bluuebunny/crossref_metadata_2025 huggingface dataset:
The importance of dynamic contrast-enhanced MRI in preoperative prediction of glioma genotype and prognosis
Authors: Ma Hui , Zhao Jing , Zeng Shanmei , Xie Dingxiang , Mazu Liwei , Deng Kan , Chu Jianping
Published: November 2024

Motivation: Accurate preoperative noninvasive diagnosis and classification of diffuse gliomas have a significant impact on clinical management and are of significant prognostic importance. Goal(s): To investigate the diagnostic efficacy and clinical value of preoperative DCE-MRI in predicting genotype and prognosis of glioma. Approach: Adult diffuse gliomas were enrolled. Univariate and multivariate logistic or cox regression analysis are performed for diagnosing glioma gene and prognosis. Results: Preoperative DCE-MRI exhibits favorable diagnostic capabilities in identification of IDH mutation status with AUC value of 0.77, 1p19q codeletion with AUC value of 0.71, and CDKN2A/B homozygous deletion with AUC value of 0.93, and assessment of glioma prognosis. Impact: This study highlighted potential clinical application of DCE-MRI by demonstrating the robust diagnostic performance of DCE-derived parameters. These parameters exhibited not only excellent diagnostic accuracy in identifying functional biomarkers such as IDH, 1p19q and CDKN2A/B, but also in predicting prognosis.
Case studies evaluating the enhanced visualization and characterization of brain tumor using multi-contrast MRI at 7T
Authors: Liu Jiaen , Huang Yujia , Chan Kimberly , Jani Mahrshi , Rodriguez Yeison , Thomas Binu , Dimitrov Ivan , Maher Elizabeth , Patel Toral , Henning Anke
Published: November 2024

Motivation: Multi-contrast MRI at 7T can potentially lead to enhanced detection and characterization of brain tumor with unpresented sensitivity and specificity. Goal(s): To evaluate the improvement in tumor boundary, vasculature and hemorrhage detection using a set of complementary MRI sequences at 3T and 7T. Approach: Five brain tumor patients were scanned with multi-contrast MRI protocols at 7T and 3T. All 7T protocols were implemented with submillimeter resolution in similar scan time as the 3T methods. Results: At 7T, high image quality was observed in all the protocols because of high contrast and resolution. This led to improved detection of tumor boundary, vasculature, and hemorrhage. Impact: Multi-contrast ultrahigh field MRI has the potential to non-invasively detect brain tumor in the early stage, provide precise tumor delineation, and visualize tumor-specific processes not seen on conventional MRI.
Low-dose Dynamic Imaging for Cerebrovascular Evaluation (LD-DICE)
Authors: Chen Yang , Xiao Jiayu , Christodoulou Anthony , Li Debiao , Chow Frances , Zada Gabriel , Chang Eric , Shiroishi Mark , Fan Zhaoyang
Published: November 2024

Motivation: Gadolinium retention in the human body following dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI remains a health concern to many patients, especially those who need long-term imaging follow-up. Goal(s): This work aims to investigate the feasibility of using a recently developed technique, MR multitasking-based dynamic imaging for cerebrovascular evaluation (DICE), to quantify permeability and perfusion with a 0.03 mmol/kg dose. Approach: Numerical simulations were conducted to determine the optimal dose level. Assessments for the agreement of low-dose DICE (LD-DICE) with full-dose DICE (FD-DICE) were performed. Results: Good correlation was achieved. Brain tissue perfusion and permeability can be quantified simultaneously with LD-DICE. Impact: Low-dose DICE will allow for comprehensive tumor vascularity evaluation with considerably less contrast agent than clinical standard protocols, which will benefit brain tumor patients who need frequent imaging follow-ups.
Role of Gd-EOB-DTPA-enhanced MRI in Hepatic Fibrosis Staging: Insights from Hepatobiliary Phase Imaging
Authors: Ren Yufan , Hu Genwen , Li Xinming , Li Min , Lyu Jiaqi , Lu Haojun , Xu Yongzhou , Guo Congyue , Zhang Ge , Quan Xianyue
Published: November 2024

Motivation: Noninvasive assessment of hepatic fibrosis progression in individual patients. Goal(s): To explore the value of signal intensity ratio (SIR) and radiomics analysis based on hepatobiliary phase (HBP) of Gd-EOB-DTPA-enhanced MRI in evaluating hepatic fibrosis staging. Approach: The liver–muscle contrast ratio (LMCR) and liver–spleen contrast ratio (LSCR) were measured in HBP image, and the radiomics model and SIR–radiomics combined model were established. Finally, the diagnostic accuracy of SIR and both models in hepatic fibrosis staging were evaluated. Results: The radiomics model exhibited higher diagnostic efficacy than SIR. The SIR–radiomics combined model demonstrated superior diagnostic performance in evaluating hepatic fibrosis staging . Impact: This study constructed a noninvasive hepatic fibrosis diagnostic model using accessible routine sequence and improved the diagnostic efficiency. It offers a reliable approach for early diagnosis and treatment assessment, potentially reducing the need for unnecessary invasive biopsies in the future.
MRI-based Radiomics Nomogram in Preoperative Prediction of Lymph Node Metastasis of Endometrial Cance
Authors: Yao Yao , Pylypenko Dmytro , Huang Zebo , Tang Wenwei
Published: November 2024

Motivation: Routinely MRI can’t assess lymph node metastasis (LNM) status in endometrial cancer (EC) accurately. Goal(s): This study aimed to investigate whether the MRI radiomics model can predict LNM status in EC. Approach: 313 EC patients who underwent preoperative MRI were recruited with the status of LNM confirmed by pathology. 2880 radiomic features were extracted and three models including a clinical model, a radiomics model and a combined model were built. Results: Area under the receiver operating characteristic curves showed all of the three models can predict LNM and the combined model showed the best discrimination ability on the training set and test set. Impact: This study built an accurate preoperative and non-invasive model to assess lymph node metastasis status in endometrial cancer. It could help doctors to determine the extent of lymphadenectomy and provide useful information for prognosis prediction and treatment decision.
Multimodal MRI-Based Radiomics Combining 3D Deep Transfer Learning for Predicting Cervical Stromal Invasion in Endometrial Carcinoma
Authors: Wang Xianhong , Bi Qiu , Bi Guoli , Wu Yunzhu
Published: November 2024

Motivation: Cervical stromal invasion (CSI) plays a critical role in distinguishing between stage I and II endometrial carcinoma (EC) and serves as a key prognostic indicator. Goal(s): Assisting clinicians in achieving precise preoperative treatment and prognostic assessments. Approach: This study constructed innovative machine learning models that merge radiomics and 3D deep transfer learning to preoperatively and non-invasively predict CSI. Results: Novel machine learning model has significant superiority over radiologists for preoperative prediction of CSI. Impact: Constructing a non-invasive preoperative prediction model to increase the diagnostic accuracy of CSI, makes up for the limitations of traditional imaging observation in the assessment of CSI and subsequently directs clinicians in preoperative precise treatment and prognostic evaluation.
Cervical cancer diagnosis from diffusion weighted imaging using deep convolutional neural networks
Authors: Aouadi Souha , Torfeh Tarraf , Bouhali Othmane , Chandramouli Suparna , Barzegar Mojtaba , Hammoud Rabih , Al-Hammadi Noora
Published: November 2024

Motivation: Conventional grading of cervix cancer (CC) requires biopsy that can lead to potential side-effects. Radiologists typically rely on multi-imaging for CC staging, which can result in patient discomfort, higher costs, and increased workload. Goal(s): This study aims to introduce noninvasive methods that leverage a single MRI for both grading and staging prediction. Approach: EfficientNetB0 and EfficientNetB3 were applied for tumor classification (binary and four-class) based on apparent diffusion coefficient maps of 85 patients. They were evaluated using the area under the receiver operating characteristic curve (AUC). Results: High AUC=0.924 and AUC=0.931 were obtained for grade and stage predictions respectively. Impact: The results demonstrated the feasibility of noninvasive prediction of cervical cancer grade and stage from diffusion weighted images. This could significantly impact the diagnosis and management of cervical cancer, as it can provide valuable information without biopsy or extensive imaging.
Multiparametric MRI based deep learning model for automatic segmentation of tumor and lymph nodes in rectal cancer
Authors: Xia Yihan , Zhu Lan , Shi Bowen , Feng Weiming , Wang Kangning , Wu Pu-Yeh , Zhang Huan
Published: November 2024

Motivation: A significant challenge in RC management is the precise delineation of tumor boundaries and the accurate evaluation of LNs. The existing manual processes for this are time-consuming and subject to high variability. Goal(s): This study aimed to develop a deep learning approach for simultaneous RC tumor and LN segmentation. Approach: We constructed the model with mpMRI data input, ResUNet architecture, and focal cross entropy loss. Results: The ResUNet model achieved a mean SEN of 0.824, PRE of 0.619, and DSC of 0.694 in the validation dataset, indicating promising results. However, some false positives and false negatives were observed in LN segmentation. Impact: We introduced a ResUNet model for RC segmentation and achieved satisfactory results. While our findings are preliminary and may benefit from larger samples, this approach could improve tumor and LN segmentation and ultimately enhance clinical utility in RC management.
MRI radiomics and machine learning for prediction of adherent perinephric fat
Authors: Le Binh , Chung Ho Seok , Heo Suk Hee , Park Ilwoo
Published: November 2024

Motivation: Sticky perinephric fat (SPF) poses a surgical challenge for patients with renal cell carcinoma and the pre-operative identification of SPF is of clinical interest. Goal(s): The aim of this study was to investigate the effectiveness of using MRI-based radiomics features in predicting the presence of SPF. Approach: Machine learning algorithms were trained using radiomics features from T1-weighted contrast-enhanced MRI images and clinical factors (gender and BMI). Results: The promising results on internal and external test sets pave the way to validate the current approach in a larger data set. Impact: Machine learning models trained with MRI-derived radiomics features can provide a tool for preoperative prediction of sticky perinephric fat. The results from this study suggest that this approach may assist in improving surgical prognosis and outcomes.
The Value of Magnetic Resonance Imaging Peritumoral Radiomics in Predicting 21-gene Recurrence Score of ER+/HER2- Breast Cancer
Authors: Chen Yang , Peng Weijun , Xie Lizhi
Published: November 2024

Motivation: 21-gene assay is recommend to guide decision on the use of adjuvant chemotherapy. However, this test is expensive and time-consuming, so it is not widely used in clinic. Goal(s): To develop a radiomics model for predicting 21-gene recurrence score based on MRI intratumoral and peritumoral features. Approach: Prediction models in tumor and different peritumoral regions (2 mm, 4 mm, 6 mm, 8 mm, 10 mm) were established using machine learning method. Feature-fusion and logistics-regression methods were used to fuse information. Results: Combining 4-mm peritumoral model on T2WI (AUC = 0.66) with intratumoral and clinical-imaging features, the fusion model performed best (AUC = 0.75). Impact: To provide an alternative for patients who cannot afford Oncotype 21-gene assay and to reduce the medical costs for those who could afford it.
Noninvasive Identification of Breast Cancer HER2 Status by Deep Learning on Multiparametric MRI Images
Authors: YANG YANG , Luo Zixin , Pan Haoyu , Guo Yuan , Tang Wenjie , Wei Xinhua , Huang Bingsheng
Published: November 2024

Motivation: Multiparametric magnetic resonance imaging (mpMRI) offers valuable insights for predicting HER2 expression. However, when fusing mpMRI features, redundancy or wastage of information may impact model performance. Goal(s): Our aim was to construct an effective deep learning model by incorporating the interrelated and complementary features of different MRI sequences. Approach: Leveraging a contrastive learning approach, we aligned features across sequences and within each sequence separately to obtain sequence-shared and sequence-specific features. Subsequently, these two features were fused by utilizing an adaptive weighting scheme. Results: When compared to widely used deep learning approaches, our method achieved the best AUC of 0.743. Impact: The method explored the interrelated and complementary features of different MRI sequences, which outperformed widely used deep learning methods in terms of performance. This method was expected to have a positive impact on the accurate prediction of HER2 expression status.
Few-shot Learning for Differentiation of Malignant and Benign Breast Cancer Lesions Using Dynamic Contrast-enhanced MRI
Authors: Zabihollahy Fatemeh , Pinto Renata , Haider Masoom , Freitas Vivianne
Published: November 2024

Motivation: Breast cancer (BrCa) is the most prevalent malignancy among women. MRI is a useful tool for BrCa early detection and characterization. However, high false-positive rates can lead to unnecessary biopsies and patient distress. To enhance diagnostic accuracy, deep learning presents a promising avenue, but training deep neural networks (DNN) requires a large, annotated dataset. Goal(s): Introduce a novel method for BrCa classification, utilizing a minimally labeled dataset. Approach: We employ a few-shot learning (FSL) approach to differentiate between benign and malignant breast tumors. Results: Our FSL-based model significantly surpasses the diagnostic performance of trained radiologists in breast cancer classification (p < 0.0001). Impact: Our FSL model streamlines machine learning by reducing data labeling needs outperforms radiologists in detecting breast cancer, and could reduce unnecessary biopsies, sparing patients from potential harm.
Prostate Cancer Diagnosis Using an Explainable Credibility Estimation Network Incorporating a Rejection Mechanism
Authors: Wei Rong , Xia Yu , Zhu Yi , Yang Jinyu , Gao Ge , Wang Xiaoying , Zhang Jue , Lian Jianxiu
Published: November 2024

Motivation: The need to improve prostate cancer diagnosis through advanced understanding of lesion characteristics and reducing false positives led to this research. Goal(s): To create a pioneering integrated system using deep learning, capable of accurately assessing the benignity or malignancy of prostate MRI images, whilst reducing labeling costs and enhancing the reliability of classifications. Approach: The approach involves training a convolutional network with multi-parametric MRI images, incorporating credibility analysis to provide visually interpretable prostate cancer prediction results and reject low-credibility predictions. Results: The results showed improved reliability and efficacy, with the model discarding low-credibility predictions, thus mitigating potential risks associated with prediction failures. Impact: This study equips clinical practitioners with the ability to comprehend the decision-making process of the CAD system and manage the output results through an intuitive display. This results enhance diagnostic accuracy, potentially impacting clinicians' decision-making and patient outcomes.
T2-weighted image radiomics nomogram to predict pancreatic serous and mucinous cystic neoplasms
Authors: Fang Xu , Bian Yun , Wang Li , Shao Chengwei , Lu Jianping
Published: November 2024

Motivation: Cystic fluid appears hyperintense via T2WI, the most sensitive detection method and T2WI is a conventional sequence. However, distinguishing pancreatic MCNs from SCNs using T2WI is difficult because both neoplasms appear as hyperintense lesions, especially when both are unilocular. Goal(s): We aimed to develop and validate a T2WI radiomics nomogram for the differentiation of SCNs from MCNs. Approach: A radiomics model that was included clinical characteristics, MRI characteristics, and T2WI rad-scores for differentiating MCNs from SCN. Results: We developed and validated a T2WI radiomics nomogram that functions as a non-invasive and convenient tool for preoperatively predicting the presence of SCNs and MCNs. Impact: The tool has the potential to help clinicians identify patients requiring surveillance or surgery.
MRI Radiomics Enhances Radiologists' Ability for Characterizing Intestinal Fibrosis in Patients with Crohn's Disease
Authors: Zhang Mengchen , Zeng Yinghou , Fang Zhuang-nian , Wang Yang-di , Zhang Ruonan , Ye Ziyin , Cao Qing-hua , Zhao Chen , Mao Ren , Sun Canhui , Chen Zhi-hui , Huang Bingsheng , Li Xuehua
Published: November 2024

Motivation: Fibrostenosis is a severe complication of Crohn's disease that significantly impacts patients' quality of life. Currently, there are no effective medical interventions for severe intestinal fibrosisGoal(s): We aimed to develop MRI-based radiomic models to improve the diagnostic accuracy of radiologists in characterizing intestinal fibrosis in patients with Crohn’s disease.Approach: Radiomic models based on different MR sequence combinations were developed and validated in an independent test cohort. Results: The radiologists’ interpretation aided by MRI-radiomics outperformed visual interpretation in characterizing intestinal fibrosis in Crohn's disease (AUC=0.86-0.93 vs. AUC=0.63-0.77). Impact: The utilization of MRI-based radiomic models significantly enhances the diagnostic accuracy of radiologists in characterizing intestinal fibrosis.
FetalSurfer: Automated Fetal Cortical Surface Reconstruction
Authors: Li Haoxiang , Liu Mingxuan , Zheng Jialan , Yang Hongjia , Li Zihan , Tian Qiyuan
Published: November 2024

Motivation: The cerebral cortex of the fetus is undergoing intricate development. Abnormal cortical development may potentially alter brain function. However the methods for processing fetal MRI images, especially cortical reconstruction, are still far behind those used for adults. Goal(s): To develop an accurate cortical surface reconstruction method and perform morphological calculations for fetal MRI. Approach: Trustworthy AI segmentation and refined Freesurfer were used to process T2-weighted fetal brain image. Results: The proposed mothod (FetalSurfer) allows for trustworthy reconstruction of the cortical surfaces of the fetal brain and calculation of indicators to measure the morphology of fetal development. Impact: FetalSurfer implements a novel fetal cerebral cortex reconstruction method without manual refinement by professional doctors, filling the gap of fetal image processing methods. Calculated indicators as curvature, thickness and sulcal depth can be used to perform morphological analysis.
Evaluation of multi-frequency MRE repeatability in healthy people and CKD diagnosis combined with automatic segmentation technique
Authors: Chen Yueyao , Luo Peiyin , Qi Ruirui , Lin Haiwei , Liang Qiumei , Li Junfeng , Chen Qiuyi , Qin Haodong , Meng Fanqi , Lyu Hanqing , Pan Jingtong , Qu Feifei , Wu Yanglei
Published: November 2024

Motivation: The incorporation of automated kidney segmentation technology with multi-frequency MRE represents a novel approach in evaluating kidney diseases. Goal(s): To explore the reliability of multi-frequency MRE combined with an automatic segmentation method and its diagnostic potential for CKD patients. Approach: Constructed an automatic kidney segmentation model based on the nnU-Net network and measured the renal stiffness of MRE, employing T-tests, ROC curves, and Spearman correlation for data analysis. Results: The incorporation of an automatic kidney segmentation model and multi-frequency MRE shows promise in effectively evaluating and monitoring kidney fibrosis. Impact: The incorporation of automated kidney segmentation and MRE presents a new tool for reliably evaluating and monitoring kidney diseases, providing potential advancements in non-invasive diagnoses.
Evaluation of Common Bile Duct (CBD) dilatation by CT data using Synthetic MRCP data by Cycle-GAN and 3D VGG Network
Authors: Kim Sojeong , Park Sunghong , Kim Kyung Ah
Published: November 2024

Motivation: Differentiating CBD dilatation with CT alone is challenging, often necessitating MRCP(Magnetic Resonance Cholangiopancreatography). Yet, patients forego MRCP due to cost and time constraints. Hence, predicting CBD dilatation using CT is vital for diagnosis. Goal(s): Developing deep neural networks to assess CBD dilatation only with CT data. Approach: Cycle-GAN and 3D VGG Networks predicted CBD dilatation, where Cycle-GAN generated synthetic MRCP from CT and 3D VGG Network predicted dilatation using this synthetic data. Results: The network trained with synthetic MRCP data predicted CBD dilatation with an AUROC of 0.7231, 30% improvement over using CT data alone, enabling CT-only diagnosis. Impact: This study introduces a transformative solution for CBD dilatation diagnosis, enabling assessments using Only-CT data from Cycle-GAN and 3D VGG Network. Achieving a 30% improvement in AUROC, it enables reliable CT-only diagnoses, overcoming scarce MRCP data and improve patient care.
Deep learning reconstructed 3D zero echo time MRI for lung imaging: a preliminary study
Authors: Tang Shixiong , Liu Weiyin , Fan Yang , Liu Jun
Published: November 2024

Motivation: Lung MRI using UTE and ZTE techniques is limited by its SNR and tissue interface blurring. Deep learning based reconstruction (DLR) technique has been used to improve MRI image quality via noise reduction. Goal(s): To evaluate potentially clinical applications of breath-hold DLR ZTE lung MRI in ventilation function. Approach: DLR and conventional reconstructed ZTE lung images of thirty patients with pulmonary nodules were compared for image quality and image-based pulmonary ventilation estimation. Results: Compared to conventional reconstructed results, DLR ZTE images demonstrated improved image quality and better correlation with clinical measurements for ventilation estimation. Impact: This preliminary study demonstrated the feasibility of DLR ZTE technique in lung MRI. DLR ZTE images showed improved image quality and better correlation with clinical measurements for ventilation estimation.
Evaluation of deep learning HASTE sequence for liver MRI at 3.0 Tesla: a qualitative and quantitative prospective study
Authors: Dong Sanyuan , Rao Shengxiang , Chen Caizhong , Zeng Mengsu , Fu Caixia , Nickel Dominik
Published: November 2024

Motivation: Liver T2-weighted imaging usually requires a long scan time. A faster sequence with adequate image qualities is essential in clinical practice. Goal(s): To evaluate deep-learning reconstruction accelerated T2-weighted HASTE sequence in liver application on the image quality and diagnostic confidence. Approach: One hundred and five patients were imaged using both sequences. Images were reviewed independently by two blinded observers. Results: The DL HASTE sequence can detect more liver lesions and improve the CNR of the lesion compared to the conventional T2-weighted BLADE sequence, with a 2.5-fold reduction in acquisition time. Impact: DL HASTE sequence has the potential to replace the conventional BLADE sequence in routine clinical liver MRI, reducing the scan time and detecting more liver lesions.
Radiomics Analysis Based on Preoperative Multiparametric MRI for Prediction of TACE Refractoriness in Hepatocellular Carcinoma
Authors: Tan Yan , Xu Wenji , Zhang Hui
Published: November 2024

Motivation: Parts of HCC respond poorly to TACE, and its efficacy declines as the number of procedures increases in the clinical practice, which is called TACE refractoriness. Goal(s): To develop and validate a preoperative multiparametric MRI-based radiomics model to predict TACE refractoriness in HCC. Approach: Radiomics feature selection was performed using PCC and RFE, and SVM was used to construct radiomic models based on each sequence and their combination, clinical-radiological model based on selected clinical-radiological predictor and combined model. Results: The combined model exhibited excellent predictive performance. The multi-phase radiomics signature performed better in predicting TACE refractoriness compared to the best single-phase radiomics signature. Impact: The preoperative multiparametric MRI radiomics analysis can predict TACE refractoriness in hepatocellular carcinoma, which may provide better guidance for decision-making regarding further TACE treatment and optimize the mode of treatment and patient management, ultimately resulting in patient survival benefits.
RAIDER: Rapid, anatomy-independent, deep learning-based chemical shift-encoded MRI
Authors: Bray Timothy , Minore Giulio , Bainbridge Alan , Hall-Craggs Margaret , Zhang Hui
Published: November 2024

Motivation: Despite recent advances, chemical shift-encoded MRI (CSE-MRI) remains a challenging problem and many algorithms are computationally expensive, leading to interest in deep learning-based methods. However, initial attempts have used convolutional neural networks (CNNs), which are limited by data requirements, poor generalisability across different anatomies (‘anatomy-dependence’) and training time. Goal(s): To address these limitations, we propose a deep learning-based method known as RAIDER. Approach: RAIDER uses two multilayer perceptrons (MLPs), each trained separately with simulated single-voxel data, to achieve ultrafast parameter estimation. Results: RAIDER is several orders of magnitude faster than conventional fitting, with similar/better performance, and avoids the inherent limitations of CNN-based methods. Impact: RAIDER delivers ‘ultrafast’ CSE-MRI processing whilst avoiding the data and training-time requirements and anatomy-dependence of CNN-based methods. It could simplify, accelerate and reduce the cost of CSE-MRI processing in both research and clinical practice.
Spectrally-selective and Interleaved Water Imaging and Fat Imaging (siWIFI) for Model-free Fat Quantification
Authors: Shin Soo Hyun , Tang Qingbo , Carl Michael , Chung Christine , Bydder Graeme , Chang Eric , Du Jiang , Ma Yajun
Published: November 2024

Motivation: Reliable water-fat separation and quantification are of critical importance in the MRI assessment of diseases involving metabolic disruption and fat infiltration. Goal(s): To develop a model-free approach for spectrally selective and interleaved water imaging and fat Imaging (siWIFI). Approach: We designed a new sequence that selectively acquires water and fat signals in an interleaved fashion. This new sequence was tested on phantoms and healthy subjects. Results: The measured fat fraction showed excellent correlation with fat concentrations of phantoms. Both phantom and healthy subject images were comparable to those from standard IDEAL scans. Impact: Our new method, termed siWIFI, selectively images water and fat for water-fat quantification which does not require complicated post-processing. Combining with MT preparation shows the feasibility of simultaneous quantification of fat infiltration and fibrosis development.
4D spectral-spatial pulse design for subject-specific fat saturation at 1.5 T
Authors: Eisen Christian , Groß-Weege Nicolas , Herrler Jürgen , Liebig Patrick , Uder Michael , Nagel Armin , Grodzki David , Malik Shaihan
Published: November 2024

Motivation: Insufficient fat saturation compromises image quality in clinical examinations. Goal(s): To improve the quality of spectral fat saturation resulting in less residual fat signal in the acquired image. Approach: Individual 4D spectral-spatial pulses based on subject-specific field maps and a numerically found trajectory are designed within an online workflow. A universal RF solution is also calculated. Performance is compared to Gaussian and SLR pulses on ten cervical spine datasets and one in-vivo measurement. Results: Simulations show significantly improved fat saturation with individual and universal spectral-spatial pulses, while average water excitation remains low only for individual pulses. The in‑vivo measurement supports the simulation results. Impact: Customized and universal spectral-spatial fat saturation pulses outperform currently used spectral pre-saturation pulses enabling more definitive interpretation of fat‑suppressed MR images. Potential application to a variety of sequences is straightforward by replacing the pre-saturation pulse with our design.
A Novel Magnetic Field Gradient Based Thresholding Method to Improve Brain Masking for QSM
Authors: Kiersnowski Oliver , Shmueli Karin
Published: November 2024

Motivation: Artefacts in QSM reconstructions can persist even after optimal noise-based masking methods are used, especially in EPI-QSM, due to challenging regions caused by large field gradients rather than noise. Goal(s): To reduce brain QSM artefacts using a novel, automated brain mask thresholding method. Approach: Regions with high magnitude of the field gradients (MFG) were removed from the brain mask used for background field removal and/or susceptibility calculation. Results: Thresholding the mask based on the MFG of the local field map was superior to thresholding the total field map. MFG-based thresholding reduced artifacts in 2D-EPI and 3D-GRE QSM compared to noise-based thresholding. Impact: Thresholding the brain mask based on the magnitude of local field gradients improves brain QSM quality by reducing streaking artefacts compared to state-of-the-art noise-based thresholding. This automated MFG-based masking method particularly improves rapid 2D-EPI-QSM as well as conventional 3D-GRE QSM.
Artifact Reduction for Rapid Phase-Based EPT in the Human Brain In Vivo Using a Multi-Echo 2D EPI Sequence
Authors: Arsenov Oriana , Luo Jierong , Karsa Anita , Kiersnowski Oliver , Fuchs Patrick Patrick , Arridge Simon , Shmueli Karin
Published: November 2024

Motivation: To date, phase-based electrical properties tomography (EPT) has been performed using time-consuming (~5-minute) gradient-echo sequences. Goal(s): To calculate EPT conductivity maps from a rapid multi-echo 2D-EPI acquisition (TR~4s), overcoming slice-to-slice phase inconsistencies. Approach: We investigated the effect of four different methods to remove slice-to-slice inconsistencies from the phase offset (φ0) of the multi-echo 2D-EPI data on conductivity maps in a numerical phantom and in vivo. Results: Removing the median φ0 in each slice in the brain provided high quality conductivity maps with clear delineation between grey matter, white matter and CSF. Tissue conductivity values showed good inter- and intra-subject repeatabilityImpact: We developed a rapid, repeatable method for phase-based EPT from multi-echo 2D-EPI, overcoming slice-to-slice phase inconsistencies. This will facilitate clinical applications of EPT, particularly in studies already using multi-echo 2D-EPI for fMRI, and paves the way towards functional EPT.
Ethanol reduces brain tissue electrical conductivity
Authors: Cao Jun , Summerell Elizabeth , Denson Tom , Rae Caroline
Published: November 2024

Motivation: Ethanol is a sedative which reduces brain metabolism and activity. We hypothesise that brain tissue conductivity is related to brain activity and tested to see if ethanol ingestion reduces tissue conductivity. Goal(s): To demonstrate a relationship between baseline brain activity and tissue conductivity. Approach: Using MREPT, data were acquired from 41 participants before and after ingestion of vodka and the resultant electrical conductivity maps were compared. Results: Spatially similar decreased electrical conductivity was seen in most participants. The spectrum of response was unrelated to the amount of alcohol consumed or to breath alcohol reading. No increased conductivity was seen. Impact: The results support the hypothesis that tissue conductivity is related to brain activity. This suggests that changes in electrical conductivity may be used as a surrogate for baseline brain activity which could be a useful biomarker of injury or degeneration.
Preliminary Whole-Brain Functional Electrical Properties Tomography using Gradient-Echo Echo-Planar Imaging
Authors: Luo Jierong , Nassar Jannette , Kiersnowski Oliver , Arsenov Oriana , Fuchs Patrick , Shmueli Karin
Published: November 2024

Motivation: Electrical properties tomography (EPT) can reveal brain tissue conductivity changes during functional activation. Previous attempts have used sequences with low resolution and limited coverage, and required separate acquisitions to generate functional MRI (fMRI) and EPT (fEPT). Goal(s): To investigate the feasibility of high-resolution whole-brain fEPT and simultaneous fMRI using gradient-echo EPI (GRE-EPI). Approach: Two healthy volunteers were scanned using GRE-EPI during a visual stimulation paradigm. Conductivity maps calculated using phase-based EPT were analysed for functional activation. Results: We observed small regions of positive and negative fEPT visual activation, co-localised with fMRI activations. These findings are reproducible across subjects and coil configurations. Impact: Calculating whole-brain functional Electrical Properties Tomography (fEPT) and BOLD fMRI simultaneously using a high-resolution multi-echo GRE-EPI sequence will allow fMRI studies to reveal functional conductivity changes, opening up a new dimension with potential for new clinical and research applications.
2D vs 3D Electrical Properties Tomography reconstruction: The impact of disregarding the third dimension.
Authors: Meerbothe Thierry , Jung Kyu-Jin , Cui Chuanjiang , Kim Dong-Hyun , van den Berg Cornelis , Mandija Stefano
Published: November 2024

Motivation: In Electrical Properties Tomography, often 2D reconstructions ignoring derivatives in the slice direction (often z) are performed instead of 3D reconstructions, without proper compensation. Goal(s): In this work, we investigate the quantitative influence on the reconstructed conductivity. Approach: This is done by experiments in a cylindrical phantom with homogeneous electrical properties in simulation and measurement. Furthermore, using simulations an indication is given of the importance of 3D reconstruction in several anatomical areas. Results: The contribution of the third dimension on the reconstructed conductivity is shown to be highly dependent on sample geometry. Therefore, disregarding this can only be done in specific cases. Impact: This work shows that the assumption of a negligible third dimension contribution as done in 2D EPT reconstruction is only accurate in specific cases. For most applications 3D reconstructions or proper compensation is needed.
Age-related changes of electrical conductivity in adults: preliminary results with MR-EPT
Authors: He Zhongzheng , Soullié Paul , Ambarki Khalid , Lefebvre Pauline , Odille Freddy
Published: November 2024

Motivation: Age-related study of electrical conductivity may be useful for personalized SAR modelling. Goal(s): To evaluate in-vivo conductivity changes in a preliminary study in adults (brain and liver), using MR Electrical Properties Tomography, at 3T. Approach: We used a UTE sequence to obtain RF-weighted images suitable for conductivity reconstruction. Data from 10 subjects were analyzed: in the brain, white matter and grey matter values were quantified; in the liver, fat volume fraction was also obtained for comparison. Results: In-vivo conductivities were close to literature values, except for white matter which was higher in vivo. A significant dependency with age was found in the liver. Impact: Knowledge of electrical properties of tissues is required for SAR modelling. Currently, values from ex-vivo/animal studies are used. In-vivo measurements by MR-EPT could bring valuable insights, in particular age-related changes, if existing, could be considered for MRI safety studies.
Relationship between QSM, R2*, and a polygenic risk score for unusual and psychotic experiences
Authors: Garcia-Saborit Marisleydis , Perez-Palma Eduardo , Villaman Camilo , Repetto Gabriela , Milovic Carlos , Crossley Nicolás , Tejos Cristian
Published: November 2024

Motivation: Psychosis has been studied from different perspectives, including genetic factors and dopamine dysfunction. However, those perspectives have been studied independently. Goal(s): To investigate the relationship between genetic factors (i.e., Polygenic Risk Score, PSR) and magnetic tissue properties associated with dopamine (QSM, R2*) in a cohort of individuals with psychotic experiences. Approach: Analyze the potential correlations among QSM, R2* and PRS scores using linear mixed models in a cohort of patients and controls obtained from the UK Biobank. Results: We identified significant predictors for QSM and R2* values with PRS, revealing differences in specific brain regions associated with dopamine pathways. Impact: The changes found in the brain regions associated with dopamine pathways provide further evidence to support that psychosis may be related to a dopamine dysfunction, and those changes may also be related to genetic factors.
7T MRSI-based k-means clustering of glioma
Authors: Cadrien Cornelius , Lazen Philipp , Sara Huskic , Sagar Acharya , Licandro Roxane , Furtner Julia , Hingerl Lukas , Strasser Bernhard , Matthias Preusser , Barbara Kiesel , Mario Mischkulnig , Thomas Rötzer-Pejrimovsky , Adelheid Woehrer , Michael Weber , Christian Dorfer , Siegfried Trattnig , Karl Roessler , Wolfgang Bogner , Widhalm Georg , Hangel Gilbert
Published: November 2024

Motivation: Preoperative glioma classification and management is still a challenge to be solved. Goal(s): To enhance the understanding of glioma characteristics and potentially improve patient outcomes, we analyzed 7T Magnetic Resonance Spectroscopic Imaging (MRSI) data in 36 glioma patients. Approach: Our approach focused on k-Means clustering of 60 metabolic ratios in the tumor to identify an overlap with the WHO 2021 diagnosis. Important metabolic ratios identified include Glu+Gln/tCho and Ins/tCr ratios, highlighting their biomarker significance. Results: Our 7T MRSI can add metabolic profiles across the entire glioma and brain, possibly contributing to future glioma research. Impact: We performed k-Means clustering of preoperative 7T MRSI metabolic ratios in 36 glioma patients. Correlation with histological WHO 2021 diagnosis was identified. With this approach, we could potentially enhance surgery planning and optimize glioma treatment and targeted drug monitoring.
Machine learning based contrast-enhancement and IDH status prediction of gliomas using 7T MR spectroscopic imaging
Authors: Schwarzhans Florian , George Geevarghese , Cadrien Cornelius , Mahbod Amirreza , Bogner Wolfgang , Zaric Olgica , Preusser Matthias , Rötzer-Pejrimovsky Thomas , Widhalm Georg , Rössler Karl , Trattnig Siegfried , Woitek Ramona , Furtner Julia , Hangel Gilbert
Published: November 2024

Motivation: IDH-mutant diffuse adult type gliomas almost invariably lead to fatality. The INDIGO trial found vorasidenib significantly improving progression-free survival in non-contrast enhancing IDH mutant CNS grade 2 glioma patients. Goal(s): The purpose of this study was to non-invasively predict both contrast enhancement and IDH mutation in glioma patients. Approach: We employed a machine learning approach on 7T MRSI data to forecast IDH mutation status and contrast-enhancing tumor tissue in adult diffuse gliomas. Results: Our models performed well in the training and the testing set (AUC ≥ 0.8) for both, IDH mutation and contrast enhancement prediction. Impact: With regard to emerging IDH inhibition therapies in IDH mutant non-contrast enhancing diffuse gliomas, non-invasive prediction of IDH mutation status and contrast enhancement are of utmost importance for glioma patients. 7T MRSI can be successfully applied to this task.
Towards predicting tumor pathology with radiopathomic analysis of multi-parametric MRI in patients with newly-diagnosed gliomas
Authors: Adegbite Oluwaseun , Tran Nate , Molinaro Annette , Phillips Joanna , Ellison Jacob , Li Yan , Luks Tracy , Shai Anny , Nair Devika , Villanueva-Meyer Javier , Berger Mitchel , Hervey-Jumper Shawn , Aghi Manish , Chang Susan , Lupo Janine
Published: November 2024

Motivation: Noninvasive identification of malignant regions in glioma can help guide diagnosis and subsequent treatment planning. Goal(s): This study aims to create models to predict and elucidate limitations in radiopathomic mapping of invasiveness in glioma using multiparametric physiologic and metabolic MRI. Approach: A large, unique multiparametric MRI dataset with tissue is leveraged to compare various machine learning models of %ki-67 and cellularity (cells/mm2). Results: : The best binary model achieved a CV-AUC =0.82 and CV-AUC = 0.75 for a binarized ki-67 and cellularity. Best ki-67 continuous predictions were in the 10-fold CV SVM and 4-fold ensemble model for continuous cellularity. Impact: Multiparametric MRI can non-invasively predict histopathology. Including physiologic and/or metabolic MRI boosts histopathological predictions, however performance is also impacted by standardization of data quality.
Radiomics-based prediction of intraoperative bleeding rate during intracranial meningiomas surgery
Authors: Filimonova Elena , Abdilatipov Abdishukur , Amelina Evgenia , Poptsova Aleksandra , Rzaev Jamil
Published: November 2024

Motivation: The usefulness of radiomics in predicting intraoperative bleeding rate remains underestimated. Our objective was to examine the potential of radiomic characteristics to predict the intraoperative bleeding rate in patients with intracranial meningiomas. Goal(s): Как To predict intraoperative bleeding rate in patients with intracranial meningiomas using radiomics, machine learning and regression methods.Approach: Brain 3T MRI was performed with subsequent tumor segmentation and radiomics analysis. Results: The combination of six ADC- and ASL-based radiomics features allowed us to predict the intraoperative bleeding rate with raw residuals estimation -23 (-101; 68) (Me (1; 3 quantile)) in patients with intracranial meningiomas. Impact: Our results provide an additional non-invasive tool for the evaluation of meningiomas, which potentially could impact the treatment tactic(for example, making a decision about performing a pre-surgicalembolization in cases with high-risk).
Histogram Analysis of Perfusion and Diffusion MR Metrics in Predicting the Consistency of Meningiomas
Authors: Zheng Lingmin , Lin Danjie , Zheng Hui , Song Yang , Xue Yunjing , Lin Lin
Published: November 2024

Motivation: The consistency of intracranial meningiomas is essential for determining the necessary surgical instruments and influencing the outcome of surgery. However, no specific feature of conventional MRI is reliable in predicting the meningiomas consistency. Goal(s): To evaluate and compare the potential of various MRI perfusion and diffusion metrics in predicting the meningiomas consistency. Approach: Histogram parameters of metrics obtained from DKI, DTI, ASL and DSC were included in logistic regression models to predict meningiomas consistency. Results: DTI, ASL, and DSC metrics could significantly differentiate between soft and hard meningiomas. The DSC combined model yielded the highest AUC of 0.858. Impact: The differentiation of soft and hard meningiomas was feasible by combining histogram parameters of DSC and DTI metrics.
MRI and tumor-infiltrating CD8+ T cell-based nomogram for predicting meningioma recurrence risk stratification
Authors: Han Tao , Liu Xianwang , Zhou Junlin
Published: November 2024

Motivation: To investigate the efficacy of MRI features and CD8+ T cells in predicting risk stratification for meningioma recurrence. Goal(s): To develop a reliable nomogram incorporating MRI features and CD8+ T cells to predict meningioma recurrence. Approach: Conventional MRI features, ADC histogram parameters, and CD8+ T cells were recorded and compared. This model was the first to combine clinical, imaging, and TME data to predict meningioma recurrence. Results: The ADCp1 and CD8+ T cells as predictive variables for meningioma recurrence and patients with low ADCp1 or CD8+ T cell counts had higher recurrence rates than those with high ADCp1 or CD8+ T cell counts. Impact: The findings will improve prognostic accuracy for patients with meningioma and potentially allow for targeted treatment of individuals who have the recurrent form.
Evaluation of the grading efficacy of preoperative MRI for grades Ⅱ and Ⅲ intracranial solitary fibrous tumor
Authors: Ran Yuncai , Wang Xiao , Zhang Yong , Wang Mengzhu , Cheng Jingliang
Published: November 2024

Motivation: Retrospective studyGoal(s): This study analyzed the general clinical features and preoperative MRI features of patients with Grade Ⅱ and Ⅲ intracranial solitary fibrous tumor (ISFT). Approach: Intergroup comparison was conducted according to postoperative pathological grade. Binary logistic regression was performed to identify effective imaging indexes that could predict pathological grade. Results: There were differences between grades in tumor location; skull invasion; signal characteristics of T2-FLAIR and DWI images; and ADCmax, ADCmean and ADCmin. ADCmin was the only effective imaging index that could predict pathological grade. Impact: Retrospective analysis showed that preoperative ADCmin can effectively predict grade Ⅱ and Ⅲ tumors in patients with ISFT, which will provide an important reference basis for preoperative grading of ISFT.
A Novel Approach for Simultaneous T1, T2, T2*, and Proton Density Mapping via Longitudinal Magnetization Control MOLED Acquisition
Authors: Chen Weikun , Guo Xinyu , Lin Qing , Wu Jian , Li Simin , Kang Taishan , Lin Liangjie , Cai Shuhui , Cai Congbo
Published: November 2024

Motivation: Traditional multi-parameter mapping methods use different sequences to acquire images with varying TE or TI, posing challenges like registration, time consumption, and physiological asynchrony among parameters. Goal(s): To rapid acquire self-registered multi-parametric maps. Approach: A longitudinal magnetization control multiple overlapping-echo detachment (LMC-MOLED) imaging method was proposed. In the first phase of LMC-MOLED, T2-T2*-MOLED was acquired using the blip-up blip-down SE-EPI/GRE-EPI readout. Subsequently, after five T2-MOLED acquisitions were applied with varying delay intervals, MOLED images with distinct T1 weighting were captured. Results: LMC-MOLED can simultaneously acquire five slices of multi-parametric maps, including T1, T2, T2*, PD, ΔB0, and B1, within 5.5 s. Impact: LMC-MOLED is a novel and rapid simultaneous multi-parameter magnetic resonance quantitative method that may aid in the clinical diagnosis of diseases such as multiple sclerosis and tumors.
Fast Spin Echo based T2 Mapping with Point Spread Function Correction
Authors: Parasram Tristhal , Xiao Dan
Published: November 2024

Motivation: Fast Spin Echo (FSE) based method can provide faster T2 mapping compared to the multi-echo spin echo method. However, the varying Point Spread Functions (PSFs) among different echo time images lead to artifacts in the T2 and proton density ($$$\rho$$$) maps. Goal(s): Developing an algorithm to address PSF artifacts in the FSE-based method that enables faster and more accurate T2 mapping. Approach: An optimization process was designed to determine voxel-wise T2 and $$$\rho$$$ values consistent with the acquired data, especially considering various PSFs. Results: The method was validated through simulations, phantom measurements, and mouse brain scans, resulting in improved T2 and $$$\rho$$$ maps. Impact: The method enabled high-quality, fast T2 mapping without the need for any modifications to the FSE pulse sequence. Therefore, it can be readily applied to quantitative studies on subjects that can be imaged with FSE.