bluuebunny/crossref_metadata_2025 huggingface dataset:
Hyperplane-based tract parcellations for improved anatomical coherence in tractometry
Authors: Neher Peter , Peretzke Robin , Maier-Hein Klaus
Published: November 2024

Motivation: The subdivision of WM tracts into individual parcels required for tractometry analysis, suffers from blurred parcel borders and wrong assignments of complete tract regions, particularly in fanning tracts. Goal(s): The goal of this work is to overcome these limitations with a new parcellation approach that yields clearly delineated tract parcels and avoids wrong parcel assignments even in challenging tracts. Approach: We propose a self-supervised approach based on optimally separating hyperplanes, obtained using large-margin classifiers, to separate tracts into parcels. Results: We compare our approach to two popular state-of-the-art approaches and clearly show markedly improved results in 24 tracts and 98 subjects. Impact: A new method to parcellate tracts for fiber tractometry, avoiding frequent errors of state-of-the-art approaches, particularly in complex tracts with a fanning topology. This might lead to improved tractometry analysis and potentially insights that were not possible with previous approaches.
Myelin Water Imaging on MAGNUS (high-performance Mesoscale Anatomy Gradient for Neuroimaging with Ultrafast Scanning) system
Authors: Zhang Jing , Abad Nastaren , Banerjee Suchandrima , MacKay Alexander , Foo Thomas
Published: November 2024

Motivation: Myelin water fraction (MWF) is conventionally measured using the T2 decay curve. Due to hardware limitations, for spin-echo based sequences, TR and TE times were restricted by SAR and gradient slew rate. Goal(s): Our objective is to reduce the total scan time by shortening the TR time. A shorter TE time should also capture more signal from the short T2 components of myelin water. Approach: We implemented myelin water imaging (MWI) on the MAGNUS system with reduced TR and TE times. Results: When implemented on the MAGNUS system, MWI could be carried out in a shorter time and the MWF were slightly larger. Impact: The work aims to optimize MWI by shortening scan times. It successfully achieves this, leading to quicker, high-quality MWI acquisition, benefiting medical diagnostics and research efficiency.
Discriminating High-Grade and Low-Grade Pediatric Gliomas by time-dependent diffusion MRI
Authors: Wang Xinyun , Zheng Hui , Zhao Xiance , Wu Zhigang , Huang Shan , Ren Gang , Wang Dengbin
Published: November 2024

Motivation: Gliomas are the most common type of central nervous system tumors in children, and the histological classification plays a crucial role in determining the prognosis and the treatment. A non-invasive approach is needed for pre-surgery characterization of the histopathology of the tumor. Goal(s): The aim of this study was to explore the feasibility of applying time-dependent diffusion magnetic resonance imaging (td-dMRI) parameters in discriminating between high-grade gliomas (HGG) and low-grade gliomas (LGG). Approach: Nine pediatric glioma patients were involved and underwent td-dMRI scan. Results: The td-dMRI parameters may serve as potential markers for the differentiation between high-grade gliomas (HGG) and low-grade gliomas (LGG). Impact: Time-dependent diffusion MRI parameters can distinguish pediatric high-grade and low-grade gliomas noninvasively, aiding personalized treatment and enhancing prognosis, among other potential applications.
Modified Look-Locker PETRA MRI for Quiet 3D T1 Mapping
Authors: Wang Yulin , Zeng Jie , Zhang Jichang , Zhu Yuliang , Ke Shiying , Niu Shengyang , Lin Lili , Yao Chendie , Wang Chengbo
Published: November 2024

Motivation: T1 mapping is essential for demyelination disease diagnosis and brain development assessment, but conventional sequences suffer from considerable acoustic noise or long scanning time. Goal(s): This study aims to develop a silent, accurate and fast 3D T1 mapping method. Approach: It uses inversion recovery-prepared rotating radial trajectories to avoid significant gradient switching, forming a series of images collected at different TIs. Results: Its accuracy is compared with IRSE method, acquiring 0.9994 R2, and its feasibility is tested on the human brain with gray and white matter T1 values close to literatures. The SPL is measured only 1.5 dBA higher than the background noise. Impact: The high acoustic noise of sequences like MPRAGE has disadvantages for pediatric imaging due to its risks of neonatal hearing loss and decreased scanning comfort. Our study might provide a solution with nearly silent sound and highly accurate measurements.
Brain iron metabolism in neonates with hyperbilirubinemia by Quantitative Susceptibility Mapping
Authors: Wang Yuanyuan , Yang Linfeng , Chen Tao , Li Xianglin , Guo Lingfei
Published: November 2024

Motivation: The motivation of this study was to investigate the changes in brain iron metabolism in neonates with hyperbilirubinemia (NHB). Goal(s): The goal was to assess the diagnostic value of QSM in NHB and explore its brain iron metabolism. Approach: Clinical trialResults: Brain iron content can dynamically change with serum bilirubin level in NHB, and brain iron content was significantly higher than that of healthy neonates in the putamen. Impact: The QSM value of the putamen can accurately diagnose NHB, and provide early warning of brain injury.
Diffusion tensor imaging quantification of brain matter tracts in gene therapy treated GM1 gangliosidosis patients
Authors: Shazeeb Mohammed Salman , Vardar Zeynep , Peker Ahmet , Kuhn Anna , Lindsay Clifford , Gray-Edwards Heather , Lebel Catherine , Vedantham Srinivasan , Johnston Jean , D'Souza Precilla , Acosta Maria , Tifft Cynthia , Vachha Behroze
Published: November 2024

Motivation: Type II GM1 gangliosidosis is a rare disease that lacks reliable quantitative biomarkers to assess neuronal health. Goal(s): We sought to quantify diffusion tensor imaging (DTI) parameters of different brain regions known to be affected in GM1 to track neuronal changes especially with the advent of gene therapy in treating GM1. Approach: We quantified fractional anisotropy and radial diffusivity changes at different timepoints using DTI-MRI to evaluate myelination changes in GM1 patients treated with gene therapy and compared them to untreated patients and healthy controls. Results: DTI can be used to demonstrate efficacy of gene therapy in monitoring disease progression/regression in GM1 patients. Impact: This study addressed the need for reliable biomarkers in assessing neuronal health in type II GM1 gangliosidosis. Using DTI parameters, we demonstrated the efficacy of gene therapy in reliably monitoring myelination changes in GM1 patients.
Degree of endometrial fibrosis evaluated using diffusion weighted imaging: initial findings
Authors: Zhou Zhengyang , Zhou Nan , Jiang Peipei
Published: November 2024

Motivation: Patients with endometrial fibrosis suffer from uterine infertility. Evaluating the degree of fibrotic endometrium can help clinicians to select the optimal treatment scheme. Goal(s): To investigate the value of DWI in distinguishing the degree of endometrial fibrosis. Approach: Mean intensity and standard deviation of endometrial ADC value in region of interest (ROI) (ADCROI and ADC-SDROI) and volume of interest (VOI) (ADCVOI and ADC-SDVOI) were measured and analyzed between healthy women, mild to moderate endometrial fibrosis (MMEF) patients and severe endometrial fibrosis (SEF) patients. Results: Endometrial MRI parameters were significantly correlated to the degree of endometrial fibrosis, which can well differentiate each group. Impact: This prospective study demonstrated the feasibility of DWI for quantitatively evaluating the degree of endometrial fibrosis, which can help clinicians to select the optimal treatment scheme and to perform dynamic follow-ups of therapy effect for patients with endometrial fibrosis.
Characterization of Myocardial Microstructure using DT-CMR with Ultra-High-Performance Gradient Scanner in Healthy Subjects
Authors: Chen Shi , Kara Danielle , Garrett Thomas , Orlandi Peyton , Kwon Deborah , Nguyen Christopher
Published: November 2024

Motivation: DT-CMR has emerged as a promising tool for in vivo use in cardiovascular disease diagnosis, but there is currently a lack of reporting on the normal values for comparison. Goal(s): Our goal was to evaluate the possibility of performing DT-CMR with an ultra-high-performance gradient MR scanner and provide normal ranges of quantitative DT-CMR measures. Approach: Seventy healthy subjects underwent DT-CMR, and the results were compared between men and women. Results: It is feasible to perform in vivo DT-CMR with a maximum gradient strength of 200 mT/m. The MD, FA and HAT for healthy subjects are (1.54±0.02) x10-3mm2/s, 0.33±0.005 and -0.81±0.02 º/% respectively. Impact: Our study reports on the initial experience of conducting DT-CMR on a new investigational ultra-high-performance gradient scanner. The normal ranges of quantitative DT-CMR values are instrumental to establish baseline and distinguish the diseased from healthy in the future.
Test-retest repeatability of intravoxel incoherent motion (IVIM) MRI in the cervical cord at 3T
Authors: Lebret Anna , Lévy Simon , Freund Patrick , Callot Virginie , Seif Maryam
Published: November 2024

Motivation: High precision of intravoxel incoherent motion (IVIM) MRI parameters in the cervical cord can facilitate studies targeting perfusion impairment and substantiate clinical findings. Goal(s): To determine the test-retest repeatability of IVIM in the cervical cord using four different common model fitting approaches. Approach: Test-retest cardiac-gated IVIM acquisitions, sensitive to perfusion, were performed at 3T in 10 healthy subjects at C1-C3 levels. Reliability was assessed using relative test-retest differences, within-subject coefficients of variation and intraclass correlation coefficients. Results: IVIM parameters in the cervical cord showed high repeatability at the group level in the white and grey matter. Impact: The high test-retest repeatability of IVIM parameters in the cervical cord will help the interpretation of future clinical findings and support its relevance for applications in various neurological diseases affecting the spinal cord.
Effect of simultaneous multislice imaging and repetition time on biexponential liver intravoxel incoherent motion
Authors: Loh Martin , Stuprich Christoph , Führes Tobit , Benkert Thomas , Uder Michael , Laun Frederik
Published: November 2024

Motivation: Intravoxel incoherent motion (IVIM) measurements are often performed with various acquisition parameters among different studies. Previous studies showed differences in f during the application of simultaneous multislice (SMS). Goal(s): The goal is to investigate the influence of SMS and the concomitant T1-effects due to the reduction of TR. Approach: Diffusion-weighted images of the liver were acquired for conventional and SMS excitation with long and short TR each. Results were verified with a biophysical model. Results: The evaluation indicates no dependence of IVIM parameters on SMS. Yet, the reduction of TR appears to be relevant for small gaps between slices in the simulations. Impact: This study indicates that while comparing different IVIM studies, TR and gaps between slices must be taken into account, as they may substantially alter the measured f. IVIM parameters for SMS excitation are well comparable with conventional excitation, though.
Deep-learning reconstruction of under-sampled readout-segmented echo-planar diffusion-weighted images at multiple b values
Authors: Kaushik Sumit , Zijlstra Frank , Kaandorp Misha , While Peter
Published: November 2024

Motivation: Readout-segmented (rs-) EPI typically yields improved DWI image quality compared to single-shot EPI, but it is time-consuming. This presently precludes its clinical use for multiple b-value diffusion modeling like IVIM. Goal(s): To accelerate rs-EPI image acquisition without compromising quality using convolutional neural networks (CNNs) trained on high-resolution and under-sampled low-resolution images. Approach: Three CNNs were trained and tested on synthetic and in vivo DWI datasets. The CNNs were tasked with reconstructing high-resolution images at multiple b values, and IVIM parameter maps were estimated for comparison. Results: The CNNs reconstructed high-resolution DWI images and IVIM parameter maps of comparable quality to the fully-sampled data. Impact: This approach could substantially reduce the scan times of readout-segmented EPI when used for multiple b-value diffusion modeling. It therefore offers the potential for improved image quality for IVIM imaging, at scan times comparable to conventional single-shot EPI acquisition.
Association of Cognitive Reserve and Glymphatic Function in Mild Cognitive Impairment Patients Using DTI-ALPS
Authors: Zhou Liang , Yang Wenxia , Liu Yang , Li Jiaceng , Ai Kai , Wu Peng , Zhang Jing
Published: November 2024

Motivation: Cognitive reserve (CR) could moderate the cognitive decline. But the association between the glymphatic function and CR in mild cognitive impairment (MCI) remains unclear. Goal(s): To evaluate the association between CR and glymphatic function. Approach: By using diffusion tensor imaging along the perivascular space (DTI-ALPS) to acquire an index (ALPS-index) in MCI and normal controls (NCs). The education level was used as a representation of CR. We used mediator analysis to explore the association among ALPS-index, CR and cognitive function. Results: MCI exhibited decreased glymphatic activity than NCs and CR mediates the link between the glymphatic system and cognition. Impact: MCI patients exhibited decreased glymphatic activity compared to NCs. Our study confirms the protective effect of CR in MCI patients, and this protective effect can against the cognitive impairment by improving CR.
Reduced Glymphatic System Activity Induces Elevated GABA Level in Hepatic Encephalopathy Patients: A Potential MRI-Based Mechanism
Authors: Zhang Qian , Zhong Ming , Nie Lisha , Wang Rongpin
Published: November 2024

Motivation: Hepatic encephalopathy (HE) is a severe complication of decompensated cirrhosis. Understanding the pathogenesis is crucial for developing effective treatments and improving outcomes. Goal(s): This study delved into the HE pathogenesis by employing diffusion tensor imaging analysis focused on the perivascular space (DTI-ALPS), alongside the quantification of GABA levels in the thalamus of patients with cirrhosis. Approach: 48 patients with decompensated cirrhosis were enrolled, with all of them undergoing DTI and MRS assessments. Results: We found that patients with HE exhibited impaired glymphatic system function and increased levels of the inhibitory neurotransmitter GABA in the thalamus, with a noteworthy correlation found between them. Impact: The discovery of decreased glymphatic system activity and increased GABA levels may hold significant promise in understanding the potential mechanisms behind HE patients. These findings could contribute to the development of targeted therapies for HE and improve patient care and outcomes.
A low rank k-space approach to channel-by-channel reduced FOV imaging
Authors: Ali Fadil , Bydder Mark , Zbyn Stefan , Eck Brendan , Garelick Ben , Saucedo Andres , Ghodrati Vahid , Joy Ajin , Finn J. , Li Xiaojuan
Published: November 2024

Motivation: To introduce a channel-by-channel reduced field-of-view (FOV) method for an arbitrary region of interest (ROI). Goal(s): With Nc channels as input, we output Nc channels, with the signal outside of the target ROI vectorially nulled while preserving the original channel sensitivity profiles and relative phases for each channel. Approach: Using a full-FOV calibration set, we learned the linear operators needed to be applied on local k-space neighbors across all channels in order to vectorially cancel the signal outside of the target-ROI for any channel. Results: We were able to generate reduced FOV images in phantom and in vivo settings. Impact: A reduced FOV method is introduced that determines the linear operators needed to vectorially cancel the signal outside the target ROI. The output is the Nc channels with the region outside of the target ROI cancelled while preserving channel sensitivities.
Using Pilot Tone for Sequence-Independent Motion Detection in the Head
Authors: Philippe Jocelyn , Romanin Ludovica , Ledoux Jean-Baptiste , Speier Peter , Bacher Mario , Huang Yantu , Kober Tobias , Hilbert Tom
Published: November 2024

Motivation: Motion artifacts are a major cause for rescans or for the patient to be recalled for another scanning session; this results in patient discomfort, workflow disruptions and additional costs. Goal(s): Prospectively provide MR operators with a “motion sensor signal” indicating subject motion to assist them in detecting and reacting to motion events. Approach: Design a sequence-agnostic Pilot Tone processing pipeline to detect motion and predict image quality degradation. Results: We showed that Pilot Tone can be used to detect head movement and predict with high accuracy whether the resulting image will be usable for diagnosis. Impact: This work shows that Pilot Tone can be used for prospective motion detection and image degradation prediction. Such a method may lead to a practically applicable solution to improve the MR workflow, thus reducing patient burden and costs.
Streaming-MoCo: Real-Time Motion-Compensated Image Reconstruction from 3D Non-Cartesian MRI
Authors: Calakli Fatih , Warfield Simon
Published: November 2024

Motivation: A real-time motion compensation monitoring system is vital for structural MRI: to assess the effectiveness of motion correction even during k-space data collection, to ensure the rapid delivery of corrected images afterwards.Goal(s): Develop a system that enables real-time: motion-compensation, image reconstruction, and visualization, during k-space data collection. Approach: Introduced a framework that includes online: coil compression, rigid motion-tracking, motion-compensated gridding, image generation/streaming. Tested it at 3T by imaging two volunteers with: no motion, instructed motion. Results: Streaming motion-compensated image reconstructions from 3D radial MRI and the progression of image quality improvements were demonstrated in real-time during k-space data collection. Impact: Our streaming-MoCo framework enables rapid reconstruction of motion-compensated images both during k-space data collection and post scan. This facilitates immediate assessment of image quality during scans, and potentially trigger corrective action well in advance before the scan concludes.
Rapid Free-Breathing 3D MRI of the Liver for Simultaneous Quantification of PDFF, R2*, and Water-Specific T1 at 0.55 Tesla
Authors: Chen Jingjia , Li Zhitao , Xia Ding , Block Kai Tobias , Chandarana Hersh , Ohliger Michael , Sodickson Daniel , Yang Yang , Feng Li
Published: November 2024

Motivation: 0.55T MRI may be a promising platform for quantitative multiparametric liver imaging with improved cost-effectiveness and robustness. Goal(s): This study demonstrates the feasibility of rapid free-breathing 3D MRI of the liver with simultaneous quantification of PDFF, R2*, and water-specific T1 at 0.55 Tesla. Approach: We tested free-breathing GraspT1-Dixon sequence with in vivo experiment and the quantitative maps are validated using R2* and PDFF phantoms. Results: We show that (1)the estimated parameters are accurate; (2)the influence of fat on liver T1 mapping can be effectively mitigated to ensure accurate and robust T1 quantification; and (3)R2* mapping may be insensitive to respiratory motion at 0.55T. Impact: 0.55T MRI offers a cost-effective platform for quantitative multiparametric MRI of the liver to improve the management of various chronic disease conditions. In particular, the improved magnetic field homogeneity at 0.55T may enable more robust estimation of R2* maps.
Feasibility of Lung MR Fingerprinting at 0.55T using a Deep Image Prior Reconstruction
Authors: Liu Zexuan , Lugogo Njira , Agarwal Prachi , Hamilton Jesse
Published: November 2024

Motivation: Parametric mapping may enable early detection and monitoring of pulmonary diseases but is hampered by low signal levels, short T2/T2* values, and susceptibility artifacts. Goal(s): This project aims to develop an MRF approach for simultaneous 2D T1, T2, and M0 mapping in the lungs on a commercial 0.55T scanner. Approach: MRF data were acquired in five healthy subjects during a 16s breathhold, repeated at end-inspiration and end-expiration, and reconstructed using a deep image prior. Results: Feasibility of lung MRF at 0.55T was demonstrated in healthy subjects, with lung T1 of 868±57ms (expiration) and 806±59ms (inspiration), and T2 of 54.1±3.2ms (expiration) and 58.7±4.0ms (inspiration). Impact: This study demonstrates the feasibility of MR Fingerprinting for 2D T1, T2, and M0 mapping of the lung parenchyma at 0.55T in healthy subjects, which may have future clinical implications for various pulmonary diseases including asthma, emphysema, and cystic fibrosis.
Two-point Dixon with balanced steady-state free precession (bSSFP): phantom experiments at 0.55T
Authors: Miao Xin , Su Pan , Keerthivasan Mahesh , Pang Jianing , Yang Yang
Published: November 2024

Motivation: Fat suppression is challenging at low field strengths due to small fat frequency shifts. Dixon imaging with bSSFP offers an attractive solution by combining the robust fat-water separation performance of Dixon with the high SNR and scan speed of bSSFP sequence. However, the distinctive spectral response of bSSFP signals was overlooked in existing Dixon implementations. Goal(s): This study aims to improve fat suppression with bSSFP sequence at low field by incorporating bSSFP signal model in a two-point Dixon algorithm. Approach: Numerical simulations and phantom experiments were performed at 0.55T. Results: Results suggest that integration of bSSFP signal model can improve fat suppression. Impact: Our proposed bSSFP-based two-point Dixon method could improve imaging with fat suppression at low field, which has been a challenging task due to small fat frequency shift and low SNR.
Fast and pseudo-random: optimization of settings for rapid quantification of T1 in white and grey matter at 64 mT
Authors: Lena Beatrice , Teixeira Rui , Padormo Francesco , Dong Yiming , Sundgren Pia , Webb Andrew , Ljungberg Emil
Published: November 2024

Motivation: Low-field MRI holds promise for efficient diagnostics. T1 mapping is valuable in neuroscience for studying myelination and brain development. To reduce scan time, incoherent, variable density trajectories are often used. Goal(s): to reach high image quality and T1 accuracy for fast T1 mapping at 64 mT. Approach: Using the 64-mT, Hyperfine Swoop scanner, we compared T1 maps acquired with fully sampled and undersampled trajectories (with and without incoherence) and reconstructed them with locally low rank regularization at different regularization factors λ. Results: Our findings showed that the most effective approach involves the use of a customized trajectory with λ around 0.004. Impact: Since fast and accurate T1 mapping in the context of low-field MRI is achievable, it would now be interesting to study brain developement in children, that present a unique challenge due to movement.
Low-rank reconstruction of variable-density random undersampled data for low-field MRI: accelerated T1 mapping at 46 mT
Authors: Dong Yiming , Najac Chloé , van Osch Matthias , Webb Andrew , Börnert Peter , Lena Beatrice
Published: November 2024

Motivation: Limited access to medical equipment makes low-field MRI an interesting option in many settings. One challenge is the prolonged scan time, especially for quantitative imaging. However, parallel imaging is typically not used due to the very high sensitivity of a single solenoid receive coil. Goal(s): This study aims to accelerate T1 mapping (used for estimating brain myelination) on a Halbach low-field system. Approach: A locally low-rank reconstruction was applied to diminish undersampling artifacts from the variable-density random sampling trajectory. Results: The study highlights the potential for both, fast lower-resolution (2.5mm2 in-plane) and higher-resolution (1.5mm2 in-plane) T1 mapping with an acceleration factor of R=4. Impact: Our study's advanced low-rank reconstruction approach for low-field MRI could transform imaging methods in regions where high-field MRI is inaccessible, enabling precise and fast T1 brain mapping, which is critical for assessing myelination-related diseases with newfound speed and reliability.
Diffusion tensor imaging in portable low-field MRI
Authors: Dai Ke , Qiu Yueqi , Chen Suen , Chen Hao , Zhang Zhiyong
Published: November 2024

Motivation: DWI/DTI are very challenging in portable MRI system, while they play a pivotal role in timely triage, diagnosis and treatment for patients with suspected acute conditions such as stroke. Goal(s): To mitigate the effects of eddy currents and achieve multi-directional diffusion-weighted imaging and diffusion tensor imaging in portable MRI system. Approach: With the three-axis gradient coil accompanied with anti-eddy plate and image-phase based eddy current correction, we applied multi-scaning average to suppress noise and adopted eddy correction to mitigate the motion blurring. Results: We succeed obtain visible multi-direction diffusion-weighted imaging and DTI imaging in portable MRI system. Impact: With the three-axis gradient coil accompanied with anti-eddy plate and image-phase based eddy current correction, we succeed in obtaining multi-direction diffusion-weighted imaging and DTI images with the portable MRI system.
Principal component analysis assisted deep learning method for motion artifacts correction in MR thermometry
Authors: Wang Ning , Ying Kui
Published: November 2024

Motivation: To improve the performance of convolutional neural network (CNN) for motion artifacts correction and demonstrate the feasibility of using motion-related information provided by principal component analysis (PCA). Goal(s): To achieve high accuracy of temperature mapping for motion existing organs like abdominal and thus expand a wide range of MR-thermometry in clinical applications. Approach: We proposed a combination method of PCA and basic CNN model to correct artifacts in abdominal MR-thermometry. Results: Preliminary results showed that the proposed method outperforms conventional CNN in terms of temperature mapping accuracy. The new method reduces the motion-related phase errors by leveraging PCA. Impact: Our proposed method has a high potential to handle motion organs with non-rigid motion. The PCA method in combination of CNN for its efficient reduction of motion induced errors may improve the feasibility and accessibility of MR-thermometry in abdominal applications.
Correction of LIFU Intensity Using MR Acoustic Radiation Force Imaging (ARFI): Improved Correlations with Treatment Outcomes
Authors: Eldirdiri Abubakr , Chang Linda , Martin David , Calu Donna , Cunningham Eric , Bernard Segun , Meesala Prathibha , Ernst Thomas
Published: November 2024

Motivation: The effectiveness of low-intensity focused ultrasound (LIFU) for neuromodulation may be compromised by inaccuracies in target localization and variable energy deposition caused by skull characteristics. Goal(s): We aimed to improve the estimation of LIFU pressure using MR-acoustic radiation force imaging (ARFI) and test whether the adjusted LIFU intensity correlates with treatment outcomes in a behavioral economic task using sucrose pellets. Approach: Eight female rats underwent LIFU targeting the nucleus accumbens (NAc) bilaterally with three different pressures. MR-ARFI confirmed the targeting and corrected the intensities during LIFU treatments. Results: Improved correlations between behavioral outcomes and ARFI-adjusted LIFU intensity were observed. Impact: Our preliminary findings demonstrate the beneficial effects for using MRI-ARFI not only to verify the FUS target location but also to refine LIFU intensities in neuromodulation procedures.
MR-ARFI for transcranial ultrasound neuromodulation: The effect of varying frequency and depth.
Authors: Mohammadjavadi Morteza , Glover Gary , Pauly Kim
Published: November 2024

Motivation: Previous MR-ARFI optimizations have incorporated tissue response times, but assumed those were constant in a homogenous medium. Goal(s): To demonstrate that tissue response times vary with ultrasound parameters such as frequency and depth, and to use those to optimize MR-ARFI. Approach: We measured displacement response time in a tissue-mimicking phantom at 0.5MHz and 1.0MHz ultrasound frequencies and depths of 40- and 60-mm. Results: With our tissue-mimicking phantom, the time constants were approximately 3ms and 6ms for 1.0MHz and 0.5MHz ultrasound frequencies, respectively. Impact: Ultrasound parameters of frequency and depth affect tissue response and can be used to optimize the acquisition to maximize SNR and minimize ultrasound dose.
Interventional navigation at a portable MRI system: A pilot study
Authors: Qiu Yueqi , Xu Can , Wei Xinkai , Wang Changyue , Chen Hao , Zhang Zhiyong
Published: November 2024

Motivation: Low-field MRI's portability and safety make it appealing for real-time interventional navigation and bedside evaluations, but it lacks efficient navigation strategies. Goal(s): Our goal was to demonstrate the potential of low-field MRI in navigation by designing efficient and high-resolution echo-planar acquisition sequences. Approach: We harnessed low-field MRI's long T2 and short T1 properties to design short-TR EPI sequences for real-time navigation and multi-shot EPI sequences for bedside postoperative assessments. Results: With the short-TR 2D EPI sequence, real-time needle insertion images were successfully obtained, and was improved after denoising. In a self-made resolution phantom, the use of multi-shot EPI greatly improved the image resolution. Impact: The combination of low-field portable EPI sequences and denoising algorithms highlights their time efficiency in navigation, thus expanding the prospects for low-field portable MRI in the field of navigation.
Intraoperative Neurophysiological Monitoring During MRI-Guided Procedures: RF-Heating Risk Considerations
Authors: Treb Kevin , Favazza Christopher , Woodrum David , Thompson Scott , Hoffman Matt , Adamo Daniel , In MyungHo , Stinson Eric , Gorny Krzysztof , Lu Aiming
Published: November 2024

Motivation: Neuromonitoring is critical during interventions near nerves. During MRI-guided interventions, a conventional system using metallic electrode needles has been used clinically, since MRI-conditional neuromonitoring systems are currently unavailable. This introduces elevated RF-heating risk. Goal(s): To identify factors that contribute to RF-heating of neuromonitoring electrodes and strategies to mitigate the risk. Approach: Neuromonitoring electrodes were inserted into porcine tissue and imaged with various clinically relevant MRI sequences for various neuromonitoring equipment configurations with high RF-heating risks. Temperatures were recorded using fiber-optic sensors. Results: Substantial temperature elevations were observed during MRI, and several RF-heating mitigation strategies were identified to enable neuromonitoring during MRI-guided procedures. Impact: The identified electrode RF-heating mitigation strategies can significantly reduce the RF-heating risk during MRI-guided ablations where neuromonitoring is critical. Understanding factors affecting RF-heating can also help identify high-risk procedures and guide risk-benefit analysis.
Super Resolution MRI using Multiple Signal Classification (MUSIC) Reconstruction
Authors: Sun Dongbiao , Zhuo Yan , Chen Lin , Zhang Zihao
Published: November 2024

Motivation: The Fourier transform (FT) reconstruction has convenient implementation and stable performance; however, it has the problem of poor resolving power. Goal(s): Our goal is to bypass the Fourier transform to obtain MR images, thereby solving the problem of poor resolution and achieving super-resolution imaging. Approach: We were inspired by array signal processing theory and proposed an approach based on the Multiple Signal Classification (MUSIC) algorithm called MUSIC-MRI. Results: Our phantom experiments suggest that the resolution ability of MUSIC-MRI is approximately 2x2 better than that of the 2D Fourier transform. Our in-vivo vascular imaging experiments show that the MUSIC-MRI significantly promotes the actual resolution. Impact: MUSIC-MRI can break through the Rayleigh Limit of Fourier transform and significantly increase the actual resolution ability of the reconstructed images. Scientists or clinicians may use MUSIC-MRI to image very small structures and lesions without modifying MR sequences.
Efficient Standardization of Clinical T2-Weighted Images: Phase-Conjugacy e-CAMP with Projected Gradient Descent
Authors: Zhang Horace , Elsaid Nahla , Sun Heng , Tagare Hemant , Galiana Gigi
Published: November 2024

Motivation: Routine clinical images are a massive data source for machine learning. The previously introduced e-CAMP method can convert T2-weighted images of clinical TSE acquisition to quantitative T2 maps, but it requires tuning of many parameters, impeding widespread implementation. Goal(s): To present an algorithm that requires few parameter choices, is robust to those parameter values, and is faster to convergence. Approach: Projected Gradient Descent ensures efficient enforcement of the T2-decay model constraint and greatly eliminates parameter tuning. e-CAMP is further enhanced by phase conjugacy with Virtual Conjugate Coils. Results: The efficient and robust implementation of e-CAMP shows accurate T2 map reconstruction. Impact: Rather than acquire specific yet time-consuming quantitative images, e-CAMP can efficiently standardize the existing qualitative images from routine clinical scans and exploit the enormous amount of images to create dataset for large-scale machine learning.
Automated Fetal Brain Volume Reconstruction from Motion-corrupted Stacks with Deep Learning
Authors: Ma Laifa , Lin Weili , Zhang He , Li Gang
Published: November 2024

Motivation: The fetal brain MRI 3D volume is critical for development assessment. However, the inevitable fetal motion during MRI acquisition makes it challenging to reconstruct a high-quality fetal brain 3D volume from multiple stacks. Goal(s): Herein, we propose a novel deep learning method for automated fetal brain MRI 3D volume reconstruction. Approach: Firstly, a multi-scale feature fusion model is proposed to solve arbitrary motion correction. Secondly, an initial 3D volume is estimated by point spread function. Next, the proposed residual-based model is used to improve the quality of the initial 3D volume. Results: The results demonstrate that the proposed method is effective and efficient. Impact: The proposed end-to-end method based on deep learning can solve arbitrary motion correction of 2D slices and reconstruct high-resolution fetal brain MRI 3D volumes effectively and efficiently.
Using a Deep Learning Prior for Accelerating Hyperpolarized 13C Magnetic Resonance Spectroscopic Imaging on Synthetic Cancer Datasets
Authors: Wang Zuojun , Luo Guanxiong , Li Ye , Cao Peng
Published: November 2024

Motivation: Hyperpolarized (HP) 13C magnetic resonance spectroscopic imaging (MRSI) is efficient and reliable in assessing the aggressiveness of tumors and their response to treatments. Goal(s): To incorporate a deep learning prior with k-space data fidelity for accelerating HP 13C MRSI. Approach: Singular maps were generated from synthetic phantom datasets simulated by two-site exchange models and used to train the deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled k-space data. Results: The proposed method was demonstrated feasibility and generalizability on varied synthetic cancer datasets, and showed improved accuracy in value and location of tumors compared to other methods. Impact: The proposed model could be considered as a general framework that extended the application of deep learning to MRSI reconstruction, which could be applied in varied cancer datasets.
Advancing Ultralow-field Brain MRI through K-space Undersampling and Deep Learning Image Reconstruction
Authors: Li Xiang , Man Christopher , Lau Vick , Leong Alex , Zhao Yujiao , Wu Ed
Published: November 2024

Motivation: While the emerging ULF MRI shows potential of low-cost and point-of-care imaging applications, its image quality is poor and the scan time is long. Goal(s): To reduce the ULF brain MRI scan time through deep learning image reconstruction from partial Fourier and uniformly undersampled data. Approach: We proposed a DL reconstruction method for fast 3D brain MRI at 0.055T by applying the 3D DL image reconstruction to undersampled 3D k-space data, achieving speed up of 2x over our newly developed partial Fourier reconstruction and superresolution (PF-SR) method. Results: Our preliminary results show the proposed method could reduce noise, artifacts, and enhance spatial resolution. Impact: Our model can work with uniformly undersampled data, leading to acceleration factor of 2, and a PF sampling of at a fraction of 0.7. Our development enables fast and quality whole-brain MRI at 0.055T, indicating potential for widespread biomedical applications.
Technical recommendations for renal DCE-MRI: Preliminary results from the consensus project of the ISMRM Renal MR study group
Authors: Gunwhy Ebony , Kurugol Sila , Lim Ruth , Zhang Jeff , Jones Richard , Zöllner Frank , Reynolds Hayley , El-Ghar Mohamed Abou , Block Kai , Pedersen Michael , Hockings Paul , Wentland Andrew , Buckley David , Serpa Luis Sanmiguel , Mendichovszky Iosif , Serai Suraj , Sourbron Steven , Dekkers Ilona
Published: November 2024

Motivation: Clinical translation of renal functional and quantitative MRI is hindered by a lack in standardisation of scan protocols. Goal(s): To develop expert consensus-based technical recommendations on the acquisition and post-processing of renal DCE-MRI. Approach: Experts were recruited and surveyed following the Delphi method to create consensus-based technical recommendations in renal DCE-MRI. Preliminary results will be used to construct the second survey round. Results: 16 experts responded to the preliminary survey. Consensus statements informed by the responses were drafted and will be circulated and refined in the next phase of the project. Impact: The insights obtained from this work will be invaluable in delivering recommendations that are comprehensive and widely accepted. Consensus-based technical recommendations for renal DCE-MRI aim to contribute to harmonisation of MRI scan protocols across sites, facilitating clinical translation.
Volumetric Layer Based Analysis for Quantitative Renal MRI
Authors: Daniel Alexander , Francis Susan
Published: November 2024

Motivation: To provide improved methods to estimate cortical-medullary changes in multiparametric MRI measures of the kidney. Goal(s): To develop an analysis method for use with 3D data to generate quantitative-depth-based cortical-medullary layers which can be applied to any multiparametric map. Approach: 3DQLayers segments the kidney into layers based on their distance from the renal surface using the Trimesh Python library. Results: Generated 3D layers can be applied to multiparametric MRI scans collected in the same session. Here, this is applied to assess cortical layer profiles and contour plots of quantitative T1-mapping, R2*-mapping and perfusion measures, and to estimate renal cortical thickness. Impact: 3DQLayers provides a layer-based analysis technique for renal multiparametric MRI data, extending traditional ROI-based methods. Layer profiles of any quantitative MRI data can be output and average renal cortical thickness estimated, these are important measures to study in renal disease.
FID-navigated motion correction for post-contrast T1w-imaging of kidneys
Authors: Ariyurek Cemre , Wallace Tess , Kober Tobias , Chow Jeanne , Afacan Onur , Kurugol Sila
Published: November 2024

Motivation: Post-contrast T1-weighted imaging is vital for diagnosing renal MRI by assessing vasculature, finding vascular abnormalities, and evaluating kidney function. However, breathing motion degrades image quality, which is problematic since breath-holding is difficult for some patients, especially children. Goal(s): There is an urgent need for motion-robust renal imaging for breath-holding challenged patients. Approach: In this study, we demonstrated the effectiveness of FIDnavs in correcting motion-related artifacts in clinical post-contrast T1w-imaging of kidneys. We evaluated our pipeline on 5 pediatric patients, both retrospectively and prospectively, using FIDnav-based motion correction (MoCo). Results: Utilizing FIDnavs for MoCo, reduced motion artifacts and blurring in post-contrast T1w-imaging. Impact: Use of FID-navigated motion correction for post-contrast T1w-imaging of kidneys can reduce motion artifacts and minimize the scan time by eliminating the need of gating. However, the calibration step required for prospective motion correction should be eased for pediatric patients.
Assessment of Renal Water Exchange by T2 Selective Labeling
Authors: Jaafar Narjes , Taso Manuel , Alsop David
Published: November 2024

Motivation: Alterations of renal water transport and exchange may reflect kidney function and disease. Goal(s): To develop and test a novel sequence for quantitative assessment of intrarenal fluid exchange. Approach: We developed a T2 selective labeling sequence for water exchange quantification and tested it on healthy subjects. Results: Images showed a characteristic spatial distribution of signal with increased signal in the renal medulla, in support of its detection and assessment of fluid exchange. Impact: Exchange imaging by T2 selective saturation may open new possibilities for the study of water transport and exchange and the diagnosis of renal disorders.
MRE-derived Anisotropic Stiffness Estimation in Kidneys
Authors: McClanahan Gregory , Kolipaka Arunark
Published: November 2024

Motivation: Magnetic Resonance Elastography (MRE) can determine tissue isotropic stiffness measurements to assess kidney disease and injury. Similarly, Diffusion Tensor Imaging (DTI) in combination with MRE can provide additional information regarding tissue health through anisotropic stiffness. Goal(s): The goal of this study is to compute tissue isotropic and anisotropic stiffness of the cortex, medulla, and whole kidney to compare against both measurements. Approach: 9 subjects were imaged for both MRE and DTI scans to estimate isotropic and anisotropic stiffness, respectively. Results: Preliminary results indicate a good correlation between isotropic stiffness and anisotropic stiffness in all regions. Impact: The application of MRI diffusion scans in conjunction with MRE shows promise in elucidating hard to detect disease and injury in human kidney.
Early renal function assessment and follow-up after renal transplantation using various diffusion models
Authors: Yang Ping , Chen Lihua , Zhu Jinxia , Grimm Robert , Stemmer Alto , Shen Wen
Published: November 2024

Motivation: Although renal biopsy is the gold standard for the diagnosis of renal allograft dysfunction, it is an invasive procedure. Magnetic resonance imaging (MRI) can be used to evaluate renal allograft function in a noninvasive manner. Goal(s): To investigate the utilities of various diffusion models in evaluating early renal allograft function. Approach: Follow-up MRI was performed at 14, 30, and 90 days after renal transplantation to evaluate correlations between MRI parameters and estimated glomerular filtration rate, along with their abilities to measure renal function after transplantation. Results: Various diffusion models can reflect changes in early renal function after transplantation. Impact: Intravoxel incoherent motion and diffusion kurtosis imaging models may serve as useful tools to investigate early renal function after transplantation.
Assessment of transplant renal artery stenosis renal function with ASL-MRI and IVIM-MRI
Authors: Guo Songlin , Wu Peng , Lu HAN , Xing Wei , Pan Liang
Published: November 2024

Motivation: Noninvasive assessment of renal function is necessary in transplanted renal artery stenosis (TRAS), which significantly impacts graft function and prognosis. Goal(s): The purpose was to evaluate renal function in patients with TRAS using ASL-MRI and IVIM-MRI. Approach: We used ASL-MRI and IVIM-MRI to evaluate renal blood flow (RBF), D, D*, and f in TRAS, and analyzed their correlation with eGFR. Results: Renal allograft in the stenosis group displayed notably lower RBF and f, both of which were positively associated with eGFR. This research highlights the importance of noninvasive techniques like ASL-MRI and IVIM-MRI in the clinical management of TRAS. Impact: This study demonstrates that TRAS reduces renal perfusion, thereby affecting transplanted kidney function. ASL-MRI and IVIM-MRI can indirectly and noninvasive reflect renal function impacted by TRAS, providing valuable information for treatment decisions and active monitoring.
Three-dimensional Phase-contrast MRA of Foot Vessels at 5T: Visualization Improved by Warm Water Immersion
Authors: Shi Zhang , Li Hao
Published: November 2024

Motivation: Unenhanced MRA on feet has always been challenging due to slow blood flow. The ultra-high field strength MR systems have been proved to offer improved visualization of distal small vessels. Goal(s): We aim to assess the visualization of foot vessels using three-dimensional phase contrast MRA (3D-PC-MRA) at 5T field-strength. Approach: Participants were prospectively recruited and underwent 5T 3D-PC-MRA on feet with and without warm water immersion. Results: Immersion of feet in warm water significantly improves visualization of distal small branches, and increases the average velocity of blood flow by 40%, resulting in the visualized enhancement in blood signal. Impact: 5T 3D phase-contrast MRA is effective in visualizing lower-extremity vasculatures and small branches, and its visualization can be significantly improved by immersing feet in warm water.
Fingerprint Representation of Metabolite Magnetic Resonance Spectroscopy with Deep Learning
Authors: Zhang Yan , Shen Jun
Published: November 2024

Motivation: One of the major challenges for spectral fitting is the modeling of background signals. Goal(s): Develop a deep learning model for quantitative detection of in vivo metabolites without relying on spectral fitting. Approach: Spectral fingerprint representation is achieved by combining manifold learning and representation learning, with the tasks that include predicting metabolite concentrations, transverse relaxation times, and reconstructing individual metabolite signals. Results: The t-SNE map illustrates that metabolites can be clustered based on the fingerprints generated by the model. The predicted metabolite concentrations and relaxation T2s agree with those found in the literature. The spectral background or unregistered signals are effectively filtered out. Impact: The deep learning model demonstrates high practical viability for the quantification of metabolite concentrations and relaxation T2s. It essentially searches for learned spectral fingerprints instead of relying on spectral fitting, the latter involves modeling all signals contained in the data.