bluuebunny/crossref_metadata_2025 huggingface dataset:
Blood Oxygen Level-Dependent Imaging of Lower Limbs: Perfusion and Clinical Assessment of Peripheral Artery Disease
Authors: Yu Xiaoxi , Liu Zhaoxi , Qu Jianxun , Wang Fengdan , Lai Zhichao , Ma Jiangyu , Fan Xiaoyuan , Ye Luming , Shao Jiang , Zhang Yan , Feng Feng , Liu Bao , Jin Zhengyu
Published: August 2023

This study used blood oxygen level-dependent (BOLD) MRI to assess the hemodynamics of the lower limbs for patients with peripheral artery disease. We obtained dynamic change of BOLD T2* with high temporal and spatial resolution in reactive hyperemia experiment and found good correlation between BOLD parameters and clinical walking performance, which verified the significance of perfusion measurement for clinical assessment.
Brain Functional Connectome prospectively encodes Distress Symptoms During COVID-19 pandemic
Authors: Pan Nanfang , Wang Song , Qin Kun , Long Yajing , Gong Qiyong
Published: August 2023

In the prospective investigation of public mental health following COVID-19 pandemic, we assessed the psychological status of a general sample. Furthermore, we built a brain-distress construct which mainly contained within- and between-DMN connection patterns, and the left hippocampus emerged as the hub region in distress-related connectome. Based on customized brain connectomes, we may identify individuals who confer high vulnerability to pandemic-induced distress symptoms with well-performing predictive modeling. Our findings may facilitate the understanding of neural correlates underlying distress on the individual macro-scale network level, and the susceptibility markers may serve as targets for prevention and intervention.
Changes of structural network in patients with early Lacunar Infarction
Authors: YU XUCHEN , Li Hongwei , He Min , Chen Weibo , Ding Jing , Wang He
Published: August 2023

Most of the lacunar infarcts are small and asymptomatic, while it could not illustrate brain function is normal. Due to the potential impact of early disseminated lacunar infarcts on brain network and function remains unclear, the objective of this study was to assess whether there were changes in brain network and functional connectivity.
Histogram Peritumoral Enhanced Features on 3.0T MRI Arterial Phase Can Predict the Microvascular Invasion of Hepatocellular Carcinoma
Authors: Wang Xinxin , Sun Yunfeng , Zhou Xueyan , Zhang Hongxia , Lian Jianxiu , Zhou Yang
Published: August 2023

Preoperative microvascular invasion (MVI) prediction plays an important role in therapeutic decision-making of hepatocellular carcinoma (HCC). We aimed to investigate the value of histogram based on the arterial phase (AP) of magnetic resonance imaging (MRI) with extracellular contrast agent compared with radiological features for predicting MVI of solitary HCC. Results showed that peritumoral AP enhanced degree on MRI showed an encouraging predictive performance for preoperative prediction of MVI, especially in sHCCs (AUC=0.798, sensitivity =54.84%, specificity =100% ). Corona enhancement thickness≤8 mm may be used as a predictive marker for MVI when compared with the group with thickness>8 mm.
To characterize clear cell renal cell carcinoma using diffusion weighted imaging with a fractional order calculus model
Authors: Shi Bowen , Xue Ke , Yin Yili , Xu Qing , Shi Binbin , Wu Dongmei , Dai Yongming , Ye Jing
Published: August 2023

Accurately characterizing ccRCC grades with a non-invasive method would be vital for guiding treatment and prognosis. This study evaluated and compared the performance of conventional ADC and the FROC parameters, Dfroc, β, μ, in differentiation of low- and high-grade ccRCC. All the FROC parameters showed better diagnostic performance than ADC in characterizing ccRCC grades, among which β yielded the highest AUC value. This implied the clinical potential of the FROC model in evaluating renal tumor aggressiveness and the dominant role of β in probing tumor heterogeneity and microstructural complexity.
Investigating the visual predicting model of metabolic biomarkers in biofluids for esophageal cancer detection using NMR-based metabolomics
Authors: Lin Yan , Ouyang Ting , Zhang Huanian , Cai Rongzhi , Zheng Peie , Fu Zhijie , Zhou Han , Wu Renhua
Published: August 2023

Constructing an optimal metabolic model by combining biomarkers in biofluids may improve its non-invasive screening efficiency for esophageal cancer (EC). In this study, urine and serum specimens representing the healthy and EC individuals were examined using high-resolution 600 MHz 1H NMR technique. Furthermore, the paralleled patient-matched metabolites of EC tumor tissues and their adjacent non-cancerous tissues were investigated, which was used as references to determine biofluids metabolic biomarkers. The visual nomogram prediction model through a combination of creatine and glycine in both serum and urine was constructed using multiple regression analysis, which improves the diagnostic efficiency for EC.
Intravoxel Incoherent Motion MR Imaging-based Virtual Elastography for the Assessment of Placenta Accreta
Authors: lu jiaojiao , liu ting , li junjun , li xianjun , yang jian
Published: August 2023

Placenta accreta may lead to life-threatening complications and is difficult to distinguish by traditional MRI sequences. In this study, IVIM-based virtual elastography was used for the first time to detect the stiffness of the placenta, and it was found that the virtual (IVIM) stiffness values for the AP-ROIs were mostly higher than those for the NP-ROIs; the IR-ROIs were also significantly higher than the NIR-ROIs. Thus, the implanted regions of the placenta could be significantly stiffer than those of the non-implanted regions. That is helpful for adding a new method for clinical diagnosis of placenta accreta.
Dissecting Brain-wide Olfactory Dysfunctions in Accelerated Aging Rodent Model
Authors: Ma Teng , Wang Xunda , Xie Linshan , Chong Pit Shan , Sin Venice , Cao Peng , Khong Pek-Lan , Lim Lee Wei , Leong Alex T. L. , Wu Ed. X
Published: August 2023

The olfactory system plays a pivotal role in driving behavioral responses that are critical to survival. In particular, the decline in ability to detect and discriminate odors in aged humans lead to an overall decrease in quality of life. However, our present understanding of olfactory dysfunction in aging brains beyond the cellular and micro-circuit level is scarce and incomplete. In this study, we deployed optogenetic fMRI to reveal the changes of brain-wide odor-associated regions brought about by aging in an accelerated aging rat model. We found diminished activations brain-wide indicating dysfunction at the systems level across multiple long-range olfactory pathways.
Sex-related Changes in GABA+ Signaling in the Right Thalamus in Patients with Knee Osteoarthritis through Acupuncture Stimulation
Authors: Gao Nan , Hu Sheng , Wang Xiaoxiao , Qiu Bensheng
Published: August 2023

This study aimed to investigate GABA variation between KOA patients and healthy people without therapy and how GABA changes through acupuncture stimulation. MEGA-PRESS sequence was used to detect GABA signaling in the right thalamus before and after acupuncture stimulation on EX-LE5 acupoint of the left leg. Our study demonstrated a sex-related GABA+/Cr difference in patients with KOA chronic pain, proving the quantitative sex differences in human pain processing. Moreover, the therapeutic effect of acupuncture stimulation was positively proved and quantified by measuring GABA+/Cr.
3D texture analyses of quantitative susceptibility maps to differentiate patients with Wilson’s disease from healthy controls
Authors: Li Gaiying , Wu Rong , Du Yasong , Song Yang , Wang Yi , Wang Xiaoping , Li Jianqi
Published: August 2023

Abnormal metal accumulation in deep gray matter (DGM) nuclei of patients with Wilson’s disease (WD) could be detected using quantitative susceptibility mapping (QSM), yet no study has quantitatively evaluated how the textures of susceptibility maps might evolve with WD. The aim of this study was to evaluate texture features extracted from susceptibility maps of DGM nuclei for differentiating WD from healthy controls (HC). The results showed that part of the texture parameters was significantly different between WD and HC, meanwhile the receiver operating characteristic curve revealed that some second-order texture parameters were more suitable and sensitive for diagnosis of WD.
Comparison of fat quantification in abdomen and vertebrae by IDEAL-IQ and mDIXON Quant
Authors: Liu Na , Song Wei , Miao Wei , Liu Lian , Lin Jie , Xu Fang
Published: August 2023

IDEAL-IQ by GE and mDixon Quant by Philips are two MR fat quantification techniques that have been widely used for clinical disease evaluation and diagnosis in recent years. This study aims to compare performance of the two similar methods for fat quantification in the liver, pancreatic and lumbar vertebral. Results showed no significant difference between IDEAL-IQ and mDixon Quant on the liver, pancreas, and lumbar vertebral fat quantification in the same cohort of heathy volunteers
4D-MRI image enhancement via a deep learning-based adversarial network
Authors: Wang Yinghui , Zhi Shaohua , Xiao Haonan , Li Tian , Cai Jing
Published: August 2023

We developed and evaluated a deep learning technique for enhancing four-dimensional MRI (4D-MRI) image quality based on conditional adversarial networks. The quantitative and qualitative evaluative results demonstrated that the proposed model was able to reduce artifacts in low-quality 4D-MRI images and recover the details obtained from high-quality MR images, and performed better as compared with a state-of-the-art method.
Higher reliability and validity of Wavelet-based ALFF of resting-state fMRI: evidence from multicenter eyes open and eyes closed databases
Authors: Yue Juan , Zhao Na , Qiao Yang , Feng Zi-Jian , Hu Yun-Song , Zhang Yong , Zang Yu-Feng , Wang Jue
Published: August 2023

Using multi-center resting-state fMRI databases under eyes closed (EC) and eyes open (EO), this study systematically investigated the intra- and inter-scanner reliability and validity of Wavelet-mALFF across multiple frequency bands. We found that Wavelet-mALFF outperformed FFT-mALFF in terms of intra- and inter-reliability and validity, particularly in the higher frequency band slow-2 (0.1992-0.25 Hz), where db2-mALFF is the top performer in Wavelet-mALFF. We propose that instead of FFT-ALFF, db2-ALFF can be employed to measure the spontaneous oscillations of local brain activity in future studies.
Learning White Matter Streamline Representations Using Transformer-based Siamese Networks with Triplet Margin Loss
Authors: Zhong Shenjun , Chen Zhaolin , Egan Gary
Published: August 2023

Robust latent representation of white matter streamlines are critical for parcellating streamlines. This work introduced a novel transformer-based siamese network with triplet margin loss, that learns to embed any lengths of streamlines into fixed-length latent representations. Results showed that a minimum of two layers of transformer encoders were sufficient to model streamlines with a very limited number of training data.
Systematic investigation of gamma knife in the treatment of brain tumors: an MR perfusion imaging study
Authors: huang ling , bai hui , ZHONG HAI , DOU WEI QIANG
Published: August 2023

The main purpose was to evaluate the gamma knife radiosurgery(GKRS) treatment effect of multiple kinds of brain tumors by MR perfusion imaging. We collected 45 patients with three kinds of brain tumors treated with GKRS. Perfusion imaging, including 3D-ASL and DSC-PWI, was performed before and after treatment. ASL-rCBF, DSC-rCBF and DSC-rCBV decreased significantly after GKRS treatment, and ASL-rCBF before GKRS treatment showed the most robust performance with high AUC in predicting GKRS treatment effect. With these findings, MR perfusion imaging can be considered a time and effective method understanding the treatment effect of GKRS for brain tumor patients.
Rapid Acquisition using Water pre-saturation for quantitive CEST imaging (RAW-qCEST): exchange rate quantitation at 3-Tesla
Authors: Chen Wenxuan , Ma Lele , Chen Zhensen , Song Xiaolei
Published: August 2023

We proposed a Rapid Acquisition using Water pre-saturation for quantitative CEST imaging (RAW-qCEST) sequence, which validated by phantom experiments at 3-Tesla. Raw-qCEST allowed rapid acquisition of multiple readouts with varied saturation powers within one TR, by adding a water pre-saturation module that suppress interference among readouts and a seconds-long CEST saturation module before each readout. Simulation and phantom experiments showed that RAW-qCEST could accurately determine pH-related exchange rates, calculated using a modified omega-plot analysis. Owing to its fast speed and quantitative capability, RAW-qCEST could be useful for imaging stroke and cancers, which will be investigated in future.
Dynamic glucose enhanced MRI detects glucose-related responses in mouse brain under normoxia and hyperoxia
Authors: Huang Jianpan , Chen Zilin , van Zijl Peter , Law Lok Hin , Prabakaran Rohith Saai Pemmasani , Park Se Weon , Xu Jiadi , Chan Kannie W. Y.
Published: August 2023

Dynamic glucose enhanced (DGE) MRI can detect glucose-related events in the brain, however, the influence of oxygen levels on DGE signal remains unknown. Here, we investigated the DGE signal changes under normoxia and hyperoxia on mouse brain, using on-resonance variable delay multi-pulse (onVDMP) MRI. Significantly higher signal change under normoxia than that under hyperoxia was observed in parenchyma but not in cerebrospinal fluid (CSF). Without glucose infusion, a signal increment of about 3% was found in both parenchyma and CSF from hyperoxia to normoxia, interpreted as related to BOLD effect. These data provide insight into the origin of glucoCEST contrast.
Amide proton transfer weighted MRI in differentiating fat-poor angiomyolipoma from clearcell renal cell carcinoma
Authors: Xu Qing , Dou Weiqiang , Ye Jing
Published: August 2023

This study aimed to explore the feasibility of amide proton transfer weighted (APTw) imaging in discriminating fat-poor angiomyolipoma (AML) from clear cell renal cell carcinoma (ccRCC). 19 ccRCC patients and 9 fat-poor AML patients were recruited for APTw imaging. ccRCC group showed significantly higher MTRasym compared to fat-poor AML group (P < 0.05). High AUC of 0.827 in ROC analysis validated the efficacy of APTw imaging in differentiating fat-poor AML from ccRCC. With these findings, APTw imaging may be considered an effective method providing added clinical value in differentiating fat-poor AML from ccRCC.
Quantification of the impact of Gadolinium agent on amide-proton-transfer weighted MRI: an ex vivo and in vivo study
Authors: Yang Qian , Liu Zhou , Wu Yin , Qian Long , Luo Dehong
Published: August 2023

The clinical value of Amide proton transfer (APT) imaging in brain tumors has been intensively studied and validated. However, whether and how much Gadolinium-based (Gd) agent in clinical dosage has impact on the APT-weighted signal in clinical scenarios remains controversial. Hence, this study designed an in vitro study using a phantom with a gradient dosage of Gd-based agent and then validated the result in 6 patients with brain tumors. The result revealed that APT effect was significantly influenced by Gd contrast agent both in the phantom in vitro study and in vivo study.
Temperature Dependence of the Electric Properties in MR Phantoms
Authors: Zanovello Umberto , Kilian Wolfgang , Troia Adriano , Ittermann Bernd , Arduino Alessandro , Zilberti Luca
Published: August 2023

Phantom realization represents an important activity for the development and validation of advanced MR-based quantitative imaging techniques such as Magnetic Resonance Fingerprinting or Electrical Properties Tomography. In this regard, phantoms represent a reliable ground truth with the requirement that the sought properties are properly characterised in the variety of conditions at which the phantoms will be finally deployed. In this abstract, measurements of the electrical properties of two different gel-based tissue-mimicking materials are presented as a function of their temperature. A linear dependence with positive (negative) slope between temperature and electrical conductivity (relative permittivity) is observed.
Diagnostic Ultra-low-dose 18F-PI-2620 Tau PET/MRI with Generative Adversarial Network-based Enhancement
Authors: Chen Kevin , Tesfay Robel , Koran Mary Ellen , Ouyang Jiahong , Shams Sara , Liang Tie , Khalighi Mehdi , Mormino Elizabeth , Zaharchuk Greg
Published: August 2023

With the focal and lower uptake in tau PET imaging compared to other tracers such as amyloid, we aim to use multimodal simultaneous PET/MR imaging combined with training a generative adversarial network (GAN) to enhance ultra-low-dose 18F-PI-2620 tau PET images. We showed that the deep learning-enhanced images greatly reduced image noise as compared to the ultra-low-dose images, outperformed the ultra-low-dose images metrics-wise, and were able to be read clinically for regional uptake patterns of tau accumulation similarly as the full-dose images.
Local BOLD correlations in white matter measured under constraints of fiber orientation
Authors: Gao Yurui , Zhao Yu , Schilling Kurt , Li Muwei , Anderson Adam , Ding Zhaohua , Gore John
Published: August 2023

Correlations between BOLD signals from adjacent voxels in white matter (WM) are anisotropic and appear similar to metrics that describe functional connectivity in gray matter. We propose a method to measure the fiber-oriented correlation (FOC) between BOLD signals in WM. This method was implemented on both resting state and motor task fMRI data across 64 HCP subjects. The FOC map revealed WM areas apparently engaged in default mode network and visual function in both rest and task conditions. The motor task map also delineates the corticospinal tract. The proposed metric FOC may be valuable for investigating functional WM pathways.
Neural excitability through BOLD variability in the preterm brain: A longitudinal resting-state fMRI study
Authors: Loukas Serafeim , de Almeida Joana Sa , Meskaldji Djalel , Van De Ville Dimitri , Hüppi Petra
Published: August 2023

Resting-state functional connectivity based on simultaneous BOLD oscillations has been described both in preterm infants and fullterm newborns. During this period of rapid cerebral cortex development, different brain activity patterns (networks) have been previously described. Regional changes in spontaneous BOLD signal variability on the other hand reflect local intravoxel BOLD changes related to neural excitability/flexibility. In the present study, we aimed to explore the longitudinal evolution of the BOLD variability between birth and term-equivalent-age (TEA), a period of rapid brain development, in preterm-born infants. Our findings suggest increased variability at TEA as compared to birth in important primary cortices.
PET attenuation correction in lung with ZTE based pseudo-CT generation
Authors: Sun Chang , Manavaki Roido , Tarkin Jason , Wall Christopher , Rudd James , Gilbert Fiona , Graves Martin
Published: August 2023

Attenuation correction methods in body PET/MRI do not consider lung density variations within and between participants, which can significantly affect PET image quantification in thoracic areas. We developed a hybrid ZTE-based/Dixon method for MR-based attenuation correction, which resulted in a pseudo-CT image (Dixon/pCT) and used for attenuation correction in the lung. PET images corrected with the Dixon/pCT attenuation map were compared to images produced with manufacturer’s Dixon attenuation map and a hybrid Dixon/CT image. Overall, the Dixon/pCT method reduced the absolute SUV error in the lung by 3%, when compared to the Dixon attenuation correction.
Brain SMS-EPI reconstructions with prospective motion correction: improved quality with updated coil sensitivity maps
Authors: Li Bo , Li Ningzhi , Balan Radu , Wang Ze , Ernst Thomas
Published: August 2023

We investigated prospective motion correction (PMC) for brain SMS-EPI using camera-based motion tracking and coil sensitivity interpolation. Phase compensation for the receiver was performed to eliminate motion-induced aliasing artifacts. Coil sensitivity profiles were interpolated and extrapolated with Makima piecewise cubic Hermite algorithm to remove residual SMS reconstruction artifacts caused by the misregistration of coil sensitivities between updated (mobile) SMS slices and stationary single-slice reference profiles.
Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network
Authors: Li Chao , Zhang Hang , Zhang Jinwei , Spincemaille Pascal , Nguyen Thanh , Wang Yi
Published: August 2023

An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinson’s disease patients. The results show that motion artifacts, such as ringing and ghosting, were successfully suppressed.
Understanding domain shift in learned MRI reconstruction: A quantitative analysis on fastMRI knee and neuro sequences
Authors: He Shizhe , Zimmer Veronika , Rueckert Daniel , Hammernik Kerstin
Published: August 2023

In this work, we investigate the problem of domain shift in the context of state-of-the-art MRI reconstruction networks with respect to variations in training data. We provide visualization tools and support our findings with statistical analysis for the networks evaluated on fastMRI knee and neuro data. We observe that the signal-to-noise ratio of the examined sequences plays an essential role, and we statistically prove the hypothesis that the type/amount of training data is less important for low acceleration factors. Finally, we provide a visualization tool facilitating the examination of the networks’ performance on each individual subject of the fastMRI data.
Learning to segment with limited annotations: Self-supervised pretraining with Regression and Contrastive loss in MRI
Authors: Umapathy Lavanya , Fu Zhiyang , Philip Rohit , Martin Diego , Altbach Maria , Bilgin Ali
Published: August 2023

The availability of large unlabeled datasets compared to labeled ones motivate the use of self-supervised pretraining to initialize deep learning models for subsequent segmentation tasks. We consider two pre-training approaches for driving a CNN to learn different representations using: a) a reconstruction loss that exploits spatial dependencies and b) a contrastive loss that exploits semantic similarity. The techniques are evaluated in two MR segmentation applications: a) liver and b) prostate segmentation in T2-weighted images. We observed that CNNs pretrained using self-supervision can be finetuned for comparable performance with fewer labeled datasets.
Deep learning-based slice resolution for improved slice coverage in abdominal T2 mapping
Authors: Ahanonu Eze , Fu Zhiyang , Johnson Kevin , Philip Rohit , Martin Diego , Altbach Maria , Bilgin Ali
Published: August 2023

This study presents a deep learning based technique for slice super resolution in RADTSE T2 mapping. The proposed method may be used to accelerate full liver imaging, while maintaining sufficient through-plane resolution for detection of small pathologies.
Detection of IDH mutation in brain tumors using RAFF, continues wave T1rho, adiabatic T1rho, adiabatic T2rho and high b-value DWI
Authors: Merisaari Harri , Steiner Aida , Pesola Marko , Gardberg Maria , Frantzén Janek , Jokinen Pekka , Aronen Hannu , Liimatainen Timo , Minn Heikki , Jambor Ivan
Published: August 2023

We evaluated the potential of Relaxation Along a Fictitious Field in second rotating frame (TRAFF2), continues wave T1rho (T1ρcw), adiabatic T1rho (T1ρadiab), adiabatic T2rho (T2ρadiab), DWI and anatomical MRI to differentiate isohydrogenase dehydrogenase (IDH) status between IDH wild type and IDH mutation in 22 patients with glioma. In voxel level analysis, DWI derived parameters using bi-exponential model (0-4000 s/mm2) provided improved performance in classification of IDH status of brain gliomas compared with TRAFF2, T1ρcw, T1ρadiab, T2ρadiab and anatomical MRI.
Correction of Bias in Single Reference Variable Flip Angle T1-mapping with Simple Extrapolation Method
Authors: Malmberg Michael , Odéen Henrik , PARKER DENNIS
Published: August 2023

Single reference variable flip angle T1 mapping is subject to some remaining bias due to T2* effects between baseline and dynamic acquisitions. Previous work determined a computationally expensive correction for this bias. In this work, we present a much simpler extrapolation method to correct for T2* related bias in T1. This study provides an analysis through simulation of the benefits and drawbacks of this simpler extrapolation correction compared to the previous correction for bias on the SR-VFA T1 calculation.
Rapid T2’ quantification by 10-echo GESE-EPIK sequence with application to oxygen extraction fraction imaging
Authors: Küppers Fabian , Yun Seong Dae , Shah N. Jon
Published: August 2023

Owing to the advantages afforded by the simultaneous acquisition of T2/T2* in several practical applications, interest in this area remains high. In light of this, here we present an improved NLSQ fitting algorithm and a more detailed validation based of our previously introduced 10-echo GESE-EPIK sequence. The validation consists of two new phantoms, including a spectroscopic comparison to reference methods, and data from five in vivo subjects at 3T. In addition, GESE-EPIK is applied to OEF quantification during a breath-hold experiment to demonstrate its sensitivity to challenge-related changes.
Iterative MR-Electrical Properties Tomography using physics-based deep learning
Authors: Zumbo Sabrina , Bevacqua Martina , Meliadò Ettore , Stijnman Peter , Meerbothe Thierry , Isernia Tommaso , van den Berg Cornelis , Mandija Stefano
Published: August 2023

We
introduce
for the first time an iterative MR electrical properties tomography reconstruction method by exploiting a cascade of multi-layer convolutional neural networks (CNNs) able to learn spatial priors in an iterative fashion, alternated with physics-based gradient descent direction calculations. This method was tested on 2D simulated human brain data. The presented results demonstrate the feasibility of this methodology to reconstruct conductivity and permittivity maps at 128MHz. Ultimately, this method allows computational advantages compared to standard contrast source inversion electrical properties tomography (CSI-EPT), i.e. faster reconstructions, which will be extremely relevant when moving to 3D reconstructions.
Optimized variable winding pitch coil designs for miniature B0 field modification.
Authors: Wehkamp Niklas , Rovedo Philipp , Zaitsev Maxim
Published: August 2023

We present numerical optimization of variable winding pitch coil designs for miniature coils with respect to target field homogeneity. The optimization is based on a continuous coil winding layout that was used to simulate field homogeneity in the target volume via the Biot-Savart law. The example coil designs expand the range of applications for frequency adjustable field probes, as they increase the B0 modification field’s homogeneity by factor of 7. Due to the specific requirements of miniature B0 modification coils, the dimensions of the coil are restricted in length, width and wire diameter.
T2-relaxation effects on NAA concentration reductions in psychotic disorders
Authors: Chen Xi , Kuan Elliot , Du Fei , Öngür Dost
Published: August 2023

We used 1H MRS at 4 T to quantify NAA concentrations and apparent T2 relaxation times in 104 psychosis patients compared to 50 matched healthy controls with four TEs (TE = 30, 90, 150 and 200 ms). Even at short TE (30 ms), NAA concentration without T2 correction was significantly lower in chronic psychosis compared to age-matched healthy controls. After T2 correction, no significant differences remained. Thus, it may be neuronal microenvironment indexed by T2 relaxation time, but not neuronal integrity indexed by NAA concentration that underlies the widely reported NAA concentration reductions in psychotic disorders.
Standardization of breast dynamic contrast-enhanced MRI signal for assessment of background parenchymal enhancement rate
Authors: Medved Milica , Tsuchiya Keiko , Fan Xiaobing , Karczmar Gregory , Abe Hiroyuki
Published: August 2023

Breast DCEMRI signal is standardized for variable imaging protocols and the linear rate of background parenchymal enhancement (BPE) is calculated, to quantitatively and objectively describe changes in BPE rates following preventative tamoxifen treatment. This is in contrast with the current practice of using 4 subjective categories to describe BPE. Decreased BPE rates post-treatment agree with earlier results showing that BPE, like breast density, correlates with breast cancer risk. Standardization for imaging parameters and contrast agent relaxivity increases the observed effect size, pointing to increased sensitivity to treatment-induced changes and the potential as a tool for individual breast cancer risk management.
Deep learning identification of clear cell renal carcinoma cancer using MR imaging
Authors: Guo Junyu , Husley Keith , Xi Yin , Pedrosa Ivan
Published: August 2023

Clear cell renal carcinoma cancer (ccRCC) is the most common subtype among renal masses. ccRCC identification helps in decision making between active surveillance and definitive intervention. A clear cell likelihood score (ccLS) using subjective interpretation of multiparametric MRI by radiologists was proposed recently. In this study, we investigate whether deep learning (DL) using the three main MR sequences for ccLS can facilitate the diagnosis of ccRCC. We compared the results of twelve trained DL models with the reported ccLS performance. Our results demonstrate that DL may achieve a performance comparable to radiologists and provide useful information for identification of ccRCC.
Observations on Rapid Dynamic Contrast Enhanced MRI in Prostate Cancer
Authors: Tartaro Armando , Renzetti Roberto , De Cristofaro Aulisa Michele , Morrone Federica , De Nicola Andrea , Bliakharskaia Ekaterina , Summers Paul
Published: August 2023

Prostate dynamic contrast-enhanced (DCE) MRI is often criticized for its weaknesses in terms of discriminating transition zone lesions and tumors in general. Using high-temporal resolution DCE MRI in patients who went on to prostatectomy, we evaluated time to peak (TTP), as well as the enhancement rate factor, and initial enhancement slope of a simple empirical mathematical model of early contrast uptake. Differences between tumors and surrounding healthy tissues were seen for the mean values of all three parameters. The AUCs were good (>0.8 in all cases except for TTP between tumor and transition zone) but case numbers limited.
Histological validation of prostate tissue composition measurement using Hybrid Multidimensional MRI: Agreement with pathologists’ measures
Authors: Chatterjee Aritrick , Antic Tatjana , Gallan Alexander , Paner Gladell , Lin Lawrence , Karczmar Gregory , Oto Aytekin
Published: August 2023

We validated prostate tissue composition measured using HM-MRI by comparing with reference standard results from pathologists’ interpretation of clinical histopathology slides following whole mount prostatectomy. We are 95% confident that 90% of absolute paired differences (TDI0.9) between HM-MRI and consensus results of pathologists were within 20.6% and 24.2% in measuring epithelium and lumen fractional volumes, respectively. These were less than our criterion of 30% and inter-pathologists’ agreement (22.3% for epithelium and 24.2% for lumen). Therefore, we accept the agreement performance of HM-MRI in measuring tissue composition measurement and consensus of pathologists is on par with the inter-raters (pathologists) agreement.
Renal mass assessment using Ultrashort Time-to-Echo sequences with ferumoxytol
Authors: Zhou Tianyi , Timms Liam , Hamadeh Aya , Drew David , Harisinghani Mukesh , Sridhar Srinivas
Published: August 2023

A ferumoxytol-enhanced MRA technique using Ultrashort Time-to-Echo (UTE) sequences is demonstrated for kidney vasculature mapping and renal mass assessment. The blood volume (BV) map is calculated by scaling the post- and pre-contrast subtraction image from 0 to 1. BV is measured in one cystic mass (-6±7)% and one complex mass (17±8)% with sub-regions BV up to (32±10)% and (30±7)%. The quantification of BV provides a measurement of the distribution of micro-vessel density, reflecting the vascularity of the kidneys and masses. This technique potentially benefits surgical planning, renal mass characterization, and disease progression in patients with impaired renal function.
A Propensity Score Matched Analysis of Quantitative Background Parenchymal Enhancement on MRI for Improved Breast Cancer Risk Stratification
Authors: Yan Ran , Murakami Wakana , Mortazavi Shabnam , Yu Tiffany , Lee-Felker Stephanie , Sung Kyunghyun
Published: August 2023

High background parenchymal enhancement (BPE) on breast MRI is related to increased breast cancer risk. In this study, we investigate possible differences in BPE among different breast cancer risk groups stratified based on BRCA 1/2 mutation status and lifetime risks. We use propensity score matching to adjust for potential confounders. We performed a quantitative BPE analysis on the 3D volume of the breast and fibroglandular tissue based on the enhancement maps and principal component analysis. The results showed a significant difference in BPE among different risk groups, indicating that BPE may improve breast cancer risk stratification.
Detecting schizophrenia using structural and synthetic cerebral blood volume MRI with deep learning
Authors: Zhang Junhao , Acosta Nicolas , Rao Vishwanatha Mitnala , Lee Pin-Yu , Gjerswold-Selleck Sabrina , Wan Zihan , Feng Xinyang , Lieberman Jeffrey A. , Small Scott A. , Guo Jia
Published: August 2023

Fast translate Icon translate Currently discrimination of patients with schizophrenia from normal healthy subjects remains challenging. In this study, it is aimed to improve the performance of schizophrenia detection using structure T1-weighed scans and synthetic functional maps with one 3D Convolutional neural networks. The results show that our approach could outperform the current state-of-the-art method in terms of AUROC and discriminative brain regions related are also localized.
Differentiation of Pediatric Brain Tumor Grades Using High b-Value Diffusion MRI with a Varying Diffusion Curvature Model
Authors: Fu Jay , Hou William , Karaman Muge , Zhou Xiaohong
Published: August 2023

Imaging-based determination of tumor grade is highly desirable, particularly for pediatric brain tumors where surgical biopsy carries considerable risks. We have applied a novel diffusion model – varying diffusion curvature (VDC) model, with two b-value ranges (0-2000 sec/mm2 and 0-4000 sec/mm2), to differentiate between low- and high-grade pediatric brain tumors on 70 patients. Our results showed that the combination of the VDC parameters substantially outperformed the ADC for differentiating low- from -high grade pediatric brain tumors. Increasing the maximal b-value from 2000 to 4000 sec/mm2 did not noticeably improve the performance.
Disrupted structural-functional coupling in children with prenatal alcohol exposure
Authors: Liang Xiaoyun , Yeh Chun-Hung , Anderson Peter
Published: August 2023

In this study, we investigated whether prenatal alcohol exposure (PAE) disrupts the coupling strength between SC and static FC or dynamic FC using multimodal connectomes. Greater coupling strengths were identified in the PAE T1 and PAE T1-T3 groups within males, indicative of brain structural and functional changes in children with PAE. Distinct age effects on coupling strength were also revealed. Further, the reduction of assortative coefficients indicated that PAE could compromise the robustness of brain networks. These findings suggest that multimodal connectomes, especially integrating network dynamics, could be advantageous to revealing brain structural and functional changes.
Pancreatic PDFF in persons with obesity: change during weight loss and correlations with adipose tissue PDFF
Authors: Junker Daniela , Rupp Selina , Han Jessie , Wu Mingming , Reik Anna , Wiechert Meike , Hauner Hans , Makowski Marcus , Holzapfel Christina , Karampinos Dimitrios
Published: August 2023

Risk stratification strategies in patients with metabolic diseases are urgently needed for the selection of appropriate lifestyle interventions. Pancreatic fat content has been shown to be associated with the metabolic syndrome and type 2 diabetes mellitus. MRI-based proton density fat fraction (PDFF) mapping based on a multi-echo gradient echo acquisition is the method of choice for non-invasive pancreatic fat quantification. This study evaluates how the PDFF of the pancreas correlates to adipose tissue PDFF and evolves after an 8-week weight loss intervention.
Sex-differences in resting-state brain activity in children with concussion in comparison to matched healthy controls.
Authors: Sharma Bhanu , DeMatteo Carol , Noseworthy Michael , Timmons Brian
Published: August 2023

There are known sex-differences with respect to the clinical presentation of pediatric concussion, with girls reporting more symptoms and symptoms with greater severity than boys. This is the first study to show that there are also sex-differences in resting state brain activity in children with concussion, suggesting that the sex-specific clinical presentation may have neurological underpinnings. Specifically, girls with concussion had resting state disturbances that were not present in boys, which is consistent with the variable symptom presentation of the injury. Continued research into these resting state differences is encouraged.
Gender-specific age-related changes in glymphatic function as measured by resting-state fMRI
Authors: Han Feng , Yang Yifan , Liu Xiao
Published: August 2023

The glymphatic system responsible for brain waste clearance may play an important role in aging and dementias. However, a lack of non-invasive tools for gauging glymphatic function in human subjects hindered the research on the age-related glymphatic changes. The global component of low-frequency (<0.1 Hz) resting-state fMRI signals was recently found coupled with CSF flow, and their coupling was used for quantifying the glymphatic function and found reduced significantly in neurodegenerative patients. Here we used the gBOLD-CSF coupling to evaluate glymphatic function change along healthy aging, and found it starts to decrease around age 55, particularly in females.
Diffusion MRI in a transgenic mouse model of Alzheimer's disease
Authors: Maharjan Surendra , Dybing Kaitlyn , Wang Nian
Published: August 2023

Alzheimer’s disease (AD) is common neurodegenerative dementia. Though the disease has no cure, early intervention can help to maintain normal mental function and slow down the progression of the disease. Brain imaging, especially Magnetic Resonance Imaging (MRI) has been extensively used to characterize the location and progression of the disease. Diffusion MRI is a novel imaging technique that exploits the microscopic mobility of water molecules. However, a comprehensive study incorporating high-resolution diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and Neurite Orientation Dispersion and Density Imaging (NODDI) is still lacking. So, we aim to apply DTI, DKI and NODDI.
Evaluation of the Effects of Capsinoids on Brown Adipose Tissue Recruitment and Activation in Obesity Using PET/MRI
Authors: Deng Jie , Neff Lisa , Zhang Bin
Published: August 2023

In this randomized, placebo-controlled trial in subjects with obesity, we implemented multi-parametric MRI including fat fraction (FF), R2*, and T1 values on PET-MRI in both thermoneutral and cold-activated non-shivering thermogenesis conditions to investigate whether brown adipose tissue property changes measured by MRI can reflect its activation secondary to cold-activated stimulation and chronic capsinoids ingestion before and after weight loss. In subjects taking capsinoids, FF decreased and R2* increased significantly after weight loss as compared with baseline, and these changes were significantly larger than the changes seen after weight loss in the group taking placebo.
Zero-Shot Self-Supervised Learning for 2D T2-shuffling MRI Reconstruction
Authors: Zhang Molin , Xu Junshen , Arefeen Yamin , Adalsteinsson Elfar
Published: August 2023

Resolving a time series of T2-weighted images from a fast-spin-echo (FSE) sequence with traditional techniques requires long acquisitions, but T2-shuffling enables clinically feasible scan times by combining subspace models, which reduce degrees-of-freedom, with random spatial and temporal undersampling. Supervised machine learning achieves impressive reconstruction, but lack of labeled training data preclude its use in reconstructing signal dynamics. Recent zero-shot-self-supervised-learning (ZSSS) techniques enable high quality structural MRI reconstruction without training data. In this work, we combine ZSSS with the subspace model to further accelerate 2D T2-shuffling acquisitions. Our ZSSS-subspace models show significant reconstruction improvement in comparison to standard T2-shuffling in simulation.
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple Contrasts
Authors: Liu Victoria , Ryu Kanghyun , Alkan Cagan , Pauly John , Vasanawala Shreyas
Published: August 2023

Model-based accelerated MRI reconstruction networks leverage large datasets to reconstruct diagnostic-quality images from undersampled k-space. To deal with inherent dataset variability, the current paradigm trains separate models for each dataset. This is a demanding process and cannot exploit information that may be shared amongst datasets. In response, we propose multi-task learning (MTL) schemes that jointly reconstruct multiple datasets. Introducing inductive biases to the network allows for positive information sharing. We test MTL architectures and weighted loss functions against single task learning (STL). Our results suggest that MTL can outperform STL across a range of dataset ratios for two knee contrasts.
General Analytical Solution for Phase-Cycled bSSFP Imaging with Three Acquisitions
Authors: Xiang Qing-San
Published: August 2023

Elliptical signal model for bSSFP imaging has found various applications such as banding artifact removal and quantitative mapping of physical parameters. Typically, four or more phase-cycled acquisitions are needed and thus impose a limiting factor in term of total scan time. In this work, it is found that the phase-cycled bSSFP system can be unlocked using a general analytical solution with only three acquisitions, leading to a significant reduction of total scan time. Mathematical frame work is presented, followed by validation with simulated data. Future work includes further demonstration, optimization, and applications with phantoms and in vivo subjects.
Development and robust analysis of 3D fat-suppressed T2 MRI for head and neck radiotherapy treatment planning
Authors: Salzillo Travis , Dresner Alex , McDonald Brigid , Way Ashley , Ahmed Sara , Andring Lauren , Corrigan Kelsey , Manzar Gohar , Yoder Alison , Mohamed Abdallah , Pinnix Chelsea , Stafford Jason , Wang Jihong , Fuller Clifton
Published: August 2023

In order to improve head and neck radiotherapy treatment planning, a series of 3D fat-suppressed T2-weighted sequences were developed with varying pulse sequence parameters. One non-fat-suppressed and five fat-suppressed sequences were acquired on five patients, and on each image, four structures were segmented by six radiation oncology physicians (observers). Robust and comprehensive analysis was performed to assess qualitative and quantitative image quality metrics. This included geometric distortion, SNR and CNR measurements, structure conspicuity, interobserver segmentation variability, and qualitative image quality rankings. The results of these were used to determine the optimal fat-suppressed sequence for head and neck radiation treatment planning.
Simulation of a Virtual Liver Iron-overload Model and Estimation of R2* Using Complex Fat-Water Models
Authors: Neupane Prasiddhi , Shrestha Utsav , Tipirneni-Sajja Aaryani
Published: August 2023

Multi-spectral fat-water-R2* modeling techniques fit either a single R2* or estimate independent R2* for water and fat molecules. In this study, a virtual liver model with hepatic iron overload was created based on true histological data and MRI signals were synthesized using Monte Carlo simulations. Our results demonstrate that the dual R2* values predicted by the ARMA model exhibit relaxivity behavior similar to in vivo and thus can be used for R2* estimation in vivo in varying concentrations of iron overload.
The dependence of the resting-state macrovascular fMRI signal power on vascular volume and orientation: A simulation study
Authors: Zhong Xiaole , Chen J. Jean
Published: August 2023

The resting-state fMRI (rs-fMRI) signal fluctuation amplitude is widely used as the resting-state fMRI marker, but it could be biased by the contribution of macrovessels. We demonstrate the dependence of the macrovascular contribution on blood oxygenation, vascular occupancy and orientation. This work paves the way for more appropriate interpretation of rs-fMRI signal amplitude given different vascular morphology across brain regions and populations.
Disposable, Handheld Clinical-Scale HP Propane Hyperpolarizer for Pulmonary MRI
Authors: Chekmenev Eduard , Samoilenko Anna , Birchall Jonathan , Kovtunov Kirill , Kovtunova Larisa , Bukhtiyarov Valeriy , Koptyug Igor , Qian Chunqi , Goodson Boyd , Ariyasingha Nuwandi
Published: August 2023

We demonstrate a disposable and handheld hyperpolarizer for clinical-scale production of hyperpolarized propane gas for application as an inhalable contrast agent for pulmonary MRI. The device consists of an aluminum commercial spray can, which contains a mixture of pressurized propylene and parahydrogen gases. This filled container can be easily transported and stored for weeks. The hyperpolarized propane production is initialized by valve actuation, which releases the propylene-parahydrogen mixture into a mini-catalytic reactor producing a stream of pure (without catalyst) hyperpolarized propane gas on demand. The feasibility of 3 T EPI, FIESTA, and GRE MRI in hyperpolarized phantoms is demonstrated.
Quantum Sensing of Local Neuronal Firings (qsLNF) in Human Brain via Proton (1H) MRI: Proof of Concept
Authors: Qian Yongxian , Calderon Liz , Chen Xingye , Liu Anli , Lui Yvonne , Boada Fernando
Published: August 2023

Neuronal firing generates fast action potentials along axon and slow postsynaptic currents at dendrites, which are difficult to detect and locate by scalp EEG and MEG placed above the skull. Here we propose local quantum sensing via endogenous proton (1H) nuclear spins inside firing neurons and detect magnetic fields indued by both action potentials and postsynaptic currents. Computer simulations and human studies showed that the proposed technique has the potential to non-invasively detect and locate neuronal firings in the brain through the acquisitions of FID signal on a 3T MRI scanner with multi-channel array coil.
Imaging hypoxia in two brain tumor models using GdDO3NI
Authors: Moghadas Babak , Scirone Jonathan , Scarpelli Matthew , Fuentes Alberto , Quarles C. Chad , Kodibagkar Vikram
Published: August 2023

In this study we have used the hypoxia-targeting MRI contrast agent GdDO3NI, (a nitroimidazole-based T1 contrast agent) to image the development of hypoxia in two types of rodent brain tumor models. Our results indicate a range of signal enhancements from 0-17% over baseline in the C6 and 9L tumors using GdDO3NI with clearance from contralateral brain and muscle tissue. This study further demonstrates the utility of GdDO3NI in non-invasive imaging of tissue hypoxia with high resolution.