechanism but the temporal lobe regions show a progressive changing pattern over time after RT. Besides, we also found that the change of DKI metrics has a stronger dose-correlation than DTI and NODDI metrics. Our study indicates the DKI and NODDI metrics can provide more comprehensive brain microstructure changes compared with conventional DTI.
Quantitative assessment of neonatal white matter injury stiffness by virtual magnetic resonance elastography
Authors: Wang Miaomiao , Liu Congcong , Li Xianjun , Yang Jian
Published: August 2023

Punctate white matter lesion (PWML) is the most common injury in neonates. Due to newborns are a vulnerable population, the limited imaging protocols are not sufficient to fully understand the injury. Le Bihan et.al recently proposed a virtual MR elastography (vMRE) method based on multiple b-values diffusion sequences, which is attractive for evaluation of brain development and injury. This study aims to quantitatively assess the stiffness of PWML using vMRE. Compared with white matter regions, a significant increased virtual sheer stiffness is observed in lesions, and it may be a feasible clinical evaluation of the pathophysiological state of brain tissue.
Accelerating Myelin Water Content Quantification using Deep Non-Local Sparse Model
Authors: Li Bowen , She Huajun , Chen Quan , Wang Zhijun , Du Yiping
Published: August 2023

A grouping sparse coding (Group-SC) model is used in this study for the acceleration of myelin water content quantification. Images with improved quality are obtained using the Group-SC algorithm, in the aspect of minimal artefacts and good data consistency with fully-sampled labels. Myelin water fraction (MWF) maps reconstructed using Group-SC demonstrate more natural spatial distribution of myelin water in the brain. The proposed Group-SC algorithm has demonstrated its potential for the acceleration of the myelin water content quantification at R = 6.
Analysis of Dynamic Function Connectivity in a Normal Rat Model After Focused Ultrasound-Neuromodulation in VPM/VPL
Authors: Hung Yu-Chieh , Wang Yi-Cheng , Liu Hao-Li , Peng Hsu-Hsia
Published: August 2023

Focused ultrasound (FUS) has been considered as a noninvasive neuromodulation method. In addition to evaluate the functional connectivity (FC) by an average of BOLD signals across the whole scan, a dynamic FC (dFC) can help to comprehend the instant alternation of FC. We aimed to investigate the alterations of dFC after FUS-neuromodulation at VPM/VPL in a normal rat model.

We used k-means and dynamic network analyses to evaluate changes of dFC at Pre-FUS, Sham, 35-min, 3-hr, and 3-day for rats with FUS sonication at VPM/VPL. We found an instant effect of FUS-neuromodulation (35-min) and a recovering trend in 3-day.
Improving laminar functional specificity and sensitivity using combined spin- and gradient-echo EPI at 7 T
Authors: Han SoHyun , Eun Seulgi , Cho HyungJoon , Uludaǧ Kâmil , Kim Seong-Gi
Published: August 2023

Both spatial specificity and sensitivity are important for high spatial resolution fMRI. In this study, we applied a filter designed based on the ΔR2*/ΔR2 ratio to enhance both the sensitivity and specificity of the BOLD signal using SAGE-EPI sequence. fMRI experiments during fist-clenching with touching with 0.8 mm isotropic resolution were performed and we directly compared the layer profile of SAGE-BOLD with that of GE- and SE-BOLD in the primary sensory and motor cortices. Vessel-size tuned SAGE-BOLD fMRI provided enhanced laminar specificity and sensitivity in the gray matter region.
Relationship between GABA+ level in occipital lobe and topological characters of cerebral EEG functional connectivity network in young adults
Authors: Liu Yanting , Zhao Sihui , Gao Yanan , Steve Hui , Mark Mikkelsen , Richard A.E.Edden , Zhang Chen , Yu Bing
Published: August 2023

We carried investigation to explore the relationship of GABA level of occipital lobe on topological and organizational properties of the entire brain using Hadamard Encoding and Reconstruction of Mega-Edited Spectroscopy (HERMES) method. Synchronous EEG-MRS data were obtained from forty healthy volunteers. GABA+ level and the global topological topological characters of the EEG functional connectivity network were calculated. The results demonstrate that the GABA+ level of the occipital lobe showed a significant negative correlation with the small world index of EEG functional connectivity network in the EEG α band.
Auditory functional MRI in the conventional pig at 1.5T
Authors: COUDERT Paul , ELIAT Pierre Antoine , SERRAND Yann , COQUERY Nicolas , TANGUY Gwenole , LAURENT Stéphane , SAINT-JALMES Hervé , LAILLET David VAL , GODEY Benoit
Published: August 2023

This work presents a functional MRI model to study the activation of central auditory pathways in a pig model at 1.5T. The conventional pig is a good model due to its anatomical similarity to humans, and therefore could be relevant to study and understand brain responses to auditory stimulations.
Functional connectome-based brain features are related to hopelessness in healthy adolescents and young adults
Authors: Xu Xin , Lai Han , Yang Cheng , Sweeney John , Gong Qiyong
Published: August 2023

The neural correlations that characterize hopelessness may help identify brain mechanisms and individuals at risk of depression and suicide. Here, we examined the functional connectivity (FC) patterns of resting state associated with hopelessness in healthy later adolescents and young adults by using CPM. We found that the level of hopelessness was negatively correlated with the FC between the right MTG and the bilateral PoG and PrG, as well as the FC between the right cerebellum VI and the left thalamus. The finding suggested that cortical-cerebellum networks underlying negative future expectation processing characterized hopelessness.
Machine Learning based Structural and Diffusion MRI to Identify Suicide Risk in Depressed Patients
Authors: Li Huiru , Zhang Huawei , Yin Li , Jia Zhiyun , Gong Qiyong
Published: August 2023

In this study, we used 5 machine learning algorithms with structural and diffusion MRI to classify suicidal risk in depressed patients and the best performance was acquired by support vector machine. In addition, the most important 10 features were most located in fronto-temporal–parietal regions. This study found a classification model using MRI data can help diagnosis and assess suicidal risk in depressed patients.
A multimodal meta-analysis of regional functional and structural brain abnormalities in obsessive-compulsive disorder
Authors: Yang Zibin , Xiao Shu , Qian Long , Wang Ying
Published: August 2023

Numerous neuroimaging studies have revealed abnormalities in specific brain regions in obsessive-compulsive disorder (OCD), but results have been inconsistent. We conducted a whole-brain voxel-wise meta-analysis on resting-state functional imaging and VBM studies between OCD patients and HCs by using Seed-based d Mapping (SDM) software. The meta-analysis demonstrated that OCD exhibits similar abnormalities in both function and structure in the prefrontal and insula. Few regions exhibited only functional or only structural abnormalities in OCD. These results expand the current understanding of functional and structural brain abnormalities in OCD patients, which would provide additional potential targets for therapeutic intervention.
Alteration in White Matter Microstructural Integrity and Network Differentiate Drug-naïve Obsessive compulsive disorder and Healthy controls
Authors: Zhang Suming , Hu Xinyu , Bu Xuan , Li Hailong , Cao LingXiao , Liu Jing , Liang KaiLi , Li Xue , Huang Xiaoqi
Published: August 2023

We investigated the alteration of white matter in obsessive compulsive disorder from voxel-level, tractography-level and connectome-level by combining analysis of TBSS, AFQ and graph theory. TBSS and AFQ revealed widespread changes in WM. Network-based statistic (NBS) analysis demonstrated decreased structural connectivity in patients mainly comprising frontal, temporal, limbic areas, which belong to DMN, FPN, limbic, SMN and VAN network. Along with regions commonly described in the CSTC model of pathophysiology, our results indicate an involvement of white matter in frontal-temporal and limbic regions to differentiate OCD patients and healthy controls, supporting their importance for neurobiological alterations in OCD.
Simultaneous PET-MRI reveals differences in aerobic glycolysis in temporal lobe versus extra temporal lobe epilepsy
Authors: Qin Qikai , Zhang Miao , Li Biao , Thompson Garth
Published: August 2023

Locating epileptic foci is critical for surgery in drug-resistant epilepsy. However, conventional methods (MRI, PET, SEEG) often cannot provide sufficient information alone. We have developed a new method, relative oxygen-glucose index (OGI), based on simultaneous PET and calibrated fMRI, which we used as a biomarker to locate epileptic foci during inter-ictal period. Relative OGI reflects aerobic glycolysis relative to global levels, which is useful as many diseases are characterized by metabolic pathway alterations. In this study, we not only demonstrate its potential in foci localization in epilepsy, but also distinguish temporal-lobe and extra-temporal-lobe epilepsy based on their different pathogenesis.
Lateralization of Memory Function in Temporal Lobe Epilepsy using Scene Memory fMRI
Authors: Tackett William , Mechanic-Hamilton Dawn , Das Sandhitsu , Davis Kathryn , Detre John
Published: August 2023

Surgery to treat mesial temporal lobe epilepsy (TLE) offers freedom from seizures but risks postsurgical memory decline. Lateralizing memory function is essential for mitigating the risk of resecting “eloquent” cortex. Memory laterality has traditionally been assessed using the Wada test, but it is invasive and poses its own risks. The noninvasive method of functional magnetic resonance imaging (fMRI) has become a possible alternative. However, questions remain about fMRI’s reliability in lateralizing memory. Our group has shown that a scene memory task is capable of activating hippocampus and lateralizing memory function. Here, we present an improved version of that experimental paradigm.
Pudendal nerve involvement in young men with erectile dysfunction: demonstration by 3T MR neurography
Authors: Gong Tao , Huang Guoquan , Lin Liangjie , Wang Guangbin
Published: August 2023

More evidences have been identified that pudendal nerve was involved in the pathogenesis of erectile dysfunction (ED) in young men. This prospective study aimed to detect and quantify pudendal nerve alterations in young men with ED using 3T MR neurography protocol. Results certified that pudendal nerve could be visualized and quantified. T2 signal contrast ratio and nerve diameter of pudendal nerve were increased significantly in ED patients compared with healthy controls, suggesting the involvement of pudendal nerve in young men with ED and thus offering a new insight into the pathophysiology and treatment of ED.
The effect of total sleep deprivation under workload on spontaneous brain activity in medical staff: a resting-state functional MR imaging study
Authors: Peng Cong , Guo Ding bo , Nie lisha , Liu Liuheng , Xiao Dongling , Yang Hua
Published: August 2023

The current study aims to assess the effect of total sleep deprivation (TSD) under workload on spontaneous brain activity in medical staff and to further investigate its latent associations with clinical markers. It was concluded that spontaneous brain activity abnormalities occurred during TSD under workload, which might explain the reduced performance of these participants on neurocognitive tests.
Association Between the Recall Verbal Memory and Side of Asymptomatic Carotid Artery Stenosis
Authors: Chen Jyun-Ru , Lin Chun-Jen , Lee I-Hui , Lu Chia-Feng
Published: August 2023

Previous studies reported that the patients with asymptomatic internal carotid stenosis (aICS) had a higher risk of stroke and recall verbal memory impairment. However, whether the side of aICS could cause different verbal memory outcomes was less explored. In this study, significant difference of functional connectivity (FC) was found between the left and right aICS groups. The correlation analysis estimating the association between FC and recall verbal memory also presented different profiles between two aICS groups.
Altered spontaneous brain activity in patients with comitant exotropia before and after surgery: a resting-state fMRI study
Authors: Wu Qian , Hu Hao , Chen Wen , Chen Lu , Zhou Jiang , Xu Xiaoquan , Wu Feiyun
Published: August 2023

We investigate the brain functional alterations in patients with comitant exotropia (CE) before and after surgery, using resting-state functional magnetic resonance imaging with amplitude of low-frequency fluctuation. Our study suggested that CE may lead to decreased brain functional activities in visual-associated areas and the brain function could partially restore along with recovery of exodeviation after strabismus surgery. Besides, our results provided an explanation to the postoperative remnant of stereopsis impairment, which would be helpful to improve the clinical interventions for patients with CE.
Mathematically constrained intravoxel incoherent motion (IVIM)
Authors: Wang Jiaqiu , Barrick Thomas , Hall Matt , Karaman Muge , Magin Richard , Reiter David , Yang Qianqian , Yu Qiang , Nasrallah Fatima , Koh Weeyao , Vegh Viktor
Published: August 2023

Intravoxel incoherent motion (IVIM) provides a method of mapping slow and fast diffusion using a two-exponential model. Whilst this method has been applied in acute stroke, brain cancer and muscle, for example, it does have challenges. These include how to best fit the three model parameters which on top of the two diffusion coefficients includes the fast diffusion volume fraction (i.e., perfusion fraction), and how to best sample b-values which lead to robust model parameter fits. Here we propose an approach based on quasi-diffusion which helps constrain the model fit and show reproducibility under different b-value sampling regimes.
Assessment of relationship between chemotherapy-induced brain structural alteration and neuropsychological scale revealed by GQI
Authors: Chuang Wei , Chen Vincent Chin-Hung , Tsai Yuan-Hsiung , Weng Jun-Cheng
Published: August 2023

Breast cancer is the most prevalent cancer among women, and most of them suffered from chemotherapy-related cognitive impairment (CRCI). In this study, we aimed to use generalized q-sampling imaging (GQI) to detect the relationship between GQI indices and mood symptoms caused by chemotherapy, post-traumatic stress disorder (PTSD), or post-traumatic growth (PTG). Using statistical multiple regression, we found the relationship change between GQI indices and depression, and a consistent relationship was obtained between GQI indices and anxiety with or without chemotherapy. The results indicated that neurotoxicity or traumatic stressors may affect brain structures.
Decreased LC MRI Contrast and Relationship with Orthostatic Hypotension in Multiple System Atrophy
Authors: Lv Haiying , Li Qing , Lu Yong
Published: August 2023

MRI imaging with Neuromelanin contrast has been thrived in recent years. We adopted this method again to explore the LC contrast differences in multiple system atrophy (MSA). Three methods for quantifying LC contrast were used, which all had the ability to reproduce the results that LC contrast decreases in MSA. We further did subgroup analyses of MSA with orthostatic hypotension (MSA-OH) and MSA without orthostatic hypotension (MSA-nonOH). Interestingly, although LC contrast of the two subgroups were not significantly different, adverse correlations between LC contrast and autonomic dysfunction severity were found in MSA-OH and MSA-nonOH subgroups.
Aberrant Intrinsic Functional Connectivity of Hippo-Orbitofrontal-Insular Circuit in Drug-naive Adolescents with Major Depressive Disorder
Authors: Zhou Zilin , Gao Yingxue , Bao Weijie , Qiu Hui , Zhang Lianqing , Feng Ruohan , Wang Yingying , Tang Mengyue , Gong Qiyong , Huang Xiaoqi
Published: August 2023

Through seed-based functional connectivity (SBFC) analysis, we investigated the altered intrinsic connectivity patterns of hippocampus, orbitofrontal cortex (OFC) and insula in adolescents with major depressive disorder(aMDD) comparing to typically developing control(TDC). We found distinct network alterations of those three structures at regional and subregional level. Moreover, we located the aberrant connectivity in hippo-orbitofrontal-insular circuit with family conflict and depressvie symptoms in aMDD which may underline the neural mechanism for depressvie symptoms in adolescents.
Assessment of idiopathic inflammatory myopathy using muscle T2 mapping segmented by Deep Anatomical Federated Network (DAFNE)
Authors: Wang Fengdan , Santini Francesco , Zhu Jinxia , Hilbert Tom , Kober Tobias , Jin Zhengyu
Published: August 2023

Idiopathic Inflammatory Myopathy (IIM) is a group of immune-mediated myopathies, with high morbidity and mortality. Prior work showed that dedicated accelerated T2 mapping of bilateral thighs can be performed within 3 minutes at high resolution and can detect IIM-induced muscle inflammation. However, manual drawing methods to delineate regions of interest are subject to sampling errors. This study investigated the first utility of a fully automated deep-learning method for segmentation of T2 maps in 64 patients with IIM against healthy controls, with results confirming its feasibility, accuracy, and efficiency.
The potential role of MRI-based bone marrow Radiomics in survival prediction of newly diagnosed multiple myeloma
Authors: LI Yang , Hong Nan
Published: August 2023

Multiple myeloma (MM) is the second most common hematologic malignancy. Despite the more effective therapies were introduced, this incurable disease remains highly heterogeneous in clinical outcome. Accurate predicative markers for prognosis are needed to develop appropriate treatment in newly diagnosed MM.
Intravoxel Incoherent Motion-Diffusion Weighted Imaging derived parameters for survival prediction of multiple myeloma
Authors: LI Yang , Hong Nan
Published: August 2023

The prognostic biomarkers for multiple myeloma (MM) was urgently needed, and this study was the first to confirm the value of Intravoxel Incoherent Motion-Diffusion Weighted Imaging (IVIM-DWI) for predicting progression-free survival in patients with MM.Purpose: To evaluate the prognostic ability of Intravoxel Incoherent Motion-Diffusion Weighted Imaging (IVIM-DWI) derived parameters in newly diagnosed multiple myeloma (MM).
Evaluation of Multimodal MRI in RA Efficacy Based on Quantitative Bone Marrow Edema
Authors: Yuan Wenzhao , Dai Yi , Lei Yiwu , Qin Fang , Zhang Huiting , Zeng Zisan , Long Liling
Published: August 2023

This study evaluated the efficacy of MRI bone marrow edema imaging for rheumatoid arthritis (RA). Wrist joints of RA patients were examined at baseline and follow-up with T1 mapping, T2 mapping and IVIM, and their clinical indicator DAS28-ESR was also collected. Results showed that the changes of both T1 and D values had statistically positive correlations with DAS28-ESR. And the results showed that T1 and D values of bone marrow edema area in lunate bone, triangle bone and capitate bone can significantly reflect the curative effect of RA, and the change rate of T1 can predict the curative effect.
Dynamic T2 mapping of human skeletal muscles via multiple overlapping-echo detachment planar imaging
Authors: Qizhi Yang , Xiaoyin Wang , Shuhui Cai , Hongjian He , Congbo Cai , Zhigang Wu , Jianhui Zhong
Published: August 2023

Magnetic resonance (MR) parametric mapping and MR dynamic imaging are both valuable clinical tools. Nevertheless, quantitative imaging and high time resolution usually cannot be achieved simultaneously with conventional dynamic MR imaging methods. Herein, the multiple overlapping-echo detachment imaging (MOLED) method with short echo time (TE), dubbed MOLED-short, is proposed to fetch the dynamic variation of T2 value on in vivo extremities with high temporal resolution (about 110 ms). The robustness of MOLED-short was demonstrated. MOLED-short can capture immediate T2 changes of muscles and may provide new insights into the metabolic responses of musculatures to human activities.
Predicting Knee Osteoarthritis Progression with an Interpretable Deep Learning Approach on MRIs: Data from the Osteoarthritis Initiative
Authors: Hu Jiaping , Zheng Chuanyang , Zhong Lijie , Yu Keyan , Chen Yanjun , Wang Zhao , Zhang Zhongping , Dou Qi , Zhang Xiaodong
Published: August 2023

Identifying knee osteoarthritis progressors is significant. MRIs can reflect the structures of the knee. However, currently no tool could rapidly and objectively predict knee osteoarthritis progression based on MRI. Therefore, we applied deep learning algorithms on MRIs of the whole knee to predict progression at three time points. The Gradient-weighted Class Activation Maps were employed for interpretability, and the highlighted infrapatellar fat pad (IPFP) was segmented for progression prediction. We showed that the deep learning framework performed well on discrimination of progressors, especially at 24th months, and that the infrapatellar fat pad plays an important role in predicting progression.
Altered segregation of gray matter structural Covariance Networks in prediabetes
Authors: Deng Lingling , Liu Huasheng , Liu Wen , Liao Yunjie , Liang Qi , Zhao Chen , Wang Wei
Published: August 2023

The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of grey matter structural covariance networks in prediabetes remains unclear. In this study, the topological features and robustness of gray matter structural covariance networks in prediabetes were examined. Results showed that the prediabetes group retained the small-worldness characteristics. The prediabetes group showed higher clustering coefficient, higher local efficiency and more vulnerable to random failure than healthy controls (HCs) group, suggesting that prediabetes disturbed the segregation of gray matter structural covariance networks, which provided new insights into the pathophysiology of this disease
Clinical variables, deep learning and radiomics features help predict the prognosis of anti-NMDA receptor encephalitis in Southwest China
Authors: Xiang Yayun , Dong Xiaoxuan , Li Yongmei
Published: August 2023

The establishment and validation of accurate prognostic models in anti-NMDA receptor (NMDAR) encephalitis is lacking. This study aims to conduct an artifificial intelligence (AI) scheme to predict the prognosis of patients with anti-NMDAR encephalitis using clinical and machine learning features. We first bulid the clinical, deep learning and radiomics models, respectively. Then, we fuse the three schemes to build a fusion model and use an independent external dataset for further validation. The new fusion model significantly outperforms all other models. It demonstrates that applying AI method is an effective way to improve the performance of prognosis prediction in anti-NMDAR encephalitis.
Altered brain white matter connectom underlie affective disorders after mild traumatic brain injury at the acute stage
Authors: Huang Wenjing , Zhang Jing , Hu Wanjun , Zhang Pengfei , Wang Jun , Wang Shaoyu
Published: August 2023

Mild traumatic brain injury (mTBI) is not a static event, but a progressive injury. The neuroanatomical alterations of mTBI at the acute stage can be an initial step of damage leading to cognitive and emotional deficit. The potential mechanisms conferring these deficits are poorly understood. In this study, we employed graph theory to explore alterations of white matter connectome and to study their relationship with neuropsychological examinations. We found disrupted topologic manner of the brain’s structural connectome, and the changes of right middle occipital gyrus explain depressive symptoms in the acute phase of mTBI due to white matter network alterations.
Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum
Authors: Qian Xing , Ng Kok Pin , Ng Kwun Kei , Ji Fang , Rosa-Neto Pedro , Gauthier Serge , Kandiah Nagaendran , Zhou Juan Helen
Published: August 2023

While emerging evidence suggests the association between network neurodegeneration and memory varies with pathology in Alzheimer’s disease (AD), the trajectory of this relationship remains elusive. We stratified 708 participants into non-amyloid/non-tau, tau-only, and AD pathology groups and examined the associations between individual-level structural and metabolic network integrity and memory across cognitive stages (cognitively normal, mild cognitive impairment, and probable AD) in each pathology group. The associations of hippocampal and default mode networks with memory exhibited differential pathology-dependent trajectories across cognitive stages. Our findings pave the way for early interventions and stage-dependent remedies to modify disease trajectory and improve clinical outcomes.
The application of multi-mode MR imaging technology in the tumor infiltration boundary of cerebral glioma
Authors: Li Jin , Li Xiangrong , Zhang Huiting
Published: August 2023

We explored the applied value of multi-mode MR imaging technology, including diffusion kurtosis imaging (DKI), arterial spin labeling (ASL) and 1H-MRS, in glioma tumor invasion boundary. Results showed mean kurtosis from DKI and apparent diffusion coefficient had the highest diagnostic efficiency between high-grade (HGG) and low-grade gliomas (LGG). There were significant differences in CHO/Cr and NAA/Cr from 1H-MRS between the solid area and the proximal peritumoral edema area (PEA), and CHO/Cr and NAA/Cr in different PEA of LGG or HGG. PEA within 1cm around the solid area is recommended to be resected to decrease recurrence rate.
Characterizing meningioma subtypes using MRI-cytometry
Authors: Chu Ying-Hua , Yuan Yifan , Zhao Xueying , Wang He , Feiweier Thorsten , Hsu Yi-Cheng
Published: August 2023

We used MRI-cytometry to estimate the mean cell diameter and intracellular volume of tumors of the meningioma subtype classification. We found cell size and intracellular volume to be very different between the three meningioma subtypes in this preliminary study. Our results reflected high collagen volume among the extracellular space in fibrous meningioma. We plan to scan more patients in the future to find a useful biomarker using MRI-cytometry.
A time-related random forest survival model based on MR imaging markers to predict the survival of patients with nasopharyngeal carcinoma
Authors: Luo Chao , Li Haixia , Deng Kan , Li Haojiang
Published: August 2023

This study aimed to identify magnetic resonance (MR) imaging markers associated with the overall survival (OS) of patients with nasopharyngeal carcinoma (NPC) and establish a random survival forest (RSF) model, which is a time-related machine learning model for survival analysis, to predict their survival.
The value of Combining ADC value with Texture Analysis to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma
Authors: jiang xueyan
Published: August 2023

In conclusion, a combination of ADC values and TA based on three MRI sequences was developed to provide a noninvasive method for preoperatively predicting the expression levels of Ki-67 and p53 in EC. To some extent, this noninvasive imaging marker can compensate for the limitation of endometrium curettage biopsy and adverse impact of tumor heterogeneity, and it provide an objective imaging basis for clinical and accurate individualized treatment.
Highly accelerated sub-millimeter 3D T2 FLAIR based on deep learning and its application in robot-assist PBC for trigeminal neuralgia
Authors: Liu Qiangqiang , Zhang Shuheng , Xu Jiwen , Zhu Jiachen , Shen Yiwen , Wang Changquan , Chen Wenzhe , Yang Jun , Yuan Jianmin
Published: August 2023

With 3D T2 FLAIR imaging, trigeminal nerve is clearly demonstrated with CSF nulled, while low SNR and low spatial resolution is always the limitation. In this study, we introduced a 0.75mm isotropic resolution whole brain 3D T2 FLAIR imaging in 5min 40sec based on a novel deep learning framework, and evaluated on a small patient cohort who underwent MR-guided robot-assist percutaneous balloon compression (PBC). To our knowledge, this is the first clinical report of MR-guided robot-assist PBC surgery based on DL accelerated 3D scan.
Automatic analysis of carotid vessel wall in MR black blood images using custom convolutional trajectories
Authors: Shen Shuai , Xu Wenjing , Ma Hongbing , Lv Xiaoyi , Cheng Guanxun , Wan Liwen , Zhang Lei , Li Ye , Liang Dong , Liu Xin , Zheng Hairong , Zhang Na
Published: August 2023

Atherosclerotic plaque is a major cause of ischemic stroke. Some arterial morphological features obtained from MR vessel wall images show great potential for identifying high-risk plaques. Deep learning has now been applied to the automatic segmentation of vessel walls to accurately and efficiently measure arterial morphological features. However, the accuracy of the existing segmentation methods is not yet high enough for clinical practical applications. This study proposed a new segmentation framework with custom convolutional trajectories for automatic segmentation of arterial vessel wall and the framework improved the accuracy of vessel wall segmentation.
A unified deep learning method for 4D-MRI motion deformation estimation and image enhancement
Authors: Zhi Shaohua , Xiao Haonan , Wang Yinghui , Li Wen , Li Tian , Cai Jing
Published: August 2023

We proposed a unified deep learning framework for predicting the motion deformation between different phases of 4D-MRI with simultaneous image quality enhancement. The network combines a coarse-to-fine unsupervised registration model to estimate the deformation vector fields (DVFs) in different image scales and a GAN-based enhancement network to restore anatomic features. Particularly, a prior knowledge of 4D-MRI is incorporated into the unified model, guiding an accurate DVF prediction and maintaining image topology. Both qualitative and quantitative results showed that the predicted DVFs and resultant 4D-MRI images achieved improved performance compared with the traditional method without modifications.
CE-MRI Based Radiomics Model to identify Crohn’s disease and Ulcerative colitis
Authors: Wang Jun , Liu Guangyao , Zhang Pengfei , Ai Kai , Ma Laiyang , Hu Wanjun , Zhang Jing
Published: August 2023

Diagnosis and differential diagnosis of Inflammatory Bowel Disease (IBD) remains challenge since the particularity of small bowel. The objective of this study was to use the contrast enhanced MRI (CE-MRI) radiomics to distinguish subtypes of IBD patients. A total of 216 patients confirmed by pathology underwent small bowel CE-MRI. After the pipeline of radiomics, it was found that wavelet transformed texture features can effectively identify ulcerative colitis (UC) and Crohn’s disease (CD) with high performance. This work provides more detailed and microscopic details for the differential diagnosis of IBD subtypes based on the preliminary results.
Correlations between functional connectivity and glucose uptake in white matter
Authors: Guo Bin , Zhou Fugen , Li Muwei , Ding Zhaohua , Gore John
Published: August 2023

Blood oxygenation-level dependent (BOLD) MRI signals have been reliably detected in white matter (WM) in both task and resting states in numerous studies. However, the relationship between WM BOLD signals and regional metabolism remains to be elucidated. In the present study, we investigated the relationship between resting state functional connectivity and glucose uptake in WM using simultaneous MRI and PET studies of human subjects. We find a significant correlation between these two measurements, suggesting that functional involvement of WM in neural activities was accompanied by an increase in glucose metabolism.
Tracking forearm muscle fibers from Diffusion MRI during dynamic contractions
Authors: Li Yang , Ma Shihan , Li Qing , Xiong Bo , Wang Weiwei , Sheng Xinjun , Zhu Xiangyang
Published: August 2023

To reconstruct the trajectories of forearm muscle fibers during dynamic contractions, we designed a customized MRI-compatible rig to help acquire MRI data of the forearm muscles under three wrist postures. A piecewise registration method was proposed to correct the misregistration between DTI and anatomical images. The reconstructed supinator was highly consistent with the cadaveric dissection reference. Diffusion and architectural parameters were calculated to characterize voluntary contractions. During the wrist flexion and extension, the fascicle length of the supinator was inversely related to the pennation angle. The diffusion parameters increased slightly from neutral position to the other two postures.
Data Selection for Deep Learning via diversity visualization and scoring
Authors: Anand Deepa , Shanbhag Dattesh , Mullick Rakesh
Published: August 2023

Data diversity is a key ingredient for robust deep learning models, especially in the medical domain. We present a diversity visualization and quantification scheme which enables decisions on data selection different enough from already existing data. Out experiments amply validate the usefulness of the proposed diversity metric in terms of enhancement in accuracy of models resulting from using them in data selection decision process with accuracy improvement from 3%->10% across different sites.
Preoperative Prediction of Lymph Node Metastasis in Patients With Rectal Cancer Using A Multi-Modality Radiomics Model
Authors: Wu Rui , Dou Weiqiang , Li Aiyin
Published: August 2023

This study mainly aimed to build a multi-modality radiomics model for predicting lymph node metastasis (LNM) of rectal cancer (RC) preoperatively. 82 RC patients were enrolled in this study. Two, six, four and eleven optimal features were separately selected for venous phase CT, HR-T2WI, DWI and multi-modality by incorporating CT, HR-T2WI and DWI together. Together with MRI-reported LN status feature, clinical features, and the combination of multi-modality and clinical features, in total seven support vector machine (SVM) classification models were respectively built. Incorporated with clinical features, multi-modality radiomics model provided the most robust predictive performance in predicting LNM.
Screening detection of abnormalities in knee joint MRI using deep machine learning
Authors: Inaoka Tsutomu , Wada Akihiko , Ishikawa Rumiko , Nakatsuka Tomoya , Tomobe Hisanori , Sonoda Masaru , Yamamoto Akinori , Sakai Ryousuke , Terada Hitoshi
Published: August 2023

The DML model including fat-suppressed contrast generation, normal image restoration, and classification and determination models may make it possible to detect all abnormalities in knee joint MRI once.
Self-supervised Liver T1rho Mapping with Physics-constrained Regularization
Authors: Huang Chaoxing , Qian Yurui , Hou Jian , Jiang Baiyan , Chan Queenie , Wong Vincent , Chu Winnie , Chen Weitian
Published: August 2023

Quantification of liver T1rho has gained interest in liver pathological study. Traditional fitting method requires acquisition of multiple T1rho-weighted images and it can be affected by respiratory motion. We propose a physics-informed self-supervised mapping method by taking only one T1rho-weighted image to do the mapping. Our preliminary experimental results show that our method has the potential to outperform the traditional multi-TSL acquisition method, particularly in the scenario of free-breathing MRI scan.
Prediction of Drug Treatment Outcome among Epilepsy Children with Tuberous Sclerosis Complex based on Deep Neural Network and Multi-contrast MRI
Authors: Jiang Dian , Hu Zhanqi , Zhao Cailei , Zhao Xia , Yang Jun , Zhu Yanjie , Liao Jianxiang , Liang Dong , Wang Haifeng
Published: August 2023

Distinguishing epilepsy drug treatment outcomes is crucial for treating children with tuberous sclerosis complex (TSC). Here, a deep-learning framework named AE-net was proposed to analyze epilepsy drug treatment outcomes using multi-contrast MRI data. Firstly, multi-contrast image-based models were respectively generated using the EfficientNet3D-B0 networks. Then, an averaging ensemble network was created as the final model. The proposed AE-net achieved the best AUC performance of 0.800 and sub-optimal AUC performance of 0.763 in the testing cohort, better than others. And the proposed method can predict epilepsy drug treatment outcomes to help clinical radiologists formulate more targeted treatments in the future.
Deep Learning Algorithm for Automated Liver Segmentation Using Portal Venous Phase Magnetic Resonance Images
Authors: Han Xinjun , Yu Niange , Xiao Qianjiang , Gao Mingyang , Zheng Dandan , Yang Zhenghan
Published: August 2023

Accurate segmentation of liver not only facilitates the subsequent quantitative assessment of the regions of interest but also benefits precise diagnosis, and surgical planning. These tasks are usually performed by radiologists via visual inspection and manual delineations, which are tedious, labor-intensive, time-consuming. Convolutional neural networks (CNNs) have shown promise for performing automated liver segmentation for CT examinations, but there is less research on MR images. In this study, we provide a 3D U-Net based model for robust whole-liver and Couinaud segment measurements to support the treatment decision-making process on MR images.
Applying Continuous-Time-Random-Walk (CTRW) diffusion model based Radiomics in Predicting HER-2 Expression in Breast Invasive Ductal Cancer
Authors: Du Siyao , Wang Mengfan , Liu Shasha , Bian Xiaoqian , Chen Xinyue , Guo Liangcun , Huang Guoliang , Zhao Ruimeng , Peng Can , Jiang Wenhong , Shi Qinglei , Yan Xu , Yang Guang , Zhang Lina
Published: August 2023

In this study, we built a support vector machine (SVM) model based on quantitative parameters of continuous-time random-walk (CTRW) diffusion model in predicting the human epidermal growth factor receptor-2 (HER-2) expression in breast invasive ductal carcinoma. An AUC of 0.753 was achieved, which may have a great potential in future clinical practice.
3D T1-weighted sequence of volumetric isotropic turbo spin-echo acquisition with a deep learning constrained Compressed SENSE reconstruction
Authors: Ma Yue , Li Linna , Sun Yang , Zhai Chang , Tong Dan , Zhu Yi , Jiang Ke
Published: August 2023

Compressed SENSE-AI reconstruction system was introduced with its sufficient noise removal to maintain high image quality while reducing scanning time. Intracranial and carotid vessel wall MR imaging (VW-MRI) are widely available in detecting and characterizing atherosclerosis occurring in the two vascular ranges, however, long scan time had so far limited high resolution VW-MR imaging. In this study, we investigated the impact of Compressed SENSE-AI acceleration factor on the diagnostic quality of magnetic resonance images and compared with standard vessel wall MR imaging protocol.
Human Knowledge Guided Deep Learning with Multi-parametric MR Images for Glioma Grading
Authors: Wang Yeqi , Li Longfei , Li Cheng , Zheng Hairong , Lin Yusong , Wang Shanshan
Published: August 2023

Automatic glioma grading based on magnetic resonance imaging (MRI) is crucial for appropriate clinical managements. Recently, Convolutional Neural Networks (CNNs)-based classification models have been extensively investigated. However, to achieve accurate glioma grading, tumor segmentation maps are typically required for these models to locate important regions. Delineating the tumor regions in 3D MR images is time-consuming and error-prone. Our target in this study is to develop a human knowledge guided CNN model for glioma grading without the reliance of tumor segmentation maps in clinical applications. Extensive experiments are conducted utilizing a public dataset and promising grading performance is achieved.
Classification and visualization of chemo-brain in breast cancer survivors with deep residual and densely connected networks
Authors: Lin Kai-Yi , Chen Vincent Chin-Hung , Tsai Yuan-Hsiung , Weng Jun-Cheng
Published: August 2023

Our goal was to establish objective 3D deep learning models that differentiate cerebral alterations based on the effect of chemotherapy and to visualize the pattern that was recognized by our model. The average performance of SE-ResNet-50 models was accuracy of 80%, precision of 78%, and 70% recall, and the SE-DenseNet-121 model reached identical results with an average 80% accuracy, 86% precision, and 80% recall. The regions with the greatest contributions highlighted by the integrated gradients algorithm for differentiating chemo-brain were default mode and dorsal attention networks. We hope these results will be helpful in clinically tracking chemo-brain in the future.
Overall survival analysis of esophageal squamous cell carcinoma using MRI-based radiomics features
Authors: Liu Yun , Wang Chenglong , Chu Funing , Qu Jinrong , Yan Xu , Yang Guang
Published: August 2023

A total number of 439 patients with esophageal squamous cell carcinoma (ESCC) were enrolled in this study. All patients scanned using StarVIBE sequences. We split the data randomly into training and independent test cohort in a ratio of 7 to 3. We proposed a method for feature selection to find the most useful features for survival analysis. The radiomics score combined with clinical variables achieved the highest consistency in the prediction of disease-free survival (DFS) with a C-index value of 0.682 in the test cohort and overall survival (OS) with a C-index value of 0.691 in the test cohort.
Investigation of synthetic magnetic resonance imaging applied in the evaluation of the prognostic factors of rectal adenocarcinoma
Authors: ma lidi , lian shanshan , liu huimin , meng tiebao , zeng weilong , zhong rui , zhong linchang , qian long , xie chuanmiao
Published: August 2023

The clinical significance of synthetic MRI in rectal adenocarcinoma remains unclear. This study aimed to explore the of quantitative parameters derived from SyMRI clinical stage according to “DISTANCE” criteria and differentiation of rectal adenocarcinoma. Our preliminary study demonstrated that quantitative T2 and PD values obtained by SyMRI might be used for noninvasive evaluation of prognostic factors of rectal adenocarcinoma. Furthermore, combining the two quantitative relaxation metrics further improved their diagnostic performance of mrN stage in rectal adenocarcinoma.
In Vivo Relaxation-Diffusion Correlation Revealed MRI Distribution Spectra of the Human Brain Using Genetic Algorithm Optimized Acquisitions
Authors: Wang Lixian , Zhang Baogui , Liang Huilou , Wu Yue , An Jing , Zhuo Yan , Xue Rong , Zong Fangrong
Published: August 2023

Multidimensional MRI experiments have shown unique significance in resolving the correlations between physical parameters and relaxation properties. This study showed T1 Relaxation-Diffusion correlation imaging spectra of an in vivo human brain and a multicomponent phantom with optimized acquisitions. The acquisition paradigm was obtained from genetic algorithm (GA) by maximizing the singular values of the corresponding kernel functions. We detected significant differences in these spectra inversed from different components, which might help to identify tissue microstructure at the sub-voxel resolution. Our study offered a high probability of applying multidimensional MRI techniques in diagnosing neurological diseases.
Design of a double-layered quadruple-nuclear birdcage coil system for 1H / 19F / 23Na/31P MR imaging at 3T
Authors: Li Nan , Du Feng , Zhang Xiaoliang , Liu Xin , Zheng Hairong , Li Ye
Published: August 2023

The lower natural abundance and gyromagnetic ratios of non-hydrogen nuclei require the use of a highly efficient RF coil to optimize the quality of the MR image and spectroscopy. In this study, a new quadruple-tuned RF coil system capable of 1H / 19F / 23Na /31P imaging was proposed, including the inner high-pass birdcage coil (31P) and a triple-tuned outer four-ring birdcage coil (1H / 19F / 23Na). The feasibility and performance of this proposed RF coil were evaluated and validated by the numerical electromagnetic simulation in the scattering parameters and B1+field distributions.
Bumped Self-decoupled RF Coil
Authors: Lu Ming , Gore John , Yan Xinqiang
Published: August 2023

In this work, we propose a novel “bump” design to further improve the coil robustness versus loadings. For the loop-mode self-decoupled coil, the rotating magnetic field (composed of Bx and By) and electric field are mainly determined by the feed and arm conductors; the frequency shift that decreases coil robustness is mainly caused by the conductor with small-valued mode capacitors (referred to as mode conductor for short). Therefore, a bumped coil with a spaced mode conductor but unchanged arm and feed conductors potentially provides more robust tuning/matching performance without decreasing coil efficiency.
Design of a 5-channel local B0 shimming coil for rat brain imaging at 3T
Authors: Chen Qiaoyan , Luo Chao , Zhang Xiaoliang , Liu Xin , Zheng Hairong , Li Ye
Published: August 2023

To improve B0 magnetic field homogeneity and minimize interferences on RF coils, local shimming coils with a few channel number can be applied. In this study, we designed and constructed a 5-channel local B0 shimming coil for the rat brain. There was a marginal SNR loss within 5% after converting the local shimming coil into a 3-channel RF coil. The reduction of B0 inhomogeneity for the rat brain with respect to the basic set was about 34%. A large portion of the distortion in EPI images was recovered and some image artifacts were eliminated after using the local shimming coil.
Multiparametric MRI method development for clinical prostate imaging using 5T MR imaging
Authors: Zheng Liyun , Yang Chun , Sheng Ruofan , Dai Yongming , Zeng Mengsu
Published: August 2023

Moving to higher magnetic field strength (> 3 T) may have clinical advantages because of an intrinsic increase of the signal-to-noise ratio (SNR), while heterogeneity in the transmit radiofrequency (RF) field or B1 can cause signal voids throughout images. This study aimed to evaluate the development of prostate MRI at 5T by providing assessment of image quality on clinical sequences, including T1W, T2W and DWI images. According to current results about image quality, present of artifacts, visibility of anatomical structures of T2W images, and geometric distortion of DWI images, clinical prostate imaging is feasible at 5T MRI.
Morphological 5T MRI of hip in comparison to 3T: An initial study
Authors: Zheng Liyun , Yang Chun , Sheng Ruofan , Dai Yongming , Zeng Mengsu
Published: August 2023

Imaging the hip cartilage using magnetic resonance imaging (MRI) is more challenging compared with other joints. The utilize of higher magnetic field strengths (>3T) is able to improve the assessment of cartilage structures in the hip. In this study we evaluated morphological 5T hip MRI in healthy subjects and compared image quality at 5T with 3T MRI. The results revealed that morphological 5T hip MRI in healthy subjects was superior to 3T MRI in image quality, visibility of the anatomical structures and contrast ratios.