bluuebunny/crossref_metadata_2025 huggingface dataset:
Relationship between Microvascular Obstruction and Regional Myocardial Strain by CMR after ST-segment-elevation Myocardial Infarction
Authors: Zhao Yanan , Cui Jianing , Liu Xueqian , Li Tao , Yue Xiuzheng
Published: August 2023

Effective risk assessment and stratification are essential for the clinical management of ST-segment elevation myocardial infarction (STEMI) patients. CMR imaging has become a beneficial imaging modality to assess myocardial morphology, function and infarct characteristics simultaneously. This study quantitatively evaluated relationship between microvascular obstruction and regional myocardial strain by Cardiac MRI after ST-segment-elevation myocardial infarction. Our data showed that regional strain parameters may be s new noninvasive imaging markers allowing comprehensive evaluation regional myocardium deformation.
Improved myocardial T1 mapping accuracy with Deep Learning reconstruction of low flip angle MOLLI series
Authors: Delso Gaspar , García-Polo Pablo , Gorodezky Margarita , Ariff Ben , de Vega Vicente Martínez , Urmeneta Javier
Published: August 2023

In this study, we explore the possibility of leveraging Deep Learning regularized reconstruction to enable lower flip angle MOLLI acquisition for myocardial T1 mapping. It has been shown in the past that lowering flip angle helps reduce various artifact sources, at the cost of lower signal to noise ratio. Regularized reconstruction can effectively manage image noise as well as increase feature sharpness. This hypothesis has been tested on a group of clinical patients referred for a cardiac MR exam.
Biplane Area-length Method Underestimates 3D Left Atrial Volume in Patients with Atrial Fibrillation
Authors: Maroun Anthony , Baraboo Justin , Gunasekaran Suvai , Hwang Julia , Liu Sophia , Kim Daniel , Greenland Philip , Passman Rod , Allen Bradley , Markl Michael , Pradella Maurice
Published: August 2023

The biplane area-length method is routinely used in clinical settings for left atrial volume (LAV) estimation. This technique is time-resolved but relies on geometrical assumptions of an ellipsoidal shape to estimate the LAV. In contrast, quantification derived from 3D segmentations on late gadolinium-enhanced magnetic resonance imaging and contrast-enhanced magnetic resonance angiography are static but do not rely on geometric assumptions. We compared the LAV estimation from these 3 methods in patients with atrial fibrillation and found a significant underestimation by the biplane area-length technique, indicating that this method may not capture the entire LAV in patients with complex anatomy.
Improving Accuracy of Myocardial T1 Estimation in MyoMapNet
Authors: Guo Rui , Chen Zhensen , Amyar Amine , El-Reiwady Hossam , Pierce Patrick , Goddu Beth , Nezafat Reza
Published: August 2023

MOLLI T1 mapping is routinely used for myocardial tissue characterization. Despite excellent precision, MOLLI has great T1 underestimation and a long breath-holding time. We previously developed a deep learning-based approach (MyoMapNet) for myocardial T1 mapping using four T1-weighted images. MOLLI T1 was used to train a fully connected neural network (FC), which resulted in a similar accuracy error as MOLLI. In this study, numerically simulated and phantom signals with the presence of different B0, flip angles, and heart rates were used to improve MyoMapNet T1 accuracy. Evaluation showed that MyoMapNet T1 could be improved and had higher precision than SASHA.
Cartesian dictionary-based T1 and T2 mapping of the myocardium
Authors: Henningsson Markus
Published: August 2023

A method for simultaneous T1 and T2 mapping of the myocardium is proposed, termed Multimapping, based on dictionary matching using a simple Cartesian single-shot acquisition across 10 cardiac cycles. The method is evaluated in a phantom, 12 healthy subjects and 43 patients with suspected cardiomyopathy.
Automated Time Adjusted Inversion Time Selection for Late Gadolinium Enhancement Imaging - Validation and Application on Patient Datasets
Authors: Yoon Seung Su , Schmidt Michaela , Rick Manuela , Chitiboi Teodora , Sharma Puneet , Emrich Tilman , Tillmanns Christoph , Seitz Andreas , Mahrholdt Heiko , Toupin Solenn , Pezel Théo , Garot Jérôme , Wetzl Jens , Maier Andreas
Published: August 2023

In cardiac MRI, the Late Gadolinium Enhancement technique is usually performed after inversion recovery scout sequences that are acquired to null the myocardial properly for optimal image contrast. In clinical practice, the selection and adjustment of the inversion time for healthy myocardium nulling are manually performed. To standardize and automate the process, we propose an automated deep-learning-based system combined with a linear regression model, which automates the selection of the inversion time, taking into consideration the time delay between the inversion time scout and LGE sequences. In this work, we validated the system in a large retrospective study (N=765).
Simultaneous learning of group-wise registration and joint segmentation of cardiac cine MRI with sparse annotation
Authors: Yang Junwei , Qian Pengfang , Kuestner Thomas , Lio Pietro , Shen Dinggang , Qi Haikun
Published: August 2023

Numerous deep learning methods have been proposed for cardiac cine MRI segmentation, while most of them require laborious annotation for supervised training. Herein, we propose an approach to perform group-wise registration and joint segmentation of cardiac cine images, by training a registration network in a self-supervised manner to align dynamic images to their mean image space and also a segmentation network in a weakly-supervised manner using sparsely-annotated data and predicted motions from the registration network. By training these two (registration and segmentation) networks simultaneously, our proposed joint learning approach provides better segmentations than the direct segmentation network.
Quantification of in-vivo myocardial stiffness in the rat heart using transient mechanical waves
Authors: Mota Marco Barbero , Annio Giacomo , Rucher Guillaume , Wittgestein Anna , Nordsletten David , Martorell Jordi , Sinkus Ralph
Published: August 2023

Heart biomechanics play a crucial role in the diagnosis and prognosis of cardiac diseases. However, current gold standard methods are highly invasive and preclude continuous monitoring of patients’ myocardium stiffness as a biomarker. In this study, we report the first results of a novel magnetic resonance elastography technique that provides with high temporal and spatial resolution enabling accurate transient shear wave speed values quantification, as thereof travel through heart tissue. Although biased due to the thin-plate geometry of the heart, early and mid-late systole, and early diastole preliminary results intuitively match the expected dynamical biomechanics of the human heart.
Data driven whole-brain cardiac signal regression from highly accelerated fMRI acquisitions
Authors: Marino Marco , Colenbier Nigel , Filippini Nicola , Pellegrino Giovanni , Marinazzo Daniele , Ferrazzi Giulio
Published: August 2023

Cardiac pulsation is a physiological confound of fMRI analysis pipelines and so accurate mapping of cardiac contributions to the BOLD signal is highly warranted. To overcome cardiac aliasing associated with the limited temporal resolution in fMRI, we developed a data-driven methodology to spatially and temporally resolve cardiac contributions from the BOLD signal itself (i.e without the need of processing external physiological recordings such as PPU/ECG signals). This is achieved by combining simultaneous multi-slice imaging and a dedicated hyper-sampling decomposition scheme.

The proposed methodology is fully data driven and it does not make specific assumptions on the shape of cardiac pulsation.
Brain microstructural changes in stroke patients at subacute/chronic stages by mean of advanced diffusion MRI: a longitudinal study
Authors: Wang Shi-Ming , Genovese Guglielmo , Diaz Belen , Huang Fan , Lehericy Stéphane , Zhang Hui , Rosso Charlotte , Branzoli Francesca , Palombo Marco
Published: August 2023

This study characterises microstructural changes in stroke lesions using NODDI and WMTI, two advanced diffusion MRI techniques, and compares their ability to detect WM alterations on the ipsi-lesional and contralesional sides of 12 patients scanned 2 weeks, 1 month and 3 months after onset. Region-of-interest analysis showed that ODI and FWF from NODDI were significantly altered at the lesion. Significant changes were also found in the WMTI parameters of several ipsi-lesional white matter tracts. Our prospective analysis suggests that the application of enhanced dMRI methods such as NODDI and WMTI can help to further comprehend the biophysical mechanism behind ischemia.
A pilot study on the application of explainable deep learning to ADC maps for predicting functional outcome of ischemic stroke patients
Authors: Zihni Esra , McGarry Bryony , Guo Jen , Sah Rani , Tadros George , Barber Philip , Kelleher John
Published: August 2023

Applying deep learning models to MRI scans of acute stroke patients to extract features indicative of functional outcome could assist a clinician’s treatment decisions. Here, we trained convolutional neural network models on ADC maps from hyper-acute ischemic stroke patients to predict 3-month mRS and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of good and poor outcomes. Although the models had poor predictive power, the visual explanations supported our previous findings that predictions might be based not on ischemic regions, but on other relevant information inherent in the image.
Deep learning Circle of Willis arterial labeling strategies for pipeline of cerebrovascular disease analysis
Authors: Bizjak Žiga , Chien Aichi , FAHA  , Tasič Jan , Špiclin Žiga
Published: August 2023

The correlation between different variants of the Circle of Willis (CoW) and cerebrovascular disorders such as stroke, aneurysms and mental disorders is not yet well understood. A step towards a better understanding of the role of CoW in the aforementioned diseases is the automatic labeling of main vessels. In this work we tested three different approaches and observed high mIoU value of 0.870. As such the automatic anatomical labelling of the CoW seems feasible for clinical evaluation of the association of different anatomical variants with the risk factors of cerebrovascular pathologies.
Fast adiabatic spin-echo with whole-brain ECCENTRIC for glioma metabolic imaging at 7T
Authors: Klauser Antoine , Strasser Bernhard , Bogner Wolfgang , Thapa Bijaya , Dietrich Jorg , Uhlmann Erik , Batchelor Tracy , Cahill Daniel , Lazeyras Francois , Andronesi Ovidiu
Published: August 2023

Metabolic alterations specific to glioma can be imaged by MR spectroscopic imaging (MRSI). Adiabatic spin-echo (ASE) MRSI enables spectral editing for specific metabolites with uniform excitation over whole-brain but needs long TR at 7T due to specific absorption rate which results in long acquisition times for high spatial resolution. Acceleration of ASE can be obtained with non-cartesian compressed sense MRSI (ECCENTRIC) acquisition. Here we evaluate the performance of ASE-ECCENTRIC metabolic imaging in glioma patients. We expect that enhanced spectral sensitivity, specificity and high spatial resolution of ASE-ECCENTRIC will improve diagnosis, prognostication, and treatment response monitoring in glioma patients.
An Automated Pose and Motion Estimation Pipeline in Dynamic 3D Fetal MRI
Authors: Xu Junshen , Zhang Molin , Vasung Lana , Turk Esra Abaci , Gagoski Borjan , Golland Polina , Grant P. Ellen , Adalsteinsson Elfar
Published: August 2023

Fetal motion is an important indicator of fetal health and nervous system development. Current assessments of fetal motion with MRI or ultrasound are qualitative and do not reflect the 3D motion of each body part . To study the detailed motion of fetuses, annotations of fetal pose are required, which would be time-consuming through manually-labelled data for each scan. In this work, we demonstrate an automated and efficient pipeline for fetal pose and motion estimation of fetal MRI using deep learning. The results of experiments show that the proposed pipeline outperforms other state-of-the-art fetal pose estimation methods.
SVoRT: Slice-to-volume Registration for Fetal Brain MRI Reconstruction with Transformers
Authors: Xu Junshen , Moyer Daniel , Grant P. Ellen , Golland Polina , Iglesias Juan Eugenio , Adalsteinsson Elfar
Published: August 2023

Volumetric reconstruction of fetal brains from MR slices is a challenging task, which is sensitive to the initialization of slice-to-volume transformations. Further complicating the task is the unpredictable fetal motion. In this abstract, we proposed a novel method for slice-to-volume registration using transformers, which models the stacks of MR slices as a sequence. With the attention mechanism, the proposed model predicts the transformation of one slice using information from other slices. Results show that the proposed method achieves not only lower registration error but also better generalizability compared with other state-of-the-art methods for slice-to-volume registration of fetal MRI.
Zero-Shot Physics-Guided Self-Supervised Learning for Subject-Specific MRI Reconstruction
Authors: Yaman Burhaneddin , Hosseini Seyed Amir Hossein , Akcakaya Mehmet
Published: August 2023

While self-supervised learning enables training of deep learning reconstruction without fully-sampled data, it still requires a database. Moreover, performance of pretrained models may degrade when applied to out-of-distribution data. We propose a zero-shot subject-specific self-supervised learning via data undersampling (ZS-SSDU) method, where acquired data from a single scan is split into at least three disjoint sets, which are respectively used only in physics-guided neural network, to define training loss, and to establish an early stopping strategy to avoid overfitting. Results on knee and brain MRI show that ZS-SSDU achieves improved artifact-free reconstruction, while tackling generalization issues of trained database models.
Improving the accessibility of deep learning-based denoising for MRI using transfer learning and self-supervised learning
Authors: Tian Qiyuan , Li Ziyu , Lo Wei-Ching , Bilgic Berkin , Polimeni Jonathan , Huang Susie
Published: August 2023

The requirement for high-SNR reference data for training reduces the practical feasibility of supervised deep learning-based denoising. This study improves the accessibility of deep learning-based denoising for MRI using transfer learning that only requires high-SNR data of a single subject for fine-tuning a pre-trained convolutional neural network (CNN), or self-supervised learning that can train a CNN using only the noisy image volume itself. The effectiveness is demonstrated by denoising highly accelerated (R=3×3) Wave-CAIPI T1w MPRAGE images. Systematic and quantitative evaluation shows that deep learning without or with very limited high-SNR data can achieve high-quality image denoising and brain morphometry.
Improving Across-Dataset Brain Tissue Segmentation Using Transformer
Authors: Rao Vishwanatha , Wan Zihan , Ma David , Tian Ye , Guo Jia
Published: August 2023

Despite achieving compelling performance, many deep learning automated brain tissue segmentation solutions struggle to generalize to new datasets due to properties inherent to MRI scans. We propose TABS, a new transformer-based deep learning architecture that achieves state-of-the-art-performance, generalization, and consistency. We tested TABS on three datasets of differing field strands and acquisition parameters. TABS outperformed RAUnet on our performance testing and remained consistent across test-retest repeated scans from a separate dataset. Moreover, TABS achieved impressive generality performance and even improved in performance across datasets. We believe TABS represents a generalized and accurate brain tissue segmentation alternative.
Workflow for Personalized RF Safety Assessment of Orthopedic Implants in MRI, a Proof of Concept
Authors: Stijnman Peter , Steensma Bart , Melis Gerrit , van den Berg Cornelis , Raaijmakers Alexander
Published: August 2023

Patients with non-labelled implants are often ineligible for MRI. Sometimes non-labelled implants are scanned by some hospitals based on literature and empirical insights, power constraints are abided to avoid tissue heating. Using an accelerated simulation method we demonstrate a proof of concept workflow to obtain the patient-specific SAR increase for multiple patient and implant positions within 2-3 hours. This workflow uses two orthogonal X-ray images to construct a 3D model of the implant which is inserted in a human model. The method enables more accurate scanning constraints and, a more informed risk vs benefit analysis for each patient.
Impact of RF Frequency Variation on AIMD MRI RF Safety Assessment
Authors: Huang Xin , Xin Xiyao , Feng Kevin , Chen Vick , Sison Shiloh
Published: August 2023

To assess the impact of RF frequency variation on AIMD MRI RF safety, the tangential electric field and cardiac pacing lead electrode RF heating transfer functions were obtained at different frequencies: around 64 MHz for 1.5T scanners and 128 MHz for 3T scanners. The result shows that the impact on in vivo RF heating prediction due to RF frequency shift is insignificant.
Quantitative Mapping of Brain Tissue Oxygenation around chronically implanted neural interfaces
Authors: Sugamura Yuka , Sridharan Arati , Jalloh Umu , Moghadas Babak , Teixeira Livia , Muthuswamy Jitendran , Kodibagkar Vikram
Published: August 2023

Chronic implantation of neural interfaces often experiences decrease in signal reliability over time. Tissue response, including the alteration of the oxygen kinetics, is thought to be a major cause. Here, we report the spatial mapping of brain oxygenation around chronically implanted electrodes using a previously developed quantitative MRI-based oxygen sensing technique, PISTOL. Seven 200 um diameter electrodes implanted in four mice allowed quantitative pO2 mapping throughout the 7-week study. No changes were observed in the baseline brain tissue oxygenation as well as the response to respiratory challenge. Future studies will combine this approach with electrophysiology, and ultimately perform simultaneous assessment.
Bariatric Surgery increases Cerebral Blood Flow in Women with Obesity
Authors: Dolui Sudipto , Chao Ariana , Williams Noel , Wadden Thomas , Detre John
Published: August 2023

Obesity is a significant risk factor for cerebrovascular disease including dementia. Cerebral blood flow (CBF) provides a biomarker of cerebrovascular health and can be measured non-invasively using arterial spin labeled (ASL) perfusion MRI. Here we examined longitudinal ASL data from adult women with obesity who underwent bariatric surgery. We demonstrated that weight loss was associated with significant increases in CBF from baseline to 6 and 18 months after surgery. Change in glucose and insulin level did not mediate the relationship, suggesting a more direct association between obesity and CBF.
Highly accelerated 4D flow MRI using compressed sensing in type B aortic dissection
Authors: Kilinc Ozden , Chu Stanley , Weiss Elizabeth K. , Baraboo Justin , Maroun Anthony , Jin Ning , Chow Kelvin , Bi Xiaoming , Davids Rachel , Mehta Chris , Malaisrie S. Chris , Hoel Andrew , Markl Michael , Allen Bradley D.
Published: August 2023

4D flow MRI is an established quantitative MR imaging technique for evaluating complex blood flow patterns. However, clinical applications of standard 4D flow MRI technique is limited by long scan times associated with multidimensional imaging. Compressed sensing (CS) acceleration can significantly reduce scan time through dramatic data undersampling, but it is not clear how this undersampling impacts assessment of flow patterns in type B aortic dissection (TBAD). In this study, we investigate performance of highly accelerated CS 4D flow MRI at two different acceleration factor levels (R=7.7 and 10.2) to evaluate aortic flow dynamics in volunteers and patients with TBAD.
Quantitative myocardial perfusion using whole-heart, 2D SMS and 3D sequences alternating every heartbeat
Authors: Huang Qi , Mendes Jason , Adluru Ganesh , DiBella Edward
Published: August 2023

A sequence with 2D simultaneous multi-slice (SMS) and 3D stack-of-stars (SoS) alternating each heartbeat sequence has been proposed previously1. Here, we increase the heart-coverage of the 2D SMS portion of the scan from 3 slices to 6 slices, to match the coverage of the 3D SoS method. Three minipigs and one healthy volunteer were studied using this method at both adenosine stress and rest cases, and the sequences gave flows with reasonable agreement.
Myocardial first-pass perfusion assessed by cardiac magnetic resonance and neutrophil to lymphocyte ratio in patients with cardiac syndrome X
Authors: zhang Gang , Xing Wei , Li Tingting , Zheng Yan , Huang Ying , He Junjing , Yue Xiuzheng
Published: August 2023

Cardiac syndrome X (CSX) is also known as microvascular angina pectoral, the pathogenesis of which has not been clearly determined [1]. A coronary angiography study [2] showed that neutrophil to lymphocyte ratio (NLR), as a marker of inflammatory response in a variety of cardiovascular diseases, was elevated in CSX patients. The NLR of CSX patients with impaired myocardial perfusion was higher than that of patients without it, such as unstable angina pectoris (UAP) patients. In this study, we found that the distribution of myocardial first-pass perfusion was heterogeneous in each segment of CSX patients in the resting stage, better than that of UAP patients. The correlation between perfusion parameters and NLR was high and significant.
Augmented Perfusion Quantified by MRI Depends on Collateral Recruitment in Middle Cerebral Artery Occlusion: A Pilot Study
Authors: Liu Mira , Saadat Niloufar , Jeong Yong , Roth Steven , Niekrasz Marek , Christoforidis Gregory , Carroll Timothy
Published: August 2023

We have developed an experimental model and quantitative MRI biomarkers to quantify the effects of therapeutic intervention to enhance collateralization in acute stroke. We performed controlled experiments in a pre-clinical canine model of ischemic stroke and use quantitative cerebral MR perfusion with DSC, intravoxel incoherent motion (IVIM), and neutron capture microsphere deposition, x-ray angiography for collateralization, and MR diffusion imaging for infarction to demonstrate a significant increase in perfusion and decrease in infarct growth in the hyperacute phase of an ischemic stroke.
Iterative Refinement of Expert Contours for Improved Ground-truth Quality in Intracranial Vessel Segmentation Neural Network Training
Authors: Zhou Hanyue , Xiao Jiayu , Ruan Dan , Fan Zhaoyang
Published: August 2023

With a typical slice-by-slice labeling fashion, the manual contouring process is subject to large intra-observer variation, especially for small-sized intracranial arteries. We propose an iterative refinement approach for ground truth contours with the help of deep neural networks for intracranial lumen and vessel wall segmentations. We demonstrate that the approach improved the smoothness of the predicted contours and feature quantification, which can potentially boost the robustness of a neural network as a consequence of the reduced uncertainty in expert labels.
Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer
Authors: Ottens Tim , Barbieri Sebastiano , Orton Matthew , Klaassen Remy , van Laarhoven Hanneke , Crezee Hans , Nederveen Aart , Gurney-Champion Oliver
Published: August 2023

Quantitative physiological perfusion parameters can be obtained from dynamic contrast-enhanced (DCE)-MRI. Conventionally, fitting is done with the non-linear least squares (NLLS) approach. However, the NLLS-fit suffers from long processing times and results in noisy parameter maps. In this work, we implemented a physics-informed gated recurrent unit (GRU) network with attention layers for estimating physiological parameters using the extended Tofts model. In simulations, we show it outperforms NLLS with more accurate and precise parameter maps. We show our method produced substantially less noisy parameter maps than NLLS in a fraction of the time in pancreatic cancer patients.
3D Inversion recovery-FLASH and myocardial perfusion MRI imaging in a sheep model of myocardial infarction
Authors: Cho Steven , Darby Jack , Williams Georgia , Dimasi Catherine , Holman Stacey , Selvanayagam Joseph , Macgowan Christopher , Morrison Janna , Seed Mike
Published: August 2023

We investigated the accuracy of cardiac MRI for detecting myocardial infarction (MI) induced by coronary artery ligation surgery in a sheep model. 3D-inversion recovery (IR) FLASH sequence and first-pass perfusion imaging was feasible and provided accurate information about the size and location of MI immediately after surgery and at 15-day follow-up. The IR-FLASH sequence detected infarcts (91.2% sensitivity, 99.9% specificity) compared with gold-standard pathology staining findings. This may prove useful for preclinical research in which it is important to document the natural history of myocardial injury, such as in studies investigating interventions aimed at mitigating the long-term impact of MI.
Heart-brain MRI evaluation in one exam: a feasibility study in healthy aging adults
Authors: Jarvis Kelly , Moore Jackson , Aristova Maria , Jin Ning , Torres Valerie , Schnell Susanne , Russell Eric , Markl Michael , Rogalski Emily , Ragin Ann
Published: August 2023

Mechanisms underlying heart-brain hemodynamic coupling and effects on the brain remain unclear due to challenges of measuring both heart and brain in a single MRI exam. We have developed a comprehensive MRI protocol that incorporates 1) 4D flow MRI of chest, 2) 4D flow MRI of head and 3) structural neuroimaging into one MRI exam to assess cardiovascular and cerebrovascular flow as well as white matter lesions and brain atrophy. This study demonstrates the utility of heart-brain MRI as viable new tool for in vivo evaluation of hemodynamic coupling along the entire heart-brain pathway.
Effect of Knockout of Two Acetyl-CoA Carboxylase Isoforms in the Isolated Mouse Heart on Oxidation of Exogenous and Endogenous Energy Sources
Authors: Sharma Gaurav , Kim Chai-Wan , Wen Xiaodong , Sherry A. Dean , Khemtong Chalermchai , Malloy Craig R. , Horton Jay D.
Published: August 2023

The product of acetyl-CoA carboxylase (ACC), malonyl-CoA, inhibits oxidation of long chain fatty acids by mitochondria. Cardiac-specific deletion of ACC-2 is associated with increased oxidation of fatty acids, as expected, the effects on glucose oxidation are controversial, and increased oxidation of stored triglycerides has been postulated. Expression of the other isoform, ACC-1, is preserved in ACC-2 mutant hearts, so alternative sources of malonyl-CoA may be important. We found that knock out of both isoforms was associated with a small increase in fatty acid oxidation, a small decrease in glucose oxidation, and little effect on oxidation of stored energy supplies.
A novel patient-adjustable coil technology substantially reduces RF heating of cardiac implantable electronic devices in pediatric patients
Authors: Bhusal Bhumi , Jiang Fuchang , Sanpitak Pia , Keil Boris , Multani Gurinder , Kutscha Nicolas , Bonmassar Giorgio , Webster Gregory , Popescu Andrada , Kim Daniel , Golestanirad Laleh
Published: August 2023

Infants and children with congenital heart disease may require cardiovascular implantable electronic devices (CIEDs), which typically precludes future evaluation by MRI due to risks associated with RF heating of implants. Here we demonstrate that a novel concept based on patient-adjustable rotating RF coil technology that was originally developed to reduce RF heating of brain implants can be adopted in pediatric patients with CIEDs to substantially reduce RF heating. Our simulations in a cohort of children with both epicardial and endocardial devices showed an 87% reduction in average SAR compared to conventional body coils.
Motion Robust Segmented Delayed Enhancement MRI by Partially Reversed ReOrdering for GeneRal SupprESSion of Motion Induced Variable Errors
Authors: Rehwald Wolfgang , Rojas Rafael , Mena Nestor , Seward Ryan , Gamoneda George , Dement Jeana , Jenista Elizabeth , Wendell David , Kim Han , Chen Enn-Ling , Klem Igor , Kimbrell Vera , Kim Raymond
Published: August 2023

Standard segmented delayed enhancement (DE) is particularly motion sensitive owing to its interleaved reordering required for inversion recovery (IR). Ghosting artifacts frequently result from imperfect breath holding. The sequence cannot run free-breathing (FB). We present a novel DE technique, PROGRESSIVE, using a partially continuous reordering and dummy pulses for simultaneous motion robustness and IR-compatibility. We tested PROGRESSIVE in simulations, a motion phantom, and a cohort of patients. PROGRESSIVE images were devoid of ghosting. When acquired FB, quality was significantly better than standard DE, and equally good during perfect breath holding. PROGRESSIVE has the potential for FB self-navigated DE imaging.
Accelerated Cardiac MR Imaging with Inline Cascaded Parallel Imaging and Generative Adversarial Neural Network (PI-GAN) Framework
Authors: Yoon Siyeop , Cai Xiaoying , Sai Eiryu , Assana Salah , Amyar Amine , Chow Kelvin , Paskavitz Amanda , Cirillo Julia , Manning Warren , Nezafat Reza
Published: August 2023

We developed and evaluated an inline cascaded parallel imaging and generative adversarial network for cardiac MRI. The preliminary results show excellent image quality of the cardiac cine images in four heat-beat.
Characterization of a novel rodent model of arteriovenous malformations (AVMs) with time-of-flight magnetic resonance angiography
Authors: Han Chul , Turner Gregory , Nguyen Candice , Lawton Michael , Oh S.
Published: August 2023

A mouse model of arteriovenous malformations was developed with a genetics-based approach that conditionally deleted the causative activin receptor-like kinase 1 (ACVRL1 or ALK1) gene. This model was characterized in vivo using time-of-flight angiography (TOF_MRA). We correlated radiographic and histopathologic findings and determined that the 55 Tagln-Cre (+);Alk12f/2f mouse was a promising experimental brain AVM model for future studies of AVM pathophysiology, growth, rupture, and therapeutic regression.
Implementation of OxFlow MRI with concurrent EEG for tracking the cerebral metabolic rate of oxygen during extended-duration scans of sleep
Authors: Barclay Alexander , Caporale Alessandra , Rao Hengyi , Lee Hyunyeol , Langham Michael , Wehrli Felix
Published: August 2023

OxFlow was used to track the global cerebral metabolic rate of oxygen (CMRO2) during wakefulness and sleep, with concurrent EEG, in eleven healthy subjects scanned continuously for 80 minutes. CMRO2 was derived from phase-contrast measurements of cerebral blood flow (CBF) in the neck with susceptometry-based oximetry measurements of venous oxygenation (SvO2) in the superior sagittal sinus (SSS). Results reveal that there is negligible bias between total CBF (tCBF), obtained by upscaling the value measured in the SSS, and CBF measured at the neck, suggesting that single-slice OxFlow can be implemented to simplify data acquisition and processing without sacrificing accuracy.
Time-Dependent Diffusion Tensor Imaging and Modeling of Aging Muscles: Correlations to Muscle Strain, Histology and Physical Assessment
Authors: Malis Vadim , Sinha Usha , Smitaman Edward , Obra Jed , Langer Henning , Mossakowski Agata , Baar Keith , Sinha Shantanu
Published: August 2023

Diffusion modeling (RPBM) of the time dependence of the skeletal muscle diffusion eigenvalues was applied to probe tissue changes with age. λ2 (TM=30 ms) was significantly lower in seniors; permeability and the residence time from RPBM were significantly positively correlated with grip size and 200 m walk. Significant negative correlations of λ1 and λ2 to age and to physical λ2 / λ3 to collagen matrix angle and to systolic/diastolic blood pressure were seen. λ2 and λ3 were significantly positively correlated to the projection of the strain on the diffusion eigenvector corresponding to the secondary eigenvalue.
Quantitative Ultrashort Echo Time (UTE) imaging of Osteochondral Junction
Authors: Lombardi Alecio , Wei Zhao , Moazamian Dina , Jerban Saeed , Jang Hyungseok , Le Nicole , Du Jiang , Chung Christine , Chang Eric , Ma Ya-Jun
Published: August 2023

MRI-based compositional imaging of cartilage has higher sensitivity to detect early changes in osteoarthritis. The osteochondral junction (OCJ) is of particular interest since it has been associated with early changes and progression of cartilage degeneration. However, conventional MRI sequences cannot differentiate with high contrast the OCJ region for quantification. Here we present quantification of T1, T1rho, T2*, and macromolecular fraction (MMF) of deep and superficial layers knee cartilage using 3D ultrashort echo time (UTE) MRI cones sequences and correlate the values from the OCJ with the degree of cartilage degeneration according to the MRI Osteoarthritis Knee Cartilage Score (MOAKS).
High Prevalence of Abnormal 3T MRI Findings in Clinically Asymptomatic Siblings of Patients with Osteochondritis Dissecans of the Knee
Authors: Kajabi Abdul Wahed , Zbyn Stefan , Tompkins Marc , Nelson Bradley , Shea Kevin , Carlson Cathy , Ellermann Jutta
Published: August 2023

This study examined seven clinically asymptomatic siblings of patients with Osteochondritis Dissecans (OCD) utilizing 3T MRI, including quantitative apparent diffusion coefficient (ADC) mapping to evaluate morphological integrity of the epiphyseal cartilage, integrity of secondary physis and trabecular bone quality in the distal femoral condyles of bilateral knees. A high prevalence (86%) of morphological MRI abnormalities that can be seen in early OCD and increased diffusivity (high quantitative ADC values) in trabecular bone were detected. The high prevalence of early signs of OCD in clinically asymptomatic siblings of OCD patients supports evidence of a genetic predisposition for the disease.
Trabecular bone imaging using 3D ultrashort echo time (UTE) Cones at the fat peak frequency: feasibility study
Authors: Jerban Saeed , Song Michael , Afsahi Amir , Moazamian Dina , Carl Michael , Ma Yajun , Lombardi Alecio , Chung Christine , Chang Eric , Du Jiang
Published: August 2023

High susceptibility levels at the marrow/bone interface may significantly reduce T2* of marrow, leading to trabecular bone volume overestimation when imaged using conventional MRI sequences. The presence of fat in bone marrow further complicates trabecular bone imaging due to chemical shift artifacts. In this study, an ultrashort echo time MRI (UTE-MRI) technique focused on the fat peak frequency was investigated to image trabecular bone ex vivo and in vivo. This technique was shown to improve trabecular bone imaging by minimizing chemical shift artifacts as well as susceptibility related short T2* effects, thereby providing more accurate estimation of trabecular bone structure.
Lower functional connectivity in areas of reduced cerebrovascular reactivity in elderly patients with long COVID
Authors: Cohen Alexander , Umfleet Laura , Franczak Malgorzata , Swanson Sara , Pommy Jessica , Agarwal Mohit , Yang Baolian , Banerjee Suchandrima , Wang Yang
Published: August 2023

Patients suffering from long COVID report cognitive symptoms months after disease onset, which may be related to neurovascular changes. Here, we evaluated functional connectivity in long COVID elderly patients and control subjects using a region of lower cerebrovascular reactivity (CVR) as the seed. We found significantly lower connectivity in long COVID patients that was widespread potentially linking alterations in CVR to functional connectivity and reduced cognitive ability in long COVID patients.
Assessing redox changes induced by treatment with pharmacological ascorbate in glioblastoma using T2* and QSM
Authors: Lee Chu-Yu , Petronek Michael , Cushing Cameron , Buettner Garry , St-Aubin Joel , Monga Varun , Buatti John , Cullen Joseph , Spitz Douglas , Allen Bryan , Magnotta Vincent
Published: August 2023

Pharmacological ascorbate (P-AscH-), as an adjuvant to standard treatment for glioblastoma, may selectively enhance cancer cell killing. The mechanisms of P-AscH- have been associated with redox changes in the labile iron pool. This study applies T2* and QSM to assess the redox changes in 40 glioblastoma patients receiving standard treatment and adjuvant P-AscH-. The results demonstrate an increase in T2* values and a decrease in QSM values within contrast-enhancing lesions 1 hour after P-AscH- infusion. These changes in T2* and QSM may result from the redox changes by P-AscH-, supporting the potential for imaging assessment of response to P-AscH-.
Coronavirus Disease 2019 (COVID-19): Impact of First and Second Waves on CT and MRI Patient Volumes at a Large Tertiary Center
Authors: Chu Stanley , Collins Mitchell , Pradella Maurice , Davids Rachel , Kramer Martin , Zimmerman Mathis , Fopma Sarah , Korutz Alexander , Avery Ryan , Markl Michael , Allen Bradley , Faber Blair , Carr James
Published: August 2023

The impact of the first wave of COVID-19 (Feb-Apr. 2020) on imaging volumes has been widely explored, but responses to a subsequent wave (Oct-Dec. 2020) remain to be investigated. In this study, a cloud-based analytics tool (CBAT) collected monthly CT/MRI scan volumes at a large tertiary center from Oct 2019 to Aug 2021. The first wave of COVID-19 significantly lowered all forms of CT/MRI imaging. The second COVID-19 wave did not significantly impact CT/MRI scan volumes, which remained at pre-pandemic levels during/afterwards. CBAT has the ability to assess impact/response towards two waves of COVID-19 at a large tertiary center.
Analysis of white matter tissue integrity of COVID-19 recovered patients using diffusion weighted imaging
Authors: Santos-Díaz Alejandro , Sanchez-Alvarado Berenice , Melendez Oscar Marrufo , Mezo Roger Carrillo , Gongora Antonio Arauz , Rodriguez Monica
Published: August 2023

Evidence supports that the virus SARS-CoV-2 has a potential neuroinvasion causing neurological symptoms, even after recovery. Diffusion Weighted Imaging (DWI) allows us to detect the microstructural changes in white matter tissue integrity. In this work, we studied the neurological manifestations of COVID-19 in apparent diffusion coeficient (ADC), fractional anisotropy (FA), and metrics derived from fixel-based analysis (FBA) such as fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC). We found statistically significant differences in 34/43 fiber bundle regions from the IIT Human Brain Atlas in the five different metrics studied.
Diffusion Tensor Imaging Biomarkers for Chronic Pain Following Mild Traumatic Injury
Authors: Yang Ho-Ching , Naugle Kelly , Wen Qiuting , White Fletcher , Wu Yu-Chien
Published: August 2023

To investigate the neuropathogenesis of post-traumatic headache following mild traumatic brain injuries (mTBI), this study applied diffusion tensor imaging and functional magnetic resonance imaging to explore relationships between brain structural and functional changes and sensitization, pain inhibitory capacity, psychological factors and headache pain in mTBI subjects. The results found structural and functional alterations in mTBI. Also, the results showed that DTI metric differences between mTBI subjects and controls can be used to predict pain/psychological measurements, which indicated the relationship between white matter disruption and measurements of endogenous pain modulation and psychological distress in mTBI.
Magnetic Resonance Imaging to Identify Chronicity and Treatability of Migraine and Chronic Headache Disorders
Authors: Esteve Isaac Manzanera , Evans Adam , Johnson Brian , Robinson Ryan , Sengupta Saikat , Pollins Alonda , Thayer Wesley , Kassis Salam Al
Published: August 2023

Migraine Headaches (MH) are the 6th leading cause of years lived with disability worldwide, affect 15% of the US population, and are nearly twice as prevalent in veterans who have been deployed. Our study focuses on pathology of the greater occipital nerve (GON), which occurs with a high prevalence in individuals with a history of trauma (i.e. traffic accidents, explosions, falls). In our pilot MRI scans and operative experience, migraines associated with GON pathology displayed a pathologic thickening of the nerve validated intraoperatively. These results present MRI as a potential biomarker of headache pathologies of MH associated with GON pathology.
Evaluate Values of First-order Features based on Advanced DWI Models in Predicting the HER-2 Expression in Breast Invasive Ductal Cancer
Authors: Du Siyao , Wang Mengfan , Liu Shasha , Bian Xiaoqian , Chen Xinyue , Guo Liangcun , Huang Guoliang , Zhao Ruimeng , Peng Can , Jiang Wenhong , Shi Qinglei , Yan Xu , Yang Guang , Zhang Lina
Published: August 2023

In this study, we compared first-order features of six DWI models in predicting the HER-2 expression in breast invasive ductal carcinoma, including conventional mono-exponential model derived parameter ADC, IVIM derived parameters (D, Dstar, f), DKI derived parameters (D, K), SEM derived parameters (DDC, α), FROC derived parameters (D, β, mu), CTRW derived parameters (D, α, β), and a comprehensive diagnostic logistic regression model was established to improve the diagnostic performance. We found that CTRM and IVIM derived first-order features demonstrated the powerful performance, and their combination reached the highest performance. The finding has a great value in breast cancer treatment.
Spectrally encoded multi-spectral imaging (SEMSI) for off-resonance correction near metal with multi-spin echo and parallel imaging
Authors: Zhou Xuetong , Lee Philip , Yoon Daehyun , Hargreaves Brian
Published: August 2023

Metal implants induce severe B0 field inhomogeneities, causing severe distortion near the implant. In this work, we combined multi-echo spin echo and parallel imaging to an approach that uses echo shifting to encode spectral components and resolve the metal-induced artifacts. Phantom and in vivo experiments were conducted and indicated that the proposed method provides comparable artifact reduction and image quality to existing multispectral imaging approaches in comparable scan time.
Subject-specific multi-echo fMRI derived seed-based connectivity as a biomarker for mild traumatic brain injury
Authors: Bhushan Chitresh , Madhavan Radhika , Jog Amod , Abad Nastaren , Fernandez Brice , Marinelli Luca , Morris H. Doug , Hood Maureen , DeMarco J Kevin , Shih Robert , Kohls Gail , Kenney Kimbra , Ho Vincent , Foo Thomas
Published: August 2023

Mild traumatic injury (mTBI) patients exhibit acute symptoms including headaches and memory problems that sometimes persist for months, though CT/MRI scans appear normal. The purpose of this study was to identify functional biomarkers of mTBI during the 3-months following injury using seed-based functional connectivity derived from multi-echo resting state functional MRI. In this work, we use multi-band multi-echo resting state fMRI to estimate seed-based functional connectivity. We propose a novel quantitative analysis methodology of suitable for voxel-wise single-subject assessment of disrupted functional connectivity by using an outlier-analysis approach.
Assessing the Role of Deep Learning in Joint Motion and Image Estimation
Authors: Nghiem Brian , Wu Zhe , Haskell Melissa , Kasper Lars , Uludag Kamil
Published: August 2023

We investigated the performance of CNN-assisted joint estimation in two cases of severe motion corruption in a 2D slice of T2w FSE MRI. We showed that the inclusion of the CNN can help speed up convergence of the joint estimation algorithm, corroborating previous findings. We also showed one case in which joint estimation failed to converge to the correct image and motion parameters, with and without the CNN. A more exhaustive study is required to confirm whether deep learning can help joint estimation salvage otherwise unsalvageable corrupted data.
Prospective Motion Correction for MR Spectroscopy in the Human Brain Using Multi-Slice Spiral Navigator
Authors: Jayadev Nutandev Bikkamane , Henry Pierre-Gilles , Deelchand Dinesh
Published: August 2023

MR spectroscopy is prone to motion artifacts due to long scan times. Existing motion correction techniques require additional hardware or are inherently slow. We demonstrate a fast and accurate prospective motion correction method for MRS using spiral navigators. A multi-slice to volume registration approach accelerates the motion parameter determination and achieves artifact free spectra at intended volume of interest.
Visualizing 4,230 White Matter Tracts at Once
Authors: Chandio Bramsh , Chattopadhyay Tamoghna , Owens-Walton Conor , Reina Julio Villalon , Nabulsi Leila , Thomopoulos Sophia , Guaje Javier , Garyfallidis Eleftherios , Thompson Paul
Published: August 2023

We propose a dimensionality reduction method, based on the bundle-based minimum distance metric and the UMAP technique, to disentangle and visualize clusters in whole-brain tractography. In multishell diffusion MRI data from 141 elderly subjects, 30 tracts were extracted per subject using auto-calibrated RecoBundles in DIPY. A (141x30)x(141x30) bundle distance matrix was calculated and fed into UMAP. Embedding space maps showed that the same bundles were consistently mapped across subjects, making it easier to identify outliers and define clusters for population-based statistical analysis.
Fast full-wave patient-specific field simulations in seconds with MARIE 2.0
Authors: Guryev Georgy , Milshteyn Eugene , Giannakopoulos Ilias , Adalsteinsson Elfar , Wald Lawrence , White Jacob
Published: August 2023

It was recently demonstrated that the combination of fast low-resolution tissue mapping and fast voxel-based field simulation can be used to perform a patient-specific MR safety check in minutes[4,5].However,field simulation required several of those minutes,making it too slow to perform the dozens of simulations that would be needed for patient-specific optimization.In this abstract we describe a compressed-perturbation-matrix technique that nearly eliminates the computational cost of including complex coils (or coils+shields) in voxel-based field simulation of tissue,thereby reducing simulation time from minutes to seconds.The approach is demonstrated on a wide variety of head+coil and head+coil+shield configurations,using the latest implementation of MARIE2.0.
Reducing fluctuations in prostate DCE-MRI physiological parameters using muscle as a reference to modulate arterial input function
Authors: Fan Xiaobing , Chatterjee Aritrick , Lee Grace , Yousuf Ambereen , Antic Tatjana , Oto Aytekin , Karczmar Gregory
Published: August 2023

We introduce a method to modulate arterial input functions (AIFs) using gluteal muscle as reference. The method was tested on a split injection protocol for prostate dynamic contrast enhanced (DCE) MRI: first injecting 30% of the standard dose (30PSD), after two minutes, followed by 70% of the standard dose (70PSD) of gadoterate meglumine. The AIFs were measured from the iliac artery for both doses and used to analyze 70PSD data. By assuming gluteal muscle Ktrans=0.1min-1 and ve=0.1 to modulate AIFs for both doses, the fluctuations of calculated physiological parameters were significantly reduced when compared to using the original AIFs.
Quality Assessment of Mouse fMRI: Evaluating Temporal Stability of Preclinical Scanners
Authors: Urosevic Mila , Fouquet Jeremie , Chakravarty M.
Published: August 2023

Mouse functional magnetic resonance imaging (fMRI) has interesting potential for basic neuroscience, but there is significant variability in data quality across studies. This variability may be partly explained by differences in scanner stability, however there are no reports of scanner quality assessments in the mouse fMRI field. We evaluated the stability of our preclinical scanner by acquiring an fMRI time-series of an agarose phantom then computing stability metrics. We found that our scanner has acceptable temporal stability but unexpected periodic fluctuations. Our methods are openly available so that other groups can easily implement stability assessments on their respective preclinical scanners.