Magnetic Resonance Materials in Physics, Biology and Medicine最新文献

Osteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues' structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.

Objective: This study aims to quantify the repeatability of a 3D Magnetic Resonance Fingerprinting (MRF) research protocol in the context of a scanner software upgrade. All of MRI assumes consistent hardware performance and raw data pre-processing on the acquisition side. Software upgrades can affect hardware specifications and reconstruction chain parameters. Understanding how vendor-provided software upgrades vary MRF-derived T1 and T2 values is crucial for its application in different settings.

Materials and methods: Eight healthy volunteers were imaged with an in-house developed 3D MRF pulse sequence using a 3T scanner before and after a software upgrade (VA31A to VA50A, MAGNETOM Vida, Siemens Healthineers). Online MRF reconstruction using Singular Value Decomposition (SVD) timeseries compression and B1+ correction was performed. The study involved test-retest repeatability assessment and a comparison of pre- and post-upgrade data based on automatically extracted T1 and T2 values from MNI-152 Harvard-Oxford Subcortical Structural Atlas regions.

Results: Significant mismatches were found directly after the upgrade. However, after an information exchange with the vendor, the 3D-MRF sequence showed consistent repeatability in both intra-version test-retest scenarios and cross-version comparisons: $-$ 1.16 ± 3.18% variability in T1 and $-$ 0.54 ± 4.84% in T2 for intra-version tests, and $-$ 0.83 ± 3.68% (T1) and $-$ 0.05 ± 5.81% (T2) variability for cross-version comparisons.

Discussion: The study shows the reliable performance of 3D MRF protocols across software upgrades is possible, but it also highlights the importance of detailed evaluation and vendor collaboration in ensuring consistency. These findings support the application of MRF in longitudinal studies and emphasize the need for systematic assessments following hardware or software modifications.

Objective: This work presents an automated quality control (QC) system within quantitative MRI (qMRI) workflows. By leveraging the ISMRM/NIST quantitative MRI system phantom, we establish an open-source pipeline for rapid, repeatable, and accurate validation and stability tracking of sequence quantification performance across diverse clinical settings.

Materials and methods: A microservice-based QC system for automated vial segmentation from quantitative maps was developed and tested across various MRF acquisition and protocol designs, with reports generated and returned to the scanner in real time.

Results: The system demonstrated consistent and repeatable value segmentation and reporting, successfully extracted all 252 T1 and T2 vial samples tested. Values extracted from the same sequence were found to be repeatable with 0.09% ± 1.23% and - 0.26% ± 2.68% intersession error, respectively.

Discussion: By providing real-time quantification performance assessment, this easily deployable automated QC approach streamlines sequence validation and long-term performance monitoring, vital for the broader acceptance of qMRI as a standard component of clinical protocols.

Objective: Glioblastoma multiforme is a highly aggressive form of brain cancer, and early diagnosis plays a pivotal role in improving patient survival rates. In this regard, molecular magnetic resonance imaging has emerged as a promising imaging modality due to its exceptional sensitivity to minute tissue changes and the ability to penetrate deep into the brain. This study aimed to assess the efficacy of a novel contrast agent in detecting gliomas during MRI scans.

Materials and methods: The contrast agent utilized modified chitosan coating on manganese oxide nanoparticles. The modification included adding methotrexate and 5-aminolevulinic acid (MnO₂/CS@5-ALA-MTX) to target cells with overexpressed folate receptors and breaking down excess hydrogen peroxide in tumor tissue, resulting in enhanced signal intensity in T₁-weighted MR images but diminished signal intensity in T₂*-weighted MR images.

Results: The nanosystem was characterized and evaluated in MR imaging, safety, and ability to target cells both in vivo and in vitro. MTX-free nanoparticles (MnO₂/CS@5-ALA NPs) had no obvious cytotoxicity on cell lines U87MG and NIH3T3 after 24/48 h at a concentration of up to 160 µgr/mL (cell viability more than 80%). In this system, methotrexate enables tumor targeting and the MnO₂/5-ALA improves T₁-T₂^*-weighted MRI. In addition, MRI scans of mice with M109 carcinoma indicated significant tumor uptake and NP capacity to improve the positive contrast effect.

Conclusion: This developed MnO₂/CS@5-ALA-MTX nanoparticle system may exhibit great potential in the accurate diagnosis of folate receptor over-expressing cancers such as glioblastoma.

Objective: Many patients with long COVID experience neurological and psychological symptoms. Signal abnormalities on MR images in the corpus callosum have been reported. Knowledge about the metabolic profile in the splenium of the corpus callosum (CCS) may contribute to a better understanding of the pathophysiology of long COVID.

Materials and methods: Eighty-one subjects underwent proton MR spectroscopy examination. The metabolic concentrations of total N-acetylaspartate (NAA), choline-containing compounds (Cho), total creatine (Cr), myo-inositol (mI), and NAA/Cho in the CCS were statistically compared in the group of patients containing 58 subjects with positive IgG COVID-19 antibodies or positive SARS-CoV-2 qPCR test at least two months before the MR and the group of healthy controls containing 23 subjects with negative IgG antibodies.

Results: An age-dependent effect of SARS-CoV-2 on Cho concentrations in the CCS has been observed. Considering the subjective threshold of age = 40 years, older patients showed significantly increased Cho concentrations in the CCS than older healthy controls (p = 0.02). NAA, Cr, and mI were unchanged. All metabolite concentrations in the CCS of younger post-COVID-19 patients remained unaffected by SARS-CoV-2. Cho did not show any difference between symptomatic and asymptomatic patients (p = 0.91).

Discussion: Our results suggest that SARS-CoV-2 disproportionately increases Cho concentration in the CCS among older post-COVID-19 patients compared to younger ones. The observed changes in Cho may be related to the microstructural reorganization in the CCS also reported in diffusion measurements rather than increased membrane turnover. These changes do not seem to be related to neuropsychological problems of the post-COVID-19 patients. Further metabolic studies are recommended to confirm these observations.

Objective: To prepare and analyze soy-lecithin-agar gels for non-toxic relaxometry phantoms with tissue-like relaxation times at 3T.

Methods: Phantoms mimicking the relaxation times of various tissues (gray and white matter, kidney cortex and medulla, spleen, muscle, liver) were built and tested with a clinical 3T whole-body MR scanner. Simple equations were derived to calculate the appropriate concentrations of soy lecithin and agar in aqueous solutions to achieve the desired relaxation times. Phantoms were tested for correspondence between measurements and calculated T1 and T2 values, reproducibility, spatial homogeneity, and temporal stability. T1 and T2 mapping techniques and a 3D T1-weighted sequence with high spatial resolution were applied.

Results: Except for the liver relaxation phantom, all phantoms were successfully and reproducibly produced. Good agreement was found between the targeted and measured relaxation times. The percentage deviations from the targeted relaxation times were less than 3% for T1 and less than 6.5% for T2. In addition, the phantoms were homogeneous and had little to no air bubbles. However, the phantoms were unstable over time: after a storage period of 4 weeks, mold growth and also changes in relaxation times were detected in almost all phantoms.

Conclusion: Soy-lecithin-agar gels are a non-toxic material for the construction of relaxometry phantoms with tissue-like relaxation times. They are easy to prepare, inexpensive and allow independent adjustment of T1 and T2. However, there is still work to be done to improve the long-term stability of the phantoms.

Object: In a typical MR session, several contrasts are acquired. Due to the sequential nature of the data acquisition process, the patient may experience some discomfort at some point during the session, and start moving. Hence, it is quite common to have MR sessions where some contrasts are well-resolved, while other contrasts exhibit motion artifacts. Instead of repeating the scans that are corrupted by motion, we introduce a reference-guided retrospective motion correction scheme that takes advantage of the motion-free scans, based on a generalized rigid registration routine.

Materials and methods: We focus on various existing clinical 3D brain protocols at 1.5 Tesla MRI based on Cartesian sampling. Controlled experiments with three healthy volunteers and three levels of motion are performed.

Results: Radiological inspection confirms that the proposed method consistently ameliorates the corrupted scans. Furthermore, for the set of specific motion tests performed in this study, the quality indexes based on PSNR and SSIM shows only a modest decrease in correction quality as a function of motion complexity.

Discussion: While the results on controlled experiments are positive, future applications to patient data will ultimately clarify whether the proposed correction scheme satisfies the radiological requirements.

Objectives: To evaluate a new motion correction method, named RT + NV Track, for upper abdominal DWI that combines the respiratory triggering (RT) method using a respiration sensor and the Navigator Track (NV Track) method using navigator echoes.

Materials and methods: To evaluate image quality acquired upper abdominal DWI and ADC images with RT, NV, and RT + NV Track in 10 healthy volunteers and 35 patients, signal-to-noise efficiency (SNR_efficiency) and the coefficient of variation (CV) of ADC values were measured. Five radiologists independently performed qualitative image-analysis assessments.

Results: RT + NV Track showed significantly higher SNR_efficiency than RT and NV (14.01 ± 4.86 vs 12.05 ± 4.65, 10.05 ± 3.18; p < 0.001, p < 0.001). RT + NV Track was superior to RT and equal or better quality than NV in CV and visual evaluation of ADC values (0.033 ± 0.018 vs 0.080 ± 0.042, 0.057 ± 0.034; p < 0.001, p < 0.001). RT + NV Track tends to acquire only expiratory data rather than NV, even in patients with relatively rapid breathing, and can correct for respiratory depth variations, a weakness of RT, thus minimizing image quality degradation.

Conclusion: The RT + NV Track method is an efficient imaging method that combines the advantages of both RT and NV methods in upper abdominal DWI, providing stably good images in a short scan time.

Objective: Diffusional kurtosis imaging (DKI) extends diffusion tensor imaging (DTI), characterizing non-Gaussian diffusion effects but requires longer acquisition times. To ensure the robustness of DKI parameters, data acquisition ordering should be optimized allowing for scan interruptions or shortening. Three methodologies were used to examine how reduced diffusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (Opt_EEM); 2) spherical codes (Opt_SC); 3) random (Random_TRUNC).

Materials and methods: Pre-acquired diffusion multi-shell data from 14 female healthy volunteers (29±5 years) were used to generate reordered data. For each strategy, subsets containing different amounts of the full dataset were generated. The subsampling effects were assessed on histogram-based DKI metrics from tract-based spatial statistics (TBSS) skeletonized maps. To evaluate each subsampling method on simulated data at different SNRs and the influence of subsampling on in vivo data, we used a 3-way and 2-way repeated measures ANOVA, respectively.

Results: Simulations showed that subsampling had different effects depending on DKI parameter, with fractional anisotropy the most stable (up to 5% error) and radial kurtosis the least stable (up to 26% error). Random_TRUNC performed the worst while the others showed comparable results. Furthermore, the impact of subsampling varied across distinct histogram characteristics, the peak value the least affected (Opt_EEM: up to 5% error; Opt_SC: up to 7% error) and peak height (Opt_EEM: up to 8% error; Opt_SC: up to 11% error) the most affected.

Conclusion: The impact of truncation depends on specific histogram-based DKI metrics. The use of a strategy for optimizing the acquisition order is advisable to improve DKI robustness to exam interruptions.