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

Object: This study aimed to evaluate the relaxivity and uniformity of agarose gel phantoms added with relaxation modifiers. It is hypothesized that the modifiers could manipulate the T1 and T2 relaxations as well as the signal uniformity.

Materials and methods: Twenty agarose gel phantoms with different GdCl₃ and FeCl₃ volume fractions were prepared. The phantoms were scanned using a 3-T scanner implementing a turbo spin echo sequence to acquire T1 and T2 images. The SNR of the images were computed using Image-J software from 1, 3, and 25 regions-of-interest (ROIs) and were inverted as T1 and T2 curves.

Results: With the increase in relaxation modifier content, T1 SNR increased at a faster rate at very short TR and reached saturation at TR well below 400 ms. Agarose gel phantoms containing GdCl₃ showed a higher saturation value as compared to phantoms containing FeCl₃. For T2 SNR, differences between plots are observed at low TE. As TE gets larger, the SNR between plots is incomparable. The SNR for both groups was uniform among 1, 3, and 25 ROIs.

Discussions: It can be concluded that GdCl₃ and FeCl₃ solutions can be used as effective relaxation modifiers to reduce T1 but not T2 relaxation times.

Purposes: This research highlights the need for affordable phantoms for MRI education. Current options are either expensive or limited. A phantom, easy to manufacture and distribute, is proposed to demonstrate various pedagogical concepts, aiding students in understanding MRI image quality concepts.

Methods: We designed a cylindrical MRI phantom that comprises sections that can be filled with chosen liquids and gels. The dimensions were chosen to fit most consumer-grade 3D printers, facilitating widespread dissemination. It includes five modular sections for evaluating spatial resolution, geometrical accuracy, slice thickness accuracy, homogeneity, and contrast.

Results: The modular cylindrical MRI phantom was successfully fabricated. Each section of the phantom was tested to ensure it met the specified pedagogical needs. The spatial resolution section provided clear images for evaluating fine details. The geometrical accuracy section allowed for precise measurement of distortions. The slice thickness accuracy section confirmed the consistency of slice thickness across different MRI sequences. The homogeneity section demonstrated uniform signal distribution, and the contrast section effectively displayed varying contrast levels.

Conclusions: This modular MRI phantom offers a cost-effective tool for educational purposes in MRI. Its design enables educators to demonstrate multiple pedagogical scenarios with a single object. The phantom's compatibility with consumer-grade 3D printers and its modularity makes it accessible and adaptable to various educational settings. Future work could explore further customization and enhancement of the phantom to cover additional educational needs. This tool represents a significant step toward improving MRI education and training by providing a practical, hands-on learning experience.

Objective: To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources.

Methods: Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy.

Results: 20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality.

Discussion: The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.

Objective: To assess and improve the reliability of the ultrashort echo time quantitative magnetization transfer (UTE-qMT) modeling of the cortical bone.

Materials and methods: Simulation-based digital phantoms were created that mimic the UTE-qMT properties of cortical bones. A wide range of SNR from 25 to 200 was simulated by adding different levels of noise to the synthesized MT-weighted images to assess the effect of SNR on UTE-qMT fitting results. Tensor-based denoising algorithm was applied to improve the fitting results. These results from digital phantom studies were validated via ex vivo rat leg bone scans.

Results: The selection of initial points for nonlinear fitting and the number of data points tested for qMT analysis have minimal effect on the fitting result. Magnetization exchange rate measurements are highly dependent on the SNR of raw images, which can be substantially improved with an appropriate denoising algorithm that gives similar fitting results from the raw images with an 8-fold higher SNR.

Discussion: The digital phantom approach enables the assessment of the reliability of bone UTE-qMT fitting by providing the known ground truth. These findings can be utilized for optimizing the data acquisition and analysis pipeline for UTE-qMT imaging of cortical bones.

Introduction: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption.

Methods: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B₁ inhomogeneities to estimate perfusion (K^trans, v_p, v_e), tissue T₁ relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor.

Results: The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%.

Conclusion: The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T₁ scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

Object: Deep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data. However, raw data is not always available in sufficient quantities. This study investigates synthetic data generation to complement small datasets and improve reconstruction quality.

Materials and methods: An adversarial auto-encoder was trained to generate phase and coil sensitivity maps from magnitude images, which were combined into synthetic raw data. On a fourfold accelerated MR reconstruction task, deep-learning-based reconstruction networks were trained with varying amounts of training data (20 to 160 scans). Test set performance was compared between baseline experiments and experiments that incorporated synthetic training data.

Results: Training with synthetic raw data showed decreasing reconstruction errors with increasing amounts of training data, but importantly this was magnitude-only data, rather than real raw data. For small training sets, training with synthetic data decreased the mean absolute error (MAE) by up to 7.5%, whereas for larger training sets the MAE increased by up to 2.6%.

Discussion: Synthetic raw data generation improved reconstruction quality in scenarios with limited training data. A major advantage of synthetic data generation is that it allows for the reuse of magnitude-only datasets, which are more readily available than raw datasets.

Purpose: To develop a new MR coronary angiography (MRCA) technique by employing a zigzag fan-shaped centric k_y-k_z k-space trajectory combined with high-resolution deep learning reconstruction (HR-DLR).

Methods: All imaging data were acquired from 12 healthy subjects and 2 patients using two clinical 3-T MR imagers, with institutional review board approval. Ten healthy subjects underwent both standard 3D fast gradient echo (sFGE) and centric ky-kz k-space trajectory FGE (cFGE) acquisitions to compare the scan time and image quality. Quantitative measures were also performed for signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as sharpness of the vessel. Furthermore, the feasibility of the proposed cFGE sequence was assessed in two patients. For assessing the feasibility of the centric k_y-k_z trajectory, the navigator-echo window of a 30-mm threshold was applied in cFGE, whereas sFGE was applied using a standard 5-mm threshold. Image quality of MRCA using cFGE with HR-DLR and sFGE without HR-DLR was scored in a 5-point scale (non-diagnostic = 1, fair = 2, moderate = 3, good = 4, and excellent = 5). Image evaluation of cFGE, applying HR-DLR, was compared with sFGE without HR-DLR. Friedman test, Wilcoxon signed-rank test, or paired t tests were performed for the comparison of related variables.

Results: The actual MRCA scan time of cFGE with a 30-mm threshold was acquired in less than 5 min, achieving nearly 100% efficiency, showcasing its expeditious and robustness. In contrast, sFGE was acquired with a 5-mm threshold and had an average scan time of approximately 15 min. Overall image quality for MRCA was scored 3.3 for sFGE and 2.7 for cFGE without HR-DLR but increased to 3.6 for cFGE with HR-DLR and (p < 0.05). The clinical result of patients obtained within 5 min showed good quality images in both patients, even with a stent, without artifacts. Quantitative measures of SNR, CNR, and sharpness of vessel presented higher in cFGE with HR-DLR.

Conclusion: Our findings demonstrate a robust, time-efficient solution for high-quality MRCA, enhancing patient comfort and increasing clinical throughput.

Objective: To establish an image acquisition and post-processing workflow for the determination of the proton density fat fraction (PDFF) in calf muscle tissue at 7 T.

Materials and methods: Echo times (TEs) of the applied vendor-provided multi-echo gradient echo sequence were optimized based on simulations of the effective number of signal averages (NSA*). The resulting parameters were validated by measurements in phantom and in healthy calf muscle tissue (n = 12). Additionally, methods to reduce phase errors arising at 7 T were evaluated. Finally, PDFF values measured at 7 T in calf muscle tissue of healthy subjects (n = 9) and patients with fatty replacement of muscle tissue (n = 3) were compared to 3 T results.

Results: Simulations, phantom and in vivo measurements showed the importance of using optimized TEs for the fat-water separation at 7 T. Fat-water swaps could be mitigated using a phase demodulation with an additional B₀ map, or by shifting the TEs to longer values. Muscular PDFF values measured at 7 T were comparable to measurements at 3 T in both healthy subjects and patients with increased fatty replacement.

Conclusion: PDFF determination in calf muscle tissue is feasible at 7 T using a chemical shift-based approach with optimized acquisition and post-processing parameters.

Objective: The image quality of synthetized FLAIR (fluid attenuated inversion recovery) images is generally inferior to its conventional counterpart, especially regarding the lesion contrast mismatch. This work aimed to improve the lesion appearance through a hybrid methodology.

Materials and methods: We combined a full brain 5-min MR-STAT acquisition followed by FLAIR synthetization step with an ultra-under sampled conventional FLAIR sequence and performed the retrospective and prospective analysis of the proposed method on the patient datasets and a healthy volunteer.

Results: All performance metrics of the proposed hybrid FLAIR images on patient datasets were significantly higher than those of the physics-based FLAIR images (p < 0.005), and comparable to those of conventional FLAIR images. The small difference between prospective and retrospective analysis on a healthy volunteer demonstrated the validity of the retrospective analysis of the hybrid method as presented for the patient datasets.

Discussion: The proposed hybrid FLAIR achieved an improved lesion appearance in the clinical cases with neurological diseases compared to the physics-based FLAIR images, Future prospective work on patient data will address the validation of the method from a diagnostic perspective by radiological inspection of the new images over a larger patient cohort.

Objective: Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To overcome the necessity of presuming a specific model for the hemodynamic response, we introduce a semi-supervised automatic detection (SAD) method.

Materials and methods: The proposed SAD method employs a Bi-LSTM neural network to classify high temporal resolution fMRI data. Network training utilized an fMRI dataset with 75-ms temporal resolution in an iterative scheme. Classification performance was evaluated on a second fMRI dataset from the same participant, collected on a different day. Comparative analysis with the standard GLM approach was conducted to evaluate the cooperative effectiveness of the SAD method.

Results: The SAD method performed well based on the classification scores: true-positive rate = 0.961, area under the receiver operating curve = 0.998, true-negative rate = 0.99, F1-score = 0.979, False-negative rate = 0.038, false-discovery rate = 0.002, false-positive rate = 0.002 at 75-ms temporal resolution.

Conclusion: SAD can detect hemodynamic responses at 75-ms temporal resolution without relying on a specific shape of an HRF. Future work could expand the use cases to include more participants and different fMRI paradigms.