bluuebunny/crossref_metadata_2025 huggingface dataset:
MR fingerprinting for liver tissue characterization in a single breath-hold scan: A histopathological correlation study
Authors: Fujita Shohei , Sano Katsuhiro , Cruz Gastao , Fukumura Yuki , Kawasaki Hideo , Fukunaga Issei , Yoneyama Masami , Kamagata Koji , Abe Osamu , Ikejima Kenichi , Botnar Rene , Prieto Claudia , Aoki Shigeki
Published: August 2023

Magnetic resonance fingerprinting (MRF) has been proposed for simultaneous T1, T2, T2*, and fat-fraction mapping of the liver, which is suitable for the diagnosis and monitoring of liver diseases. We evaluated the repeatability of liver MRF measurements and correlated it with reference standard imaging in patients with diffuse liver disease. Furthermore, we validated MRF-derived measurements against histological grading of liver biopsies. Liver MRF provided repeatable multi-parametric maps with high agreement with measurements of separate conventional quantitative mapping as well as histological grading of liver biopsies. This approach may enable objective and non-invasive hepatic tissue characterization in a single breath-hold scan.
Evaluation of hepatic microenvironment in cirrhotic patients with collateral circulation by multiparametric MRI
Authors: Nan Wang , Wang Jiazheng , Sun Peng , Chen Lihua , Song Qingwei , Liu Ailian
Published: August 2023

Cirrhosis of the liver not only increases the risk of liver cancer but also causes portal hypertension, leading to gastrointestinal bleeding, hepatic encephalopathy, ascites, and other complications. The opening of collateral circulation caused by portal hypertension plays an important role in the occurrence and progress of diseases. Therefore, precise diagnosis of collateral circulation (CC) in cirrhotic patients is important for clinical practice. Hereby, we compared quantitative parameters from multiparametric MRI (4D flow, APT, IVIM, T1/T2 mapping) between the CC and non-CC groups in cirrhotic patients, which showed significant differences.
Evaluation of the advantage of free-breathing liver DCE-MRI with golden-angle radial sparse parallel using different contrast agents
Authors: Yang Lingjie , Duan Xiaohui , Wang Mengzhu , Jiang Wei , Wang Yu , Hu Huijun , Zeng Weike , Shen Jun
Published: August 2023

To evaluate the image quality between two sequences (GRASP vs. CDT-VIBE), as well as two kinds of contrast agents (Gd-EOB-DTPA vs. gadobutrol) in liver DCE-MRI. The qualitative and quantitative evaluations regarding of the image quality, and the detection rates of lesions among four groups were analyzed and compared. The results showed that MR images with GRASP sequence had significantly better image quality and less artifacts than that with CDT-VIBE sequence especially in arterial phase, and MR images with injection of gadobutrol could improve the hepatic vessel clarity and reduce motion artifacts compared with that of Gd-EOB-DTPA in CDT-VIBE sequence.
Feasibility and Reproducibility of Multi-frequency MR elastography in healthy and diseased pancreas
Authors: Song Qike , Shi Yu
Published: August 2023

To determine the feasibility of multi-frequency MRE for assessing pancreatic stiffness in healthy volunteers and patients with pancreatic adenocarcinoma (PDAC).A total of 40 healthy volunteers and 10 patients with PDAC were prospectively recruited between March 2020 and October 2021.Each volunteer and patient underwent 3.0-T pancreatic MRE at frequencies of 30Hz, 40Hz, 60Hz, 80Hz and 100Hz.Shear stiffness of healthy pancreas and PDAC, pancreatic width and volume, waist circumference, and wave distance were measured. Image quality assessment was performed according to image quality score (IQS: 1~4，≥3 considered qualified).
Vascular Involvement and Resectability of Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced MRI: Comparison with Pancreatic Protocol CT
Authors: Noda Yoshifumi , Kawai Nobuyuki , Kambadakone Avinash , Kaga Tetsuro , Ishihara Takuma , Hyodo Fuminori , Kato Hiroki , Matsuo Masayuki
Published: August 2023

MRI is used as a problem-solving tool when contrast-enhanced CT is not available for assessing pancreatic ductal adenocarcinoma. In this study, we evaluated the diagnostic performance of vascular involvement and resectability on MRI and compared them with pancreatic protocol CT. Our results showed that no statistical difference in terms of the detection of vascular involvement and the resectability classification based on the NCCN guideline between CT and MRI.
Diffusion and perfusion MRI and radiomics signatures for preoperative evaluation of epithelial-mesenchymal transformation in rectal cancer
Authors: Chen Yidi , long Liling , Song Bin , Zhang Huiting
Published: August 2023

This study demonstrated morphological characteristics of MRI can hardly diagnose the expression of E-cadherin and Vimentin in rectal cancer. ADC value (Ultra-high b-Value) was positively and negatively correlated with E-cadherin and Vimentin expression. Ktrans and Kep values were negatively and positively correlated with E-cadherin and Vimentin expression. ADC, Ktrans and Kep values had significant diagnostic efficiencies for low E-cadherin and high Vimentin expression. Radiomics signatures with machine learning have excellent diagnostic efficacy for E-cadherin and Vimentin expression, building the model which combined MRI quantitative parameters and radiomics features will improve the predictive performance for E-cadherin and Vimentin expression.
Development and validation of a semi-automated framework for PI-RADS v2.1 assessment
Authors: Singh Dharmesh , Kumar Virendra , Das Chandan , Singh Anup , Mehndiratta Amit
Published: August 2023

Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. An automated or semi-automated PI-RADS assessment system could be beneficial in the screening process of PCa and could improve the consistency of scoring. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automatic framework for PI-RADS assessment using machine learning classifiers.
Lung cancer in multi-parametric PET/MRI: assessment of subtype and EGFR status based on amide proton transfer-weighted imaging and 18F-FDG PET
Authors: Meng Nan , Fu Fangfang , Feng Pengyang , Li Ziqiang , Huang Zhun , Fang Ting , Wu Yaping , Yuan Jianmin , Yang Yang , Liu Hui , Wang* Meiyun
Published: August 2023

18F-Fluorodeoxyglucose positron-emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) allows multimodal quantitative MRI sequences to be scanned in parallel with PET imaging, providing a more multidimensional reflection of lesion information. Our results showed that amide proton transfer-weighted imaging (APTWI), and metabolism related parameters can be beneficial for the non-invasive assessment of subtype and EGFR status in patients with lung cancer.
Diffusion-kurtosis based texture features for differential diagnosis between prostate cancer and benign prostatic hyperplasia
Authors: Liu Yunsong , Liu Ailian , Chen Lihua , Wang Jiazheng , Lin Liangjie , Zhang Xiaoxiao
Published: August 2023

The differential diagnosis between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) remains challenging due to their overlapping atypical clinical symptoms and MRI features. This study aims to explore the performance of mean kurtosis (MK) based texture features in differentiation between PCa and BPH. Results show that the MK texture features can help distinguish PCa and BPH. The 90Percentile MK value show the best diagnostic performance (AUC: 0.93; sensitivity: 88%; specificity: 100%).
Correlation between SUV, tumor blood flow and ADC of extra-axial tumors on FDG-PET/MRI
Authors: Otani Sayo , Fushimi Yasutaka , Nakajima Satoshi , Sakata Akihiko , Okuchi Sachi , Hinoda Takuya , Sakurama Azusa , Wicaksono Krishna , Tagawa Hiroshi , Wang Yang , Ikeda Satoshi , Nakamoto Yuji
Published: August 2023

PET/MRI shows high accuracy of fused images by simultaneously acquired PET and MR images. We can obtain spatially and temporally consistent information on metabolic and anatomic information from PET/MRI. We evaluated each quantitative value in 12 patients with extra-axial tumor in this study. We manually placed the same regions of interest (ROIs) of the tumor on FDG-PET, CBF map and ADC map, and performed ROI analysis of SUV, TBF (tumor blood flow) and ADC. There were correlations between SUV and TBF, and between SUV and ADC among patients with TBF less than 110 ml/100g/min.
Improved differentiation of Primaray liver cancer by combination of Amide Transfer weighted imaging(APTw) and T2 mapping
Authors: Lin Tao , Wang Jiazheng , Wu Zhigang , Chen Lihua , Song Qingwei , Pu Renwang , Zhao Ying , Ren Xue , Xu Qihao , Liu Ailian
Published: August 2023

Hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) are the most common types of primary liver cancer, which differ greatly in terms of pathogenesis, biological behavior, histological morphology, treatment and prognosis. The accurate diagnosis of HCC and ICC is important for treatment options. In this retrospective study, we revealed that APTw combined with T2 mapping could improve the differential diagnosis of HCC and ICC. Results showed that APTw combined with T2 mapping had higher efficacy (AUC:0.910). Further analysis also implied moderate correlations between APTw and T2 mapping.
Value of ESWAN combined with DTI in the differential diagnosis of solid pseudopapillary tumor and neuroendocrine tumor of pancreas
Authors: Wang Lu , Wang Yi , Wang Xinqi , Wang Longshuang , Zhang Qinhe , Xie Lizhi , Liu Ailian
Published: August 2023

It is difficult to differentiate the solid pseudopapillary tumor of the pancreas (SPTP) from pancreatic neuroendocrine tumor (PNET). Enhanced T2* weighted angiography (ESWAN) technique can simultaneously obtain amplitude, phase, R2* and T2* values, which can quantitatively assess the magnetic sensitivity characteristics of tissues. DTI can evaluate the diffusion of water molecules in living tissues. This study founded that R2* and T2* of ESWAN, fractional aniso (FA) and volume ratio aniso (VRA) of DTI were significantly different (P<0.05) and the AUC was 0.860(sensitivity: 100%; specificity: 79.2%). Therefore, ESWAN combined with DTI may be an effective method to identify SPTP and PNET.
Memory-friendly and Robust Deep Learning Architecture for Accelerated MRI
Authors: Wang Zi , Qian Chen , Guo Di , Sun Hongwei , Li Rushuai , Qu Xiaobo
Published: August 2023

Deep learning has shown astonishing performance in accelerated MRI. Most methods adopt the convolutional neural network and perform 2D convolution since many MR images or their corresponding k-space are in 2D. In this work, we try a different approach that explores the memory-friendly 1D convolution, making the deep network easier to be trained and generalized. Furthermore, a one-dimensional deep learning architecture (ODL) is proposed for MRI reconstruction. Results demonstrate that, the proposed ODL provides improved reconstructions than state-of-the-art methods and shows nice robustness to some mismatches between the training and test data.
Radiomic feature-based assessment of deep learning-based compressed sensing reconstruction
Authors: Miyasaka Tomoki , Funayama Satoshi , Tamada Daiki , Morisaka Hiroyuki , Onishi Hiroshi , Terada Yasuhiko
Published: August 2023

Deep learning has been attracting attention as a new tool for image reconstruction. However, there is a lack of appropriate automatic evaluation metrics for reconstruction performance of small structures such as lesions, which poses a high hurdle for clinical application. Here, we explored the relationship between radiomic features of tumors and various DL reconstruction conditions, and proposed a new method based on radiomics to evaluate the reconstruction performance of DL against lesions. Based on the analysis using the concordance correlation coefficients for ground truth images, we explored several texture features that are sensitive to differences in reconstruction methods and conditions.
B1- and B1+ based bias field correction for ultra-high field MRI
Authors: Hsu Yi-Cheng , Liebig Patrick , Chu Ying-Hua
Published: August 2023

We proposed a method to approximate the magnitude of B1- and correct the bias field induced signal inhomogeneity at 7T. For 2D and 3D T2 weighted images, the signal intensity was more homogeneous and the signal void was recovered using the proposed method. This method can be implemented to correct bias field effect for other imaging methods using the estimated |B1-| and |B1+|.
Identification of NF2 loss in meningiomas using 1H-MRS at 3T
Authors: Sacli-Bilmez Banu , Baş Abdullah , Tan Kübra , Danyeli Ayça Erşen , Can Özge , Pamir M.Necmettin , Dinçer Alp , Özduman Koray , Ozturk-Isik Esin
Published: August 2023

Loss of neurofibromatosis 2 (NF2-L) is a well-known genetic alteration of meningiomas and causes meningiomas to evolve into more aggressive and infiltrating form. This study aims to investigate single-voxel proton magnetic resonance spectroscopy (1H-MRS) correlations of NF2-L in meningiomas and to develop machine learning and deep learning models to identify NF2-L in meningiomas. NF2-L meningiomas had significantly higher Ins, Lac, and Ins+Glyc, and lower tNAA than tumors with no copy number loss. While a subspace discriminant model achieved a classification accuracy of 77.25%, a 1D-CNN model obtained a classifcation accuracy of 88.9% for identifying NF2-L meningiomas.
Compressed SVD for L+S Matrix Decomposition Model to Reconstruct Undersampled Dynamic MRI
Authors: Shafique Muhammad , Qazi Sohaib , Ullah Irfan , Omer Hammad
Published: August 2023

The Low rank and Sparse (L+S) matrix decomposition model has been proposed in literature to reconstruct the undersampled dynamic MRI data. The limitations of L+S method include an effective separation of the low-rank and sparse components from the acquired dynamic MRI data; also the algorithm is computationally expensive. In this paper, Compressed Singular Value Decomposition (cSVD) is employed in L+S method. The results show that the proposed method provides effective separation of the L and S components as well as considerably reduces the computation time.
In vivo microstructural border delineation between areas of the human cerebral cortex using magnetic resonance fingerprinting (MRF) residuals
Authors: Moinian Shahrzad , Vegh Viktor , Reutens David
Published: August 2023

We previously showed that magnetic resonance fingerprinting (MRF) residual signals can be used for in vivo voxel-wise parcellation of the human cerebral cortex, using supervised machine learning classification algorithms. However, previous work relied on brain atlases to provide probabilistic masks of cortical region to label samples to train a classification model. Here, we investigate the feasibility of developing automated atlas-free cortical border delineation in individuals. We demonstrate that 90% of the cortical border voxels identified by the proposed framework are co-localised with the borders between two cortical areas on the Juelich maximum probability map of cerebral cortex in six participants.
Tumor Aware Temporal Deep Learning (TAP-DL) for Prediction of Early Recurrence in Hepatocellular Carcinoma Patients after Ablation using MRI
Authors: Li Yuze , An Chao , Chen Huijun
Published: August 2023

Hepatocellular carcinoma patients after thermal ablation suffer high recurrence rate. In this study, we proposed a deep learning method to predict the early recurrence in these patients. Compared with other predictive models, two innovations were achieved in our study: 1) integrating interconnected tasks, i.e., tumor segmentation and tumor progression prediction, into a unified model to perform co-optimization; 2) using longitudinal images to take the therapy-induced changes into consideration to explore the temporal information. Results showed that our approach can simultaneously perform tumor segmentation and tumor progression prediction with higher performance than only doing any single one of them.
A residual-spatial feature based MR motion artifact detection model with better generalization
Authors: Liu Xiaolan , Ge Yaan , Dai Qingyu , Wang Kun
Published: August 2023

Motion artifact in MRI images is the frequency existence in daily scanning and causes clinical distress. In this study, we proposed an automatic framework for MRI motion artifact detection using the residual features from neural network and spatial characteristics combination-based machine learning method, which can be applied to multiple body parts and sequences. High performance is achieved in validation with the accuracy of 97.6%. The comparison is performed with different representative methods and proving the effectiveness of the proposed architecture on limited dataset.
Efficient six-direction DTI tensor estimation using model-based deep learning
Authors: Li Jialong , Lu Qiqi , Liu Qiang , Feng Yanqiu , Zhang Xinyuan
Published: August 2023

Diffusion tensor imaging (DTI) can noninvasively probe the tissue microstructure and characterize its anisotropic nature. The images carried with heavy diffusion-sensitizing gradients suffer from low SNR, and thus more than six diffusion-weighted images are required to improve the accuracy of parameter estimation against noise effect. We propose an efficient DTI model-based 3D-Unet (DTI-Unet) to predict high-quality diffusion tensor field and non-diffusion-weighted image from the noisy input. In our model, the input contains only six diffusion-weighted volumes and one b0 volume. Compared with the state-of-the-art denoising algorithms (MPPCA, GLHOSVD), our model performs better in image denoising and parameter estimation.
Automated classification of intramedullary spinal cord tumors and inflammatory demyelinating lesions using deep learning
Authors: Zhuo Zhizheng , Zhang Jie , Duan Yunyun , Zhang Xianchang , Liu Yaou
Published: August 2023

A DL framework for the segmentation and classification of spinal cord lesions, including tumors (astrocytoma and ependymoma) and demyelinating diseases (MS and NMOSD), were developed and validated, with performance sometimes outperforming radiologists.
Differentiation between glioblastoma and solitary brain metastasis using mean apparent propagator-MRI: a Radiomics analysis
Authors: Ma Xiaoyue , Zhao Guohua , Gao Eryuan , Qi Jinbo , Zhao Kai , Gao Ankang , Bai Jie , Zhang Huiting , Yan Xu , Yang Guang , Cheng Jingliang
Published: August 2023

Preoperative differentiation of glioblastomas and solitary brain metastases may contribute to more appropriate treatment plans and follow-up. However, routine MRI has a very limited ability to distinguish between the two. Mean apparent propagator (MAP)-MRI, as a representative of diffusion MRI technology, is effective in evaluating the complexity and inhomogeneity of the brain microstructure. We developed a series of radiomics models of MAP-MRI parametric maps, routine MRI, combined routine MRI, and combined MAP-MRI parametric maps to compare their performance in the identification of two tumors. Finally, a good performance with the combined MAP-MRI radiomics model was obtained.
Fast and Calibrationless Low-Rank Reconstruction through Deep Learning Estimation of Multi-Channel Spatial Support
Authors: Yi Zheyuan , Zhao Yujiao , Xiao Linfang , Liu Yilong , Man Christopher , Hu Jiahao , Lau Vick , Leong Alex , Chen Fei , Wu Ed X.
Published: August 2023

In traditional parallel imaging, calibration data need to be acquired, prolonging data acquisition time or/and sometimes increasing the susceptibility to motion. Low-rank parallel imaging has emerged as a calibrationless alternative that formulates reconstruction as a structured low-rank matrix completion problem while incurring a cumbersome iterative reconstruction process. This study achieves a fast and calibrationless low-rank reconstruction by estimating high-quality multi-channel spatial support directly from undersampled data via deep learning. It offers a general and effective strategy to advance low-rank parallel imaging by making calibrationless reconstruction more efficient and robust in practice.
A deep learning network for low-field MRI denoising using group sparsity information and a Noise2Noise method
Authors: Lian Yuan , Ye Xinyu , Luo Hai , Wu Ziyue , Guo Hua
Published: August 2023

Owing to hardware advancements, interest in low-field MRI system has increased recently. However, the imaging quality of low-field MRI is limited due to intrinsic low signal to noise ratio (SNR). Here we propose a deep-learning model to jointly denoise multi-contrast images using Noise2Noise training strategy. Our method can promote the SNRs of multi-contrast low-field images, and experiments show the effectiveness of the proposed strategy.
Low-rank Parallel Imaging Reconstruction Imbedded with a Deep Learning Prior Module
Authors: Xiao Linfang , Liu Yilong , Yi Zheyuan , Zhao Yujiao , Leong Alex , Wu Ed
Published: August 2023

Recently, deep learning methods have shown superior performance on image reconstruction and noise suppression by implicitly yet effectively learning prior information. However, end-to-end deep learning methods face the challenge of potential numerical instabilities and require complex application specific training. By taking advantage of the multichannel spatial encoding (as exploited by conventional parallel imaging reconstruction) and prior information (exploited by deep learning methods), we propose to embed a deep learning module into the iterative low-rank matrix completion based image reconstruction. Such strategy significantly suppresses the noise amplification and accelerates iteration convergence without image blurring.
A Deep Learning Approach to Improve 7T MRI Anatomical Image Quality Deterioration Due mainly to B1+ Inhomogeneity
Authors: Akasaka Thai , Fujimoto Koji , Fushimi Yasutaka , Thuy Dinh Ha Duy , Shima Atsushi , Sawamoto Nobukatsu , Nakamoto Yuji , Isa Tadashi , Okada Tomohisa
Published: August 2023

The effect of transmit field (B1+) inhomogeneity at 7T remains even after correction using a B1+-map. By a deep learning approach using pix2pix, a neural network was trained to generate 3T-like anatomical images from 7T MP2RAGE images (dataset 1: T1WI and T1-map after B1+ correction and dataset 2: Inversion time [INV]1, INV2 and B1+-map). When the HCP anatomical pipeline was applied and compared, low regressions of original 7T data to 3T data were largely improved by using generated images by pix2pix, especially for dataset 2.
Deep learning-accelerated T2-weighted imaging of the female pelvis: reduced acquisition times and improved image quality
Authors: Ren Jing , He Yonglan , Liu Chong , Liu Shifei , Zhu Jinxia , Nickel Marcel Dominik , Jin Zhengyu , Xue Huadan
Published: August 2023

Novel deep learning (DL) reconstruction methods may accelerate female pelvis MRI protocols keeping high image quality. The value of a novel DL reconstruction of T2-weighted (T2DLR) turbo spin-echo (TSE) sequences for female pelvis MRI in three orthogonal planes was evaluated. We evaluated examination times, image quality, and lesion conspicuity of benign uterine disease. The T2DLR quantitative parameters remained similar or were significantly improved compared with that of standard T2 TSE (T2S), allowing for a 62.7% reduction in acquisition times. Applying this novel T2DLR sequence achieved better image quality and shorter acquisition time than T2S.
Advancing RAKI Parallel Imaging Reconstruction with Virtual Conjugate Coil and Enhanced Non-Linearity
Authors: Man Christopher , Yi Zheyuan , Lau Vick , Hu Jiahao , Zhao Yujiao , Xiao Linfang , Leong Alex , Wu Ed
Published: August 2023

RAKI is recently proposed as a deep learning version of GRAPPA, which trains on auto-calibration signal (ACS) to estimate the missing k-space data. However, RAKI requires a larger amount of ACS for training and reconstruction due to its multiple convolutions which resulting in lower effective acceleration. In this study, we propose to incorporate the virtual conjugate coil and enhanced non-linearity into the RAKI framework to improve the noise resilience and artifact removal at high effective acceleration. The results demonstrate that such strategy is effective and robust at high effective acceleration and in presence of pathological anomaly.
Assessment of Glx and GABA levels in the Global Developmental Disability Children: A Magnetic Resonance Spectroscopy Study
Authors: Liu Zhuo , LI Dan , Lin Liang , Jiang Ke , Ling Shan , Jia Fei , Bian Bing , Wang Zhuo , Guo Dan , Qiu Ying
Published: August 2023

This study aims to explore the correlation between brain neuro metabolites and clinical symptom scales in GDD and GDD+ASD. 11 children with GDD and 12 GDD +ASD children took MR scans, and completed the GABA and Glx spectroscopic acquisition. Results indicated that there is a significant difference in DQ language levels between GDD and GDD+ASD, and the GABA/Cr levels were significantly decreased in GDD+ASD compared with GDD. A positive correlation was observed between Glx/Cr and ADOS stereotype among all participants, and a clear link was observed between Glx/Cr and the sensory symptoms of ASD at both behavioral and perceptual levels.
Hyperpolarized 13C-pyruvate MR metabolic imaging in Aging
Authors: Nguyen Nguyen , Rasanjala Onila , Bae Eun Hui , Park Ilwoo
Published: August 2023

Hyperpolarized [1-13C1]pyruvate MRI data were obtained in vivo metabolic data from young and aged animals in rodent to elucidate the different changes in metabolism due to aging. The preliminary results in this study demonstrated the feasibility of using hyperpolarized MRI 13C technique for identifying renal metabolic changes between aged and young animals. The reduction of Lac/Pyr, Ala/Pyr and BiC/Pyr ratio may be used as biomarkers for the cellular dysfunction that accompanies the aging process. The results from this study warrant further research on the usage of hyperpolarized 13C MRI in monitoring metabolic changes in aging-related studies.
A Simulator, “MagTetris”, for Fast Calculation of Magnetic Field and Force for Permanent Magnets
Authors: Liang Tingou , Qiu Tie , Huang Shaoying
Published: August 2023

We propose a magnet simulator (named “MagTetris”) for fast calculation of both magnetic field and magnetic force generated by multiple cuboid magnets in arbitrary configurations. The accuracy of “MagTetris” is examined by comparing the calculated results to the FEM-based simulations and the experimental results through a 2-magnet experiment. The average differences between the FEM-based simulations and the “MagTetris” calculations are within 6% for field and 2% for force, while those between the measurements and the “MagTetris” are within 10% for field and 4.5% for force. For the calculation speed, “MagTetris” is 123 times faster compared to the FEM-based commercial software.
A Temperature-Insensitive Halbach Magnet Array Design for Ultra-Low-Field MRI
Authors: Huang Ziming , Leong Alex T. L. , Wu Ed X.
Published: August 2023

Halbach permanent magnet array offers lightweight and mobility but its field is sensitive to temperature, thus hindering its robust point-of-care MRI applications. This work presents a temperature-insensitive Halbach magnet array design for ultra-low-field MRI. The proposed array is of adequate bore diameter and reasonable weight (~50kg), and provides a 30 mT B0 within a 20 cm imaging field of view (FOV). Numerical simulation is conducted to test the proposed design, demonstrating a drastic reduction in temperature-induced B0 drifting and inhomogeneity.
T2 relaxometry based CSF fraction (CSFF) mapping is a better biomarker for brain drainage pathology than DTI-based free water (DTI-FW) mapping
Authors: Zhou Liangdong , Nguyen Thanh , Li Yi
Published: August 2023

We compared two free water methods, T2 relaxometry-based CSFF and DTI-based DTI-FW, by associating them with age, cognitive score RAVLT, amyloid beta deposition from PiB SUVr, tau deposition from MK6240, aqueduct CSF flow from PC-MRI and ventricle CSF clearance from MK-PET. Results show that CSFF outperforms DTI-FW in most relation pairs. CSFF works great for quantifying clearance related measures but DTI-FW failed.
R2* changes reflect the kinetics of formalin-based penetration and fixation in post-mortem tissue
Authors: Nazemorroaya Azadeh , Aghaeifar Ali , Schulz Hildegard , Scheffler Klaus , Shiozawa-Bayer Thomas , Hirt Bernhard , Hagberg Gisela
Published: August 2023

Post-mortem brain MRI can yield valuable information. However, tissue preservation requires substitution of the CSF-fluid by fixation agents, which is time-consuming for large samples. R2* maps, dominated by R2-effects, were measured at several timepoints during fixation of pig-brain tissue samples using formalin-based fixatives.
A user interface for brain cell analysis of a petascale database in cerebral cortex and its application to numerical simulations of diffusion
Authors: Sylvanus Benjamin , Huang Susie , Lee Hong Hsi
Published: August 2023

The 1.4 petabyte electron microscopy volume with fully segmented cells in the human cerebral cortex is publicly available, serving as a valuable resource of building numerical phantoms of MRI. However, even few neurite interruptions in cell segmentations can lead to substantial biases in numerical simulations of diffusion inside cells. Here we propose a user interface to automatically correct most of the neurite interruptions and manually edit the corrections. Combing the proposed user interface with Trees toolbox and SpinDoctor for quantitative cell analysis and diffusion simulations, we provide a convenient platform to validate biophysical modeling of MRI evaluating tissue microstructure.
Linear+: An optimized sequence reordering for robust scout accelerated retrospective motion estimation and correction
Authors: Polak Daniel , Splitthoff Daniel Nicolas , Clifford Bryan , Wei-Ching Lo , Tabari Azadeh , Huang Susie , Conklin John , Wald Lawrence L. , Cauley Stephen
Published: August 2023

Distributed sequence reorderings for 3D multi-shot acquisitions ensure overlap with the central k-space in every shot. This improves the stability of navigator-free retrospective motion estimation but often reduces the robustness of the image reconstruction. In this work, we have developed and optimized a novel sampling strategy (Linear+) that extends the standard sequential sequence ordering with a small number of additional calibration samples acquired near the k-space center. In simulation and in vivo, Linear+ enabled accurate motion parameter estimation and correction across multiple motion patterns while providing improved image homogeneity and spatial resolution compared to a distributed reordering.
A Review and Comparison of Unwrapping Methods in Dual Velocity-Encoding MRI
Authors: Franco Pamela , Ma Liliana , Schnell Susanne , Markl Michael , Bertoglio Cristobal , Uribe Sergio
Published: August 2023

4D flow MRI data may be inaccurate due to phase wrapping when using lower VENC than the maximum actual velocity. Accuracy can also be affected by reduced velocity-to-noise ratio (VNR) when using a too high VENC. Dual-VENC approaches have been proposed to unwrap velocity-aliasing artifacts and increase VNR, providing more reliable measurements and allowing more flexibility in the selection of VENC. Dual-VENC methods enable acquisitions of 4D flow MRI data with high dynamic range and without velocity aliasing. The purpose of this study is to compare the performance of some of these methods.
Automatic Quantification of Enlarged Perivascular Space aided with Super-resolution 2D T2 images
Authors: Zhuo Jiachen , Shao Muhan , Roys Steven , Liang Xiao , Tchoquessi Rosy Njonkou , Raghavan Prashant , Prince Jerry , Gullapalli Rao
Published: August 2023

The perivascular space (PVS) is key to brain waste clearance and brain metabolic homeostasis. Enlarged PVS (ePVS) can be automatically quantified reliably by combining the 3D T1w and 3D T2w images to produce enhanced PVS contrast followed with frangi filtering and thresholding. However often times only 2D T2w images are available, especially in clinical exams. In this study, we investigate the feasibility of using an innovative deep learning based super-resolution technique (SMORE) to produce 3D T2w images (SR T2) for ePVS quantification. We show that the SR T2 volume provided comparable ePVS estimation as a 3D T2 volume.
Accelerated whole-brain Bloch-Siegert B1-mapping using segmented 3D EPI
Authors: Soellradl Martin , Graf Christina , Stollberger Rudolf
Published: August 2023

To exploit acceleration potential of Bloch-Siegert shift based $$$B_1^+$$$-mapping, we implemented a 3D segmented EPI sequence with variable number of interleaves. Additionally, sampling of the k-space center with variable block-sizes was investigated. Compared to a fully sampled gradient-echo sequence, we show that acceleration by a factor up to 50 is feasible. Depending on the desired accuracy, whole-brain 3D $$$B_1^+$$$-mapping is possible in 5 to 10 seconds.
CEST MRI Using Golden-Angle Cartesian Acquisition with Sparse Reconstruction
Authors: Xia Ding , Feng Li , Xu Xiang
Published: August 2023

In this work, we proposed a novel dynamic CEST MRI framework, which employs golden-angle rotated variable-density Cartesian acquisition with multicoil compressed sensing reconstruction. It enables continuous data acquisition and retrospective reconstruction with desired temporal resolution. We have demonstrated the performance of this new framework for accelerated CEST MRI both in phantom and in human brain at 7T.
Comparison of Double Quantum (DQ) Suppression Methods for Inversion Recovery TQTPPI (IRTQTPPI) Sequence
Authors: Reichert Simon , Kleimaier Dennis , Schad Lothar
Published: August 2023

This study proposes an inversion recovery TQTPPI pulse sequence to create triple quantum (TQ) coherences utilizing the $$$T_1$$$ relaxation pathway. Different double quantum (DQ) suppression methods provide stable fit results with reasonable SNR. The $$$T_1$$$-TQ signal is sensitive to faster motion than the conventional $$$T_2$$$-TQ pathway and thus provides additional information about sodium-protein interactions.
Design of a 13-Channel hybrid RF array with field rectification of dielectric material for foot/ankle imaging at 7T
Authors: Bhosale Aditya , Ying Leslie L , Zhang Xiaoliang
Published: August 2023

This study proposes a 13-channel hybrid array system consisting of Microstrip transmission line resonators and half-birdcage coil and demonstrates the high-permittivity dielectric material’s positive effect on the B1 field distribution.
Quantitative evaluation of tendon enthesis using ultrashort echo time magnetic resonance imaging (UTE-MRI) techniques
Authors: Jerban Saeed , Lombardi Alecio , Ma Yajun , Afsahi Amir , Moazamian Dina , Athertya Jiyo , Jang Hyungseok , Chung Christine , Du Jiang , Chang Eric
Published: August 2023

Entheses are transition zones connecting flexible tissues such as tendons and ligaments to bone. Conventional MRI sequences detect little or no signal from entheses as a result of their short T2* values. Alternatively, ultrashort echo time MRI (UTE-MRI) with TE<50 μs can be used to image tissues with short T2 and T2* for quantitative assessment. The feasibility of using quantitative UTE-MRI techniques for the evaluation of entheses regions of Achilles tendons in cadaveric ankle specimens was investigated. Using 20 cadaveric ankle specimens, high quality images were obtained with significant entheses signal which enabled excellent UTE-T1, UTE-Adiab-T1r, and UTE-MT model fittings.
Population analysis of B0 magnetic field conditions in the human heart
Authors: Shang Yun , Theilenberg Sebastian , Peng Boyu , Jambawalikar Sachin , Schreiber Laura , Juchem Christoph
Published: August 2023

Cardiac MRI suffers susceptibility-induced artifacts due to B0 inhomogeneity across the heart. The lack of population data in cardiac B0 conditions and the practical inability to obtain such data in large populations impedes the development of optimal cardiac B0 shim strategy. Here, we establish population-based B0 conditions from readily available CT images and simulate cardiac B0 maps of 254 CT subjects with broad demographic parameters. The results are expected to develop optimal subject- and population-specific cardiac B0 shim strategies.
Simultaneously Assessing Cerebral Glucose and Oxygen Metabolism and Blood Flow in Rat Brain at Ultrahigh Field of 16.4T
Authors: Zhang Guangle , Zhu Wei , Chen Wei , Zhu Xiao-Hong
Published: August 2023

In this study, we used a newly developed tri-frequency RF surface coil that enables 1H MRI and interleaved 2H/17O MRS measurements to simultaneously assess cerebral glucose and oxygen metabolism, cerebral blood flow and oxygen extraction fraction in rat brain at 16.4T with administration of 2H-labeled glucose and 17O-labeled oxygen gas. Our results demonstrate that this approach can capture the activities of major pathways relevant to glucose and oxygen metabolism and perfusion in the same brain at the same time, thereby provide comprehensive information crucial for understanding the metabolic and vascular coupling or uncoupling relationship in healthy and diseased brains.
Reconstructing High-Quality Sodium MR Images from Limited Noisy k-Space Data with Model-Assisted Deep Learning
Authors: Zhao Yibo , Li Yudu , Guo Rong , Thulborn Keith , Liang Zhi-Pei
Published: August 2023

Sodium MRI can acquire important biological information about cell integrity and tissue viability, but its clinical application has been limited by low SNR and poor spatial resolution. We propose a novel method to reconstruct high-quality sodium images from limited and noisy k-space data. The new method synergistically integrates model-based reconstruction with deep learning. Simulation and experimental results show that the proposed method can reconstruct high-SNR and high-resolution sodium images, which clearly delineate lesions such as brain tumors.
Differentiating mild and severe nephropathies via multiparametric urea CEST, NOE and quantitative MT imaging
Authors: Shin Soo Hyun , Wendland Michael , Vandsburger Moriel
Published: August 2023

Standardized blood tests lack adequate sensitivity to renal function and its underlying pathophysiology. We examined whether urea CEST, along with NOE CEST and quantitative MT imaging, can be used to quantitatively differentiate cisplatin and aristolochic acid (AA) models of mild and severe nephropathy, respectively. The increase of T1 relaxation time and decrease of qMT contrast were parallel in two models, while the decreased intrarenal gradient of urea CEST and NOE contrast were only observed in AA models. These results indicate that our integrated approach has the potential to noninvasively quantitate renal injuries and characterize different types of renal pathophysiology.
Tetrahedral based Method for Intracranial Blood Vessels Volume Calculation
Authors: Yang Runyu , li yuze , Chen Huijun
Published: August 2023

The rupture of intracranial atherosclerotic plaque is a major cause of ischemic stroke. The atherosclerotic plaque burden was proposed to measure the risk of plaque vulnerability, which can be quantified by the volume of the vessel wall calculated on MR images. However, due to the complex shape of intracranial blood vessels, the traditional calculation method may reduce the accuracy of plaque burden measurement,especially overlapped in the curved vessels. Therefore, a tetrahedral based method was proposed to reduce the volume calculation error of intracranial vessel wall. The proposed method was validated in simulation and in-vivo datasets.
Non-contrast enhanced MR vessel wall image acquisition based on generation adversarial network
Authors: Gu Yuzhuo , Li Zongyang , Ma Weiqiong , Cheng Guanxun , Wan Liwen , Zhang Lei , Dai Yongming , Li Ye , Liang Dong , Liu Xin , Zheng Hairong , Zhang Na
Published: August 2023

High resolution magnetic resonance vessel wall imaging (MRVWI) is now widely used for the risk evaluation of patients with ischemic stroke. The use of MRVWI with and without contrast agent is a diagnostic criterion from expert consensus recommendations. However, patients with renal insufficiency who receive gadolinium-based contrast agents are at risk for developing a debilitating and potentially fatal disease known as nephrogenic systemic fibrosis. The study proposes a method based on adversarial generation network to obtain enhanced MR vessel wall image without using contrast agent. The results show the enhancement effect is consistent with the contrast-enhanced MRVWI images.
Monitoring Multi-Organ Stiffness Changes with MR Elastography in Amyloidosis Patients Treated with Stem Cell Therapy
Authors: Arani Arvin , Magnuson Jessica , Zhu Zheng , Trzasko Joshua , Sui Yi , Damani Devanshi , Glaser Kevin , Murphy Matthew , Yin Meng , Dispenzieri Angela , Ehman Richard , Venkatesh Sudhakar , Araoz Philip
Published: August 2023

Light chain (AL) cardiac amyloidosis is a disease where abnormal proteins are deposited in multiple organs and is associated with elevated tissue stiffness. In cases with poor prognosis, autologous hematopoietic stem cell transplantation (ASCT) is used for therapy. Organ response monitoring is currently limited and challenging. The objective of this study was to evaluate the feasibility of using magnetic resonance elastography (MRE) to monitor stiffness changes in multiple organs pre- and post- ASCT therapy. At 3 months post therapy, significant reduction in MRE stiffness (p<0.001) was observed in organs that exhibited successful therapy response (defined by 9 month clinical outcomes).
Predicting breast cancer treatment response using a hybrid deep learning network on multislice SyntheticMR images
Authors: Son Jong Bum , Hwang Ken-Pin , Rauch David E , Ahn Ju Hee , Lee Jiyoung , Zhou Zijian , Panthi Bikash , Adrada Beatriz , Candelaria Rosalind P , White Jason B , Guirguis Mary , Mohamed Rania M , Ravenberg Elizabeth E , Yam Clinton , Tripathy Debasish , Ma Jingfei
Published: August 2023

We developed a hybrid deep learning network combining convolutional neural (CNN) and long short-term memory (LTSM) networks to predict slice-to-slice consistent responses to neoadjuvant systematic therapy (NAST) in triple negative breast cancer (TNBC) patients using multislice quantitative SyntheticMR images. We demonstrated that neural networks originally developed for video feature classification can be adapted to predict treatment response of cancer patients using MR images. Our hybrid network was able to overcome the slice-to-slice inconsistency that would have resulted if a 2D network is applied directly, therefore providing higher prediction accuracy.
MR imaging of increased fibrotic tissue and hyaluronan deposition in the advanced stages of pancreatic ductal adenocarcinoma
Authors: Vohra Ravneet , Wang Yak-Nam , Son Helena , Totten Stephanie , Arora Akshit , Maxwell Adam , Lee Donghoon
Published: August 2023

Pancreatic cancer is expected to become the second leading cause of the cancer-related deaths in the USA by 2030. A genetically engineered mouse model (KPC) offers an alternative to transplantation models for preclinical therapeutic evaluation as it expresses mutations similar to human pancreatic cells. MRI can be used to monitor the tumor microenvironment in a variety of tumors. The goal of this study was to monitor fibrotic tissue and hyaluronan deposition in the KPC mouse model during the late stages of tumor development.
A 32-Ch Over-Overlapped Semi-Flexible RF Head Coil Array with Improved Deep Brain SNR
Authors: Duan Yunsuo , Liu Feng , Marsh Rachel , Riddle Matthew , Patel Gaurav , Gray John , Kangarlu Alayar , Kegeles Lawrence , Vaughan John
Published: August 2023

To improve the SNR in deep brain of human head images acquired using RF coil arrays with high coil element count, we proposed a 32-ch coil array with enlarged and over-overlapped coil loop elements laid out on a semi-flexible 3D-printed coil former. The experiment results showed that both the overall homogeneity of images and the SNR in deep brain are significantly improved.
In-silico shimming performance of a bore-sized 2-D UHF volume coil for different anatomies at 7T
Authors: Gokyar Sayim , Voss Henning , Taracila Victor , Robb Fraser , Ballon Douglas , Winkler Simone
Published: August 2023

We propose a two-dimensional cylindrical high-pass ladder (2D c-HPL) coil concept and investigate the contribution of the longitudinal dimension to utilize a bore-mounted general-purpose clinical volume coil for ultra-high field MRI. Theory and in-silico results show that this architecture exhibits improved B1 field uniformity for specific anatomies and regions such as brain (a coefficient of variation of 12.3%), cerebellum (4.9%), heart (16.7%), prostate (2.8%), and whole chest (36.8%). This could prove particularly useful for whole-body clinical imaging and could open new pathways for clinical diagnostics in UHF MRI.
Development of high impedance microstrip resonators for ultrahigh field MR imaging
Authors: Gao Tianyu , Zhang Xiaoliang
Published: August 2023

Microstrip resonator has been used as the radio frequency (RF) transceiver for magnetic resonance signal excitation and reception. Its application in parallel imaging could achieve faster imaging speed and better image quality. Mutual coupling is one of the major challenges in designing a multichannel RF transceiver array required in parallel imaging technology. The mutual coupling among the array elements causes resonant frequency shift, input impedance changes, and radiation pattern change, and thus degrade the imaging quality.
Dielectric transreceiver resonant coil using an ultrahigh εr ceramic disk for 17O imaging at 10.5T and comparison with a single-loop surface coil
Authors: Li Xin , Wiesner Hannes , Zhu Xiao-Hong , Chen Wei
Published: August 2023

We integrated a permittivity-tunable ultrahigh dielectric constant (uHDC) disk with copper film shielding (for tuning) and a pick-up coil (for matching) for performing 17O MRS imaging (MRSI) at 10.5T. We conducted a phantom 17O imaging study to compare the coil performance and signal-to-noise ratio between the newly designed uHDC resonator with a surface loop coil of the same diameter as the disk. The results indicate a better performance of the uHDC resonator at the room temperature(21 Co), and a much better performance when the ceramic disk temperature was cooling to 16 Co, owing to a large denoising effect.
Performance of Short, Gradient Modulated RF Pulses due to Asynchrony Between RF and Gradient Waveforms
Authors: Kumaragamage Chathura , Brown Peter , McIntyre Scott , Nixon Terence , De Feyter Henk , de Graaf Robin
Published: August 2023

The effect of asynchrony between RF and gradient modulated (GM) waveforms on the performance of 3 ms gradient modulated GOIA-WURST RF pulses (BW = 15 kHz) was evaluated in simulation and experimentally. Results demonstrate that a 20+ μs asynchrony between RF and GM functions substantially degrades inversion performance when using large RF offsets to achieve translation. A projection-based method is presented that was used to calibrate RF and GM asynchrony on a 4T human MR system, where a ~40 µs asynchrony was present. The projection method allows a quick calibration of RF and GM asynchrony on pre-clinical/clinical MR systems.
Hypo-diamagnetism on χ-separation: a potential marker for the differential diagnosis between MS and NMOSD.
Authors: Jang Jinhee , Shin Hyeong-Geol , Lee Hyebin , Park Dohoon , Kang Junghwa , Nam Yoonho , Lee Jongho , Kim Woojun
Published: August 2023

Using χ-separation, detailed characterization of MS and NMOSD lesions were possible. Majority (490/611, 80%) of MS lesions were hypo-diamagnetic on χneg maps, suggesting loss of diamagnetic myelin. On the contrary, only 14.2% (32/225) of NMOSD lesions were hypo-diamagnetic. On χpos maps, some MS and a few NMOSD lesions showed paramagnetic rim round the lesion, which was identical to paramagnetic rim sign on SWI or QSM. The proportion of hypo-diamagnetic lesions showed excellent diagnostic performances (AUROC 0.961, 95%CI 0.907-1), as compared to central vein sign and paramagnetic rim lesion sign.