bluuebunny/crossref_metadata_2025 huggingface dataset:
Continuous visual stimulation is associated with reduced visual cortex temperature
Authors: Mumuni Abdul Nashirudeen , Abubakari Mohammed Nasir , Salifu Mohammed Mamduh
Published: November 2024

Motivation: It is unclear if magnetic resonance spectral response to neural activation offers insight into brain temperature variation. Goal(s): To measure visual cortex temperature during visual stimulation. Approach: During single and continuous stimulation, spectra were collected from the activated visual cortex region (n = 4). Frequency shifts of the N-acetyl aspartate, creatine and choline peaks from the water peak were calibrated to measure temperature using pre-determined regression equations for each peak. Spectral responses to neural activation were estimated as percentage changes in their height, width and area. Results: Continuous neural activation was associated with significant decrease in visual cortex temperature. Impact: Continuous neural activation of the visual cortex increases cerebral blood flow to the activated region, which helps dissipate heat from visual cortex tissues as observed in the consistent temperature reduction. The observed spectral changes indicate clear response to neural activation.
Impact of Anesthetics on Excitatory and Inhibitory Neurotransmission in Brain
Authors: Patel Anant , Kumar Sreemantula Arun , Ramesh Akila
Published: November 2024

Motivation: Anesthetics are routinely used in surgery, and neurometabolic analysis in different neurological conditions but their impact on brain energy metabolism is not well understood. Goal(s): To evaluate the impact of anesthetics on excitatory and inhibitory neurotransmission in the brain. Approach: Infuse [1,6-13C2]glucose in mice maintained under isoflurane (1.5%) or urethane (1.5 g/kg), and monitor 13C labeling of brain metabolites by 1H-[13C]-NMR spectroscopy. Results: The glutamatergic and GABAergic neurometabolic activity was suppressed in isoflurane as well as in urethane anesthetized mice. The inhibitory and excitatory neurotransmission increase or decrease together with the brain activity. Impact: These findings will have implications for the interpretation of function signals in the brain.
Alteration of intracerebral metabolites and subjective sleepiness by acute caffeine administration in adults.
Authors: wang Qian , Li Shan , Wu Shuo , zhang Jia , Zhai Hui , Cui Yun , Mao Yu , Yan Gen
Published: November 2024

Motivation: The effects of acute caffeine intake on brain metabolite levels remain largely unknown. Goal(s): Illustrate the sensitivity of MRS to fluctuations in brain metabolites, investigate the difference among different caffeine consumption habit groups and to explore the association between metabolite changes and sleepiness. Approach: MRS was performed at three time point after the participants consumed coffee. Results: GABA+, GPC/GPC + PCH, Ins, Glu, and Glx levels were significantly altered after caffeine consumption. The levels of Glu, GPC, Cr + PCr, Glx, and Ins were significantly influenced by caffeine consumption habits. GABA+ levels in TH voxels significantly correlate with subjective sleepiness. Impact: This study should prove valuable in the study of the sensitivity of MRS to fluctuations in brain metabolites, the brain metabolites alteration differences among different coffee consumption habits, and MRS studies of GABA.
The Feasibility of Synthetic MRI in Monitoring NPC with Clival Invasion Changes During Radiation Treatment
Authors: Xu Yifan , Huang Lixuan , Tang Chuyun , Cui Wei , Zeng Zisan
Published: November 2024

Motivation: Complications from radiotherapy in nasopharyngeal carcinoma impact patient quality of life, necessitating methods to determine the optimal radiation dose for patients. Goal(s): To evaluate Synthetic MRI's ability to monitor clival invasion and its changes during treatment in nasopharyngeal carcinoma patients. Approach: T1, T2 and PD values of clivus were acquired before treatment, at radiation doses of 6420cGy and 7062cGy, and were compared with each other. Results: Prior to treatment, the clival T1 values in clival invasion patients were higher than those without clival invasion. No difference was found in Quantitative values between the radiation dose reached 6420cGy and 7062cGy. Impact: Synthetic MRI can assess clival invasion in nasopharyngeal carcinoma and its changes during radiation treatment, potentially enabling the evaluation of the optimal radiation dose for patient.
Brain Alterations in Nasopharyngeal Carcinoma patients after Chemotherapy based Multiplex MR imaging: A longitudinal MRI study
Authors: Liu Dan , Yang Kai , Guo Xiaofang , Song Xiaopeng , Yuan Zilong , Liu Yulin
Published: November 2024

Motivation: The objective of this study was to examine the potential impact of chemotherapy on the brain in patients with nasopharyngeal carcinoma (NPC) using Multiplex (MTP) MR imaging sequence, which generate multiple contrasts with one-time scan. Goal(s): We aimed to investigate the altered MTP parameters and their correlation with clinical cognitive scores before and after chemotherapy. Approach: All patients underwent MTP imaging, and the data were analyzed using BrainTool software. Results: The findings revealed that patients who underwent chemotherapy exhibited reduced brain volume and elongated T2Star in some brain regions, although no significant correlation was observed with clinical cognitive scores. Impact: The newly MTP MR sequence can evaluate brain alterations in NPC patients following chemotherapy. The observed decrease in volume values and increase in T2Star properties within brain regions may suggest neuron and myelin sheath damages in NPC patients after chemotherapy.
Within-session test-retest reproducibility and reliability of auditory cortex GABA+ in individuals with tinnitus using MEGA-sLASER at 3T
Authors: Wai Jonathan , Swanberg Kelley , Grassetti Alex , Gajdošík Martin , Martinez Diana , Juchem Christoph
Published: November 2024

Motivation: The measurement of GABA+ using in vivo proton magnetic resonance spectroscopy requires adequate reproducibility and reliability for its use in clinical research and as a potential biomarker. Goal(s): We aimed to determine the within-session test-retest reproducibility and reliability of GABA+ in the auditory cortex of individuals with tinnitus. Approach: A MEGA-sLASER sequence for GABA+ measurement using two consecutive within-session blocks was performed on 38 participants over 2 scanning days. Results: GABA+ quantification in the auditory cortex demonstrated similar reproducibility and reliability when using either a total creatine (tCr) or water reference (GABA+/tCr: CV=5.6%, ICC=0.69, 95% CI [0.52-0.80]; GABA+/water: CV=5.6%; ICC=0.73, 95% CI [0.58-0.83]). Impact: Auditory cortex GABA+ quantification with MEGA-sLASER in a clinical population with GABA+ abnormalities in the region of interest can be achieved with reproducibility and reliability that is comparable to what has been reported in other brain regions of healthy controls.
Synthetic-based histogram analysis associated with depth of invasion predicted progression-free survival in nasopharyngeal carcinoma
Authors: Yang Fan , Wei Haoran , Yu Xiaoduo , Xie Lizhi , Lin Meng , Zhang Hongmei
Published: November 2024

Motivation: Deeply infiltrating tumors have a poor prognosis and need close follow-up. Whether the depth of invasion could serve as a quantitative prognostic indicator in NPC. The prognostic assessment value of synthetic MRI (SyMRI) needs further analysis. Goal(s): To explore the value of two quantitative methods in prognostic assessment in NPC, including depth of invasion and SyMRI. Approach: This study prospectively included NPC patients, and utilized multiple statistical methods to construct and validate two methods. Results: Depth of invasion and SyMRI are important quantitative methods. Impact: Tumor invasion depth is an unignorable metric, and combining it with SyMRI may provide a quantitative and objective approach for clinical treatment.
Enhancing Eye Diagnostic Precision: Deep Learning Reconstruction Improves MRI Image Quality for Extraocular Rectus Muscle Assessment
Authors: Liu Chenchen , Ran Yuncai , Cheng Jingliang , Zhang Yong , Wen Baohong , Chen Rui , Wang Kaiyu
Published: November 2024

Motivation: Extraocular rectus muscles (ERMs) are essential for precise eye movement control, and their assessment is vital for diagnosing eye conditions. Magnetic Resonance Imaging (MRI) is preferred due to its superior soft-tissue resolution, but it has limitations. Goal(s): To determine whether Deep Learning Reconstruction (DLR) can enhance eye image quality and diagnosis. Approach: A study with 28 patients used DLR on ocular MRI. Two readers evaluated the images independently, and statistical analyses were conducted. Results: DLR significantly improved ERM depiction and overall image quality compared to conventional MRI. Interobserver agreement was good, especially for structural depiction. DLR produced clear, detailed images, enhancing diagnostic potential. Impact: This study demonstrates that DLR improves MRI image quality for assessing eye conditions, potentially leading to more accurate diagnoses, reduced repeat examinations, and enhanced patient care.
Self-Supervised Pre-Training Based Hybrid Network for Deep Gray Matter Nuclei Segmentation
Authors: Bao Lijun , Deng Yang
Published: November 2024

Motivation: Vision Transformers (ViTs) have the potentiality to outperform convolutional neural networks (CNNs) in the task of deep gray matter nuclei segmentation. However, Transformer-based models require large labeled datasets for training. Goal(s): Our goal is to design a Transformer-based model and alleviate the model's dependence on labeled data. Approach: We present a CNN-Transformer hybrid Network (CTNet) for deep gray matter nuclei segmentation. Moreover, a novel self-supervised pre-training approach that combines rotation prediction and masked feature reconstruction is proposed to pre-train the CTNet. Results: Our method achieves better performance than other comparison models on human brain MRI datasets. Impact: This is the first study that utilizes self-supervised learning methods for deep gray matter nuclei segmentation. Our method can achieve outstanding segmentation performance and effectively assist clinical doctors in the diagnosis and treatment of neurodegenerative diseases.
Longitudinal oncology lesion tracking using self-supervised vision transformers.
Authors: Anand Deepa , M Gurunath Reddy , Shanbhag Dattesh , Chatterjee Sudhanya , Mongia Aanchal , Patil Uday , Mullick Rakesh
Published: November 2024

Motivation: Automate lesion(s) delineation across longitudinal time-points to improve throughput and accuracy, reduce fatigue and determine disease velocityGoal(s): A method where user identifies an imaging lesion and wants to automatically label phenotypically similar imaging lesion on other scans . Approach: Vision Foundation model (DINO V2) features to localize and segment similar region between template mask region and new test data to obtain segmentation of similar lesion(s). Results: Reasonably well lesion segmentation capabilities on serial MRI scans in oncology patients with various MRI protocols, orientations and contrast. For Ferret diameter metric, a mean difference (95% CI) = -3.5 mm (-7.6 to 0.7 mm). Impact: Ability to automatically delineate phenotypically similar lesions on serial imaging data with user interaction on first time point only. Methodology is generalizable irrespective of imaging orientation, contrast and without need for extensive data labelling or geometric synchronization on serial scans.
Intracranial Large Vessel Severe Stenosis and Occlusion Detection Based on Vision Transformer Fusion Model from Multi-parametric MRI
Authors: Sun Mengzhou , Liang Xiaoyun , Zhang Jing , Wu Di , Zhu Wenzhen
Published: November 2024

Motivation: Traditionally, large vessel severe stenosis and occlusion (LVSSO) detection based on CTA needs contrast agent exposure. It is important to develop a LVSSO detection approach using contrast agent-free MR images that can achieve results comparable to clinical doctors. Goal(s): To develop a new fusion algorithm that can achieve the accuracy comparable to clinical diagnostic levels. Approach: A new fusion algorithm model based on vision transformer was developed. A total of 380 patients were enrolled in the current study. Results: The proposed model achieved an AUC of 0.963 and an accuracy of 94.7%. Impact: The proposed model achieved satisfactory accuracy for LVSSO detection, i.e. 94.7%, indicating that the performance of the proposed model has reached the clinical diagnosis level.
Cross-Attention Mechanism and Vision Transformer-Enhanced Multimodal Medical Imaging Fusion for Nasopharyngeal Carcinoma Segmentation
Authors: Xie Xingyu , Zhao Wenjie , Tang Si , Huang Zhenxing , Hu Yingying , Fan Wei , Yang Yongfeng Yang , Zheng Hairong , Liang Dong , Cheng Chuanli , Hu Zhanli
Published: November 2024

Motivation: The segmentation of nasopharyngeal carcinoma (NPC) is vital for diagnostic and prognostic processes. NPC segmentation is challenging due to its intricate anatomy, variability, and closeness to essential structures. Goal(s): This study aims to improve NPC segmentation accuracy by leveraging multiple modalities, such as DCE-MRI and PET-CT. However, it is worth noting that previous research has not fully harnessed the potential of cross-modal features through cross-attention mechanisms. Approach: This paper introduces a new approach, integrating cross-attention with the Vision Transformer structure, enabling efficient interaction between features from various modalities. Results: The experiments show that the proposed model offers superior performance and state-of-the-art results. Impact: The proposed method aims to enhance NPC segmentation results by utilizing multimodal medical imaging fusion, such as DCE-MRI fusion and PET-CT fusion. This approach has the potential to benefit other segmentation tasks involving multimodal medical image data.
Transformer residual cross (T-REX) networks for volumetric super-resolution.
Authors: Grover James , Shan Shanshan , Keall Paul , Waddington David
Published: November 2024

Motivation: Higher temporal resolution is needed for many MRI-guidance applications. Reducing matrix sizes can increase temporal resolution at the cost of lower spatial resolution. Deep learning-based super-resolution could mitigate the trade-off in spatiotemporal resolution. Goal(s): Develop and evaluate a unified deep learning-based algorithm that can up-sample thick single-slice low spatial resolution MRI to a thin multi-slice high spatial resolution MRI. Approach: Developed a transformer residual cross (T-REX) neural network that simultaneously increased the spatial resolution and decreased the slice thickness providing high spatial resolution multi-slice MRI. Results: T-REX was successfully trained and evaluated showing promising results for a variety of field strengths and sequences. Impact: The ability to acquire high spatial resolution volumetric MRI quicky has applications to low-field MRI and MRI-guided radiation therapy. Here, we present our initial findings of a unified neural network that applies volumetric super-resolution.
Hybrid attention SwinV2 transformer cascade design for Accelerated Multi-Coil MRI Reconstruction
Authors: Rahman Tahsin , Bilgin Ali , Cabrera Sergio
Published: November 2024

Motivation: Shifted window (Swin) Vision Transformers are increasingly outperforming CNNs in computer vision tasks, particularly if adequate GPU resources are available for training. Goal(s): In this work, we investigate cascaded Swin transformers with hybrid attention for accelerated MRI reconstruction. Approach: Our proposed Hybrid SwinV2-MRI-cascade architecture incorporates multi-coil data and k-space consistency constraints while offering a high degree of flexibility in network choice depending on performance requirements and compute capabilities. Results: Experiments show that both hybrid attention and longer cascades can be used in a granular manner to improve MRI reconstruction performance in Swin transformer networks. Impact: A highly configurable cascaded hybrid attention SwinV2 transformer architecture for MRI reconstruction is proposed. Its modular nature offers the ability to create transformer networks that fully leverage available training compute resources while producing high quality output.
Application of Swin Transformer for Alzheimer’s Disease Classification on Structural MRI and FDG-PET Brain Scans Using ADNI Data
Authors: Weber Chiara , Seeger Jakob , Isselmann Ben , Günther Matthias , Weinmann Andreas , Gregori Johannes
Published: November 2024

Motivation: The detection of Alzheimer’s Disease (AD) can be supported by using automated computer vision solutions. Those can potentially enable an earlier diagnosis and facilitate improved patient treatment. Goal(s): Our goal is to apply a state-of-the-art deep learning approach to the field of AD diagnosis based on brain scans. Approach: A pretrained Swin Transformer model is tuned on FDG-PET and structural MRI brain scans to classify AD. Results: Our model achieves a competitive area under curve of 97.8% / 99.7% and accuracy of 97.0% / 99.5% (MRI / PET) on independent test data. Impact: We show how a modern deep neural network can be trained with reasonable efforts while still achieving comparable results to established approaches. This procedure can lead the way towards classifying AD on more challenging modalities, such as ASL.
Application of deep learning–based image reconstruction in MRI of the temporomandibular joint to improve image quality and reduce scan time
Authors: Wang Chunjie , Wu Chunxue , Wu Tao , Lu Jie
Published: November 2024

Motivation: MRI examination of temporomandibular joint takes a long time to scan, especially when scanning in the open-mouth position, the patient cannot maintain the open-mouth state for a long time, it is easy to cause movements of the temporomandibular joint, resulting in motion artifacts in the scanned image and failure of the examination. On the premise of ensuring image quality, it is particularly important to reduce scan time. Goal(s): Applying deep learning reconstruction to improve temporomandibular joint MRI image quality and reduce scanning time. Approach: clinical experiments. Results: Deep learning reconstruction can improve image quality and significantly shorten scanning time of temporomandibular joint MRI. Impact: Deep learning reconstruction can significantly shorten scan time while improving image quality, helping radiologists to diagnose quickly and confidently，helping patients to spend less time feeling anxious in an MRI and more time living their life.
High fidelity, distortion-free brain diffusion-weighed imaging through Compressed SENSE combined Deep Learning reconstruction
Authors: Zhang Yajing , Wang Yiming , Su Wengu , Jiang Guangyu , Zhang Zhongping , Ren ZhongChang , Zhao Yan
Published: November 2024

Motivation: Distortion-free brain diffusion-weighted imaging (DWI) remains a challenge due to susceptibility artifacts and low signal-to-noise ratio (SNR). Goal(s): Assess the effectiveness of Compressed SENSE (CS) combined with deep learning (CS-DL) in improving brain DWI image quality. Approach: We compared various acceleration schemes and reconstruction methods on TSE-DWI brain images. Results: CS-DL with a factor of 4 improved image quality and SNR, while reducing scan time by 22%. Impact: Implementation of CS-DL in TSE-DWI holds promise for high-fidelity, distortion-free imaging, facilitating detailed analysis of small brain abnormalities in regions affected by magnetic field inhomogeneity and susceptibility.
Improved PCASL imaging of the lung using multiple labeling: Results of an ongoing study
Authors: Martirosian Petros , Oestreicher Louis , Munz Max , Pohmann Rolf , Schwartz Martin , Küstner Thomas , Liang Cecilia , Schick Fritz , Seith Ferdinand
Published: November 2024

Motivation: We have recently shown that a PCASL-bSSFP sequence provides high quality perfusion images of the lung. However, the perfusion signal is dependent on the respective cardiac cycle. Goal(s): Our aim was to further increase the perfusion signal of the lung parenchyma and/or to shorten the examination time using a new PCASL approach with multiple labeling. Approach: Four volunteers were examined using an ECG-triggered multiple labeling PCASL-bSSFP sequence with different number of labeling pulses, post labeling delays and repetition delays. Results: The PCASL sequence with multiple labeling appears to provide a higher perfusion signal of the lung parenchyma, reducing sensitivity to cardiac cycle variations. Impact: The new PCASL approach with multiple labeling appears to provide a significantly higher perfusion signal of the lung than measurements with single labeling pulse. The sequence makes the PCASL technique more robust and faster for future application in clinical practice.
Unveiling asymmetric magnetization transfer effects for BBB-ASL quantification in MCAO model rats
Authors: Nakamura Kazuhiro , Kinoshita Toshibumi
Published: November 2024

Motivation: This study explores the asymmetric magnetization transfer (MT) effects on recently developed blood-brain barrier integrity (BBB-ASL) technique, with a focus on different neck labeling gradient polarities. Goal(s): Validation of the asymmetric MT effect in BBB-ASL. Approach: BBB-ASL parameters of arrival transit time (ATT), intra-voxel transit time (ITT), exchange time (Tex) and CBF were estimated by our custom MATLAB program based on a two-compartment model. Results: Asymmetric MT effect changes not only depending on physical factors but also on the severity of the tissue. The difference might be related to changes in ITT and ATT. Impact: This study explores the asymmetric magnetization transfer (MT) effects on BBB-ASL with MCAO model rats. Asymmetric MT effect changes not only depending on physical factors such as magnetic field inhomogeneity but also on the severity of the tissue.
Arterial spin labeling in assessment of interstitial fibrosis in renal allografts
Authors: Jiang Bin , Wu Peng , Wan Jiayi , Ma Jiali , Xu Rui , Tian Yangyang , Hu Linkun , Zhu Mo
Published: November 2024

Motivation: Renal interstitial fibrosis, a prevalent, irreversible, progressive chronic kidney injury, emerges as a crucial prognostic determinant for kidney transplantation. Goal(s): To explore the feasibility and performance of arterial spin labeling (ASL) in evaluating the degree of renal fibrosis after renal transplantation. Approach: ASL was performed on 64 renal transplantation recipients. ASL parameters were obtained and analyzed. Results: The study revealed a noteworthy negative correlation between the measured renal blood flow values, obtained through ASL, and the degree of interstitial fibrosis in transplanted kidneys. ASL can effectively differentiate various degrees of fibrosis in transplanted kidneys. Impact: Using ASL technology, it is possible to non-invasively assess the degree of fibrotic changes in transplanted kidneys and the progression of kidney function, thereby achieving early detection, diagnosis, and treatment.
Age-Related Hemodynamic Variations in Children: A Study Using Single- and Multi-delay ASL
Authors: Prysiazhniuk Yeva , Petr Jan , Armindo Rui Duarte , Otáhal Jakub , Kynčl Martin , Moseley Michael , Tong Elizabeth , Zhao Moss
Published: November 2024

Motivation: There is lacking understanding of hemodynamic changes in children and their impact on MRI perfusion quantification. Goal(s): We aim to investigate age-related hemodynamic changes in developing brain. Approach: Perfusion parameters from single- and multi-delay Arterial Spin Labeling (ASL) MRI of MR-negative children are analyzed. Results: We found significant age-dependent differences between perfusion quantification with single- and multi-delay approaches. ATT followed a non-linear distribution in age and was heterogeneous across vascular territories Our findings support the use of multi-delay ASL for improved perfusion assessment in children and provide better understanding of hemodynamic changes in developing brain. Impact: This is the first study to investigate age-related arterial transit-time changes in children and their impact on perfusion quantification with perfusion MRI. Our findings prompt improved understanding of age-related perfusion changes and standardization of hemodynamic parameters in the pediatric cohort.
Quantitative measurement of renal arterial spin labeling: a noninvasive indicator of perfusion in renal artery stenosis
Authors: Zhang Xiaoxiao , Zhang Gumuyang , Xu Lili , Zhang Jiahui , Bai Xin , Chen Li , Peng Qianyu , Guo Erjia , Zhu Jinxia , Kuehn Bernd , Jin Zhengyu , Sun Hao
Published: November 2024

Motivation: There are few validated noninvasive indicators for renal perfusion and renal function in patients with renal artery stenosis (RAS)Goal(s): To explore the utility of arterial spin labeling (ASL) as a noninvasive indicator of renal perfusion and function in patients with RAS. Approach: We evaluated correlations between ASL-measured preoperative renal blood flow (RBF) and renal function, then compared preoperative and postoperative RBF. Results: ASL-measured RBF was significantly correlated with renal function. ASL was able to evaluate perfusion improvement in patients with RAS after interventional therapy. Impact: ASL can be used as an indicator of renal function and perfusion in patients with RAS. It can also be used to assess changes in renal perfusion after interventional therapy.
Calibrated fMRI with a background-suppressed PCASL and multi-echo BOLD dual-acquisition sequence
Authors: Chavarría Inés , Vidorreta Marta , Fernández-Seara María , Caballero-Gaudes César
Published: November 2024

Motivation: Calibrated fMRI quantitatively estimates cerebral metabolic rate of oxygen (CMRO2) by simultaneously measuring cerebral blood flow (CBF) with arterial spin labeling (ASL) and BOLD. Pseudo-continuous ASL (PCASL) with background suppression (BS) and 3D readout is recommended for CBF while multi-echo (ME) 2D BOLD has gained popularity. Nowadays, no calibrated fMRI sequence integrates both. Goal(s): Our goal is to combine them in a single sequence. Approach: We introduce a novel dual-acquisition calibrated fMRI sequence integrating BS-PCASL-3D GRASE for CBF and 2D multi-echo EPI BOLD. Results: Resting-state data in a healthy volunteer showed CBF values concordant with literature and improved BOLD tSNR and connectivity maps. Impact: We propose a novel sequence for calibrated fMRI that optimizes both CBF and BOLD measurements with a dual acquisition scheme, thus overcoming the limitations of previous ASL-BOLD calibrated fMRI sequences and showing promise for improved accuracy and reliability.
Clinical application of multi-delay arterial spin labeling MRI to Parkinson’s disease: more insights into hemodynamic disturbance
Authors: Wang Xue , Dou Weiqiang , Wu Jingtao
Published: November 2024

Motivation: Hemodynamic disturbance is one of the neuropathological characteristics of Parkinson's disease (PD). Multi-delay arterial spin labeling (m-ASL) MRI can optimize the accuracy of cerebral blood flow (CBF) quantification by taking into account arterial transit time (ATT). Goal(s): We aimed to comprehensively explore the detailed abnormalities of hemodynamics in PD and verify the application of m-ASL in PD diagnosis. Approach: Voxel-based analysis and machine learning approach were applied to this study. Results: Our findings identified impaired hemodynamics in PD with regional abnormalities of CBF, ATT and cerebral blood volume, providing complementary depictions of perfusion disruption in PD and highlighting the clinical feasibility of m-ASL. Impact: Our results provided complementary depictions of perfusion disruption in PD, and validated the promise of m-ASL in the investigation of underlying neurodegeneration and the clinical diagnosis of PD, providing an effective neuroimaging biomarkers for the diagnosis of neurodegenerative diseases.
Impact of Bayesian-Inference vs Least-Squares Fitting on Multi-Delay Arterial Spin Labelling Parameter Estimation
Authors: Fothergill Aisling , Kirk Thomas , Higgins David , Oliver-Taylor Aaron , Zhang Logan , Craig Martin , Chappell Michael , Parkes Laura
Published: November 2024

Motivation: Multi-delay Arterial Spin Labelling has application across multiple patient groups, but accurate quantification remains difficult, particularly for prolonged transit times and noisy data. Goal(s): Compare least-squares and Bayesian-inference model fitting for perfusion estimate accuracy. Approach: Least-squares and Bayesian-inference, specifically BASIL, pipelines were run on simulated and in-vivo ASL data with different SNR with three choices of prior/initial value for arterial transit time (ATT). The resulting cerebral blood flow (CBF) and ATT maps were compared. Results: ATT quantification is impacted by ATT prior/initial value in Bayesian-inference fitting more than least-squares fitting. Least-squares fitting is more susceptible to CBF overestimation at lower SNR. Impact: MD-ASL analysis method can impact ATT accuracy. Bayesian-inference fitting is better for lower SNR data when CBF is the primary interest. Least-squares fitting is better for higher SNR data, when prior/estimate is not well known, and for accurate ATT estimation.
Improving the Robustness of Pseudo-Continuous Arterial Spin Labeling in the Intraoperative MRI Setting.
Authors: Sánchez-Albardíaz Carmen , Calvo-Imirizaldu Marta , Aramendía-Vidaurreta Verónica , Vidorreta Marta , Bejarano Bartolomé , Gonzalez-Quarante Lain H , García Ana Aransay , Honorato Cristina , Cacho-Asenjo Elena , Martinez-Simon Antonio , Fernández-Seara María A
Published: November 2024

Motivation: Improvement of the intraoperative pseudo-continuous arterial spin labeling images for resection control in brain tumor surgery at 3T. Goal(s): Evaluate the effect of shortening RF pulse duration and gap on the labeling efficiency of PCASL and to test it in the intraoperative setting. Approach: A study in 10 healthy volunteers was done approximating the off-resonance effects at the labeling plane. 2 PCASL sequences were tested in 2 patients. Results: A PCASL sequence robust to off-resonance effects is obtained by shortening RF duration and gap. Impact: Shortening the RF duration and gap improves the PCASL labeling efficiency at high B0 off-resonance values at the labeling plane.
Arterial Transit Time and Cerebral Blood Flow Estimation in Multi-delay Pseudo-continuous Arterial Spin Labeling
Authors: Zheng Jiaxin , Shi Liangchen , Zhang Yong , Zhao Li
Published: November 2024

Motivation: The inherent low SNR of ASL limits the accuracy of cerebral blood flow (CBF) and arterial transit time (ATT) quantification. Although a postlabeling delay weighted delay method has been proposed, its benefits are not clear and cannot be used in general delay protocols. Goal(s): To compare multi-delay protocols and estimators. Approach: A general weighted delay estimator was proposed. Its performance was evaluated using Monte Carlo simulations and in-vivo experiments, compared to the direct model fitting. Results: The multi-PLD/LD protocols provided a superior estimation compared to the other protocols. The L2-norm fitting and the GWD estimator may provide improved CBF and ATT estimation, respectively. Impact: The multi-PLD/LD protocols provided superior accuracy and precision which may provide a feasible option for quantifying the dynamic characteristics of perfusion. The L2-norm fitting and the general weighted delay method may provide improved CBF and ATT estimation, respectively.
Optimizing pseudo-continuous ASL at 7 Tesla using dynamic low order-shim update
Authors: Chang Yulin , Stockmann Jason , Vidorreta Marta , Wheeler Natalie , Potthast Andreas , Benner Thomas , Taso Manuel , Detre John , Juttukonda Meher
Published: November 2024

Motivation: The labeling efficiency of pseudo-continuous ASL (pCASL) is reduced in the presence of large B0 off-resonance, which makes pCASL challenging at ultra-high fields due to susceptibility-induced off-resonance. Goal(s): We aim to significantly reduce the labeling efficiency dependence on B0 off-resonance of pCASL to achieve highest allowed labeling efficiency at a given labeling flip angle. Approach: 0th- (frequency) and 1st-order (x,y,z) shim components were used dynamically in the duration of labeling to mitigate B0 off-resonance without affecting imaging readout. Results: Improved labeling efficiency was achieved, which enabled higher-resolution perfusion imaging using pCASL. Selective labeling of blood was also demonstrated possible using our approach. Impact: Our approach provides a simple method to substantially remove the labeling-efficiency dependence on B0 off-resonance for pCASL at 7T. This enables high-quality ASL-based perfusion imaging at ultra-high fields for study of brain function, physiology, and pathology.
Direct myelin imaging using a 3D double adiabatic inversion recovery prepared ultrashort echo time (3D DIR-UTE) cones sequence
Authors: Athertya Jiyo , Lo James , Tang Qingbo , Doi Kenichiro , Shin Soo Hyun , Moazamian Dina , Lou Melissa , Suprana Arya , Malhi Bhavsimran Singh , Chang Eric , Du Jiang , Ma Yajun
Published: November 2024

Motivation: Many neurological disorders are characterized by myelin damage and loss. Robust long T2 suppression is of critical importance for accurate myelin quantification due to myelin's low proton densities. Goal(s): To develop a new UTE imaging approach that enables sufficient long T2 suppression for selective myelin imaging. Approach: A 3D DIR-UTE sequence was developed for selective myelin imaging on a 3T clinical scanner. The technical feasibility was tested by phantom and in vivo studies. Results: The long T2 signals were sufficiently suppressed with the DIR scheme. The myelin proton fraction in white matter regions quantified by the DIR-UTE was 5.42±0.35%. Impact: The long T2 signals were sufficiently suppressed with the DIR scheme. The myelin proton fraction in white matter regions quantified by the DIR-UTE was 5.42±0.35%.
Advanced Diffusion Detects Possible Myelin Changes in Alzheimer’s and Huntington’s Mouse Models and Treatment
Authors: Liu Andy , Simmons Danielle , Alvarez Connor , Wang Yi , Spincemaille Pascal , Longo Frank , Zeineh Michael , Georgiadis Marios
Published: November 2024

Motivation: Myelin degeneration is implicated in many neurological diseases. Diffusion and susceptibility MRI provide metrics to assess myelin degeneration. Goal(s): Treatment can affect myelination in neurodegeneration and diffusion metrics can be used to follow these changes. Approach: After detailed registration to the Allen Atlas, we examined key white matter areas (corpus callosum, fornix). Results: We observed diffusivity increases and kurtosis/axonal water fraction decreases in AD mice. We also saw an inverse trend in HD mice. Interestingly, this was reversed by treatment and confirmed histologically. Impact: We find diffusion changes suggesting myelin loss in Alzheimer’s mice, and inverse changes in Huntington’s mice that are reversed by disease-modifying treatment and confirmed histologically. Advanced diffusion metrics can be useful biomarkers to monitor myelin changes and treatment in neurodegeneration.
Cardiorespiratory fitness is associated with cerebral myelin content in aging
Authors: Faulkner Mary , Laporte John , Gong Zhaoyuan , Guo Alex , Bae Jonghyun , Palchamy Elango , Bouhrara Mustapha
Published: November 2024

Motivation: While higher cardiorespiratory fitness (CRF) is recognized as vital for brain health, its specific connection with white matter integrity, especially cerebral myelination, remains unclear. Goal(s): This study aims to investigate the association between CRF and myelin in cognitively unimpaired adults spanning a wide age range. Approach: We employed our advanced multicomponent MR relaxometry method to measure myelin water fraction, a direct proxy of myelin content, while CRF was assessed using the maximum rate of oxygen consumption, peak VO2. Results: Our results indicate that higher peak VO2 is associated with greater myelin content across several white matter structures, particularly among older adults. Impact: This work lays the foundation for future investigations to further elucidate the mechanisms underlying the relationship between cardiorespiratory fitness and cerebral myelination, as well as its potential as an interventional target in addressing age-related neurodegeneration, including in Alzheimer’s disease.
Intracranial EEG information flow is associated with metabolic and structural connectivity in temporal lobe epilepsy
Authors: Cai Bingyang , Jiang Shize , Huang Hui , Li Jiwei , Yuan Siyu , Cui Ya , Chen Liang , Luo Jie
Published: November 2024

Motivation: EEG measured directional information flow, cerebral metabolism and structural connectivity are altered in temporal lobe epilepsy, although their interplay have not been elucidated. Goal(s): To investigate whether and how structural and metabolic connectivity may separately or jointly affect the directional information flow. Approach: This study proposed a step-wise analysis to study the correlation between directional information flow measured by SEEG and glucose metabolism measured by FDG PET, and to explore their associations with different structural connectivity. Results: The inward information flow was negatively correlated with FDG uptake. White matter structural connectivity modulated the relationship between SEEG information flow and metabolism. Impact: Unraveling the underlying association of white matter connectivity and FDG metabolism with directional information flow strength could offer a comprehensive view of neuronal signal propagation and potentially improve seizure onset localization of focal epilepsy.
Mapping the Functional Role of White Matter Tracks by fusing Diffusion and Functional MRI
Authors: Lv Jinglei , Shine Mac , Kong Fei , Calamante Fernando
Published: November 2024

Motivation: The axonal connections in white matter(WM) are vital for brain function, but there is a knowledge gap of the functional role of WM tracks. Goal(s): To address this, we proposed a method to combine diffusion MRI (dMRI) and functional MRI (fMRI) to map the functional role of WM tracks during tasks. Approach: To each WM track generated with dMRI, we define the dynamic functional connectivity (DFC) that reflects the functional interaction of the GM it connects. General linear model(GLM) is then employed to measure the activation level during task. Results: Meaningful tracks that are associated with various tasks, implicating the activated networks. Impact: Our method paves the road to generate a functional atlas of white matter. It is important for understanding the functional architecture of the brain, the mechanism of brain disorders, such as, Schizophrenia, Epilepsy and Traumatic Brain Injury.
White Matter Tract Specific R2 Reflects Fiber Size and Microstructure
Authors: Wang Yicun , van Gelderen Peter , de Zwart Jacco , Zhou Jiazheng , Duyn Jeff
Published: November 2024

Motivation: T2 (or R2=1/T2) contrast is widely used for the study of neurological disease. Understanding how the relaxation relates to tissue microstructure may improve its clinical utility. Goal(s): To explore white matter microstructural correlates to R2. Approach: R2 mapping in healthy volunteers using Gradient Echo Sampling of Spin Echo at 7T. Atlas based analysis of fiber tracts, and analysis of corpus callosum subsections. Results: R2 variation was about 25% across the white matter and correlated inversely with the diffusion MRI metric of axonal diameter. Impact: T2 contrast is complex and influenced by factors including tissue iron and lipid content. Tissue microstructure, in particular white matter fiber thickness, may also play a role. Studying their relative contribution will help understand the nature of pathological tissue changes.
Mapping structural disconnection and morphometric similarity in Multiple Sclerosis: a longitudinal study
Authors: Tranfa Mario , Scaravilli Alessandra , Petracca Maria , Moccia Marcello , Quarantelli Mario , Cocozza Sirio , Brunetti Arturo , Pontillo Giuseppe
Published: November 2024

Motivation: Multiple sclerosis can be modelled as a network disorder. Progressive demyelination and neurodegeneration lead to structural disconnection and disruption of the morphometric similarity between gray matter regions. Goal(s): To obtain measures of structural disconnection and morphometric similarity networks from conventional MRI sequences and test whether they are sensitive to disease status and clinical disability. Approach: 461 patients were imaged. Using publicly available software, we computed structural disconnection using white matter lesions masks and normative tractography atlases. Likewise, morphometric similarity was computed from standard FreeSurfer outputs. Results: Structural disconnection and morphometric similarity networks are sensitive to disease status and explain clinical disability. Impact: Measures of structural disconnection and morphometric similarity networks obtained from conventional MRI sequences are sensitive to multiple sclerosis and its related physical and cognitive disability. Our approach could represent a way to overcome the limitations of the standard network analyses.
Abnormal white matter integrity revealed by Tracts Constrained by Underlying Anatomy (TRACULA) in obsessive-compulsive disorder
Authors: Chai Shuangwei , Li Hailong , Jiang Jiaxin , Cao Lingxiao , Zhou Huan , Li Bin , Huang Xiaoqi
Published: November 2024

Motivation: Diffusion tensor imaging (DTI) is a useful imaging technique to investigate white matter (WM) integrity and previous studies had revealed the disruption of WM related to neuropathology in obsessive-compulsive disorder (OCD). However, few study had reliably located the defect at track level. Goal(s): To investigate the WM integrity in OCD on a whole brain level using newly developed tractography method. Approach: We used Tracts Constrained by Underlying Anatomy (TRACULA) software to investigate the WM integrity in OCD. Results: We found widespread disruption of WM integrity in OCD, and for the first time, we observed abnormality in bilateral acoustic radiation in OCD. Impact: Using the innovated fiber tractography methodology, we were able to detect abnormality in bilateral acoustic radiation in OCD, which had been suggested by previous study using resting state fMRI.
Alterations in the lateralization of the infant brain with white matter injury
Authors: Bai Peng xuan , Wang Miaomiao , Liu Congcong , Zhu Linlin , Zhu Linlin , Feng Yuying , Yang Jian
Published: November 2024

Motivation: To investigation of cerebral asymmetry in full-term newborns and alterations in the lateralization of the infant brain with white matter injuryGoal(s): To explore brain volume laterality in normal full-term newborns and the effect of infant with WMI on laterality. Approach: Calculated asymmetry index, used non-parametrically to test the significant, compared the difference of AI with the WMI group. Results: The distribution of lateralized significance in brain regions between groups was near the same. Significant differences were found between the groups in the hippocampus, amygdala, superior temporal gyrus middle part GM, occipital lobe GM, superior temporal gyrus middle part WM, and caudate nucleus. Impact: WMI can cause localized lateralized changes in brain regions, which may be associated with future adverse developmental outcomes and might be a biomarker for predicting prospective developmental outcomes of WMI.
Changes in Brain Metabolites in People with HIV Before and After ART Treatment Using Whole-Brain MRSI
Authors: Salan Teddy , Sheriff Sulaiman , Aggarwal Deepika , Vyas Sameer , Singh Paramjeet , Govind Varan
Published: November 2024

Motivation: Chronic HIV infection is associated with widespread brain metabolite imbalance. However, few studies attempted to quantitate changes in brain metabolites in people with HIV (PWH) before and after initiating antiretroviral therapy (ART) treatment. Goal(s): the aim of this study is to evaluate changes in brain metabolites in PWH, before and after initiating ART, at the whole-brain level using MRSI spectroscopic imaging (MRSI). Approach: In this longitudinal study, changes in MRSI derived metabolite are evaluated before and after ART treatment using an atlas-based approach. Results: Our results showed persistent neuro-inflammation in HIV subjects post-ART despite maintaining viral suppression. Impact: This study advocates the need for continued monitoring of their brain health of PWH even after starting ART.
Neuroinflammatory Imaging Markers: Insights into the Cerebral Consequences of Post-Acute Sequelae of COVID-19 (PASC)
Authors: Huang Chuan , Ying Jia , Sekendiz Zennur , Fontana Ashley , Clouston Sean , Kritikos Minos , Bangiyev Lev , Luft Benjamin
Published: November 2024

Motivation: The motivation behind this study stems from the urgent need to decipher the neuroinflammatory processes contributing to Post-Acute Sequelae of COVID-19 (PASC) and to identify potential biomarkers for this condition through neuroimaging. Goal(s): The primary goal is to investigate the presence of cerebral imaging markers indicative of neuroinflammation in individuals experiencing neurological PASC. Approach: The study employed multi-shell diffusion MRI, to investigate changes in individuals with PASC. Diffusion parameters indicative of neuroinflammation were analyzed, by comparing among PASC patients, never-infected individuals, and acute-COVID controls. Results: PASC patients demonstrated increased whole-brain alterations consistent with axonal injury with inflammation. Impact: The findings of altered diffusion parameters in PASC patients shed light on the presence of axonal injuries with inflammation. These insights can potentially impact the diagnosing and treating long-term neurological symptoms of COVID-19, ultimately improving patient care and recovery strategies.
Scalable and Transferable U-Net for Accurate Simultaneous 3D MRI Segmentation of Gestational Sac and Decidual Tissue in Cesarean Scar Pregnancy
Authors: Shi Jie , Ye Jin , Fu Le , He Junjun , Li Tianbin , Cheng Jiejun
Published: November 2024

Motivation: Cesarean scar pregnancies (CSP) pose significant risks and complications. Accurate segmentation of the gestational sac (GS) and decidual tissue (DEC) in CSP through MRI is crucial for diagnosis, but current methods are limited in effectiveness. Goal(s): Introduce a large-scale and pre-trained model, Scalable and Transferable U-Net (STU-Net), to accurately segment GS and DEC simultaneously. Approach: 151 CSP females with structural MRI were enrolled. STU-Net was trained and evaluated. Results: The proposed STU-Net achieved promising segmentation performance. Impact: The proposed STU-Net enables precise segmentations of GS and DEC, potentially enhancing the diagnostic accuracy of CSP.
Contrastive Learning with Multi-Contrast Constraints for Segmentation in Renal Magnetic Resonance Imaging
Authors: Ghoul Aya , Umapathy Lavanya , Zhang Cecilia , Martirosian Petros , Seith Ferdinand , Gatidis Sergios , Küstner Thomas
Published: November 2024

Motivation: Supervised deep learning provides state-of-the-art medical image segmentation when large labeled images are accessible. However, manual segmentation suffers from prolonged delineation. Goal(s): In response to the 2024 ISMRM Challenge “Repeat it With Me: Reproducibility Team Challenge”, we aim to show the effectiveness of contrastive learning to find suitable initialization for segmentation with limited annotation. Approach: We use a multi-contrast contrastive loss guided by representational constraints to learn discriminating features within multi-parametric renal MR images and fine-tune the pretrained model on segmentation tasks. Results: Our findings validate that pretraining diminishes the needed annotation effort by 60% for different imaging sequences and enhances segmentation performance. Impact: Multi-contrast contrastive learning reduces annotation effort to train deep-learning segmentation models, confirming prior findings in a new cohort, within the 2024 ISMRM Challenge “Repeat it With Me: Reproducibility Team Challenge” and indicating its potential to improve multi-parametric imaging workflows.