bluuebunny/crossref_metadata_2025 huggingface dataset:
Resting-State Functional Connectivity of the Central Autonomic Network (CAN) in Parkinson’s Disease
Authors: Seow Pohchoo , Shih Yao-Chia , Hartono Septian , Yin Li Rong , Kuek Aeden Zi Cheng , Ng Samuel Yong Ern , Tan Eng King , Tan Louis , Chan Ling Ling
Published: August 2023

Vast evidence for network-level functional dysfunctions have been reported in Parkinson’s disease (PD). However, brain abnormalities that underlie common autonomic symptoms in PD have not been fully investigated. Resting-state functional integration of the Central Autonomic Network (CAN) of 79 PD patients and 43 healthy controls (HC) were evaluated using seed-based analysis. Significantly decreased functional connectivity between the right anterior insular seed and left lateral occipital cortex, right lingual gyrus, and left occipital pole were found in HC compared to PD. Functional connectivity analysis of the CAN facilitates further understanding of the potential mechanisms underlying altered autonomic regulation in PD.
Disrupted brain functional network connectivity in Parkinson’s disease patients with Mild Cognitive Impairment.
Authors: Sreenivasan Karthik , Zhuang Xiaowei , Caldwell Jessica , Ritter Aaron , Cordes Dietmar , Mari Zoltan , Mishra Virendra
Published: August 2023

The underlying cause of cognitive deficits in PD (PD-MCI) is not well understood and there are no biomarkers to diagnose PD-MCI. Although previous studies have found disrupted functional connectivity several factors make it difficult to generalize findings from fMRI studies. In the current study, we investigate functional network abnormalities in PD-MCI and PD patients who are cognitively normal (PD-NC). Our results were in line with earlier studies that showed altered connectivity was observed involving the frontal and striatal regions in the PD-MCI group, and we also found altered connectivity between regions in the temporal and the parietal cortex.
Altered functional connectivity in Parkinson’s disease with psychosis
Authors: Safai Apoorva , Prasad Shweta , Saini Jitender , Lenka Abhishek , Pal Pramod , Ingalhalikar Madhura
Published: August 2023

Visual hallucinations (VH) is a commonly occurring psychosis symptom in Parkinson’s disease (PD). However, neuropathology of VH in PD is not clearly known, thereby limiting the efficacy of therapeutic strategies. To mitigate this gap, we evaluated functional connectivity (FC) changes and network organisation, related to VH in PD. PD patients exhibited widespread reduction in interhemispheric FC and local network topology of temporal, frontal, parietal, striatal, limbic, cerebellar, occipital and sensory motor regions. Patients with psychosis displayed larger FC reductions, particularly in cerebellar regions and their connections with frontal and occipital regions. Topological changes in temporal regions seen across PD patients
Effective Connectivity within the Resting-State Network using Spectral Dynamic Causal Modeling
Authors: Mohammadian Fatemeh , Sadeghi Arash Zare , Salari Hanieh Mobarak , Talebi Mahsa , Hashemi Hassan , Rad Hamid Reza Saligheh , Noroozian Maryam
Published: August 2023

Alzheimer's disease (AD) is a network connection dysfunction syndrome. An approximate picture of functional integration and statistical dependence on responses between different regions of the brain can be defined by functional connectivity (FC). Explanation of the statistical dependencies and estimating how the dynamics of neurons affect each other remotely is done by effective connectivity (EC). Studying directional interactions between different regions of the brain plays a key role in our understanding of the functional integration of brain networks.
Choroid plexus changes in clinically defined Alzheimer disease spectrum: Volumetry, QSM and BBB permeability imaging
Authors: Moon Won-Jin , Choi Jong Duck , Moon Yeonsil , Kim Hee Jin , Yim Younghee , Lee Subin
Published: August 2023

In patients with cognitive impairment spectrum, 3T MRI revealed that choroid plexus volume and permeability was associated with cognitive impairment severity. l In patients with cognitive impairment, choroid plexus (CP) volume followed this order: Alzheimer dementia > late mild cognitive impairment (MCI) > early MCI or subjective cognitive impairment (p<.001) l Large CP volume was associated with lower Ktrans (r=-0.403, p=.006) and Vp (r=-0.543, p=.003) l Increased CP volume was negatively associated with memory function (B=-0.6662, SE=0.2089, p=.002), frontal executive function (B=-0.8973, SE=0.3145, p=.005), and mini-mental status examination score (B=-0.8158, SE=0.3153, p=.01) after adjusting for covariates.
Early microstructural aberrations in a mouse model of Alzheimer’s disease detected by Soma and Neurite Density Imaging
Authors: Ianus Andrada , Cruz Renata , Chavarrias Cristina , Palombo Marco , Shemesh Noam
Published: August 2023

Alzheimer’s disease induces early morphological tissue changes which precede the onset of symptoms and can be important targets for neuroimaging. In this work we employ Soma and Neurite Density Imaging (SANDI), a biophysical modelling approach for diffusion MRI, to investigate tissue microstructure alterations in the 3xTg AD mouse model and compare it with the state-of-the-art Diffusion Kurtosis Imaging (DKI).
Ultra-High Field MRI reveals amyloid plaques-like in a familial Alzheimer’s Disease patient-derived brain organoids
Authors: Santin Mathieu , Galet Benjamin , Daube Clément , Ravassard Philippe
Published: August 2023

This study presents preliminary results obtained on APP muted brain organoids. In vitro images were obtained using ultra-high field MRI and Gd passive staining of samples. MR images were compared to histology and immunohistochemistry slices to confirm the presence of amyloid plaques of mutated APP brain organoid compared to isogenic (control) brain organoid.
Quantitative Dixon imaging to characterise early alterations in liver fat in a novel model of diet-induced MAFLD
Authors: Ball Christopher , Low Zun Siong , Yaligar Jadegoud , Verma Sanjay , Sahib Norliza Binte Esmail , Cheng Hong Sheng , Yip Yun Sheng , Lee Justin Yin Hao , Lim Debbie , Wahli Walter , Poptani Harish , Tan Nguan Soon , Velan S. Sendhil
Published: August 2023

A novel diet – LIDPAD – was developed to fulfil the need for a wild-type, diet-induced preclinical model of metabolic associated fatty liver disease that matches all physiologically relevant changes observed during human MAFLD progression. Stages of MAFLD may be distinguished through MRI and the generation of proton density fat fraction maps. C57BL/6 mice fed on the LIDPAD diet for 4-16 weeks underwent hepatic fat quantification using multi-gradient eight-point Dixon MRI. Physiological changes were assessed through transcriptomic and insulin measurements. Liver fat increased rapidly, alongside body weight, while transcriptional, metabolic and histological changes mirrored human MAFLD progression.
MR Elastography-Based Staging of Liver Fibrosis in Fontan Associated liver Disease is Confounded by the Effects of Venous Congestion
Authors: Serai Suraj , Ghosh Adarsh , Wilkins Benjamin , Cahill Anne Marie , Biko David , Rand Elizabeth , Rychik Jack , Goldberg David
Published: August 2023

In patients with Fontan, although liver stiffness has been found to increase significantly with age and has been directly correlated with diminished cardiac function, the increased liver stiffness could be influenced by underlying conditions of vascular congestion. The purpose of this study was to compare stiffness as measured by MRE localized by liver biopsy with histological results from biopsy, and to determine the change in MRE defined liver stiffness over time. Our findings have important implications for how liver MRE might be a useful tool in following the progression of liver stiffness longitudinally in patients with Fontan associated liver disease.
Measuring Fixation in Cementless Total Knee Replacement Tibial Baseplates with RSA and MRI
Authors: Broberg Jordan , Koff Matthew , Howard James , Lanting Brent , Potter Hollis , Teeter Matthew
Published: August 2023

This prospective evaluation of cementless total knee replacement tibial baseplate fixation using RSA and MRI at paired time points identified a stabilization of migration and mostly normal integration of the bone-implant interface by 3 months post-operation. A potential relationship between RSA migration magnitude and the evaluated bone-implant interface supports the use of MRI as a tool for evaluating implant fixation.
Knee cartilage radiomics biomarkers are associated with osteoarthritis pain phenotypes
Authors: Peake Edward , Kluzek Stefan , Auer Dorothee , Radojčić Maja
Published: August 2023

Pain is a hallmark of knee osteoarthritis, a chronic condition with considerable health and socio-economic burden. Phenotyping this heterogeneous disease is a step towards improved and personalised treatment, and long-term pain phenotypes based on latent class trajectory analysis and knee MRI may facilitate it. In this study, we aimed to examine the correlation between baseline knee cartilage MRI features with established 9-year pain trajectories. Using data from the Osteoarthritis Initiative (n = 9385, 9-year follow-up), we demonstrated a weak correlation between knee cartilage, pain scores and pain trajectories and significant differences in cartilage radiomics between the knee pain trajectories.
Infrapatellar fat pad is predictive of incident knee osteoarthritis one year prior to diagnosis: data from the osteoarthritis initiative
Authors: Ying Jia , Yu Keyan , Zhao Tianyun , Zhang Xiaodong , Huang Chuan
Published: August 2023

The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). However, whether the IPFP can serve as an independent biomarker for OA development is yet unknown. Radiomics is a powerful tool that can extract high-dimensional quantitative features for clinical outcomes. In this work, we proposed a prediction model for incident radiographic knee OA (iROA), using radiomic features of the IPFP, one year prior to diagnosis. The prediction performance was assessed, and our results from 604 knees demonstrated that MR-based radiomic features from the IPFP are predictive of iROA.
Hypercapnia does not change CMRO2: Data from mouse studies using multimodal NIRS-MRI at 9.4T
Authors: Hashem Mada , Wu Ying , Dunn Jeff
Published: August 2023

Non-invasive quantitative imaging of cerebral metabolic rate of oxygen (CMRO2) is essential to understand oxidative metabolism in health and disease. Hypercapnia is often used as a calibration for MRI methods associated with quantifying CMRO2. Here we show that CMRO­2 remains constant during hypercapnia. We also provide a novel multimodal NIRS-MRI system that can assess absolute values of multiple metabolic correlates noninvasively in living mouse brain, and with no need for calibration with gas mixtures. Moreover, this technique is capable to monitor mitochondrial status by measuring the redox state of cytochrome oxidase, the enzyme responsible for oxygen consumption in the cell.
Characterization of Self-assembled G4·K+ Hydrogel using CEST MRI
Authors: Gong Xiaoling , Zhang Xiaoxiao , Zhang Xiaoyong , Wu Bing
Published: August 2023

CEST MRI is a novel molecular imaging technique in which the contrast is generated by the dynamic exchange between the exchangeable proton and water. Self-assembled G4·K+ Hydrogel can be fully characterized by its intrinsically endowed CEST contrast.
DEXSY Reveals Water Exchange Coupled to Activity
Authors: Williamson Nathan , Ravin Rea , Cai Teddy , Falgairolle Melanie , O'Donovan Michael , Basser Peter
Published: August 2023

Currently, no MRI method exists to non-invasively and absolutely measure cellular activity. One approach is to quantify steady-state transmembrane water exchange rates. Diffusion Exchange Spectroscopy (DEXSY) non-invasively and directly encodes for exchange of endogenous components. Using DEXSY-based methods implemented on a low-field, high-gradient MR system, we show in viable ex vivo neonatal mouse spinal cord samples that the rate at which water exchanges across cell membranes decreases drastically after perturbation known to induce persistent membrane depolarization. We also show that the exchange rate recovers to normal values after perturbations known to restore membrane potential.
Accelerated Quantitative 3D UTE-Cones Imaging using Compressed Sensing
Authors: Athertya Jiyo , Ma Ya-Jun , Afsahi Amir , Ji Alicia , Chang Eric , Du Jiang , Jang Hyungseok
Published: August 2023

Quantitative ultrashort echo time (qUTE) imaging suffers from long acquisition time due to multiple acquisitions required for parameter estimation. In this study, feasibility of accelerated qUTE Cones imaging with compressed sensing (CS) reconstruction is investigated for fast variable flip angle UTE-T1 mapping, adiabatic UTE-T1ρ mapping, and UTE quantitative magnetization transfer (MT) modelling of macromolecular fraction (MMF). We explored these biomarkers for qUTE-Cones imaging of in vivo human knee joints at various undersampling rates. The performance of CS-reconstruction and parameter mapping was evaluated in tendons, ligaments, menisci, and cartilage.
Training a tunable, spatially-adaptive denoiser without clean targets
Authors: Pfaff Laura , Hossbach Julian , Preuhs Elisabeth , Wuerfl Tobias , Camejo Silvia Arroyo , Nickel Dominik , Maier Andreas
Published: August 2023

Accelerating MRI is intrinsically limited by the thermal noise from the imaged object. In this work we aim to optimize MR image denoising using an unsupervised deep learning-based method. Stein's unbiased risk estimator and spatially resolved noise maps indicating the standard deviation of the noise for every pixel were incorporated into the training process. It was shown that this approach can achieve results that are equal or superior to those of state-of-the-art supervised and unsupervised methods. Furthermore, we show how to control the tradeoff between denoising and image sharpness by using a model conditioned on the noise map.
Rapid high-resolution cranial bone MRI using deep-learning prior image reconstruction
Authors: Boroojeni Parna Eshraghi , Commean Paul , Eldeniz Cihat , Gan Weijie , Skolnick Gary , Patel Kamlesh , Kamilov Ulugbek , An Hongyu
Published: August 2023

A high-resolution (HR) MRI capable of resolving the detail of bony structures at sub-millimeter resolution is desired. A short MR acquisition results in under-sampled k-space data below the Nyquist rate, leading to artifacts and high noise. We developed an HR reconstruction method regularized by a complex deep-learning prior (RECD). We achieved high-resolution MR (0.6x0.6x0.8mm3) with a one-minute acquisition time. Using images reconstructed from a 5-minute MR scan as the gold standard, we compared the peak signal to noise ratio (PSNR) and similarity index (SSIM) for 1-min RECD and 1-min compressed sensing (CS) reconstructed images. RECD outperformed CS.
Magnetization transfer optimization to enhance 3D EPI image contrast for high-resolution fMRI applications at 7T
Authors: Malekian Vahid , Graedel Nadine , Josephs Oliver , Callaghan Martina
Published: August 2023

3DEPI is widely used for cortical depth-dependent fMRI studies at 7T. However, it suffers from poor image contrast that can be problematic for fMRI post-processing steps. One solution to bolster contrast is to implement a magnetization transfer (MT) module. Here, we experimentally investigated the different MT pre-pulse characteristics to optimize image contrast, while considering power limitations at 7T. Contrast gains eased co-registration to an MP2RAGE reference, and improved cortical segmentation. Our analyses confirmed that the MT contrast is spatially variable and dependent on transmit field inhomogeneity.
Channel Pruning to Improve Robustness of Pilot Tone-based Cardiac Trigger Detection
Authors: Hatef Jacob , Vijaykumar Abhishek , Chen Chong , Liu Yingmin , Ahmad Rizwan
Published: August 2023

In this work, we develop and apply a method to prune channels from multi-coil Pilot Tone (PT) data. The method suppresses contributions from noisy or corrupt channels and thus improves the quality of the PT-driven cardiac signal. The method, called Spectrum Optimized Channel pruning (SOC), is based on maximizing the ratio of the signal energy in the frequencies that correspond to physiological motions to the signal energy in the frequencies that are outside the physiological motions. Using data from 15 subjects, we highlight that the proposed channel pruning method improves the quality of the extracted cardiac signal.
Adaptive k-Space Sampling in Magnetic Resonance Imaging Using Reinforcement Learning
Authors: Baader Edith , Theißen Fabian , Pezzotti Nicola , Schulz Volkmar
Published: August 2023

One approach to accelerate MRI scans is to acquire fewer k-space samples. Commonly, the sampling pattern is selected before the scan, ignoring the sequential nature of the sampling process. A field of machine learning addressing sequential decision processes is reinforcement learning (RL). We present an approach for creating adaptive two-dimensional (2D) k-space trajectories using RL and the so-called action space shaping. The trained RL algorithm adapts to a variety of basic 2D shapes outperforming simple baseline trajectories. By shaping the action space of the RL agent we achieve better generalization and interpretability of the agent.
Compression sensing based uterine 3D-T2WI isotropic imaging: Optimization of the optimal accelerator
Authors: Ma Changjun , Tian Shifeng , Chen Lihua , Wang Nan , Song Qingwei , Lin Liangjie , Liu Ailian
Published: August 2023

3D-ISO-T2WI can obtain ISO volume images compared with traditional 2D-T2WI imaging, reducing partial volume effects, and volume data post-processing can be omni-directional, Three-dimensional, multi-angle display of the overall picture of the uterine structure and the scope of the lesion, but its scanning time is longer [1], the peristalsis of the subject's uterus, bladder and intestine during the scanning time may affect the image quality, and the scanning time The long-term reduction of the patient’s coordination and examination circulation.In this study, different AF will be selected to optimize the best AF based on the CS-based 3D-ISO-T2WI sequence of the uterus.
Single shot multiple overlapping-echo acquisition combined with deep learning for ultra-fast T2 mapping in glioma: A preliminary study
Authors: Bao Jianfeng , Ma Xiaoyue , Yang Qinqin , Wang Xiao , Zhang Yong , Lin Liangjie , Cheng Jingliang , Cai Congbo
Published: August 2023

Quantitative MRI (qMRI) are expected largely to provide more accuracy clinical diagnostic information. However, bound by the relative long duration for the routine MRI exam and much longer qMRI acquisition time, the qMRI is not widely used for diagnostic purpose. The change of MR signal caused by contrast agent extravasation is affected by many factors, which will increase difficulty in diagnosis. Quantitative MRI (qMRI) are expected to overcome this issue. Recently, an ultra-fast multi-parameters qMRI technique was developed by our group, and herein we aim to apply the proposed strategy on brain tumor imaging and to preliminary access the potential performance.
Detection of Iron Oxide Nanoparticles (IONPs)-Labeled Stem Cells Using Quantitative UTE Imaging
Authors: Athertya Jiyo , Akers Johnny , Dwek Sophia , Wei Zhao , Du Jiang , Chang Eric , Thu Mya , Jang Hyungseok
Published: August 2023

Non-invasive, clinically applicable tracking of therapeutic cells by magnetic resonance imaging (MRI) offers unparalleled insight into the safety and efficacy of cell-based therapies in the body. Here we used a series of 3D quantitative UTE techniques including UTE-QSM, UTE-T1, and UTE-T2* mapping to evaluate the MR characteristics of stem cells labeled with a proprietary nanoparticle formulation that simultaneously labels cells for both optical and MR imaging. In a phantom experiment, all quantitative UTE parameters showed strong correlation with concentrations of labeled stem cells. Interestingly, in ex vivo mouse imaging, only UTE-QSM and UTE-T2* mapping detected the injected, labeled stem cells.
A New, Simple Two-Pass Masking Approach for Streaking Artifact Removal in Any QSM Pipeline
Authors: Karsa Anita , Shmueli Karin
Published: August 2023

Tissue magnetic susceptibility maps calculated using any Quantitative Susceptibility Mapping (QSM) pipeline are often corrupted by streaking artifacts. Large streaking artifacts originating from extreme-susceptibility regions, such as interhemispheric calcifications or intracerebral bleeds, are common, not only in patients, but also in healthy, elderly subjects. Several variations on the two-pass masking approach have been proposed previously to suppress these artifacts. Here we propose a broadly-applicable two-pass masking method that is easy to implement and integrate into any QSM pipeline. We show that two-pass masking greatly reduces streaking from calcifications and cerebral bleeds without affecting susceptibility map anatomical features and values.
QALAS + QSM: Efficient Multi-parameter Mapping Allows Disentangling Para- and Dia-magnetic Contributions in Brain Tissue
Authors: Shin Hyeong-Geol , Chen Jingjia , Lee Jongho , Kim Tae Hyung , Cho Jaejin , Varela-Mattatall Gabriel , Gagoski Borjan , Liu Chunlei , Bilgic Berkin
Published: August 2023

Quantitative MRI has demonstrated potential in clinical and neuroscience applications, but its adoption has been hampered by excessively long scan times. Recent biophysical models have enabled differentiation of para- and dia-magnetic contributions to the tissue, but the acquisition of multi-parametric maps required by such models have further increased the encoding burden. We propose a comprehensive 9.5 min exam at high isotropic resolution to address this, from which T1, T2, proton density, T2*, para- and dia-magnetic susceptibility maps are estimated. This exam flexibly lends itself to generation of contrast-weighted images, allowing it to be used for clinical reads as well.
A Proximal Step Enables Fast and Accurate Single-Step QSM Reconstructions, Preventing Susceptibility Underestimation
Authors: Milovic Carlos , Bredies Kristian , Langkammer Christian , Shmueli Karin
Published: August 2023

Single-step quantitative susceptibility mapping (QSM) algorithms simplify the processing pipeline and promise to be more robust against background fields than traditional two-step methods but they often underestimate tissue susceptibilities. Here, we propose a highly efficient gradient descent Tikhonov-regularized proximal solver and a highly accurate ADMM TV-regularized proximal solver to improve the accuracy of two Laplacian-based single-step methods. Our solvers outperformed current single-step methods and showed in-vivo performance very similar to traditional two-step methods. This will simplify QSM processing pipelines, allowing further automation in future, although more research is needed to improve robustness against noise and boundary-conditions-related artifacts.
Age and sex contributions to variance in resting state fMRI (rs-fMRI) temporal complexity analysis
Authors: Simard Nicholas , Kumbhare Dinesh , Ulmer Stephan , Noseworthy Michael
Published: August 2023

Understanding the process of aging and the differences in sex with regards to large data repositories can help improve the implementation of machine learning and artificial intelligence paradigms in neuroimaging. The following research presents data that identifies a relationship between aging and sex in resting state functional magnetic resonance imaging (rs-fMRI) data. Using over 10,000 age and sex matched healthy controls and performing a homebuilt processing pipeline for rs-fMRI data, significant relationships between aging and reduced temporal complexity (TC) was found (p=0.03058), along with women having a higher TC than men (p=0.000623).
Performance of small field of view rotated 2D selective RF excitation Diffusion Tensor (DTI) Imaging of kidneys with automatic motion correction
Authors: Lim Dee Zhen , Williams Julia , Wong Jason , McKenna Lucy , Smith Julie , Hornsey Emma , Italiano Dominic , Churilov Leonid , Staeb Daniel , Benkert Thomas , Ekinci Elif , Lim Ruth P
Published: August 2023

Renal diffusion tensor imaging (DTI) is a promising, non-invasive biomarker for kidney function. Cortical and medullary fractional anisotropy (FA) and mean diffusivity (MD) correlates with early microstructural and functional changes in kidney diseases. However, reliability of DTI metrics is affected by artifacts. The ideal renal DTI sequence for robust and reproducible measurements remains under investigation. We report on a prototype for small field-of-view (FOV) DTI incorporating 2D selective and rotated RF excitation and automatic motion correction to minimize artifacts in 9 diabetic patients and 9 controls, demonstrating high inter-reader agreement for measured FA and MD and high image quality.
Preclinical Investigation of Kidney Volume and Perfusion after Nephrectomy using Dynamic Contrast-Enhanced MRI.
Authors: Cristinacce Penny Hubbard , Lausecker Franziska , Li Shanshan , Dickie Ben , Berks Michael , Little Ross , Hutchison Alastair , O'Connor James , Lennon Rachel
Published: August 2023

Compensatory hypertrophy of the remaining kidney is observed in mice after nephrectomy using structural MRI. By fitting the two-compartment filtration model to dynamic contrast-enhanced MRI data we examined the effects of nephrectomy on the passage of blood through the remaining kidney over time. Total kidney plasma volume (vp) increases at 1 week post-nephrectomy, along with T1. This is followed by a decrease at week 4, which is coupled to a decrease in total and per unit volume plasma flow and an increase in plasma mean transit time.
Dixon-MRI to quantitatively evaluate the correlation between abnormal fat deposition and chronic kidney disease with diabetic complications
Authors: Xia Fangjie , Shen Wen , Chen Lihua , Zhu Jinxia
Published: August 2023

The study use Dixon and DKI sequence to quantitative analysis the ectopic fat deposition in chronic kidney disease and diabetic nephropathy. Compare the degree of fat infiltration in different organs and tissues including Liver, pancreas, kidney and muscles, combined with clinical laboratory experiments, to study the relationship between fat deposition and the development of CKD or diabetic nephropathy. Aim to point out the significance of treatment to fat deposition for CKD or diabetic nephropathy patient.
Multiparametric MR imaging in diabetic nephropathy: New insights to evaluate early diabetic nephropathy noninvasively
Authors: Yamamoto Akira , Tamada Tsutomu , Ueda Yu , Takeuchi Mitsuru , Kido Ayumu , Higaki Atsushi , Kanki Akihiko
Published: August 2023

The purpose of this study was to identify the changes in multiparametric magnetic resonance imaging (MRI) findings in early diabetic nephropathy (DN). Multiparametric measurements were made of the renal cortex and medulla. Significant differences were seen between the healthy and early DN (grade1) in optimal TI and inverted TI value. T2 value of cortex is in marginal significant (p=0.064). This study suggests the possibility that MRI using the values of optimal TI, inverted TI value and T2 value of cortex can be used to evaluate early diabetic nephropathy non-invasively and in a short period of time.
Analysis of postural effect on kidney volume using multiposture MRI
Authors: Nakagawa Seiya , Miyati Tosiaki , Ohno Naoki , Kawano Koga , Oda Yuki , Kobayashi Satoshi
Published: August 2023

We assessed the effect of posture on kidney volume measured in the supine and upright positions using an original MRI system that can image in any posture (multiposture MRI). We compared the kidney volume (whole kidney, renal cortex, medulla, and pelvis) between the supine and upright positions. Upright position reduced whole kidney, renal cortex, and medulla volumes, and these differences between postures potentially provide new diagnostic information.
Chemical Exchange Saturation Transfer MR Fingerprinting (CEST-MRF) Imaging of Brain Metastases: Initial Results
Authors: Yu Victoria , Tringale Kathryn , Young Robert , Otazo Ricardo , Cohen Ouri
Published: August 2023

Chemical Exchange Saturation Transfer (CEST) imaging is a promising method for cancer imaging and was recently combined with MR fingerprinting (CEST-MRF) for fast quantitative relaxation and exchange mapping. The CEST-MRF parametric maps reflect different biophysical processes, and their combination provides a comprehensive picture of complex pathologies, like brain tumors. The goal of this work is to demonstrate the potential utility of CEST-MRF in imaging brain metastases (BM). The in vivo reproducibility of the method is quantified in a healthy subject and the clinical utility is shown in subjects with metastatic brain tumors.
Magnetic Resonance Fingerprinting with Total Nuclear Variation Regularisation
Authors: Singh Imraj , Jaubert Olivier , Jin Bangti , Thielemans Kris , Arridge Simon
Published: August 2023

Magnetic Resonance Fingerprinting (MRF) accelerates quantitative magnetic resonance imaging. The reconstruction can be separated into two problems: reconstruction of a set of multi-contrast images from k-space signals, and estimation of parametric maps from the set of multi-contrast images. In this study we focus on the former problem, while leveraging dictionary matching for the estimation of parametric maps. Two different sparsity promoting regularisation strategies were investigated: contrast-wise Total Variation (TV) which encourages image sparsity separately; and Total Nuclear Variation (TNV) which promotes a measure of joint edge sparsity. We found improved results using joint sparsity.
Through-plane Motion in Magnetic Resonance Fingerprinting: Simulations and Phantom Experiments
Authors: Nagtegaal Martijn , McGrath Charles , Günthner Christian , Bauman Manuel
Published: August 2023

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Amide Proton Transfer (APT) Mapping from Undersampled Z-spectra in the Brain Using Deep Learning
Authors: Cheema Karandeep , Han Pei , Han Hui , Xie Yibin , Christodoulou Anthony , Li Debiao
Published: August 2023

CEST Imaging requires radio frequency pulses at several frequency offsets to generate CEST maps (APT, NOE, DS, MT). In this study, we aimed to generate APT maps from CEST images of undersampled frequency offsets using deep learning, which can potentially reduce the total scan time of CEST imaging. The Z-spectrum was undersampled by a factor of 3.5 using the Fisher information gain analysis. Fitting results from the proposed method were compared with those from multi-pool fitting with fully sampled Z-spectrum. We have shown that it is feasible to reconstruct APT maps from undersampled, uncorrected CEST images.
Improved detection of multiple kidney pH compartments by deep learning in MRS and MRSI with hyperpolarized 13C-labelled zymonic acid
Authors: Grashei Martin , Fok Wai-Yan Ryana , Skinner Jason , Menze Bjoern , Schilling Franz
Published: August 2023

Accurate determination of peak position is challenging for spectra with dense spectral regions paired with low SNR as occuring in pH measurements using hyperpolarized [1,5-13C2,3,6,6,6-D4]zymonic acid in kidney of mice. Despite scarcity of available data from preclinical experiments, convolutional neural networks (CNN) and multilayer perceptrons (MLP) could be trained by complementing real and augmented data with synthetic spectra. While MLPs do not achieve suitable performance, CNNs predict pH compartments with an accuracy comparable or superior to supervised line fitting in synthetic test spectra. Further, CNNs allow generation of composite pH maps with improved quality while quantitatively agreeing with line-fitted maps.
Deep learning framework for accelerating revised-NODDI parameter estimation with tensor-valued diffusion encoding
Authors: Guerreri Michele , Azadbakht Hojjat , Zhang Hui
Published: August 2023

This work demonstrates the feasibility of using deep learning (DL) to accelerate revised-NODDI parameter estimation with data acquired using tensor-valued diffusion encoding (TVDE). Revised-NODDI is a recently proposed version of NODDI which showed improved compatibility with TVDE. Thanks to this compatibility the model has an extra free parameter to be estimated which, with conventional fitting methods, further slowdown NODDI’s time-demanding parameter estimation. DL methods can vastly accelerate this process. We show that accurate estimation of revised-NODDI parameters can be obtained via a DL framework. We compare the results with those obtained with conventional fitting methods.
SUPER-IVIM-DC, A supervised deep-learning with data consistency approach for IVIM model parameter estimation from Diffusion-Weighted MRI data
Authors: Rotman Elad , Afacan Onur , Kurugol Sila , Warfield Simon , Freiman Moti
Published: August 2023

Recently, unsupervised deep-learning showed improved performance in estimating the “Intra-Voxel incoherent motion” (IVIM) signal decay model parameters from Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) data compared to classical methods. However, such deep-learning models do not generalize well on acquisitions with a high signal to noise ratio (SNR). In this work, we introduce SUPER-IVIM-DC, a supervised deep learning network coupled with a data consistency term to improve the capacity of deep-learning-based models to generalize the IVIM signal decay model. We demonstrated an improvement in model generalization, accuracy, and homogeneity using simulation, phantom, and in-vivo experiments.
Reducing Artifacts in Readout-Segmented Spectroscopic Imaging at 7T
Authors: Seginer Amir , Keith Graeme , Porter David , Schmidt Rita
Published: August 2023

Readout-Segmented COKE (RS-COKE) is an Echo Planar Spectroscopic Imaging (EPSI) variant supporting larger spectral-widths and is therefore useful for spectral imaging at 7T. However, mismatches between the segments at the overlaps lead to artifacts, especially if the lipids signal is not suppressed (to avoid adversely affecting the metabolites signal). We developed a procedure to measure and reduce the readout segment mismatches – through signal and trajectory corrections – leading to improved spectral images and spectra. The procedure was tested by scanning both a special 3D head-shaped phantom – which includes a lipid layer – and human volunteers.
Does Magnetic Resonance Spectroscopy Show Changes in Brain Metabolites At 3-Months Post Concussion in Pediatric Patients?
Authors: Walker Robyn , La Parker , Bell Tiffany , Joyce Julie , Beauchamp Miriam , Craig William , Doan Quynh , Zemek Roger , Yeates Keith , Harris Ashley
Published: August 2023

This study uses 1H magnetic resonance spectroscopy (MRS) to determine metabolite differences in the chronic phase (~3-month) of pediatric concussion compared to orthopedic injury (OI) controls. In the first analysis, significant differences in N-acetylaspartate (NAA), choline (Cho) and inositol (Ins) were seen between concussion and OI controls. Secondly, in a series of 3-way ANCOVAs across OI, symptomatic and asymptomatic concussion groups showed group differences in NAA, Cho and Ins depending on the symptom scale. When metabolites were different in this analysis by symptoms, it was generally driven by the asymptomatic group.
Measurement of gastric emptying with dynamic 3D DMI using a deuterium body array at 7 T
Authors: Gursan Ayhan , Hendriks Arjan , Welting Dimitri , Klomp Dennis , Prompers Jeanine
Published: August 2023

Gastric emptying abnormalities are frequently observed in diabetes patients. Here we explored whether dynamic 3D DMI could be a radiation-free alternative for scintigraphy to measure the gastric emptying rate. One volunteer was scanned after oral intake of deuterated glucose and proximal and distal gastric emptying was monitored. The distribution of the glucose load in the proximal and distal parts of the stomach and the rate of gastric emptying agreed well with scintigraphy results. DMI has potential to investigate gastric emptying abnormalities in patients with diabetes, while at the same time providing information on glucose uptake and metabolism in the liver.
Frequency and Phase Drift Correction of Rosette MRSI Data using Spectral Registration
Authors: Senthil Sneha , Kadota Brenden , Near Jamie
Published: August 2023

MRSI is a non-invasive in-vivo technique for mapping tissue concentrations in clinical and neuro-scientific research. Although, in vivo MRSI has made progress with respect to spatial resolution, acquisition time and the number of detectable metabolites; frequency and phase drifts in the acquired data are still a persisting problem, resulting in SNR losses, broadening of spectral peaks and deformities in line spectra of metabolites. In this abstract, we show how rosette MRSI sampling trajectories offer the possibility to perform frequency and phase drift correction which is otherwise not possible to do in most other commonly used cartesian and non-cartesian sampling trajectories.
Respiratory motion affects peak spatial SAR value and location – impact on safety supervision for 7T cardiac imaging
Authors: Schoen Natalie , Seifert Frank , Metzger Gregory , Speck Oliver , Ittermann Bernd , Schmitter Sebastian
Published: August 2023

Respiratory motion impacts peak spatial specific absorption rate (psSAR) at cardiac UHF MR imaging as shown previously for individual$$$B_1^+$$$shim vectors. Here two saftey supervision modes were compared: a local SAR control mode (SCM) and a channel-wise power control mode (PCM).

Results show amplitude and location changes of the psSAR between inhale and exhale and this effect depends on the coil setup, element type and breathing pattern. Respiratory-induced psSAR variations are lower when applying PCM compared to SCM, while PCM is more conservative with respect to the total power allowed.
On the complexity of pTx systems in SAR assessment
Authors: Petzold Johannes , Ittermann Bernd , Seifert Frank
Published: August 2023

Different methods for SAR control (SAR limit, phase-agnostic SAR limit, amplitude limit) are evaluated in a simulation study of parallel transmit (pTx) body coil systems (2,4, or 8 channels) at different B0 (0.5T, 1.5T, 3T). Criteria are the necessary safety factors to accommodate for subject variability, and the resulting mean(B1+). Due to the high safety factor necessary for a phase-based SAR limit, its mean(B1+) performance becomes effectively the same as the much simpler to implement amplitude limit.
MR signal conditioning for an electro-optic conversion with a polarization state modulator
Authors: nobre paul , Gaborit Gwenaël , Sablong Raphael , Courjal Nadege , Behague Florent , Coste Antoine , Godet Adrien , Suarez Miguel , Duvillaret Lionel , Beuf Olivier
Published: August 2023

The coaxial cables connecting coils to the MRI are subject to many unwanted interactions with RF pulses and with patients’ tissues. Currents flowing on the shielding of the cable can lead to local SAR increase and RF-induced burns. Despite their limitations and the amount of effort to discard them, there is still no satisfying alternative. One of the issue for competing technologies is to safely provide on coil power supply without cables inside the MRI. In this work, we propose to evaluate the feasibility of passive optical conversion and transmission of the signal to the MRI console.
The vestibular neuromatrix in patients with post-concussive vestibular dysfunction and healthy controls
Authors: Smith Jeremy , Trofimova Anna , Ahluwalia Vishwadeep , Garrido Jose Casado , Hurtado Julia , Frank Rachael , Hodge April , Gore Russell , Allen Jason
Published: August 2023

Convergent clinical and neuroimaging evidence suggests that cognitive-affective and vestibular symptoms are interrelated: affective disorders not only co-occur with vestibular dysfunction but may also influence vestibular processing. However, the topology of the vestibular/cognitive/affective network (‘vestibular neuromatrix’) is not well-defined. The present study leveraged graph theory metrics to assess the functional and structural connectivity among 82 regions of interest in healthy controls and in patients with subacute post-concussive vestibular dysfunction. Patients exhibited deficiencies in connectivity among vestibular, pre- and orbitofrontal, and visual regions, as well as in the integration of visual and vestibular information, visuospatial attention, and monitoring of internal state.
Emerging functional connectivity differences in children with autism spectrum disorders and younger siblings affected with Autism
Authors: Kumar Manoj , Patalasingh Sachin , Hiremath Chandrakanta , Saini Jitender , VijaySagar K. John , Yamini BK , Bhrath Rose
Published: August 2023

Resting-state fMRI studies performed during natural sleep indicate that functional-connectivity in ASD-children begins to differentially mature from earliest periods of life and continues to have distinct patterns throughout early development. In current study, we have performed rs-fMRI in younger (12 to 36-months-of-age) cohorts of ASD, ASD-siblings, and typically developing children to look for functional-connectivity patterns. We observed significantly reduced functional-connectivity in language, social-cognition, and motor regions than controls and ASD-siblings. Interestingly, some of these regions are also considerably different when compared between ASD and ASD-siblings. Altered connectivity may use for targeted interventional therapy for better clinical outcomes in ASD-children.
Altered dynamic functional network connectivity in post-stroke dementia with subcortical lesion
Authors: Wu Qianwen , Cai Huaying , Ni Linhui , Han Guocan , Zhao Zhiyong , Wu Dan
Published: August 2023

This study aimed to explore the changes of functional network connectivity (FNC) in post-stroke dementia (PSD) using a dynamic FNC (dFNC) method. We collected resting-state fMRI data from16 PSD patients, 20 post-stroke non-dementia (PSND) patients and 19 normal controls, and identified 13 functional networks. We found PSD-specific decreases in the dFNC between default mode network and executive control network as well as visual network, which were not observed in static FNC analysis. Moreover, PSD showed a significant decrease in time-variance of global efficiency in whole brain compared with PSND. These changes were associated with the clinical assessments of patients.
Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity.
Authors: Cai LiMin , Chen HuaJun
Published: August 2023

We explored dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity. FNC states were determined by k-mean clustering, and state-specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated. ALS patients showed increased FNC between DMN-SMN in state 1 and between CCN-SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time and remained in state 1 (showing a relatively strong FNC) for a shorter time. A significant correlation was observed between ALSFRS-R and mean dwell time in state 2 and transition number.
Dynamic Functional Connectivity in Pediatric Spinal Cord Injury Patients Versus Healthy Controls
Authors: Schaefer Joseph , Miao Jingya , Matias Caio , Mohamed Feroze , Krisa Laura , Harrop James , Alizadeh Mahdi
Published: August 2023

The purpose of this study is to investigate the distributed network properties of subjects with pediatric spinal cord injury using static and dynamic functional connectivity analysis. 36 subjects of which 18 had history of SCI between the ages of 0-18 years underwent 3-Tesla resting state functional MRI (rs-fMRI). Static and dynamic functional connectivity was calculated. Results showed patients with a history of pediatric spinal cord injury did not show widespread changes in static functional connectivity but did have significant changes in dynamic functional connectivity, including spending more time in a relatively hypoconnected brain state and less brain state transitions.
Readout-segmented Echo-planar Imaging for Determining Cervical Cancer’s Lymph-Vascular Space Invasion and Lymph Node Metastasis Status
Authors: Song Huizhen , Bai Jiao , Wang Yu , Xie Juan , Huang Mengping , Wu Yunzhu , Wang Shaoyu , Shu Jian
Published: August 2023

This study compared the image quality of single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI) in cervical cancer (CC). ADC values derived from these two sequences were considered as diagnostic capabilities for CC’s lymph-vascular space invasion (LVSI) and lymph node metastasis (LNM). The results showed that rs-EPI images of cervical cancer had higher subjective image quality scores, lower SNR, but no difference in CNR and ADC values. ADC was able to predict the CC’s LNM status，but couldn’t identify the LVSI status. ADC of rs-EPI sequence was superior to diagnose CC’s LNM status.
Non-Linear Optimization for Enhanced Parameter Retrieval in MR Fingerprinting
Authors: Lundstrom John , Poorman Megan , Dienstfrey Andrew , Keenan Kathryn
Published: August 2023

We investigate non-linear optimization to produce quantitative parameter maps in MR Fingerprinting. Our Monte Carlo analysis shows non-linear optimization to be robust with respect to noise. We also find non-linear optimization to yield consistent results when initialized by dictionary matching using either sparse or densely populated dictionaries. Our research outcomes suggest non-linear optimization is an effective enhancement to the MR Fingerprinting pipeline, allowing for smaller dictionary sizes and more accurate parameter retrieval. Future work will include enhancements to, and analysis of, the computational efficiency of non-linear optimization.
Mitigation of Magnetisation Transfer Effects using Off-Resonance Pulses in MR Fingerprinting
Authors: Kukran Simran , Dragonu Iulius , Statton Ben , Allen Jack , Lally Pete , Quest Rebecca , Bangerter Neal , Koh Dow-Mu , Orton Matthew , Grech-Sollars Matthew
Published: August 2023

Previous work has shown an inherent bias in quantitative T1 values for current standard MRF techniques as compared to gold standard methods. In our study we show how magnetisation transfer effects are a component of this bias and that the introduction of off-resonance pulses within an MRF sequence may mitigate these effects.
Solving T2-blurring: Joint Optimization of Flip Angle Design and DenseNet Parameters for Reduced T2 Blurring in TSE Sequences
Authors: Dang Hoai Nam , Endres Jonathan , Glang Felix , Weinmüller Simon , Loktyushin Alexander , Scheffler Klaus , Doerfler Arnd , Maier Andreas , Zaiss Moritz
Published: August 2023

We propose an end-to-end optimization approach to reduce T2 blurring in single-shot TSE sequences, by performing a joint optimization of Flip Angle Design and DenseNet parameters using he original transverse magnetization image at a certain TE as target. Our approach generalizes well to in vivo measurements at 3T, revealing enhanced white and grey matter contrast and fine structures in human brain.
Comparison of machine learning methods for detection of prostate cancer using bpMRI radiomics features
Authors: Ulrich Ethan , Dhaouadi Jasser , Schat Robben , Spilseth Benjamin , Jones Randall
Published: August 2023

Multiple prostate cancer detection AI models—including random forest, neural network, XGBoost, and a novel boosted parallel random forest (bpRF)—are trained and tested using radiomics features from 958 bi-parametric MRI (bpMRI) studies from 5 different MRI platforms. After data preprocessing—consisting of prostate segmentation, registration, and intensity normalization—radiomic features are extracted from the images at the pixel level. The AI models are evaluated using 5-fold cross-validation for their ability to detect and classify cancerous prostate lesions. The free-response ROC (FROC) analysis demonstrates the superior performance of the bpRF model at detecting prostate cancer and reducing false positives.