bluuebunny/crossref_metadata_2025 huggingface dataset:
Physiological contributions of ECG-derived respiration to BOLD fluctuations during resting-state and respiratory modulations
Authors: Esteves Inês , Fouto Ana , Ruiz-Tagle Amparo , Caetano Gina , Figueiredo Patrícia
Published: August 2024

Physiological signal acquisition during fMRI may be used for multiple purposes, though it usually requires additional setup which may increase complexity and cause subject discomfort. Since ECG is modulated by respiration, an ECG-derived respiration (EDR) may be obtained without needing extra equipment for EEG-fMRI studies, which inherently use the ECG. In this work, EDR signals were computed for resting-state and two respiratory challenges modulating respiration patterns, to validate their use in the MR environment. Their performance for estimating physiological regressors of BOLD-fMRI signals was similar to the one obtained by using concurrently acquired respiratory signals.
Multi-shot Diffusion-weighted Imaging of the Breasts in the Supine versus Prone Position
Authors: Moran Catherine , Middione Matthew , Mazzoli Valentina , McKay-Nault Jessica , Guidon Arnaud , Waheed Uzma , Rosen Eric , Poplack Steven , Rosenberg Jarrett , Ennis Daniel , Hargreaves Brian , Dnaiel Bruce
Published: August 2024

Diffusion-Weighted Imaging (DWI) allows for the detection of breast cancer without a contrast injection, while supine positioning may improve the comfort and efficiency of a breast MRI screening exam. This work investigates multi-shot DWI of the breasts in the supine versus prone positions, in both asymptomatic volunteers and patients with breast lesions. Supine multi-shot DWI outperformed prone multi-shot DWI based on an image quality observer study, receiving significantly higher ratings for sharpness, aliasing, and overall image quality. Lesion Apparent Diffusion Coefficients (ADCs) were highly correlated between the two positions, while fibroglandular tissue ADCs were significantly higher in the supine position.
DSC-MRI and ASL-MRI Measured Perfusion in Glioblastoma: Are they Competitive or Complementary?
Authors: Zhou Limin , Wang Yiming , Pinho Marco , Udayakumar Durga , Youssef Michael , Maldjian Joseph , Madhuranthakam Ananth
Published: August 2024

We evaluated whether arterial spin labeled (ASL) MRI is competitive or complementary to dynamic susceptibility contrast (DSC) MRI measured perfusion in 20 glioblastoma (GBM) patients among 78 imaging sessions. In over 90% of scans, ASL was competitive to DSC. Among these competitive cases, ASL was either equivalent or superior to DSC, making ASL a very attractive alternative to DSC. In some cases (less than 10%), however, ASL and DSC were complementary to each other and combining their strength could provide a more comprehensive information of the whole tumor.
Detectability of changes in the blood-brain barrier permeability to water using optimised multi-TE pseudo-continuous arterial spin labelling
Authors: Zhang Logan , Chappell Michael
Published: August 2024

The blood-brain barrier integrity can be reflected by the pre-exchange lifetime of water molecules, Tex, which can be measured using multi-TE multi-delay ASL. Nevertheless, the low SNR of ASL limits the detectability of subtle Tex changes within a feasible scan time for clinical applications. In this study, optimised protocols were evaluated for the theoretical detectability of 10% changes in haemodynamic parameters. Simulation shows that while changes in CBF and arterial transit time (ATT) could be detected on individual level in a five-minute scan, the same detectability of Tex would require a minimum of 6 subjects within ten minutes of scanning.
Increased Blood-brain Barrier Permeability in Response to Caffeine Challenge
Authors: Mahroo Amnah , Konstandin Simon , Hoinkiss Daniel , Hirsch Jochen , Günther Matthias
Published: August 2024

Caffeine is a commonly used stimulant drug and is known to change brain physiology by constricting vessels leading to decreased perfusion. We tested the impact of caffeine ingestion on blood-brain barrier (BBB) permeability by measuring exchange time using multi-TE ASL technique. Six healthy volunteers were examined for five pre-caffeine sets and six post-caffeine sets. The multi-TE two-compartment model was used to estimate exchange time (Tex) along with perfusion and ATT. We found that perfusion and Tex decreased over time while ATT increased. Decreased Tex indicates that the labelled water transferred at a faster rate reflecting increased BBB permeability.
Phase contrast motor unit MRI (PC-MUMRI) to investigate changes to muscle twitch dynamics in a healthy ageing cohort
Authors: Birkbeck Matthew , Heskamp Linda , Schofield Ian , Hall Julie , Whittaker Roger , Blamire Andrew
Published: August 2024

Functional changes to the muscle’s contractile and relaxation properties occur with age. Current techniques which measure twitch dynamics provide limited information about individual muscles. We used phase contrast motor unit MRI (PC-MUMRI) to non-invasively measure the contraction and relaxation times of lower leg muscles in a healthy ageing cohort, relating this to isometric maximum voluntary contraction (MVC). Contraction and relaxation times increased with ageing and were negatively correlated with MVC. PC-MUMRI offers a sensitive method to measure muscle twitch dynamics and could be combined with other MR techniques to characterise functional changes to muscle with ageing.
Elucidating the role of diabetes mellitus on multiple sclerosis pathology using diffusion MRI
Authors: Lan Michael , Stepanov Valentin , Chen Jenny , Ades-Aron Benjamin , Novikov Dmitry , Fieremans Els
Published: August 2024

Diabetes mellitus (DM) has been potentially associated with multiple sclerosis (MS), but has not been definitively shown to worsen its severity. Our study used diffusion kurtosis imaging (DKI) analyzed with tract-based spatial statistics to identify significant differences in DKI metrics in addition to measuring lesion volume in MS and Control patients with and without DM. We observed significantly increased diffusion and axial kurtosis and increased periventricular lesion volume in MS patients with DM compared to those without DM, indicating worsening inflammation and demyelination, suggesting that DM may serve as an exacerbating factor for worsening MS prognosis.
Dual-Task Gait Predicts Changes in Choline in the Primary Motor Cortex of Older Individuals with Mild Cognitive Impairment
Authors: Elkas Jack , Pieruccini-Faria Frederico , Montero-Odasso Manuel , Bartha Robert
Published: August 2024

The dual-task cost on walking speed (DTC) can predict the progression of dementia in people with mild cognitive impairment (MCI). In MCI, levels of choline in the primary motor cortex, an MRS metabolite associated with inflammation, has been negatively correlated with dual-task speed. However, it is unknown whether DTC can predict choline changes in MCI. 65 MCI patients were assessed for DTC speed and absolute choline concentration in the motor cortex before and after 6 months. DTC at baseline was positively correlated with changes in choline after 6 months, suggesting that gait impairment may precede neuroinflammation in MCI.
Enlarged choroid plexus volume in multiple sclerosis is related with disability, cognition and brain atrophy
Authors: Noteboom Samantha , Vellema Jelle , Steenwijk Martijn , de Vries Helga , Barkhof Frederik , Killestein Joep , Strijbis Eva , Schoonheim Menno
Published: August 2024

Enlargement of the choroid plexus (ChP) has been recently suggested in multiple sclerosis (MS), but relations with clinical and MRI outcome measures remain unclear. In this study, we compared automated segmentation approaches to assess ChP volume on 3D-T1 to manual outlines. Next, ChP volume was assessed in 327 patients with MS and 78 healthy controls. Gaussian Mixture Modelling (GMM)-based segmentation showed best agreement with manual segmentations in MS and controls. Enlargement of ChP was observed in MS compared to controls, and was associated with worse physical disability and cognitive impairment and more severe brain, cortical and thalamic atrophy.
Examining T2* Relaxometry in Diffusion Tensor Space to Quantify Cerebral Microbleeds in Alzheimer’s Disease Mouse Model
Authors: Hunter Emani , Foley Lesley , Zhao Yifan , Aizenstein Howard , Wu Minjie , Iordanova Bistra
Published: August 2024

Cerebral microbleeds are morphologic changes contributing to the progression of Alzheimer’s Disease and vascular dementias. However, the effects of microbleeds on brain connectivity across age and sex remain to be understood. In this study, we performed T2* mapping, that was registered to a DTI space to measure diffusion changes in microbleed locations. R2* and diffusion tensor indices demonstrate an inverse relationship, where diffusion is low and R2* rates are high. Our results suggest that microbleeds decrease water diffusion in the brain possibly due to increased neuroinflammation and hemosiderin accumulation, shedding light on abnormal tissue structure in Alzheimer’s disease subjects.
Task functional magnetic resonance imaging-guided individualized repetitive transcranial magnetic stimulation after a brief nap attenuates the sustained attention performance deterioration induced by sleep deprivation
Authors: Zhu Yuanqiang , Guo Fan , Chang Yingjuan
Published: August 2024

Our pilot study validated the effectiveness of rTMS after a brief nap in terms of improving sustained attention in the context of SD. An investigation of the dynamic changes of PVT task-related cerebral responses across the three conditions showed that the middle frontal gyrus recovered least after the nap and was selected as the stimulation target. Through modulating the functional connectivity within the FPN and DMN, individualized, 10-Hz rTMS showed promise in terms of improving the vigilance of military academy cadets accepting real stimulation.
Identify the paraventricular thalamic nucleus in humans using a structural connectivity approach
Authors: Kamagata Koji , Uchida Wataru , Andica Christina , Nagai Yasuhito , Nishioka Masaki , Owaki Mana , Saito Yuya , Takabayashi Kaito , Hagiwara Akifumi , Wada Akihiko , Akashi Toshiaki , Aoki Shigeki , Kato Tadafumi
Published: August 2024

We attempted to identify the paraventricular thalamic nucleus (PVT), which is known to regulate emotion, motivation, stress, and drug- and alcohol-related behaviors in humans. We used data-driven connectivity profiles obtained using probabilistic tractography and a k-means clustering method with diffusion-weighted imaging data. We consistently identified an anatomical connectivity-based parcellation of the PVT in two independent cohorts that included 601 healthy subjects. Furthermore, we discerned the specific structural pattern of the PVT, which agreed with findings from animal studies. Finally, we noted significant correlations between PVT structural and functional connectivity with the limbic structures and drug-, nicotine-, or alcohol-related scores.
Optimized model architecture and generalization for deep learning-based SAR prediction (MRSaiFE)
Authors: Moghadam Mina Chookhachizadeh , Panjwani Nawal , Motovilova Elizaveta , Zhang Mengying , Robb Fraser , Hoang Adrian , Afrin Tasmia , Winkler Simone Angela
Published: August 2024

Predicting the SAR distribution in ultra-high field MRI is a crucial task to prevent tissue damage due to the hotspots, though it is challenging. The MRSaiFE deep learning framework predicts SAR based on anatomical images, but it does not guarantee model generalization due to data leakage in the training process. To improve the model, we extended the UNet architecture to include residual and inception modules in its encoder part. Further, we implemented customized loss functions, and evaluation metrics to improve the predictive performance. The results show that the model predicts SAR with an SSIM=86% and MSE=0.14% for unseen body models.
Brain Decoding and Reconstruction of concepts of visual stimuli from fMRI through deep diffusion models
Authors: Ferrante Matteo , Boccato Tommaso , Toschi Nicola
Published: August 2024

In vision, the brain is a feature extractor which works from images. We hypothesize that fMRI can mimic the latent space of a classifier, and employ deep diffusion models with BOLD data from the occipital cortex to generate images which are plausible and semantically close to the visual stimuli administered during fMRI. To this end, we mapped BOLD signals onto the latent space of a pretrained classifier and used its gradients to condition a generative model to reconstruct images. The semantic fidelity of our BOLD response to visual stimulus reconstruction model is superior to the state of the art.
Functional MRI denoising Using Data-Driven Multi-Step Deep Neural Network
Authors: Ghaffarzadeh Sina , Malekian Vahid , Makhsousi Faeze , Seyyedsalehi Seyyed Ali
Published: August 2024

In this study a novel method for sampling the active and noisy areas is proposed by using the purification of gray and non-gray matter areas of fMRI data. Also, a data-driven network is proposed in a parallel, multi-step and integrated manner for optimal noise reduction of t-fMRI data. Besides, the proposed method reduces substantially physiological noise without considering the specific noise source and only by using the ROI of noise and activity. Based on the results, the proposed method provides a more accurate and improved activity map than previous methods, which increases the power of activity analysis in fMRI data.
Depth-dependent effects of thermal and physiological noise reduction in BOLD fMRI
Authors: Guidi Maria , Giulietti Giovanni , Moeller Harald , Norris David , Giove Federico
Published: August 2024

In this study, we evaluated the effect of common denoising steps (NORDIC, regression for motion parameters, RETROICOR and aCompCor) on a high-resolution resting-state BOLD fMRI dataset. We extracted the temporal standard deviation and the spectral power density at different cortical depths in the primary motor cortex and found that each denoising algorithm had a distinct signature on the profile shape. We further estimated the effect of denoising by calculating the temporal signal-to-noise ratio and delta variation signal (DVARS) for different tissue types and found that NORDIC and aCompCor had the largest impacts on the metrics considered.
Validating NORDIC denoising on high-resolution fMRI data at 7 T
Authors: Pfaffenrot Viktor , Norris David
Published: August 2024

High-resolution fMRI is dominated by thermal noise. The NORDIC PCA filter is a promising method to remove thermal noise but its effect on the spatial fidelity of layer-dependent profiles is sparsely investigated. We evaluated the performance of NORDIC with several parameter setting for laminar fMRI at 7 T and sought to investigate the effect NORDIC has on the shape of the profiles. Our results suggest that NORDIC indeed affects high-resolution profiles but the overall effect can be seen to be small and can be further reduced with careful parameter settings.
Where is each finger area in brain? Enhanced characterization of the somatosensory area using high-resolution EPIK at 3T
Authors: Oh Sung Suk , Chang Min Cheol , Kwak Soyoung , Choi Jong-ryul , Shah N. Jon , Yun Seong Dae
Published: August 2024

The tactile stimulus of fingers in fMRI can effectively reveal the somatosensory functional region. However, this research requires a high-resolution fMRI technique, enabling a distinct delineation of functional areas for each finger. This work demonstrates the use of high-resolution EPIK (1.25 x 1.25 mm2) for enhanced characterization of somatosensory areas at 3T. EPIK provides improved spatial resolution than EPI (1.72 x 1.72 mm2) with increased brain coverage while keeping the same temporal resolution as EPI. The activation region from EPIK was shown to be more locally specific, thereby enabling a clearer recognition of functional areas for each finger.
Diagnostic performance of DCE-MRI for the axillary sentinel lymph nodes status in breast cancer patients
Authors: Xiao Fang , Jin Mingli , wang Peng , Pan Zhihua , Zhao Qi , Jiang Ying , Zhang Miaoqi , Zhang Yong
Published: August 2024

Sentinel lymph nodes(SLN) status is one of the most important indicators affecting breast cancer patient treatment and prognostic. Thus, a non-invasive diagnostic method capable of accurately predicting of the SLN status is of great value in selecting breast cancer patients who would benefit from therapy. In this study, we investigate the diagnostic performance of different SLN status [pN0, pN0 (i+), pN1mi, and pN1] using dynamic contrast-enhanced (DCE) MRI. We found that (1) Krans, Kep, and Ve are significantly different in different SLN status, (2) and the Ktrans is the best single parameter for the detection of SLN status.
Comparison of DWI score and Kinetic score as a Prognostic Marker in Breast Cancer Patients receiving Neoadjuvant Systemic Treatment.
Authors: Ota Rie , Kataoka Masako , Iima Mami , Honda Maya , Okazawa Aika , Yamada Yosuke , Takeuchi Yasuhide , Toi Masakazu , Kubo Takeshi , Nakamoto Yuji
Published: August 2024

This study aimed to investigate the association of DWI score of breast cancer derived from MRI obtained after neoadjuvant systemic treatment (NST) with disease-free survival (DFS). Kinetic score from standard DCE-MRI at the same timing was analyzed for comparison. Kaplan-Meier analysis showed that patients with DWI score of 2 showed significantly shorter DFS than those with DWI score 0 or 1. Patients with kinetic score of 2 or 3 showed significantly shorter DFS than those with kinetic score of 0 or 1. DWI score demonstrated excellent inter-reader agreement and is suitable for predicting prognosis of breast cancer patients after NST.
Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer
Authors: chen yanhong , wang lijun , luo ran , liu huanhuan , Wang dengbin
Published: August 2024

This study described the application of MRI-based deep learning radiomics in patients with breast cancer, presenting a novel individualized clinical decision nomogram that could be used to predict axillary lymph node metastasis providing a noninvasive approach to assist clinicians in clinical decision-making.
Multiparameter estimation from DANTE-prepared multi-delay ASL using artificial neural network
Authors: Ishida Shota , Fujiwara Yasuhiro , Takei Naoyuki , Matta Yuki , Kanamoto Masayuki , Kimura Hirohiko , Tsujikawa Tetsuya
Published: August 2024

A simulation-based supervised neural network was developed for simple and robust parameter estimation from multi-delay DANTE-prepared arterial spin labeling (ASL). The network was trained using 15 million simulation data points. Accuracy and precision were compared between the proposed and conventional methods. The neural-network-based estimation presented higher accuracy and precision than the conventional method that used table lookup. A higher noise immunity was also observed with the proposed method. A simulation-based supervised neural network simplifies the estimation process of multiparametric ASL. The estimation performance of cerebral blood flow and arterial cerebral blood volume was particularly improved by the proposed method.
Spatiotemporal redundancy based denoising method in arteprial spin labeling MRI: A ticket to free sensitivity improvement
Authors: Chen Xinran , Lin Liangjie , Wei Zhiliang , Chen Lin , Chen Zhong
Published: August 2024

Arterial-spin-labeling (ASL) MRI has been widely used in neurological studies for investigating brain activity via focusing on regional perfusion differences. However, sensitivity is a general issue in ASL MRI to prevent the high-resolution comparisons of perfusion heterogeneity, which are associated with pathological process, or lead to prolonged scan time, which inhibits the temporal resolution in monitoring acute-stage perfusion changes (e.g., perfusion recovery after spontaneous resuscitation in cardiac arrest). Therefore, in this study, a post-processing method exploring spatiotemporal redundant information of ASL data is proposed to enhance the sensitivity without extending scan durations.
A novel approach to derive robust arterial input functions for DCE-MRI in small animals
Authors: Gunwhy Ebony , Tadimalla Sirisha , Waterton John , Hockings Paul , Schütz Gunnar , Green Claudia , Kenna J. Gerry , Sourbron Steven
Published: August 2024

Accurate biomarker quantification is hindered in small animal MRI due to difficulties in reliably deriving arterial input functions (AIFs). This study provides a robust alternative to commonly used approaches by deriving AIFs from a simple, whole-body circulation model. This method is compared with individual and population spleen-derived AIFs by evaluating performance in gadoxetate DCE-MRI of the rat liver. Results demonstrated that the whole-body circulation model-derived AIF yields greater repeatability, reproducibility, and goodness-of-fit to observed data, indicating that it provides more accurate biomarker quantification than the individual or population spleen-derived AIFs.
Synthetic MRI in the assessment of the classification of rotator cuff injury
Authors: Yabo Ni , zhijun Wang , Zhaorong Tian
Published: August 2024

In this study,to investigate the diagnostic value of quantitative parameters of synthetic magnetic resonance image in the grade of rotator cuff injury, we found that the T2 values had high diagnostic efficacy in grading the degree of rotator cuff injury, especially the T2 values of the lateral subregion and the middle subregion had high diagnostic efficacy in differentiating complete tears from partial tears, tendinopathy and normal tendons.
Evaluation of a deep learning-based acceleration technique for ankle MRI protocol in clinical applications
Authors: Zhao Qiang , Xu Jiajia , Yang Yu Xin , Zhao Yuqing , Wang Qizheng , Dai Yongming , Yuan Huishu
Published: August 2024

A deep learning-based compressed sensing (ACS) technology was recently introduced for an integrative MR acceleration solution. This study assessed the effectiveness of using ACS to evaluate ankle injuries. The ACS acceleration technique allows faster imaging than conventional acceleration methods, providing adequate image quality and diagnosis performance.
Regional Achilles Tendon Segmentation for Quantitative Magnetic Resonance Imaging at 1.5 Tesla
Authors: Vilimek Dominik , Kahankova Radana , Martinek Radek , Kryl Martin , Janacova Veronika , Golian Milos , Uchytil Jaroslav , Hanzlikova Pavla , Szomolanyi Pavol , Trattnig Siegfried , Juras Vladimir
Published: August 2024

Changes in the Achilles tendon composition are associated with an increased risk of tendinopathy which is common in middle-aged overweight patients and is one of the most common sports injuries. However, measuring and quantifying such properties is a challenging task. The purpose of this paper is to introduce an end-to-end pipeline for segmenting Achilles tendon using deep convolutional neural networks and automatic segmentation into three segments. Our model shows promising results outperforming state-of-the-art approaches (Dice 90.6% and Jaccard 84.0%). This is one of the key steps for short and long T2* value analysis from 1.5T data.
Improving T2 distribution estimation using deep generative priors
Authors: Doucette Jonathan , Kames Christian , Rauscher Alexander
Published: August 2024

$$$T_2$$$ distributions are typically computed using point estimates such as nonnegative least-squares (NNLS). This characterizes the most likely $$$T_2$$$-distribution arising from the data, but disregards other plausible solutions - of which there are many, due to the ill-posed nature of the inverse problem. Here, we instead propose to use Bayesian posterior sampling methods. To guide the difficult high-dimensional sampling problem, a data-driven domain transformation is learned alongside a deep generative prior. The resulting posterior samples produce more spatially consistent myelin water fraction (MWF) maps compared to NNLS, despite the purely voxelwise analysis, and additionally yields novel MWF uncertainty estimates.
Fast T1 mapping of whole brain using Multiplied Added Subtracted Divided Inversion Recovery (MASDIR)
Authors: Losa Letizia , Peruzzo Denis , Bydder Graeme , Agarwal Nivedita
Published: August 2024

Whole brain T1 mapping has been performed using several different sequences but remains mostly confined to research applications. A clinically reliable and fast method to evaluate the white matter (WM) is important for accurate diagnosis, evaluating the severity of disease and monitoring disease progression over time. In this study we assessed Multiplied, Added, Subtracted and/or Divided Inversion Recovery (MASDIR) technique in nine healthy subjects and found evidence that such technique greatly improves the visualisation of tissue of interest and allows calculation of T1 values in a reliable, fast and clinically usable manner.
Comparison of machine learning models for discriminating obsessive-compulsive disorder based on automated fiber quantification
Authors: Zhang Suming , Bu Xuan , Cao Lingxiao , Li Hailong , Liang Kaili , Zhou Zilin , Gao Yingxue , Zhang Lianqing , Li Bin , Huang Xiaoqi
Published: August 2024

In this study, we compared the classification performance of different machine learning models for discriminating OCD patients based on DTI tractography. Firstly, we extracted DTI metrics and tract volumes as features. Following feature selection, four machine learning models were performed for classification. Finally, a novel SHapley Additive exPlanations (SHAP) analysis was used to intepret the value of importance for each feature. We found that XGBoost exhibited the best classification performance among the four models. The model explanation by SHAP suggested that the volume of callosal orbital frontal tract was the most important factor in differentiating OCD from healthy controls.
Modified cerebral small vessel disease total burden is useful to screening study based on community people
Authors: Mu Ronghua , Zheng Wei , Qin Xiaoyan , Li Xin , Deng Kan , Liu Fuzhen , Zhuang Zeyu , Yang Peng , Lv Jian , Zhu Xiqi
Published: August 2024

The objective of this study was to create a modified total load score suitable for community screening study by modifying mild WMH and EPVS. Our study found NCT-A (DST) variable abnormalities even in people with mild EPVS and WMH, suggesting that modified cerebral small vessel disease total burden is useful to screening study based on community people.
Validation Study of MRI Contrast Agent of Gd-DOTA Conjugated to DNA Aptamer for Detecting Oligomeric Amyloid-beta
Authors: Jahng Geon-Ho , Kim Sang-Tae , Kim Hyug-Gi , Kim Yu Mi , Cho Jee-Hyun
Published: August 2024

The oligomeric amyloid-b (oAβ) is a reliable feature for an early diagnosis of Alzheimer's disease (AD). Therefore, the objective of this study was to demonstrate imaging of oAβ deposits using our developed DNA aptamer called ob5 conjugated with gadolinium (Gd)- DOTA as a contrast agent for early diagnosis of AD using MRI. An oAβ-specific aptamer was developed by amide bond formation and conjugated to Gd-DOTA MRI contrast agent and/or cyanine5 (cy5). We verified the performance of our new contrast agent with AD model mice using In vivo and ex vivo fluorescent imaging and animal MRI experiments.
MRS combined with enhanced silhouette in the prediction of malignant glioma radiomics classification
Authors: song jing , zong hui qian , zhang ya , wang jing , wei hong yang , xie li zhi
Published: August 2024

Question: Among the studies using radiomics methods to predict glioma grading, most of them are based on conventional magnetic resonance imaging sequences, and functional magnetic resonance imaging is less studied. Methods: This study predicted malignant glioma grading based on magnetic resonance structural images and magnetic resonance spectroscopy using an radiomics approach. Results: The test set AUC of the model constructed based on T1-enhanced images and the ratio of three metabolites of MRS was 0.95. Conclusion: Radiomics based on T1-CE and MRS has a good performance in identifying both grade III and grade IV gliomas.
Improved perfusion-weighted images at 7T combining pTx and low B1+ Adiabatic pulses
Authors: Oliveira Ícaro , Schnabel Robin , Osch Matthias , Hirschler Lydiane , Zwaag Wietske
Published: August 2024

A FAIR acquisition at 7T, using a TR-FOCI pulse for background suppression, was set up on a pTx (32Rx8Tx) system and on the standard (32Rx2Tx) system. CBF-imaging results were compared in terms of CBF-weighted signal intensities and drop-off with increasing thickness of the slab-selective inversion slab. The higher and more homogeneous B1 of the pTx system translated to higher CBF-weighted signal intensities. Both setups allowed functional mapping of the hand region in the motor cortex in approximately 5 minutes.
Self-Supervised MR Image Reconstruction with Stage-by-Stage Data Refinement
Authors: Liu Xue , Li Cheng , Zhang Yu , Li Haoran , Wang Yeqi , Zheng Hairong , Wang Shanshan
Published: August 2024

Deep learning-based, especially fully supervised learning-based, methods have shown unprecedented performance in MR image reconstruction. Fully sampled data are utilized as references to supervise the learning process. However, it is challenging to acquire fully sampled data in many real-world application scenarios. Unsupervised approaches are required. Here, we propose an iterative data refinement method for enhanced self-supervised MR image reconstruction. Different from Yaman's self-supervised learning method (SSDU), training data in our method are refined iteratively during model optimization to progressively eliminate the data bias between the undersampled reference data and fully sampled data. Better reconstruction results are obtained.
MP-PCA image denoising technique for high resolution quantitative susceptibility mapping (QSM) of the human brain in vivo
Authors: Doniza Liad , Stern Neta , Radunsky Dvir , Helft Coral , Fuchs Patrick , Karsa Anita , Shmueli Karin , Ben-Eliezer Noam
Published: August 2024

Quantitative susceptibility mapping (QSM) has many clinical applications such as distinguishing between acute and chronic multiple sclerosis (MS) lesions, and probing microbleeds in traumatic brain injury. High scan resolutions improve diagnostic quality and reduce partial volume artifacts albeit at a price of a lower signal-to-noise ratio (SNR). In this study, we introduce a principal component analysis (PCA) denoising algorithm for QSM data, showing the ability to generate QSM maps of the human brain at 0.6x0.6x0.6 mm3 resolution at 3T in vivo.
Deep Learning based prediction of the planes for automated planning of MRI imaging of cervical neural foramina and lumbar pars interarticularis
Authors: Bhushan Chitresh , Shanbhag Dattesh , Patil Uday , Kolupar Trevor , Fung Maggie
Published: August 2024

We present a generalized DL-based intelligent slice placement framework for planes of cervical neural foramina (CF) and lumbar pars interarticularis (PI) for spine MRI. CF and PI scan improves assessment of foraminal stenosis and lumbar spondylolysis respectively, but requires highly skilled operator for accurate prescription. Our approach enables automatic patient-specific scan plane prescription from routine axial T2W images. In our test, it achieves mean error of <0.7 mm and <0.2 degrees and demonstrates similar/better contiguous anatomical visualization to manual scans on retrospective reformatting of 3D data. These results indicate that our approach is accurate and suitable for clinical usage.
Within Patient Contrast Adjustment Through a Self-Consistent Deep Learning Model when Imaging Near Metal: An Example in Angiography
Authors: Koch Kevin , Nencka Andrew
Published: August 2024

Multi-spectral imaging around metallic implants allows for a unique application of subject-specific deep learning applications because both high-resolution standard acquisitions and multi-spectral acquisitions are performed with significant regions of artifact-free overlap. A deep neural network can be trained in that region of artifact-free overlap within a single patient to yield a contrast transform on the multi-spectral images and achieve similar contrast to traditional acquisitions in the region of those acquisitions obscured by metal artifacts. In this proof of concept, a preliminary example of synthesizing time of flight-like contrast from a multi-spectral acquisition with vascular flow voids was shown.
Comparison between the application value of reduced FOV multi-shot echo-planar DWI and conventional single-shot echo-planar DWI
Authors: Zhang Haini , Zeng Jianwei , Meng Yankai , Wu Peng , Han Lu , Zhuang Yinping
Published: August 2024

We have compare the performance of reduced field of view (rFOV)image reconstruction using image-space sampling function (IRIS)diffusion weighted imaging (DWI)sequence and full field of view (fFOV) single-shot echo-planar imaging (SSEPI)diffusion weighted imaging(DWI)sequence for rectal cancer patients. And found that compared to fFOV ssEPI-DWI sequence, rFOV IRIS-DWI sequence has a better resolution, reduced sensitivity artifacts, image distortion, and improved image quality in rectal cancer patients.
Magnetic Resonance Imaging Based Radiomics of Axial and Sagittal Orientation in Suspected Placenta Accreta Spectrum Pregnancies
Authors: Do Quyen , Twickler Diane , Lewis Matthew , Herrera Christina , Owen David , Madhuranthakam Ananth , Spong Catherine , Fei Baowei , Lenkinski Robert , Xi Yin
Published: August 2024

Placenta accreta spectrum (PAS) is associated with significant morbidity and mortality. We applied ROI-based radiomics analysis on axial and sagittal plane MR images in women with placenta previa and a history of previous cesarean delivery. Our goal was to compare MR textural features extracted from axial and sagittal imaging planes and compare findings to surgical outcomes. Radiomics features were significant in both anatomical planes in the MRIs of women who underwent hysterectomy.
Intrinsic CSF outflow declines with age in healthy humans detected by spin-labeling MRI
Authors: Malis Vadim , Bae Won , Yamamoto Asako , McEvoy Linda , McDonald Marin , Miyazaki Mitsue
Published: August 2024

Parasagittal dura (PSD) along the superior sagittal sinus (SSS) can be visualized using FLAIR and 3D SSFSE without gadolinium-contrast administration. Visualization of intrinsic CSF outflow from the PSD to the SSS can be achieved in human brains using spin-labeling MRI at clinical 3-T MRI. The spin-labeling MRI shows intrinsically tagged CSF outflow from the upper PSD and lower PSD to SSS. The quantitative intrinsic CSF outflow metrics indicate an age-related decline of the intrinsic CSF outflow at the SSS in human brains.
Aging-related Changes in Spontaneous Brain Activity in White Matter
Authors: Li Muwei , Gao Yurui , Lawless Dylan , Xu Lyuan , Zhao Yu , schilling Kurt , Ding Zhaohua , Anderson Adam , Landman Bennett , Gore John
Published: August 2024

Recent studies have consistently reported that BOLD effects in WM reflect neural activities, and thus represent a new window into brain function. Here we evaluate the potential value of BOLD measurements in WM as an indicator of functional changes during normal aging. We observed a widespread reduction of metrics of WM BOLD effects, suggesting changes occur in information exchange in WM with aging. Our findings converge to support the notion that WM BOLD signals in specific regions and their interactions with others have the potential to serve as imaging markers of aging.
Decreased cholinergic nucleus 4 basal forebrain density is associated with disease progression in Alzheimer’s Disease.
Authors: deBettencourt Coreylyn , Kentiba Kirubel , Atalay Alexander , Newman Benjamin , Van Horn John , Druzgal T.
Published: August 2024

Density of the cholinergic nuclei of the basal forebrain has been shown to be associated with disease and symptom progression in Parkinson’s Disease. This project was aimed at determining if there is a similar effect of these brain regions in predicting cognitive impairment and disease progression in Alzheimer’s Disease. By using a longitudinal multicenter dataset of Alzheimer's patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we analyzed the relationship between grey matter density of cholinergic basal forebrain and disease progression. We demonstrated that patients with increased disease severity had significantly lower density in the cholinergic nuclei of the basal forebrain.
Studying the Impact of Diabetes on the Pathogenesis of Alzheimer’s Disease
Authors: Jue Thomas , Graham James , Rehman Usman , Datta Keshav , Liu Shie-Chau , Chen Jane , Hurd Ralph , Spielman Daniel
Published: August 2024

How type 2 diabetes (T2D) increases the risk of Alzheimer’s disease (AD) frames the central research question. The study has developed a rat model that exhibits both T2D and AD (T2D-AD +/-). These hemizygous animals contain the genes APPswe and PS1ΔE9 and display amyloid burden as early as 6 mos of age. Standard Tg344-AD +/- model shows amyloid plaque at 9 mos of age. AD impairs cognitive performance but brain metabolism as observed with Barnes Maze test and dynamic nuclear polarization experiments. MRS/MRI and physiological studies can now use the animal model to interrogate how insulin insensitivity accelerates AD onset/progression.
Multimodal deep learning for Alzheimer’s disease classification and clinical score prediction
Authors: Itkyal Vaibhavi , Batta Ishaan , Abrol Anees , Calhoun Vince
Published: August 2024

In this project, we use different neuroimaging modalities in the ADNI data, including structural magnetic resonance imaging (sMRI) and temporal transformations of resting-state functional magnetic resonance imaging (rs-fMRI) data, for several classification (e.g., diagnosis) and regression (e.g., age and clinical assessments) objectives.
Assessing cerebral perfusion: transfer function analysis of the BOLD response to hypoxia-induced changes in deoxyhemoglobin
Authors: Duffin James , Sayin Ece , Sobczyk Olivia , Poublanc Julien , Levine Harrison , Mikulis David , Fisher Joseph
Published: August 2024

We used hypoxia-induced changes in deoxyhemoglobin concentration as a susceptibility contrast agent. Transfer function analysis of the resulting changes in the BOLD signal for each voxel provided cerebral perfusion measures that indicate the distribution of the strength of the signal response to the contrast agent (gain) and the lag of the response (phase or time). We relate the gain and lag to conventional resting hemodynamic measures cerebral blood volume and mean transit time, respectively.
The ERIC phantom - Extra-dimensional respiration and inflow of contrast: an open-source DCE digital simulation tool
Authors: Schrauben Eric
Published: August 2024

An open-source MATLAB-based software tool was developed for digital simulation (using the MRXCAT framework) of DCE MRI acquisition and reconstruction strategies, termed the ERIC phantom: extra-dimensional respiration and inflow of contrast. The ERIC phantom is a user-friendly graphical user interface, allowing for variable respiratory motion, acquisition parameters and trajectories, motion correction strategies, and compressed sensing reconstructions. This realistic abdominal DCE phantom can be used to investigate varying strategies for producing high quality DCE reconstructions in the presence of respiratory motion and contrast inflow.
Slab-selection in free-running cardiac and respiratory motion-resolved bSSFP 5D whole-heart MRI
Authors: Ferincz Robin , Romanin Ludovica , Yerly Jérôme , Piccini Davide , Stuber Matthias , Roy Christopher
Published: August 2024

Recent advances have enabled high resolution cardiac and respiratory motion-resolved 5D whole-heart MRI using non-contrast enhanced non-selective 3D radial bSSFP. However, the nature of bSSFP, the subject dependent anatomy, as well as the underlying sparse reconstruction can lead to banding and streaking artifacts, which degrade image quality and reduce diagnostic utility. In this work, the impact of slab-selective RF pulses in a programmable orientation that is independent of the k-space trajectory is assessed in a cohort of healthy volunteers. Preliminary results suggest that a subject-specific slab orientation can reduce artifacts and improve image quality.
DTI-Net: Unsupervised Diffusion Tensor Reconstruction Using Implicit Neural Representation
Authors: Shi Yuting , Zhang Yuyao , Wei Hongjiang
Published: August 2024

Diffusion tensor imaging (DTI) needs a large number of diffusion-weighted images (DWIs) to reliably reconstruct the diffusion measurements of the brain white matter, making the data acquisition time-consuming. Deep learning has emerged as a powerful technique to reduce the number of acquired DWIs. While most existing deep learning methods are supervised and need high-quality ground truth data as the training labels. Here, we proposed an unsupervised and subject-specific DTI reconstruction method called DTI-Net to significantly reduce the required number of DWIs, while also can simultaneously conduct the super-resolution reconstruction of the tensors.
Stratification of Liver Histologic Fibrosis using Machine Learning on MRI Radiomic Data and Clinical Features
Authors: Li Hailong , Qian Jinzhao , Chen Ziang , Pan Wen , Reeder Scott , Harris David , Masch William , Alsam Anum , Shanbhogue Krishna , Bernieh Anas , Ranganathan Sarangarajan , Parikh Nehal , Dillman Jonathan , He Lili
Published: August 2024

Chronic liver diseases can lead to variable amounts of liver fibrosis, which impacts patient management and outcomes. Percutaneous liver biopsy is the clinical reference standard for assessment of liver fibrosis. However, biopsy is subject to sampling errors and poor patient acceptance. The aim of this study is to develop machine learning models to stratify the severity of biopsy-derived liver fibrosis using MR radiomic data and clinical data. Using clinical, routinely collected MRI and clinical data, our machine learning was able to stratify the severity of liver fibrosis with an AUROC of 0.71, demonstrating the feasibility of the machine learning approaches.
Preliminary Experience of 5.0 Tesla Higher Field Abdominal DWI: Agreement of Apparent Diffusion Coefficient With 3.0 Tesla Imaging
Authors: Zhang Yunfei , Dai Yongming , Zeng Mengsu
Published: August 2024

Recently, the 5.0 T whole-body MRI scanner was developed. This study aims to evaluate the feasibility of 5.0 T high field DWI and assess the agreement of the apparent diffusion coefficient (ADC) with that from 3.0 T DWI. The results showed that 5.0 T DWI displayed an increase in subjective image quality score. Both the inter-field and inter-observer agreements of ADC values were substantial to excellent. The substantial to excellent agreements between the ADC values measured with 3.0 T and 5.0 T DWI for liver, pancreas, spleen and kidney suggested that 5.0 T DWI can be applied for abdominal imaging.
Can pH can be a marker of hypoxia? Role of MRI CEST pH imaging in differentiating hypoxic from non-hypoxic tumors.
Authors: Singh Aruna , Stabinska Julia , Krishnamachary Balaji , Gupta Kuldeep , Sedaghat Farzad , Nimmagadda Sridhar , Bulte Jeff , Bhujwalla Zaver , Mcmahon Michael
Published: August 2024

Acidosis and hypoxia play a key role in developing metastasis and chemoresistance in tumors. pH measurement can be used as indicator of hypoxia and can provide insights into the metastatic potential of tumors. This study have explored chemical exchange-dependent saturation transfer (CEST) MRI based pH imaging to differentiate between two types of breast tumor models differing in their Hypoxia Inducible Factor-1α (mediator for hypoxia related adaptations in tumors) expression. Tumor acidification was observed in both with lower average extracellular pH (pHe = 6.1) in tumors with intact HIF-1α expression compared to those with HIF-1α silenced group (pHe = 6.2).
Simulation of a dynamic saturation CEST technique using MR fingerprinting for quantitative amide proton transfer imaging at 3 T
Authors: Ostenson Jason , Zu Zhongliang
Published: August 2024

Conventionally, quantitative CEST imaging requires steady-state acquisitions so that the obtained signals can be fitted to simple analytical models, which takes a long scan time. Recently, transient-state techniques such as MR fingerprinting were developed to shorten the scan time. This study evaluates the potential of using a new pulse-by-pulse modulation of the saturation power acquisition in conjunction with an MRF reconstruction and fitting approach, termed dynamic saturation CEST, for quantitative APT imaging. Based on signal simulations, sensitivity analysis, and image simulations, we find it is plausible to estimate amide and macromolecular pool concentration, as well as water R1.
Characterization and Optimization of New CEST Reporter Proteins with Increased Detection Specificity
Authors: Korenchan David , Fillion Adam , Cheng Leo , Gilad Assaf , Farrar Christian
Published: August 2024

CEST reporter genes could play a critical role in the optimization of novel biological therapeutics. However, existing amide proton based CEST reporter genes have poor specificity as the amide protons of the reporter gene have the same chemical shift as amide protons from endogenous proteins. Here we report on a new class of reporter proteins with CEST contrast at 5-ppm, based on tyrosine hydroxyl ring protons.
Respiratory Modulation of Cerebrospinal Fluid Flow During Paced Breathing
Authors: Banks Makaila , Cicero Nicholas , Gomez Daniel , Beldzik Ewa , Napadow Vitaly , Polimeni Jonathan , Lewis Laura
Published: August 2024

We used flow-sensitive fMRI to explore the effect of paced respiration on cerebrospinal fluid (CSF) flow. Results indicate that paced breathing tasks at rates between 0.1 Hz and 0.25 Hz increase CSF flow. All breathing paces showed a striking increase in CSF inflow-enhanced signals with the highest group-averaged amplitude being 26% during the 0.1 Hz breathing task. We conclude that diaphragmatic paced breathing is an effective modulator of CSF flow that may represent increased fluid transport relative to free breathing.
Effects of Noise on Blood-Brain Barrier Permeability Measurement Error in Diffusion Weighted and Multi-Echo Arterial Spin Labeling MRI
Authors: Zhu Yufei , Kim Donghoon , Jung Youngkyoo , Fan Audrey
Published: August 2024

Arterial spin labeling (ASL) MRI is noninvasive and has the potential to become a sensitive, clinically useful way to measure blood-brain barrier (BBB) permeability. Here, we focused on two different ASL sequences, diffusion-weighted (DW) and multi-echo (T2), and performed simulations to compare the robustness of BBB permeability (Texch) measurement to different levels of noise. We conclude that the Texch fitting for DW ASL is more robust to noise at a physiological arterial transit time (ATT) of 1000ms at the expense of being more vulnerable to bias when the assumed ATT deviates from the true underlying ATT.
Visualization of Human Neurofluids Using Intrathecal 17O-Labeled Water and MRI: A Preliminary Study
Authors: Kameda Hiroyuki , Harada Taisuke , Sugimori Hiroyuki , Bai Xiawei , Minowa Kazuyuki , Kudo Kohsuke
Published: August 2024

Imaging of neurofluids dynamics is a promising aspect in clinical imaging. Only few methods of long-term water tracking in the brain have been described for diagnostic purposes. Herein, we examined water circulation between the CSF and ISF using an intrathecal injection of 17O-labeled water and 3T-MRI in two patients with dementia. Signal changes were detected in the brain parenchyma after the intrathecal administration. This visualization technique can detect the distribution of water tracers to the CSF and brain parenchyma and could become a clinical tool for evaluating neurofluids dynamics in humans.