Motivation: High-resolution time-of-flight (TOF) magnetic resonance angiography (MRA) is needed for imaging the cerebral vasculature at the mesoscopic scale but is challenged by involuntary head movements during image acquisition. Goal(s): This study tests whether whole-brain volumetric navigators (vNavs), based on 3D-EPI, can be used for prospective motion correction (PMC) of thin-slab TOF acquisitions at 7T. Approach: Interactions between vNavs and TOF sequence modules are examined, and the performance of PMC is investigated under different head motion conditions. Results: While results show that vNavs affect TOF signal, vNav motion correction enables the robust imaging of small vessels. Impact: We show that slab-selective isotropic 0.16-mm TOF-MRA is feasible with whole-brain volumetric navigators for prospective head motion correction, enabling robust in vivo imaging of the human vasculature at an unprecedented scale.
Motivation: Classical 1D linescan acquisitions have recently been shown to be valuable for recording in-vivo MRI signals across the layers of human cerebral cortex; however, these reduced-field-of-view techniques are vulnerable to rotational head movements as well as translations parallel and perpendicular to the line. Goal(s): To acquire high-quality, high-resolution linescan data that is robust to "in-line" and "through-line" motion. Approach: 3D-EPI volumetric navigators (vNavs) were incorporated into a spin-echo-based linescan pulse sequence. Results: We demonstrate that, by combining prospective and retrospective motion correction, we can acquire reliable linescan data with 0.5-mm readout resolution at 7T, in the presence of in-line and through-line head motion. Impact: Motion-robust linescan techniques will help enable the measurement of tissue microstructure and microvascular fMRI signals at high spatial resolutions, approaching the thickness of individual cortical layers, facilitating noninvasive studies of cortical circuitry and architectonics in the living human brain.
Motivation: Dipole antennas struggle with imaging small regions at 300 MHz due to their reliance on physical length for tuning, which limits resolution. Goal(s): This study aims to develop a discretely dielectric-material-coated (DDMC) dipole antenna for dipole-length-independent frequency tuning, improving imaging quality and safety. Approach: We designed and constructed discretely dielectric-material-coated dipole antenna and compared the DDMC design with fractionated dipole designs using simulations and bench-testing, employing a high-dielectric-material to achieve 300 MHz resonance without altering physical dimensions. Results: The DDMC dipole provided a more uniform B1+ distribution and lower electric fields, demonstrating superior decoupling and a higher Q-factor for enhanced ultra-high-field MRI performance. Impact: This research advances ultra-high-field MRI technology by enhancing imaging quality and safety through innovative dipole antenna designs. Future studies can explore optimized dielectric materials, improving access to high-resolution imaging in clinical settings and benefiting patients with complex anatomical needs.
Motivation: In 3T MRI, achieving optimal B1 field efficiency and reducing specific absorption rate (SAR) are essential for enhancing image quality and patient safety. Goal(s): To develop a multimodal concentric surface coil that improves B1 field efficiency and reduces SAR compared to conventional surface coils. Approach: The novel coil design incorporates five electromagnetically coupled concentric resonators to form a multimodal resonator. Performance was evaluated through simulations and bench tests. Results: Both simulations and bench tests demonstrate that the concentric coil achieves superior B1 efficiency and lower SAR, underscoring its potential to improve image quality and safety in in vivo MRI applications. Impact: The proposed multimodal concentric coil demonstrates superior B1 field efficiency and reduced SAR compared to conventional surface coils. These improvements can significantly enhance image quality and patient safety in 3T MRI, paving the way for better clinical outcomes.
Motivation: MR Fingerprinting (MRF) enables simultaneous multi-parametric mapping but suffers from computational challenges and noise/artifact sensitivity due to dictionary matching. Goal(s): This study aims to develop a novel network called CLIP-MRF to improve pattern matching in MRF. It incorporates contrastive learning to enhance quantification accuracy in accelerated MRF. Approach: We propose a dual-encoder contrastive training method to robustly map MRF signals to tissue parameters accurately. The model maximizes similarity between matching signal-parameter pairs and minimizes mismatched ones during training. Results: CLIP-MRF demonstrates superior performance over the state-of-the-art MRF methods in T1 and T2 quantification, reducing the reconstruction time and errors. Impact: The CLIP-MRF network enables accurate parameter mapping and improves computational efficiency for accelerated MRF. Trained on simulated data only, the network offers robust generalization across signals with different noise/artifacts, paving the way for fast and reliable tissue quantification.
Motivation: Real-time catheter tracking in MRI-guided procedures is essential but is often hindered by signal loss due to catheter orientation and interference from the B1 field, which diminishes contrast. Goal(s): This study develops an omnidirectional catheter marker with an LC structure for consistent tracking across all orientations. Approach: Simulations were used to design the marker, featuring alternating inductor windings and a custom capacitor to achieve the target Larmor frequency. The marker performance was evaluated across six orientations in a torso phantom. Results: The marker demonstrated reliable B1 field elevation near the inductor, enhancing contrast and ensuring effective orientation-independent catheter tracking. Impact: This research enhances real-time catheter tracking in MRI-guided procedures through an innovative omnidirectional catheter marker. Improved tracking accuracy can lead to safer interventions and better patient outcomes, ultimately increasing the effectiveness of interventional MRI in diverse clinical applications.
We proposed an attention-based multi-offset network to exploit redundant anatomy information for the reconstruction of CEST-MR image (AMO-CEST). To the best of our knowledge, this is the first work using deep learning with varied radial sample patterns and multi-offset slices as input to accelerate CEST-MRI. Compared with other deep learning-based methods on the four times under-sampling mouse brain CEST dataset, the AMO-CEST achieved the best performance with an MMSE of, a PSNR of dB, and an SSIM. In conclusion, the proposed AMO-CEST network can accelerate the CEST-MRI at high down-sampling rate while maintaining good image quality.
Nyquist ghosting is a common artefact in echo planar imaging (EPI), typically corrected using separately acquired reference data. Here we demonstrate that reference free Nyquist ghost correction algorithms based on entropy and Ghost/Object ratio minimization can outperform navigator based methods and improve imaging efficiency for in vivo diffusion tensor cardiovascular magnetic resonance.
This is important research to assess the effects of MRgFUS thalamotomy on white matter connectivity in ET. Results showed that MRgFUS might act the topologic properties on brain networks. Rich-club and small-world organizations exist in HC and ET. The right orbital part of the superior frontal gyrus and right putamen were identified as a hub in the ET group only, whereas the left putamen identified as hubs in the group only. Importantly, gamma and sigma correlated tremor improvement after MRgFUS thalamotomy, playing a role in reflecting tremor improvement for clinical treatment.
Neurometabolite concentrations provide a direct index of infarct progression in stroke, but their relationship with stroke onset time remains unclear. Using a fast high-resolution 3D MRSI technique, this study assessed the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimated their value in predicting early (<6 hours) vs late (6–24 hours) hyperacute ischemic stroke groups. We found that lesional NAA and creatine was reduced from acute to subacute stroke patients and NAA level was inversely related to onset time in hyperacute patients. The changes in neurometabolite levels provided good discrimination between patients for early & late hyperacute time windows.
Most neuroimaging studies investigating autism spectrum disorder (ASD) have focused on static brain function and used children as research subjects. However, this study investigated dynamic changes of regional neural function in adult ASD patients. Significant differences in dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuation (dALFF) were observed based on resting state fMRI in several brain areas, such as the left middle/inferior temporal gyrus and left middle occipital gyrus. A significant correlation was found between clinical scores and the dReHo/dALFF values. These results suggested that dynamic regional brain function might be helpful in understanding neural mechanisms in ASD .
In this work, we demonstrate a parallel transmit implementation of the bSSFP sequence at 7 Tesla. The bSSFP sequence is one of the most efficient acquisition strategies however highly sensitive to B0 inhomogeneity which creates undesirable banding artefacts. A tailored pair of pTx pulses can be used to induce the desired steady-state magnetisation behaviour at all spatial locations regardless of the off-resonance frequency due to B0 inhomogeneity. Shown through simulation and experimental validation, we were able to capture signal in one acquisition with significant mitigation of banding artifacts and improvement in signal uniformity without SNR loss and time penalty.
The long acquisition time of hyperpolarized 129Xe multiple b-values DWI made it hard to apply in patients with severe pulmonary diseases. Herein, we proposed a method of variable-sampling-ratio compressed sensing patterns for accelerating hyperpolarized 129Xe DWI. A four-fold reduction in acquisition time was achieved using the proposed method while preserving good image quality. Meanwhile, the method can be used for evaluating pulmonary injuries caused by cigarette smoking.
The potential for DMI measurements of deuterated glucose metabolism to differentiate between metabolic subtypes in GBM has been demonstrated in patient-derived xenografts in mice. The glycolytic subtype showed increased lactate labelling whereas two oxidative subtypes showed increased glutamate/glutamine (Glx) labelling. There was decreased lactate labelling in a glycolytic subtype and decreased lactate and Glx labelling in an oxidative subtype within 24 h after the completion of standard-of-care chemoradiotherapy, demonstrating that the technique can also be used to detect early treatment response in this tumor type.
A primary challenge for in vivo kidney MRI is the presence of different types of involuntary physiological motion, affecting the diagnostic utility of acquired images due to severe motion artifacts. Existing prospective and retrospective motion correction methods remain ineffective when dealing with complex large amplitude nonrigid motion artifacts. We introduce an unsupervised deep learning-based method for in vivo kidney MRI motion correction. We demonstrate that our deep learning model achieved the average structural similarity index measure (SSIM) of 0.76±0.06 between the reconstructed motion-corrected and ground truth motion-free images, showing an improvement of about 0.33 compared to the corresponding motion-corrupted images.
This study aimed to develop and construct a MRI-based full-age-range brain age prediction model that can be applied in the Chinese health care system. We proposed a brain age prediction method based on transfer learning and partition modeling, which was using Atlas attention enhancement. The performance of models with different image masks were compared and the model constructed based on top60% image mask achieved the best prediction performance. The brain age prediction method proposed in this study can provide objective brain age for assessing brain health status in Chinese population.
Fetal orientation determines the mode of delivery. It is also important for sequence planning in fetal MRI. This abstract proposes Fet-Net, a deep-learning algorithm, which uses a novel convolutional neural network (CNN) architecture, to automatically detect fetal orientation from a 2-dimensional (2D) magnetic resonance imaging (MRI) slice. 6,120 2D MRI slices displaying vertex, breech, oblique and transverse fetal orientations were used for training, validation and testing. Fet-Net achieved an average accuracy and F1 score of 97.68%, and a loss of 0.06828. Fet-Net was able to detect and classify fetal orientation, which may serve to accelerate fetal MRI acquisition.
We present dynamic T2* measurements for HP [1-13C]pyruvate and metabolites in a healthy human brain volunteer and two RCC patients at 3T. The T2* of pyruvate was shown to vary during the acquisition, whereas the T2* of lactate and bicarbonate was constant through time and across organs. The T2* of lactate was constant at gray matter (30.1±5.9ms), white matter (33.8±7.6ms), healthy kidney (38.94±6.9ms) and tumor (33.27±6.4ms), and the T2* of bicarbonate over whole-brain (109.5±12.8ms) and kidney (64.6±15.8ms). These relaxometry measurements will be useful for future sequence optimization and can be included in kinetic modeling to harmonize data across different TEs.
