bluuebunny/crossref_metadata_2025 huggingface dataset:
Reduced dynamism of brain activity underlying spatial deficits in subjective cognitive decline
Authors: Chen Qian , Zhang Bing
Published: August 2024

The alterations of brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, 12 states with distinct brain activity were identified in a cohort of 80 SCD and 77 normal control (NC) participants using the hidden Markov model (HMM). The SCD group showed an inability to dynamically upregulate and downregulate the state with general network activation. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC.
Brain Age Prediction using optimized MRI Brain Features: A novel strategy to understand Aging and Aging Associated Pathologies
Authors: Majumdar Arkaprava , Yadav Neha , Tiwari Vivek
Published: August 2024

A certain subset of the aging population maintains the cognitive abilities with minimal structural changes with aging while another subset of people transforms to mild cognitive impairment and dementia with manifold structural variations. Since a subset maintains the cognitive abilities, brain structural volume with aging while another subset undergoes cognitive-impairment and drastic brain structure alterations, we believe that the brain age pattern is distinct from chronological age. Here, we have used a set of MRI determined brain volumetry to determine the brain age as a measure of normal and pathological aging.
Myelin Volume Fraction Estimation in Multiple Sclerosis Lesions
Authors: Şişman Mert , Nguyen Thanh , Dimov Alexey , Marcille Melanie , Spincemaille Pascal , Gauthier Susan , Wang Yi
Published: August 2024

Monitoring myelin content quantitatively is important in the study of Multiple Sclerosis (MS). Here, a novel approach is proposed to estimate myelin volume fraction (MVF) of MS lesions from routine multi-echo gradient echo (mGRE) using biophysical modeling of the myelin sheaths. The obtained MVF values correlated significantly with the myelin water fraction (MWF) values obtained in the same lesions using conventional multicomponent T2 relaxometry. Moreover, the change in MVF values in newly enhancing lesions over the first year also correlated significantly with the change in MWF values over the same period.
White Matter Myelin Content and Motor Function in Radiologically Isolated Syndrome and Multiple Sclerosis
Authors: Vavasour Irene , Keane Connor , Johnson Poljanka , Lee Joshua , Adelia Adelia , Oh Jiwon , Traboulsee Anthony , Kolind Shannon
Published: August 2024

Regional myelin water fraction (MWF) in radiologically isolated syndrome (RIS) white matter were compared to relapsing-remitting (RR) and primary-progressive (PP) multiple sclerosis (MS). Results showed myelin damage in some white matter regions of RIS. Early myelin damage in RIS may be associated with subclinical ambulatory dysfunction. Manual dexterity correlated with myelin content in the cortical spinal tract and corpus callosum in PPMS whereas it correlated with myelin heterogeneity in RRMS. These findings suggest that myelin changes in critical white matter tracts have differing impact in gross and fine motor function in RIS and MS subtypes.
Altered response of the functional network during pain stimulus in small fiber neuropathy
Authors: Drenthen Gerhard , Far Amir , Faber Catharina , Upadhyay Jaymin , Linden David , van Gool Raquel , Backes Walter , Hoeijmakers Janneke , Jansen Jacobus
Published: August 2024

Small fiber neuropathy (SFN) is a neuropathic disorder that is associated with chronic pain, which may have an effect on the functional organization of the brain network. Here, we study the effect of pain on the functional network by applying a painful stimulus block-design during an fMRI acquisition. We show global changes in the functional network for patient with idiopathic and genetic SFN compared to controls. Moreover, self-reported pain scores correlated with nodal importance of the somatosensory cortex (postcentral gyrus) in the pain-evoked cerebral functional network, indicating that the functional network takes an important role in the perception of pain.
Disrupted topological organization of hubs in Parkinson’s disease patients with freezing of gait.
Authors: Sreenivasan Karthik , Bayram Ece , Zhuang Xiaowei , Longhurst Jason , Yang Zhengshi , Cordes Dietmar , Ritter Aaron , Caldwell Jessica , Cummings Jeffrey , Mari Zoltan , Litvan Irene , Stover Natividad , Yacoubian Talene , Bluett Brent , Mishra Virendra
Published: August 2024

Neuroimaging studies, including resting-state functional MRI (rs-fMRI), have implicated altered resting-state functional connectivity (rs-FC) in PD patients with freezing of gait (PD-FOG) and showed that the disruption of connectivity between the resting state networks (RSNs) was correlated with FOG. However, the network organization in PD-FOG remains poorly understood. In this study, we use rs-fMRI data and graph theoretical approaches to explore the reorganization of functional networks in PD-FOG. The results of our study suggest that there is a substantial reorganization of regional brain hubs and disruption in the higher-order functional network topology in PD-FOG participants compared to PD-nFOG.
Dysregulated brain dynamics in visual-motor network of type 2 diabetes and its relation to cognitive impairment
Authors: Yu Ying , Hu Bo , Wang Wen , Yan Lin-Feng , Cui Guang-Bin
Published: August 2024

To screen type 2 diabetes mellitus(T2DM)-specific effective connective (EC) network, the dynamic features of which may contribute to distinguishing T2DM patients with mild cognitive impairment (T2DM-MCI) from controls. Screening of resting-state functional MRI (rs-fMRI) data from early T2DM, T2DM-related static causality network mainly consisted of nodes in visual and sensorimotor network. In the visual-motor network, the fractional windows and mean dwell time of strong dEC state in T2DM-MCI patients were significantly higher than controls. The sum of dECs (sumdEC) could effective distinguish the mT2DM-MCI indicating sumdEC to be a promising biomarker for the early cognitive impairment in T2DM.
Altered Gray Matter Volume and Functional Connectivity in Type 2 Diabetes Mellitus
Authors: Zhang Xue , Hu Wanjun , Zhang Jing
Published: August 2024

Keyword: Neuroimaging, type 2 diabetes mellitus, VBM, resting-state fMRI, Cognition The main purpose of this study was to evaluate the structural brain alterations and related functional abnormity of type 2 diabetes(T2DM), and its correlation with cognitive scale. Changes in brain volume were assessed using voxel-based morphology (VBM), while VBM Results were used to build ROI to analyse related functional connectivity (FC) changes in brain function. The result indicated that decreased gray matter volume and decreased functional connectivity may represent the neurobiological mechanism underlying cognitive dysfunction in patients with T2DM.
RF-induced heating of interventional devices at 0.55T
Authors: Özen Ali , Russe Maximillian , Lottner Thomas , Reiss Simon , Littin Sebastian , Zaitsev Maxim , Bock Michael
Published: August 2024

Low-field high-performance MRI systems at 0.55T are expected to cause less RF heating in conventional interventional devices compared to clinical 1.5T or 3T systems, which would facilitate MR-guided interventions significantly. Here we systematically evaluate the safety of intravascular devices through high-resolution electric field mapping, transfer function measurements, and realistic device trajectories from vascular models. Furthermore, the effects of patient size and positioning, target organ (liver and heart), and transmit body coil type are tested at a low-field RF test-bench for 6 commonly-used interventional devices (two guidewires, two catheters, an applicator and a biopsy needle).
Is MR-induced RF heating around implanted leads lower at 0.55 T than 1.5 T? Not necessarily.
Authors: Sanpitak Pia , Bhusal Bhumi , Vu Jasmine , Golestanirad Laleh
Published: August 2024

Low-field MRI systems have been advertised as “safe” for patients with implants – but is this true? In a series of systematic simulations, we looked at implants with varied length, amount of coiling, and insulation during RF exposure in both 0.55 T and 1.5 T MRI environments. We found that both the actual and apparent length of the implant can significantly impact heating. There are several cases where heating is substantially higher at 0.55 T than 1.5 T, suggesting that no broad claims should be made, and these factors need to be carefully considered before scanning a patient.
Physiological variability of the tympanic membrane temperature as an RF safety metric assessment
Authors: Le Ster Caroline , Kozlov Mikhail , Vignaud Alexandre , Boulant Nicolas
Published: August 2024

RF exposure during MR exams induces heating of the exposed tissues. Measurement of tympanic membrane temperature with an infrared sensor has been used to estimate non-invasively the global RF-induced temperature rise. In this study, the tympanic membrane temperature was measured before and after having the volunteers lying for 15 minutes with earplugs with neither an RF nor a static magnetic field. Large physiological variations of temperature rise were observed across the volunteers after this dummy “exposure”, showing the low reliability of this metric to measure small temperature rises, that make it problematic for RF-induced temperature rise estimation.
Three Delineating Radiomics Models Based on MR-determined Metastatic Lymph Nodes for Prognostic Prediction in Nasopharyngeal Carcinoma
Authors: Huang Manqian , Deng Kan , Xie Hui , Huang Wenjie , Wang Xiaoyi , Luo Chao , Li Shuqi , Cui Chunyan , Ma Huali , Liu Lizhi , Li Haojiang
Published: August 2024

The purpose of this study was to assess the performance of radiomic models based on MR-determined metastatic lymph nodes phenotype in predicting the prognosis of nasopharyngeal carcinoma (NPC) with three different feature selection methods: all lymph nodes (ALN), the largest lymph node (LLN), and the largest slice of the largest lymph node (LSLN). The results showed that LSLN radiomic features showed better accuracy in predicting the overall survival (OS).
Comparison of radiomics-based machine learning survival models in predicting prognosis of glioblastoma
Authors: Luan Jixin , Zhang Chuanchen , Liu Bin , Yang Aocai , Lv Kuan , Hu Pianpian , Ma Guolin
Published: August 2024

In this study, we aimed to compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. The Cox proportional-hazards model (Cox-PH) and SurvivalTree, Random survival forest (RSF), DeepHit, DeepSurv four machine learning models were constructed, and the performance of the models was evaluated using C-index. We found that deep learning algorithms based on radiomics in predicting the overall survival of GBM patients, and the DeepSurv model showed the best predictive ability.
A radiomic approach to the diagnosis of femoroacetabular impingement
Authors: Montin Eros , Kijowski Richard , Lattanzi Riccardo
Published: August 2024

The results of this study showed that radiomic can automatically distinguish a healthy joint from one with impingement using water-only Dixon MRI. To our knowledge, this is the first application of radiomic for FAI diagnosis. Our radiomic analysis achieved an accuracy greater than 97%, which is higher than the 90% accuracy for detecting FAI reported for standard diagnostic tests (90%). Combining our proposed radiomic analysis with methods for automated joint segmentation could be used to rapidly identify patients with FAI, avoiding time-consuming radiological measurements of bone morphology.
nnFAE: An Extended Module for FeAture Explorer (FAE) for Radiomic Feature Processing
Authors: Song Yang , Zhang Chengxiu , Zhang Jing , Wang Shaoyu , Yan Xu , Yao Yefeng , Yang Guang
Published: August 2024

The biological meaning, model robustness and the harmonization of the features are the focuses in the current radiomics development. We designed a software named nnFAE which extends the open-source FeatureExplorer (FAE) to extract habitats features using multi-parameter MR images, to extract robust features recommended by IBSI, and to harmonize features from multi-vendors, etc. nnFAE has a graphic user interface to process the images and feature matrix in batch and can be used readily in radiomics studies.
Application of radiomics approach on predicting freezing of gait in Parkinson’s disease based on rs-fMRI indices
Authors: Guo Miaoran , Liu Hu , Fan Guoguang
Published: August 2024

In the present investigation, we built a non-invasive and automatic classification model, by extracting radiomic features based on whole-brain functional alterations of rs-fMRI indices (mALFF, mReHo, and DC) combined with clinical scales (MoCA, and HAMD) using feedforward neural network (FNN) models, which is a representative of supervised learning classification methods. We found that these models can effectively differentiate PD-FOG and PD-nFOG and find potential biomarkers of PD-FOG, which might facilitate the individual diagnosis of PD-FOG patients.
Feasibility of texture analysis of cocaine use disorder patients’ MRI data to predict early brain changes
Authors: Poojar Pavan , Balchandani Priti , Hurd Yasmin , Taufique Shilpa , Geethanath Sairam
Published: August 2024

Substance use disorder (SUD) affects the structure, function, and metabolism of the brain, and MR imaging helps to track and manage the therapeutic efficacy. We analyzed cortical white matter and Amygdala regions for 5 patients with cannabis use disorder using changes in texture and volume. Each patient was scanned 4 times(at 0,2,12 and 24 weeks) with 3D fast spin echo on a 3T scanner. We found increased textural changes across intervals for cortical white matter. However, for Amygdala, volumetric changes were greater in weeks 2 and 12. Both these changes help in the early monitoring of the effectiveness of the therapy.
Six-fold enhancement in spatial-resolution of Pseudo-Continuous Arterial Spin Labeling Perfusion Mapping using a Cryogenic Coil at 9.4T
Authors: Monteiro Sara Pires , Hirschler Lydiane , Barbier Emmanuel L. , Figueiredo Patrícia , Shemesh Noam
Published: August 2024

High resolution CBF mapping using ASL can benefit multitude applications, yet its spatiotemporal resolution is limited in pre-clinical settings and at higher fields. To address this issue, we explored the possibility of improving the resolution of pCASL by using a cryogenic coil at 9.4T in the rat brain. Compared to the current state-of-the-art measurements, we managed to enhance the resolution of pCASL images by a factor of at least 6. The interpulse phase optimizations applied at the labelling plane are crucial for higher and stable inversion efficiency.
A novel Fourier-based perspective to analyze and compensate for corruption in arterial spin labeling MRI
Authors: Hwang Seon-Ha , Park Sung-Hong
Published: August 2024

A new perspective in Fourier domain to understand the ASL signal and compensate for corruption artifacts is proposed. The averaged perfusion-subtraction image corresponds to a single frequency component and the other frequency components could be interpreted as non-perfusion signals. In this viewpoint, the multiple corruptions could be compensated by finding the optimal perfusion frequency component to minimize the zigzag amplitude in the non-perfusion signals. The proposed compensation reduced the corruption without eliminating any measured data, preserving SNR. This new perspective would open up the possibility of processing the ASL signal in the Fourier domain rather than the time domain.
Identifying Brain Resting-state Networks from Arterial Spin Labeling with Spectral Clustering
Authors: Barrett Jason , Meng Haomiao , Zhang Zongpai , Chen Song , Zhao Li , Alsop David , Qiao Xingye , Dai Weiying
Published: August 2024

We propose a spectral clustering algorithm (SCA) based on the Pearson correlation metric (SCA-PC) to identify large-scale brain networks in arterial spin labeling (ASL) images. It was shown to be more robust to Gaussian distributed noise sources based on simulations. We studied the robustness of SCA-PC vs. the traditional SCA method based on a Euclidean distance metric (SCA-ED) for deriving resting-state networks from real human fMRI data. Our results indicate that SCA-PC can derive better brain networks from ASL data than traditional SCA-ED.
Deep Unfolding MR reconstruction - weighting the k-space sampling Density in training Loss (wkDeLo)
Authors: Boroojeni Parna Eshraghi , Gan Wejie , Liu Jiaming , Hu Yuyang , Chen Yasheng , Commean Paul , Eldeniz Cihat , Wang Tongyao , Skolnick Gary , Merrill Corinne , Patel Kamlesh , An Hongyu , Kamilov Ulugbek
Published: August 2024

We develop a self-supervised and physics-guided deep unfolding (DU) network for MR image reconstruction by weighting the k-space sampling Density in network training Loss (wDeLo). We have demonstrated that high-quality MR images at a spatial resolution of 0.6x0.6x0.8 mm3 could be achieved using an acquisition time of 1 minute (x6.25 acceleration) or 45 seconds (8x acceleration). Compared to SSDU and its uniform weighted counterpart (un-wDeLo), the wDeLo method significantly improves PSNR and SSIM. It had fewer artifacts, lower noise, and preserved image sharpness.
Accelerating MRI Using Vision Transformer with Unpaired Unsupervised Training
Authors: Huang Peizhou , Li Hongyu , Liu Ruiying , Zhang Xiaoliang , Li Xiaojuan , Liang Dong , Ying Leslie
Published: August 2024

In this abstract, we propose a novel deep-learning reconstruction method that enables training with only unpaired undersampled k-space data without the ground truth. The network utilizes a statistical model for the undersampling artifacts to enable unsupervised learning, and the generative adversarial network to enable unpaired training. In addition, the physics model is incorporated into the transformer network by unrolling the underlying optimization problem. Experiment results based on the fastMRI knee dataset exhibit marked improvements over the existing state-of-the-art reconstructions.
Accelerated MRI Reconstruction Using a Lightweight Recurrent Transformer: ReconFormer
Authors: Guo Pengfei , Mei Yiqun , Zhou Jinyuan , Jiang Shanshan , Patel Vishal
Published: August 2024

Accelerating MRI reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k-space. While state-of-the-art algorithms have shown a great progress based on convolutional neural networks (CNN), transformers for MRI reconstruction has not been fully explored in the literature. We propose a recurrent transformer model, namely, ReconFormer, for MRI reconstruction which can iteratively reconstruct high fertility magnetic resonance images from highly under-sampled k-space data. We validate the effectiveness of ReconFormer on multiple datasets with different magnetic resonance sequences and show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency.
Biophysical model estimation of white matter using machine learning combined dictionary-based non-negative linear least square optimization
Authors: Abdolmotalleby Hesam , Mani Merry
Published: August 2024

We estimated biophysical model parameters using a dictionary of diffusion signals. The dictionary is generated based on the set of biophysical model parameters. We then rotated this dictionary along fascicle direction derived from spherical deconvolution and tried to fit it to the diffusion signal using non-negative linear least square optimization to find the associated dictionary weights. These weights went through a deep learning network to find model parameters. Our approach shows reasonable accuracy and reliability for biophysical model estimation. It is also possible to extend our approach to more complex and general biophysical models to achieve higher specificity.
Manifold Learning and Dimensionality Estimation for the Human Functional Connectome
Authors: Gonzalez-Castillo Javier , Fernandez Isabel , Handwerker Daniel , Lam Ka-Chun , Pereira Francisco , Bandettini Peter
Published: August 2024

The exploration of time-varying aspects of the human functional connectome (FC) is challenging because the high dimensionality of connectivity matrices precludes direct visual inspection in a meaningful manner. Dimensionality reduction helps circumvent this problem, yet effective application requires a-priori knowledge of the intrinsic dimension of the data and careful selection of algorithmic hyperparameters. Here, we first estimate the intrinsic dimension of FC data. Next, we use data with known cognitive state changes to evaluate the effectiveness of Laplacian Eigenmaps, T-SNE and UMAP to generate informative low dimensional representations of time-varying FC data for explorative and predictive purposes.
Real-time style transfer for quantitative susceptibility mapping using unsupervised learning
Authors: Xiong Zhuang , Gao Yang , Sun Hongfu
Published: August 2024

Existing supervised deep learning methods for quantitative susceptibility mapping (QSM) could lead to degraded results when applied to phase images acquired with different scan parameters, such as image resolution and acquisition orientation. This work proposes a novel unsupervised learning method incorporating style transfer and deep image prior to enabling the reconstruction of susceptibility maps from local field maps acquired with any scan parameters. To speed up the inference, a pre-training strategy is also proposed, reducing reconstruction time from minutes to seconds.
Reduced acquisition time of female pelvis diffusion weighted imaging (DWI) using LIPO-only (LION) DWI
Authors: Murayama Daichi , Yoneyama Masami , Sakai Takayuki , Ball Iain , Ochi Shigehiro
Published: August 2024

We hypothesized that LIPO-only (LION) DWI might be one of the best solutions to reduce the acquisition time of female pelvis DWI if it is further optimized to increase the robustness of fat suppression. We compared the image quality of DWI in the female pelvis between the conventional method (SPAIR-DWI) and the proposed method (LION-DWI) to investigate their clinical usefulness. LION-DWI showed correct signal decay because fat suppression pre-pulse was not applied. LION-DWI can reduce acquisition time of the pelvis DWI while maintaining the SNR and sufficient image contrast.
Evaluate the value of Curved Planar Reconstruction of 3D high-resolution T2WI for the Detection of Deep Infiltrative Endometriosis
Authors: Li Ye , Liu Ailian , Wang Jiazheng , Lin Liangjie
Published: August 2024

22 DIE patients were scanned for comparison of diagnostics accuracy between ordinary reconstruction and curved planar reconstruction image of 3D-CS-HR T2WI sequence. Results indicate that Curve images improved the detection of ectopic lesions in each ligament of the pelvic floor.
Intravoxel incoherent motion MRI combined with Doppler findings in predicting very low birth weight infants
Authors: Deng Jing , Zhang Aining , Zhao Meng , Mu Xihu , Zhou Xin , Qu Feifei , Song Jiacheng , Chen Ting
Published: August 2024

Very low birth weight infants (VLBWI) are an adverse pregnancy outcome in small for gestational age infants (SGA). We explored the value of intravoxel incoherent motion (IVIM) histogram parameters and Doppler parameters in predicting VLBWI from SGA. Compared with the SGA group, true diffusion coefficients (Dmean, D50th, D75th, D90th) and perfusion fraction (fmax) were significantly lower, and umbilical artery pulsatility index, resistance index (RI), and peak systolic velocity/end-diastolic velocity were significantly higher in the VLBWI group. A combined predictive model (D90th and RI) improved diagnostic performance and demonstrated the potential of IVIM histogram analysis for clinical diagnosis of VLBWI.
Dynamic Glucose Enhanced MRI of Brain Tumors using Direct Water Saturation
Authors: van Zijl Peter , Yadav Nirbhay , Ali Sajad Mohammed , Seidemo Anina , Kamson David Olayinka , Blair Lindsay , Laterra John , Knutsson Linda
Published: August 2024

The transverse relaxation time (T2) of water is affected by the presence of exchangeable protons that are chemically shifted with respect to the water protons. We use direct saturation (DS) MRI to dynamically measure this effect during infusion of D-glucose to assess its uptake in brain tumors. The change in T2 becomes apparent as a line broadening of the DS spectrum, which is acquired using a whole-brain water saturation shift reference (WASSR) acquisition. First results using 0.5g/kg D-glucose show linewidth changes on the order of a few Hertz in glioma patients, allowing separation of tumor tissue from healthy brain tissue.
PUSHUP-CEST: Calibration free homogeneous saturation for ulta-high field CEST imaging
Authors: Völzke Yannik , Stirnberg Rüdiger , Pracht Eberhard , Löwen Daniel , Gras Vincent , Boulant Nicolas , Zaiss Moritz , Stöcker Tony
Published: August 2024

CEST imaging benefits from the increased spectral resolution at ultra-high field. Due to the inhomogeneous RF field, a B1+-correction is necessary, which requires repeated measurements. The number of required repetitions can be reduced to two by using a ptx-based saturation scheme, like MIMOSA. We present an alternative saturation scheme that is based on PUSH saturation and universal pulses, which we name PUSHUP. Using this approach, we could reduce the inhomogeneity of the CEST saturation as effectively as MIMOSA while being more SAR efficient.
Metabolic Imaging of malignant gliomas during immunotherapeutic intervention using chemical exchange saturation transfer (CEST) MRI at 9.4T
Authors: Karimian-Jazi Kianush , Sturm Volker , Schregel Katharina , Hunger Jessica , Turco Verena , Enbergs Noah , Boztepe Berin , Fischer Manuel , Streibel Yannik , von Knebel-Doeberitz Nikolaus , Korzowski Andreas , Görke Steffen , Kroh Florian , Ladd Mark , Schlemmer Heinz-Peter , Paech Daniel , Rodell Christopher , Platten Michael , Wick Wolfgang , Heiland Sabine , Bendszus Martin , Breckwoldt Michael
Published: August 2024

CDNP-R848, an experimental immunotherapeutic TLR7/8 agonist, showed high treatment efficacy with a significant tumor volume reduction and led to a re-normalization of the metabolic properties (MTRex Amide, MTRex Amine and MTRex NOE) of the tumor in relation to the healthy brain, with distinct differences to vehicle treatment. The clinical relevance of CEST imaging appears to be localizing active tumor areas and a better understanding of tumor heterogeneity. In the future, we aim to better characterize the origin of the CEST contrast by correlated histological and spatial metabolic analysis.
The exchange rate of creatine CEST in mouse brain
Authors: Zhang Ziqin , Wang Kexin , Park Sooyeon , Li Anna , Li Yuguo , Weiss Robert , Xu Jiadi
Published: August 2024

We aim to use three different approaches to estimate the exchange rate of creatine (Cr) CEST, i.e., the pure CrCEST line-shape, B1-dependent CrCEST, and the pH response with different B1 values. The pure CrCEST signal extracted using wild type and GAMT-/- mice with low Cr and PCr concentrations; the pH in the brain cells altered by hypercapnia to demonstrate the pH sensitivity of GuanCEST; a two-step Bloch-McConnell fitting implemented to quantify the exchange rates. in vivo CrCEST exchange rate was found slow (~260-350 s-1). CrCEST is the major contribution to the opposite pH-dependence of GuanCEST signal under different B1.
Z-spectral fitting for fat-correction of APT/CEST-MRI including fat-suppression pulses to improve body applications
Authors: Keupp Jochen , Dimitrov Ivan , Eggers Holger , Vinogradov Elena
Published: August 2024

Fat signal correction remains a challenge in APT/CEST-MRI for body-oncology. We show that signal background models that include direct water saturation, MT-effect, and modified fat spectra can be fitted to Z-spectra acquired with/without fat-suppression. APT/CEST signals are then extracted as the residual from the background fit (APT#). The model for fat-suppressed spectra uses a mirrored fat contribution. After B0-correction, 5 model parameters are sufficient. This allows for the acquisition of clinically feasible protocols using less than 20 Z-spectral offsets. Fat-suppression potentially increases the precision of APT# imaging by lowering the initial fat contribution.
Imaging Extracellular Lactate with Lanthanide-PCTA-based PARACEST Agents
Authors: Chiaffarelli Remy , Jurek Paul , Cruz Pedro , Zimmermann Max , Geraldes Carlos , Kiefer Garry , Martins André Ferreira
Published: August 2024

Lactate accumulation in the tumor microenvironment is a hallmark of cancer aggressiveness. Here, we report a method to image extracellular lactate using shiftCEST MRI and Yb- and EuPCTA Shift Reagents (SRs). In the presence of lactate, the SRs shift the lactate OH-CEST signal away from the water signal by ~14 ppm (EuPCTA) and ~95 ppm (YbPCTA), allowing for quantitative detection of extracellular lactate produced by cancer cells. In vivo studies confirmed the detection and fast renal elimination of the lactate*YbPCTA into the bladder by shiftCEST MRI. Overall, these results provide new insights into developing innovative non-invasive metabolic MRI.
Subspace denoising for CEST MRI with non-local low-rank constraint and spectral-smoothness regularization
Authors: Chen Xinran , Wu Jian , Lin Liangjie , Wei Zhiliang , Chen Lin , Chen Zhong
Published: August 2024

Chemical exchange saturation transfer (CEST) is a powerful technique that enables non-invasive detection of endogenous metabolites in living tissues. Since the observed water signal is decreased due to the transfer of saturated spins, CEST imaging inherently suffers from low SNR, hence degrading accuracy and reproducibility. Inspired by the spatial-spectral correlation of CEST images, here we propose a Subapace denoising method with Non-Local Low-Rank constraint and Spectral-Smoothness regularization (SNLRSS) to diminish the noise, which improves the accuracy of subsequent quantitative analyses of CEST images.
Changes in Brain Water Components in Normal Aging
Authors: Zhou Liangdong , Li Yi , Chiang Gloria , Sweeney Elizabeth , Wang Xiuyuan , Gauthier Susan , Wang Yi , Kuceyeski Amy , Leon Mony , Nguyen Thanh
Published: August 2024

Water homeostasis in brain involves the fluid dynamic through the intracellular fluid, extracellular fluid and CSF, and it’s important to help maintaining the brain function. The change of water compartments with aging may reflect the physiology and pathophysiology of the brain. We mapped the brain CSF water fraction, myelin water fraction and intro-extracellular water fraction using an MR FAST-T2 relaxometry approach. We found the brain CSF water quadratically increases with normal aging. Myelin water increases before the 50s and then decrease in adults. Intro-extracellular water decreases in the adult lifespan. This results are impactful for understanding the glymphatic clearance mechanism.
Low b-value DTI of CSF: Resolving Intravoxel Incoherent Motion into Ordered and Disordered Motions
Authors: Bito Yoshitaka , Ochi Hisaaki , Shirase Ryuji , Yokohama Wataru , Harada Kuniaki , Kudo Kohsuke
Published: August 2024

Low b-value DTI (Low-b DTI) has been recently proposed for investigating the CSF motion. Here, an analysis technique using the Low-b DTI was proposed for resolving intravoxel incoherent motion into ordered (linear) and disordered (random) motions of the CSF. A normal-subject study demonstrated that the proposed technique can differentiate characteristics of the complex CSF motion in typical ROIs. The proposed technique can be useful in investigating the dynamics of neurofluids.
Exploring microstructural changes in patients with vascular cognitive impairment using multi-b-value diffusion and T2-relaxometry MRI
Authors: Drenthen Gerhard , Voorter Paulien , van Dinther Maud , Staals Julie , van Oostenbrugge Robert , Backes Walter , Jansen Jacobus
Published: August 2024

Local increases of interstitial fluid (ISF) predates axonal damage in white matter hyperintensities (WMH). However, it is challenging to disentangle these processes on T2w-MRI. By using multi-b-value diffusion and multi-echo T2-relaxometry imaging, we aimed to quantify markers of ISF volume and mobility in patients with vascular cognitive impairment (VCI) and healthy controls (HC). A trend for a higher mobility and volume of ISF was found in VCI patients compared to HC. Moreover, in VCI, elevated mobility of ISF was already present in perilesional tissue compared to normal-appearing white matter, suggesting that it might be an early marker of WMH development.
Combining CSF hydrodynamics and 4th ventricle outlet morphology improves predictive performance of decompression for CM-I patients
Authors: Xiao Yawen , Chen Shiqi , Zhang Zhaotao , Dai Jiankun , Gui Yifei , Xiao Xinlan
Published: August 2024

Foramen magnum decompression (FMD) is the most used surgical treatment for Chiari malformation type I patients. However, 25～33% of them have persistent symptoms after surgery. This study aimed at investigating the possibility to predict the FMD outcome by using cerebral fluid (CSF) hydrodynamics and 4th ventricle outlet morphology obtained from magnetic resonance imaging. 27 patients were included and 17 of them had improved outcomes after FMD. Our results showed the peak diastolic velocity of CSF in aqueduct and the width of 4th ventricle outlet were associated with the FMD outcome and combining them can improve the prediction performance.
Changes in brain pulsatility associated with heart rate elevation using amplified MRI and phase-contrast MRI
Authors: Kumar Haribalan , Rodgers Haylea , Wright Jet , Bristow Ben , Condron Paul , Emsden Taylor , Taylor Davidson , Holdsworth Samantha , Safaei Soroush , Talou Gonzalo , McGeown Josh , Maunder Ed , Kwon Eryn
Published: August 2024

Cardiac pulsatility is a key driver of brain pulsatility. However, changes in brain pulsatility in response to changes in heart rate have been sparsely studied. Using a combination of amplified MRI and phase-contrast MRI, brain parenchyma motion, blood flow, and CSF flow were measured and assessed during rhythmic hand-grip exercise to help understand the role of heart rate on brain physiology. This approach opens opportunities for probing the role of heart rate, brain fluid, and motion flow in various pathologies that affect the brain.
Effect of nighttime wakefulness on the brain's structure and function: a study involving virtual ISMRM2021 attendees
Authors: Kitagawa Maho , Sawamura Daisuke , Urushibata Yuta , Hamaguchi Hiroyuki , Oppong Philip , Sakamoto Daiki , Tha Khin
Published: August 2024

Little had been reported about the effect of nighttime awakening (virtual jet lag) on the brain structure and function. In this prospective study which evaluated if short-time nighttime awakening due to virtual conference attendance affected the brain function, increased sleepiness, impaired information-processing ability, decreased mALPS index to suggest impaired glymphatic system functioning, a trend toward altered functional connectivity, were observed after nighttime awakening. The finding on mALPS index was similar to true jet lag.
Free-water corrected Diffusion Kurtosis Imaging exhibits modified age-related trajectories of the measures in the brain white matter
Authors: Pieciak Tomasz , París Guillem , Beck Dani , Maximov Ivan , Tristán-Vega Antonio , de Luis-García Rodrigo , Westlye Lars , Aja-Fernández Santiago
Published: August 2024

Diffusion kurtosis imaging (DKI) allows quantifying Gaussian and non-Gaussian effects in diffusion MRI signal and has already proved its relevance, including assessing age-related brain alterations. Hitherto, the standard DKI follows a single-component signal representation with no explicitly defined free-water factor that might lead to a reduced specificity of tensor-based measures. This work demonstrates the free-water corrections to the DKI and revisits the variations in the metrics across the adult lifespan. Our study shows a significant reduction in variability and dynamics of the kurtosis fractional anisotropy and the preservation of the status quo in the mean kurtosis tensor with age.
Neural correlates of anxiety in rhesus macaques exposed to early social adversity: A longitudinal DTI study across adolescence.
Authors: Errame Hind , Rayson Holly , Massera Alice , Bihan-Poudec Yann , Lamberton Franck , Froesel Mathilda , Gacoin Maeva , Ben Hamed Suliann , Richard Nathalie , Ferrari Pier-Francesco , Hiba Bassem
Published: August 2024

Using diffusion tensor imaging (DTI) and behavioral observation, this study investigated the longitudinal effects of early social adversity (ESA) on risk for anxiety and the neural mechanisms underlying this relationship across adolescence. Diffusion MRI data were collected from 21 macaques comprising two groups that differed in exposure to ESA; mother-reared and nursery-reared. Each monkey was scanned at 2.5, 3.5 and 4.5 years old, with anxiety-like behavior assessed at each time-point. Results suggest that ESA has long-term effects on WM fractional anisotropy (FA) in several regions, with differences in frontolimbic and frontostriatal WM tracts modulating the relationship between ESA and anxiety .
White matter integrity and structural connectivity disruption in cognitive impairment T2DM patients with cerebral small vessel disease
Authors: ming Huang , Hui Yu , Bao Li , Tao Liu , Aaron Zhang , Bo Gao
Published: August 2024

DTI provides an effective way to investigate the white matter integrity and white matter connectivity. In this study, almost the white matter integrity was injured in cognitive impairment T2DM patients with CSVD, which were accompanied with reduction of FA value and increasing of λ∥、λ⊥ and ADC values. These results suggested that axonal injuries and demyelinating diseases maybe exist and induce the network connectivity disruption in these patients. More over, In patients with T2DM, network parameters were associated with memory and attention, significantly correlation were found between network parameters and cognitive assessment scores. These findings may provide new insights into how white matter abnormalities and white matter network disruption can affect cognitive function in patients with diabetes.
Quantitative choroid plexus and bulk cerebrospinal fluid metrics in older adults with and without Alzheimer’s disease
Authors: Eisma Jarrod , McKnight Colin , Considine Ciaran , Hett Kilian , Song Alexander , Han Caleb , Elenberger Jason , Claassen Daniel , Donahue Manus
Published: August 2024

The goal of this work is to characterize changes in the choroid plexus (ChP) and aqueductal cerebrospinal fluid dynamics in patients with Alzheimer’s disease utilizing arterial spin labeling (ASL) and phase contrast (PC), respectively. We observed that across all participants, ChP hypertrophy was associated with reduced ChP perfusion. However, we did not observe differences in ChP perfusion or net CSF flow between AD and healthy cohorts. The observed increase in peak CSF flow with age, despite the general decrease in ChP perfusion with age, suggests that the ChP performs functions beyond simply increasing or decreasing CSF production.
When texture does not matter: Misinterpretation of deep learning-based Alzheimer's disease classification
Authors: Tinauer Christian , Ropele Stefan , Langkammer Christian
Published: August 2024

Recent studies have shown Clever Hans effects in e.g. a widely used MRI tumor dataset. Clever Hans was a horse in the early 20th century that could supposedly do some arithmetic, although it only reacted to unintended cues of its owner. Inspired by these insights, we used T1w images from the ADNI database and removed the texture using threshold binarization. By using a standard deep neural network we separated images from Alzheimer's patients (n=404) from normal controls (n=905). This study revealed that volumetric measures are overwhelmingly relevant for the classification, while textures (T1w-contrast variations) are neglectable.
Automated Intracranial Artery Labeling in Patients with Cerebrovascular Steno-occlusive Diseases
Authors: Liu Lixin , Lv Yi , Jiang Peirong , Wang He , Chen Zhensen
Published: August 2024

Labeling of intracranial arteries is important for computer-aided diagnosis of cerebrovascular diseases and quantitative analysis of intracranial vasculature. Performance of the previously proposed automated intracranial artery labeling method based on Graph Neural Network (GNN) is limited in datasets with overt cerebrovascular steno-occlusive diseases. In this study, we improved the generalizability of the GNN-based method by using dedicated data augmentation and spatial normalization strategy. The results show that our method is more robust than the previous method in ischemic stroke patients with overt intracranial stenosis or occlusion.
Carotid arterial stiffening is associated with reduced downstream territorial perfusion of internal carotid artery in elderly adults
Authors: He Yining , Tang Jianing , Zhao Tianrui , Yan Lirong
Published: August 2024

Arterial stiffness is an important risk marker for poor brain aging, vascular disease, and dementia. Greater arterial stiffness leads to the transmission of excessive pulsations from the greater vessels into the downstream capillary and tissue causing microvascular dysfunction. However, previous studies have mainly focused on central or peripheral pulse wave velocity assessment. The present study has demonstrated a significant association of the PWV of the feeding arteries to the brain and its downstream territorial perfusion assessed by using two new MRI techniques, which could provide valuable insight into the neurovascular pathology of aging and brain dysfunction.
Retrospective quantification pharmacokinetics of clinical breast DCE-MRI using deep learning
Authors: Wu Chaowei , Wang Lixia , Wang Nan , Pandol Stephen , Christodoulou Anthony , Xie Yibin , Li Debiao
Published: August 2024

Standard-of-care DCE-MRI suffers from a limited number of contrast phases and low temporal resolution, preventing the quantification of pharmacokinetic parameters. Quantitative DCE-MRI techniques have not yet been widely applied in the clinic due to the limited availability of specialized sequences and image reconstruction. To tackle this problem, we proposed to improve the temporal resolution of multi-phasic DCE-MRI by deep learning post-processing and demonstrated promising results in tumor delineation in the Duke-Breast-Cancer-MRI dataset.
Early Prediction of Response to Neoadjuvant Systemic Therapy of Triple Negative Breast Cancer using Radiomics on DCE-MRI
Authors: Panthi Bikash , Pashapoor Sanaz , Adrada Beatriz , Candelaria Rosalind , Guirguis Mary , Patel Miral , Mohamed Rania , Boge Medine , Zhou Zijian , Son Jong , Hwang Ken-Pin , Le-Petross Huong , Leung Jessica , Scoggins Marion , Whitman Gary , Xu Zhan , Lane Deanna , Moseley Tanya , Perez Frances , White Jason , Ravenberg Elizabeth , Clayborn Alyson , Chen Huiqin , Sun Jia , Wei Peng , Thompson Alastair , Moulder Stacy , Korkut Anil , Huo Lei , Hunt Kelly , Litton Jennifer , Valero Vicente , Tripathy Debu , Yang Wei , Yam Clinton , Rauch Gaiane , Ma Jingfei
Published: August 2024

We developed models based on radiomic features from dynamic contrast enhanced (DCE) MR images and demonstrated that these models have potential to serve as non-invasive biomarkers for early prediction of pathologic complete response (pCR) in triple negative breast cancer (TNBC) patients undergoing neoadjuvant systemic therapy (NAST).
Effect of pathology on quantitative metrics of image reconstruction using a deep learning-based brain MRI reconstruction model
Authors: Chen Shengjia , Johnson Patricia , Lui Yvonne
Published: August 2024

We evaluate image quality in brain MR images with pathology, reconstructed by a deep learning-based image reconstruction algorithm. We have two main contributions: 1) a procedure for evaluating the image reconstruction quality of images, both globally and in patches with labelled pathology, and 2) report quantitative differences between two groups of reconstructed images (abnormal vs. normal). The pathology evaluation results find pathology regions have more losses and lower structural similarity when compared to normal patches and entire normal brains.
Accelerated acquisition and DL reconstruction to enable single shot fast spin echo (SSFSE) imaging of diagnostic quality using single coil
Authors: Chatterjee Sudhanya , C Florintina , Patil Rohan , Rajamani Sajith , Sundaresan Rajagopalan , Patil Uday , Shankapal Preetham , Joel Suresh , Venkatesan Ramesh , Agarwal Harsh
Published: August 2024

Single shot fast spin echo (SSFSE) is a popular imaging approach for acquisition of high-resolution MR images in motion sensitive areas such as abdomen. In certain clinical settings, use of multiple coils setup for acquisition is not feasible (such as abdominal scans for obese subjects in non-wide bore MRI scanners). SSFSE imaging with single coil using the popular partial Fourier approach only presents risk of excessive blurring in the images. In this study we present a method to enable SSFSE T2 imaging using single coil. Proposed method is evaluated on prospectively accelerated data.
Learned Tensor Low-CP-Rank and Bloch response manifold priors for Non-Cartesian MRF Reconstruction
Authors: Li Peng , Li Xiaodi , Lu Xin , Hu Yue
Published: August 2024

We propose a deep unrolled network for non-Cartesian MRF reconstruction by unrolling the MRF reconstruction model regularized by the tensor low-rank and the Bloch resonance manifold priors. To avoid computationally burdensome singular value decomposition, we propose a learned CP decomposition module to exploit the tensor low-rank priors of MRF data. Inspired by the MRF imaging mechanism, we also propose a Bloch response manifold module to learn the mapping between reconstructed MRF data and the multiple parameter maps. Numerical experiments show that the proposed network can improve the reconstruction quality of MRF data and multi-parameter maps within significantly reduced computational time.
High efficient DTI reconstruction network with flexible diffusion directions
Authors: Wu Zejun , Wang Jiechao , Chen Zunquan , Wu Zhigang , Bao Jianfeng , Cai Shuhui , Cai Congbo
Published: August 2024

Deep learning has been used in diffusion tensor imaging (DTI) to fast reconstruct diffusion parameters. However, diffusion-weighted images (DWIs) as network input must maintain diffusion gradient direction consistency during training and testing for deep-learning-based DTI parameter mapping. A dynamic-convolution-based network was developed to achieve generalized DTI parameter mapping for flexible diffusion gradient directions. This proposed method uses dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. The results indicate that the proposed method can reconstruct high-quality DTI-derived maps from six diffusion gradient directions.
Tract-specific myelin mapping using magnetization transfer-prepared diffusion imaging: comparison with conventional MTR tractometry
Authors: Lu Wen Da , Nelson Mark , Leppert Ilana , Bontempi Pietro , Schiavi Simona , Rowley Christopher , Daducci Alessandro , Tardif Christine
Published: August 2024

Tractometry is a technique used to investigate the microstructure variations along white matter tracts or to reconstruct microstructure-weighted connectivity matrices. However, partial volume effects from crossing fibers bias the individual fiber measurements and conceal subtle differences. Here, we compare MTR tractometry to a novel approach using dual-encoded MT-weighted DWI analyzed using COMMIT to disentangle the MTR signal of individual white matter fibers. The results show a broader distribution in edge MTR values for the dual-encoding approach in comparison to tractometry. Both techniques show similar spatial MTR patterns, however the dual-encoded MT-weighted DWI approach shows a higher pattern of inter-individual variability.