NMR in Biomedicine最新文献

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 T_1ρ 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 T_1ρ 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 T_1ρ 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.

In clinical practice, particularly in neurology assessments, imaging multiparametric MR images with a single-sequence scan is often limited by either insufficient imaging contrast or the constraints of accelerated imaging techniques. A novel single scan 3D imaging method, incorporating Wave-CAIPI and MULTIPLEX technologies and named WAMP, has been developed for rapid and comprehensive parametric imaging in clinical diagnostic applications. Featuring a hybrid design that includes wave encoding, the CAIPIRINHA sampling pattern, dual time of repetition (TR), dual flip angle (FA), multiecho, and optional flow modulation, the WAMP method captures information on RF B1t fields, proton density (PD), T1, susceptibility, and blood flow. This method facilitates the synthesis of multiple qualitative contrast-weighted images and relaxometric parametric maps. A single WAMP scan generates multiple contrast-weighted images and relaxometric parametric maps, including PD-weighted (PDW), T1-weighted (T1W), T2*-weighted (T2W), adjusted T1-weighted (aT1W), susceptibility-weighted imaging (SWI), B1t map, T1 map, T2/R2* map, PD map, and quantitative susceptibility mapping (QSM). Both phantom and in vivo experiments have demonstrated that the proposed method can achieve high image quality and quantification accuracy even at high acceleration factors of 4 and 9. The experiments have confirmed that the rapid single scan method can be effectively applied in clinical neurology, serving as a valuable diagnostic tool for conditions such as pediatric tuberous sclerosis complex (TSC)-related epilepsy, adult Parkinson's disease, and suspected stroke patient. The WAMP method holds substantial potential for advancing multiparametric MR imaging in clinical neurology, promising significant improvements in both diagnostic speed and accuracy.

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.

MRI in vivo is a powerful clinical diagnosis tool as it allows acquiring noninvasively images with an ample range of contrasts. Advanced imaging techniques such as chemical exchange saturation transfer (CEST) allow measuring metabolic information including pH. Sodium tissue concentration, which can be measured by ²³Na MRI, is sensitive to changes in different pathological conditions. The routine clinical application of these techniques is limited by the required additional scan time. Multinuclear interleaved techniques allow reducing the total acquisition scan time by performing the pulse sequence elements of a ¹H imaging sequence during the idle times typically used in ²³Na MRI to allow magnetization recovery and reduce T₁ weighting. An interleaved radial amine CEST and sodium (INTERLACED) pulse sequence was developed on a clinical scanner to simultaneously map acidity or T₂* decay with ²³Na signal, reducing the total scan time by 46% relative to sequential mononuclear acquisitions and without introducing any significant bias, as demonstrated in vitro. Dynamic INTERLACED measures were performed in the leg during a 5-min plantar flexion exercise and during a second plantar flexion exercise immediately followed by a 5-min voluntary isometric contraction. The results showed increased T₂* and ²³Na signal during recovery in the gastrocnemius (GAS) while only an increase in ²³Na signal was observed in the soleus (SOL). During the isometric contraction, T₂* decreased in GAS, SOL, and the tibialis anterior; the ²³Na signal increased in GAS and SOL; and the magnetization transfer asymmetry increased in GAS, in agreement with an increase of intracellular sodium and acidification of the extracellular space. Our approach requires no hardware modifications, facilitating its inclusion in clinical routine at 3 T. Furthermore, it could benefit functional studies by enabling the acquisition of dynamic multinuclear information simultaneously from the same transient state.

Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole-lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10^-2) for asthma, r = 0.79 (p = 3.15 × 10^-6) for COPD, and r = 0.84 (p = 2.80 × 10^-5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole-lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative.

Gliomas are highly heterogeneous and often include a nonenhancing component that is hyperintense on T₂ weighted MRI. This can often not be distinguished from secondary gliosis and surrounding edema. We hypothesized that the extent of these T₂ hyperintense areas can more accurately be determined on high-quality 7 T MRI scans. We investigated the extension, volume, and complexity (shape) of T₂ hyperintense areas in patients with glioma on high-quality 7 T MRI scans compared to clinical MRI scans. T₂ hyperintense areas of 28 patients were visually compared and manually segmented on 7 T MRI and corresponding clinical (1.5 T/3 T) MRI scans, and the volume and shape markers were calculated and subsequently compared between scans. We showed extension of the T₂ hyperintense areas via the corpus callosum to the opposite hemisphere in four patients on the 7 T scans that was not visible on the clinical scan. Furthermore, we found a significantly larger volume of the T₂ hyperintense areas on the 7 T scans compared with the clinical scans (7 T scans: 28 mL [12.5-59.1]; clinical scans: 11.9 mL [11.8-56.6]; p = 0.01). We also found a higher complexity of the T₂ hyperintense areas on the 7 T scans compared with the clinical scans (convexity, solidity, concavity index and fractal dimension [p < 0.001]). Our study suggests that high-quality 7 T MRI scans may show more detail on the exact extension, size, and complexity of the T₂ hyperintense areas in patients with a glioma. This information could aid in more accurate planning of treatment, such as surgery and radiotherapy.