Recent advancements in magnetic resonance imaging (MRI) techniques are promising for the detection of fetal abnormalities, and MRI may supplement or replace prenatal ultrasound scans in the future. In particular, the interest of scientific and medical communities in high-field (3T) MRI continues to grow due to its improved contrast-to-noise and signal-to-noise ratios compared to clinical MRI of lower field strength (1.5T). However, 3T MRI shows more prominent dielectric artifacts due to constructive and destructive interference of standing waves inside the body at these frequencies. Here, we present a concept of passive radiofrequency shimming using metasurface-based pads to improve image quality in fetal MRI at 3T. The proposed metasurface increases the efficiency and homogeneity of the radiofrequency magnetic field, reducing dielectric artifacts in the fetal body and brain images. We offer an ultralight and compact passive way to improve 3T imaging of fetal brain and body structures, simplifying clinical workflows and decreasing the procedure time.
The progression and repair of a traumatically injured spinal cord (SCI) involves multifactored processes. Noninvasive, mechanism-informative objective biomarkers could greatly facilitate the translation of findings from preclinical animal models to patient applications. We aimed to develop and validate multiparametric chemical exchange saturation transfer (CEST) and quantitative magnetization transfer (qMT) magnetic resonance imaging (MRI) biomarkers for assessing SCI severity, demyelination, and neuroinflammation, as well as the response to neuroprotective drug treatment riluzole. Changes in CEST and qMT MRI metrics before and after a moderate contusion injury at the L1 level of the lumbar spinal cord were compared between two groups of rats that received either the riluzole or a vehicle treatment over 8 weeks. The specificity of these MRI biomarkers was validated by postmortem immunohistology. The functional relevance of these biomarkers was evaluated by correlation with hindlimb sensorimotor and pain behavior. The pool size ratio (PSR) maps from qMT acquisitions of the SCI region in riluzole-treated rats showed increased white matter macromolecular content compared to the HBC vehicle-treated group, suggesting increased myelin levels and possible remyelination of the injured spinal cord. CEST APT pool (3.5 ppm) amplitude decreased at the region rostral to the injury in riluzole-treated rats compared to the vehicle group, indicating potentially reduced neuroinflammatory activity. MRI metrics correlated temporally with behavioral measures of injury severity and recovery. Histological analysis spatially validated MRI-revealed myelination and neuroinflammation status and confirmed differences between the drug and vehicle treatment groups. Quantitative MRI is well suited for monitoring and quantifying the efficacy of pharmacological treatments in preclinical spinal cord injury models. Multiparametric MRI changes in white matter myelination (qMT PSR) and neuroinflammation (CEST APT) in the injured spinal cord were related to injury severity, behavioral deficits, and recovery progression over time. Both imaging metrics captured enhanced recovery from the neuroprotective drug riluzole, supporting the practical utility of these MRI biomarkers.
In this work, we introduce spatial and chemical saturation options for artefact reduction in magnetic resonance fingerprinting (MRF) and assess their impact on T1 and T2 mapping accuracy. An existing radial MRF pulse sequence was modified to enable spatial and chemical saturation. Phantom experiments were performed to demonstrate flow artefact reduction and evaluate the accuracy of the T1 and T2 maps. As an in vivo demonstration, MRF of the prostate was performed on an asymptomatic volunteer using saturation modules to reduce flow-related artefacts. T1, T2 and B1 + maps obtained with and without saturation modules were compared. Application of spatial saturation in prostate MRF reduced streaking artefacts from the femoral vessels. When saturation is enabled T1 accuracy is preserved, and T2 accuracy remains acceptable up to approximately 100 ms. Chemical and spatial saturation can be incorporated into MRF sequences with limited impact on T1 accuracy. Further sequence optimisation may be needed to accurately estimate long T2 components. Spatial saturation modules have potential in prostate MRF applications as a means to reduce flow-related artefacts.
This study aimed to develop and evaluate a novel magnetization-prepared, ultra-short echo time (UTE)-capable, stack-of-spirals sequence (STFL) to quantify monoexponential and biexponential T1ρ maps of the whole knee joint, addressing limitations of existing MRI techniques in assessing bone-patellar tendon-bone (BPTB) donor site healing and graft remodeling after anterior cruciate ligament (ACL) reconstruction (ACLR). Experiments were performed with agar-gel model phantoms, seven healthy volunteers (four males, average age 31.4 years old), and five ACLR patients (three males, average age 28.2 years old). Compared with a conventional Cartesian turbo fast low angle shot (CTFL) sequence, the STFL sequence demonstrated an improved signal-to-noise ratio (SNR), increasing from 16.5 for CTFL to 21.7 for STFL. In ACLR patients, the STFL sequence accurately detected increased fractions of short T1ρ components within the ACL graft, rising from 0.15 to 0.38, compared with 0.11 to 0.18 with CTFL. Furthermore, the STFL sequence revealed significant decreases in the fraction of short T1ρ components in the patellar tendon of ACLR patients (from 0.6 to 0.47) compared with healthy controls, whereas no significant changes were observed with the CTFL sequence. These findings suggest that the STFL sequence holds promise for noninvasive assessment of BPTB donor site healing and graft maturation following ACLR.
Magnetic susceptibility MRI offers potential insights into the chemical composition and microstructural organization of tissue. However, estimating magnetic susceptibility in white matter is challenging due to anisotropic subvoxel Larmor frequency shifts caused by axonal microstructure relative to the B0 field orientation. Recent biophysical models have analytically described how axonal microstructure influences the Larmor frequency shifts, relating these shifts to a mesoscopically averaged magnetic field that depends on the axons' fiber orientation distribution function (fODF), typically estimated using diffusion MRI. This study is aimed at validating the use of MRI to estimate mesoscopic magnetic fields and determining whether diffusion MRI can faithfully estimate the orientation dependence of the Larmor frequency shift in realistic axonal microstructure. To achieve this, we developed a framework for performing Monte Carlo simulations of MRI signals in mesoscopically sized white matter axon substrates segmented with electron microscopy. Our simulations demonstrated that with careful experimental design, it is feasible to estimate mesoscopic magnetic fields. Additionally, the fODF estimated by the standard model of diffusion in white matter could predict the orientation dependence of the mesoscopic Larmor frequency shift. We also found that incorporating the intra-axonal axial kurtosis into the standard model could explain a significant amount of signal variance, thereby improving the estimation of the Larmor frequency shift. This factor should not be neglected when fitting the standard model.
Functional scans in cardiovascular magnetic resonance (CMR) adopting bSSFP sequences suffer from dark band artifacts due to B0 inhomogeneity. The best remedy to mitigate this issue is through cardiac B0 shimming. The development of an optimal B0 shim strategy for the human heart is hindered by a limited understanding of B0 conditions in clinical diagnostic orientations of CMR. Here, we present high-resolution B0 distributions in cardiac imaging planes, derived from simulations utilizing high-resolution computed tomography (CT) images from 1008 subjects, and present an oblique slicing method to derive such B0 distributions. This study also presents a theoretical analysis of spherical harmonic B0 shimming at 3 T using a static global approach and slice-specific dynamic shim updating in the short-axis view of human hearts. The characteristics of cardiac B0 conditions along with spherical harmonic shimming were correlated with the subjects' demographic parameters, with weak or no correlations, suggesting limited demographic commonality and predominantly subject-specific characteristics in cardiac B0. The segmented lung volume shows more significant associations and relatively higher correlations with B0 conditions, indicating that B0 conditions in the heart rely on the anatomy surrounding the heart more than overall body shape and size. This research provides a basis for the development of optimized cardiac B0 shim strategies.
Due to the complex structure of the brain, variations in tumor shapes and sizes, and the resemblance between tumor and healthy tissues, the reliable and efficient identification of brain tumors through magnetic resonance imaging (MRI) presents a persistent challenge. Given that manual identification of tumors is often time-consuming and prone to errors, there is a clear need for advanced automated procedures to enhance detection accuracy and efficiency. Our study addresses the difficulty by creating an improved convolutional neural network (CNN) framework derived from DenseNet121 to augment the accuracy of brain tumor detection. The proposed model was comprehensively evaluated against 12 baseline CNN models and 5 state-of-the-art architectures, namely Vision Transformer (ViT), ConvNeXt, MobileNetV3, FastViT, and InternImage. The proposed model achieved exceptional accuracy rates of 98.4% and 99.3% on two separate datasets, outperforming all 17 models evaluated. Our improved model was integrated using Explainable AI (XAI) techniques, particularly Grad-CAM++, facilitating accurate diagnosis and localization of complex tumor instances, including small metastatic lesions and nonenhancing low-grade gliomas. The XAI framework distinctly highlights essential areas signifying tumor presence, hence enhancing the model's accuracy and interpretability. The results highlight the potential of our method as a reliable diagnostic instrument for healthcare practitioners' ability to comprehend and confirm artificial intelligence (AI)-driven predictions but also bring transparency to the model's decision-making process, ultimately improving patient outcomes. This advancement signifies a significant progression in the use of AI in neuro-oncology, enhancing diagnostic interpretability and precision.
Alzheimer's disease (AD) is the most prevalent form of dementia, characterized by progressive memory loss and cognitive decline, often affecting behavior and speech. Early detection of AD remains a challenge due to its symptomatic overlap with normal aging and other cognitive disorders, necessitating precise classification methods. This paper proposes a novel Skill Al-Biruni Earth Radius Optimization-enabled Deep Spiking Neural Network (SBERO_Deep SNN) for AD classification using magnetic resonance imaging (MRI). Initially, input MRI images undergo enhancement through thresholding transformations. The segmentation is done using UNeXT, which is optimized by the hybrid SBERO algorithm. The SBERO combines the Skill Optimization Algorithm (SOA) and Al-Biruni Earth Radius (BER). Statistical features, local binary patterns (LBP), and gradient directional patterns (GDP) are then extracted, and classification is performed using a Deep Spiking Neural Network (Deep SNN) trained with SBERO. The proposed method achieves 90.49% accuracy, 89.98% sensitivity, and 90.16% specificity, outperforming existing state-of-the-art techniques in AD classification. The qualitative analysis highlights the robustness of the model in differentiating AD from other cognitive disorders, particularly in early stages, providing a reliable tool for clinical diagnosis.
Advances in gene therapy, especially for brain diseases, have created new imaging demands for noninvasive monitoring of gene expression. While reporter gene imaging using co-expression of fluorescent protein-encoding gene has been widely developed, these conventional methods face significant limitations in longitudinal in vivo applications. Magnetic resonance imaging (MRI), specifically chemical exchange saturation transfer (CEST) MRI, provides a robust noninvasive alternative that offers unlimited depth penetration, reliable spatial resolution, and specificity toward particular molecules. In this study, we explore the potential of CEST-MRI for monitoring gene expression in neurons. We designed a CEST polypeptide reporter expressing 150 arginine residues and evaluated its expression in the living brain after viral vector delivery. A longitudinal study performed at one and 2 months postinjection showed that specific CEST signal was observable. In particular, the CEST contrast exhibited distinct peaks at 0.75 and 1.75 ppm, consistent with the expected hydroxyl and guanidyl protons resonance frequencies. Histological study confirmed the specific neuronal expression of the transgene evidenced by the fluorescence signal from the td-Tomato fluorophore fused to the polypeptide. The ability to image noninvasively a neuron-specific CEST-MRI reporter gene could offer valuable insights for further developments of gene therapy for neurological disorders.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：