Magnetic Resonance in Medicine最新文献

Purpose: To develop a robust and efficient multidimensional MRI (MD-MRI) data processing framework for accurately estimating joint frequency-dependent diffusion-relaxation distributions, while overcoming computational limitations and noise instability inherent to Monte Carlo (MC) inversion.

Methods: We introduced an Informed Dictionary-guided Monte Carlo (ID-MC) strategy that incorporates data-driven dictionary matching into the inversion process, followed by targeted local mutation refinement to enhance flexibility and reduce overfitting. This hybrid approach aims to improve the stability, accuracy, and reproducibility of MD-MRI parameter estimation. We evaluated ID-MC through in silico simulations across a range of signal-to-noise ratios and in vivo test-retest experiments in the human brain. Reproducibility was assessed using intraclass correlation coefficients (ICC) and within-subject variability, allowing rigorous comparison with MC.

Results: In simulations, the ID-MC approach consistently achieved lower fitting errors and higher estimation accuracy across a wide range of noise levels, demonstrating its ability to balance local flexibility and global biological plausibility. Compared to MC inversion, ID-MC also reduced computation time by approximately 69%, highlighting its potential for time-efficient large-scale applications. In in vivo test-retest analyses, ID-MC substantially improved reproducibility, doubling the number of MD-MRI parameters with ICC greater than 0.75 relative to MC. Notably, diffusion frequency-dependent parameters, previously poorly reproducible with MC, showed up to 146% higher ICC with ID-MC.

Conclusion: By integrating data-driven dictionary matching with targeted mutation refinement, ID-MC improves the robustness, reproducibility, and computational efficiency of MD-MRI inversion, supporting studies that require highly sensitive detection of subtle brain microstructural changes.

Purpose: This work aims to develop a robust Nyquist ghost correction method for multishot echo-planar imaging (EPI). The method helps correct challenging Nyquist ghosts, particularly on scanners with high-performance gradients or ultra-high fields.

Methods: A method for multishot EPI ghost correction, called multishot dual-polarity GRAPPA (msDPG), is developed by extending the DPG concept to multishot readouts. msDPG employs tailored DPG kernels to address high-order phase differences between two EPI readout polarities, which cannot be fully addressed using linear phase correction (LPC) or non-linear phase correction (nLPC). Advanced regularizers can be readily employed with the proposed msDPG for physiologic inter-shot phase variation correction during reconstruction. Additionally, a calibration refinement method is proposed to improve the quality of the DPG calibration data and enhance reconstruction performance.

Results: Phantom and in vivo experiments on scanners with high-performance gradients and ultra-high fields demonstrated that msDPG achieved superior ghost correction performance than LPC and nLPC, reducing the ghost-to-signal ratio (GSR) by over 50%. Compared to conventional DPG, msDPG provided images with lower noise amplification, particularly for acquisitions with large in-plane acceleration. Consequently, high-fidelity, submillimeter diffusion images were obtained using msDPG with regularized reconstruction.

Conclusion: The proposed msDPG provides a robust Nyquist ghost correction method for multishot EPI, enabling submillimeter imaging with improved fidelity.

Purpose: Supine breast MRI has the potential to improve patient comfort compared to prone breast MRI, in addition to providing images in the same position as subsequent treatment protocols. Novel flexible coil arrays have enabled high SNR and parallel imaging in supine breast imaging, but the combined effect of coil and patient positioning on SNR has yet to be investigated. The aim of this study is to use a tissue-independent metric to account for tissue deformation to compare SNR between prone and supine positions, using appropriate coils for each.

Methods: Relative SNR (rSNR) metric is proposed as the ratio of SNR between a breast coil and a body coil. This metric is demonstrated to be tissue-independent, allowing for easier SNR comparisons in cases of tissue deformation. We scanned 10 female subjects and compared the rSNR in segmented regions consisting of breast tissue, chest wall, and axilla between prone and supine breast imaging.

Results: The rSNR was significantly higher in the breast tissue and chest wall in the supine position for all cases. The axilla rSNR was significantly higher in supine for four cases, with another four significantly higher in prone, and two showing no statistical difference. Using a distance-from-coil analysis, we found that the tissue is closer to the coil in supine, and that the supine coil provided higher SNR at distances closer than 4cm.

Conclusion: Our results show that using a surface array coil in the supine position can provide higher SNR than a standard setup in most subjects for most relevant regions of breast MRI.

Purpose: Segmentation of cranial nerves (CNs) bundles using magnetic resonance imaging (MRI) provides a valuable quantitative approach for analyzing the morphology and orientation of individual CNs. Currently, the CN regions can be segmented directly using deep learning-based methods. However, existing methods overlook the unique characteristics of CNs, particularly their environmental features and representation in multimodal images that may lead to suboptimal segmentation outcomes.

Methods: We proposed a dynamic-guided diffusion probability model for CNs segmentation, which enhances segmentation performance by integrating the intrinsic characteristics of CNs. A dynamic-guided mechanism approach called the SE-A-NL module was proposed. The module is capable of addressing both the varying characterization abilities of multimodal images and the long-range connections of CNs within images.

Results: Quantitative and qualitative experiments demonstrate that the proposed method surpasses current state-of-the-art approaches, delivering accurate and effective segmentation of five pairs of cranial nerves. Notably, the method outperforms existing techniques in 16 out of the 20 evaluated metrics.

Conclusion: The overall network model effectively integrates multimodal information and anatomical priors by combining multi-channel attention and non-local attention mechanisms, thereby improving CNs segmentation performance. Thorough comparative and ablation studies highlight the superior performance of the proposed method.

Purpose: Chemical shift encoding (Dixon) can simultaneously remove multiple fat peaks in multi-shot EPI diffusion-weighted imaging (ms-EPI DWI) at 3 T with low sensitivity to B₀ inhomogeneity. However, this method is not applicable at 5 T, since increased fat off-resonance frequencies cause slice position mismatches between different fat peaks. In this work, we propose a two-step strategy combining slice-selection gradient modulation (SSGM) and Dixon to enhance fat suppression for ms-EPI DWI at 5 T.

Methods: In the first step, SSGM adjusts the amplitudes of excitation and refocusing slice-selection gradients according to the off-resonance frequencies of methyl/methylene fat peaks, so that these fat slices are excited but not refocused. In the second step, the olefinic fat peak is chemical shift encoded and separated from the diffusion-weighted water images through a joint water/fat separation algorithm with structured low-rank regularization. The two-step strategy was evaluated in the leg, head-and-neck, and prostate.

Results: In vivo experiments demonstrated that the Dixon-only methods cannot simultaneously suppress all fat peaks at 5 T, while this problem was addressed by combining SSGM and Dixon. SSGM showed superior suppression for methyl/methylene fat compared to SPAIR. The following Dixon further removed olefinic fat untouched by SPAIR. Qualitative analysis showed improved overall image quality for all anatomies. Prostate experiments showed that the proposed method is also applicable in reduced FOV acquisitions, high-resolution (1.6-mm isotropic) and high b value imaging (2800 s/mm²).

Conclusion: The proposed two-step strategy improved fat suppression in ms-EPI DWI at 5 T, which can potentially enhance whole-body disease screening and diagnosis.

Purpose: Investigating time-division multiplexing for parallel transmission in ultra high-field imaging, striving for homogeneous whole brain excitation with a limited number of RF channels.

Methods: A fast RF switch was built to alternately route 8 transmit channels to each row of a double-row 16-element transmit coil array at a 9.4 T human MRI system. Methods for SAR monitoring and pulse design for this temporal degree of freedom were developed and investigated in electromagnetic simulations and in vivo measurements, employing parallel transmission kT points pulses aiming for homogeneous whole-brain excitation. The achievable trade-off between local SAR and excitation homogeneity was compared for multiplexed and simultaneous transmission.

Results: Using time-division multiplexing, similar excitation fidelity as with 16 transmit channels can be achieved with only 8 channels. For instance, multiplexing reduces the flip angle inhomogeneity by 2.22-fold compared to exciting only a single row of the array, and by 1.85-fold compared to statically splitting and routing 8 channels to 16 transmit coil elements. As a trade-off, compared to simultaneous excitation, multiplexing requires either increased pulse duration or amplitudes, the latter causing increased SAR. However, with appropriate SAR-aware pulse design, the multiplexing-induced local SAR increase can be controlled. This allows for viable pulse design solutions for the considered low-flip-angle imaging scenarios.

Conclusion: Time-division multiplexing allows driving a larger number of transmit elements with a smaller number of RF channels, resulting in improved parallel transmission performance. This opens up new possibilities for using advanced multi-row transmit coil arrays in sites with only 8 RF channels available.

Our goal was to understand the barriers and challenges to clinical translation of quantitative MR (qMR) as perceived by stakeholders in the UK. We conducted an electronic survey on seven key areas related to clinical translation of qMR, developed at the BIC-ISMRM workshop: “Steps on the path to clinical translation”. Based on the seven areas identified: (i) clinical workflow, (ii) changes in clinical practice, (iii) improving validation, (iv) standardization of data acquisition and analysis, (v) sharing of data and code, (vi) improving quality management, and (vii) end-user engagement, a 40-question survey was developed. Members of BIC-ISMRM, MR-PHYSICS, BSNR and institutional mailing lists were invited to respond to the online survey over a 5-week period between September and October 2022. The responses were analysed via descriptive statistics of multiple-choice questions, Likert scores and a thematic analysis of free text questions. A total of 69 responses were received from predominantly research imaging scientists (69%) in numerous centres across the UK. Three main themes were identified: (1) Consensus; the need to develop in terminology, decision making and validation; (2) Context Dependency; an appreciation of the uniqueness of each clinical situation, and (3) Product Profile; a clear description of the imaging biomarker and its intended use. Effective translation of qMR imaging and spectroscopic biomarkers to achieve their full clinical potential must address the differing needs and expectations of a wide range of stakeholders.

Purpose: To develop a self-supervised scan-specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI).

Methods: We propose REFINE-MORE (REference-Free Implicit NEural representation with MOdel REinforcement), combining an implicit neural representation (INR) architecture with a model reinforcement module that incorporates MR physics constraints. The INR component enables informative learning of spatiotemporal correlations to initialize multiparametric quantitative maps, which are then further refined through an unrolled optimization scheme enforcing data consistency. To improve computational efficiency, REFINE-MORE integrates a low-rank adaptation strategy that promotes rapid model convergence. We evaluated REFINE-MORE on accelerated multiparametric quantitative magnetization transfer imaging for simultaneous estimation of free water spin-lattice relaxation, tissue macromolecular proton fraction, and magnetization exchange rate, using both phantom and in vivo brain data.

Results: Under 4× and 5× accelerations on in vivo data, REFINE-MORE achieved superior reconstruction quality, demonstrating the lowest normalized root-mean-square error and highest structural similarity index compared to baseline methods and other state-of-the-art model-based and deep learning approaches. Phantom experiments further showed strong agreement with reference values, underscoring the robustness and generalizability of the proposed framework. Additionally, the model adaptation strategy improved reconstruction efficiency by approximately fivefold.

Conclusion: REFINE-MORE enables accurate and efficient scan-specific multiparametric qMRI reconstruction, providing a flexible solution for high-dimensional, accelerated qMRI applications.

Purpose: To develop a deep learning (DL) denoising method to enhance high-resolution carotid vessel wall MRI quality acquired using a standard head-and-neck clinical coil.

Methods: Fifty-five scans were performed as part of an ongoing multicenter study. Routine carotid VWI protocol including 2D T1- and T2-weighted TSE, 3D TOF-MRA, and MPRAGE was performed using simultaneous acquisition from a standard 20-channel head-and-neck coil and a high-sensitivity Neck-Shape-Specific (NSS) surface coil. Paired retrospective reconstructions with and without NSS coil elements served as the reference and input, respectively. A supervised DL model employing a residual UNet architecture was optimized and trained to map low-SNR inputs to high-SNR references, benchmarked against conventional denoising algorithms using quantitative and qualitative metrics.

Results: The DL denoiser substantially reduced noise while preserving vessel-wall structures across contrast-weighted sequences. It achieved PSNR > 31 dB and structural similarity index (SSIM) > 0.93 versus reference slices. In segmented vessel-wall and lumen regions of interest (ROIs), the DL approach achieved significantly higher SNR and CNR values than input images (p < 0.05), closely approaching the reference. Furthermore, inner-wall edge sharpness was maintained (Average ERD 7.50-8.51 mm with DL vs. 7.15-8.28 mm with references), supporting confident downstream plaques assessment. Radiologists' Likert ratings corroborated these image-quality improvements.

Conclusion: A DL-based method was developed to improve high-resolution, multi-contrast carotid vessel wall MRI acquired using low-SNR standard head-and-neck coils. The resulting image quality was comparable to that obtained with specialized neck surface coils, potentially enabling broader access to advanced carotid imaging without the need for additional hardware.

Purpose: To propose a signal acquisition and modeling framework for multi-component tissue quantification that encompasses transmit field inhomogeneity, multi-component relaxation and magnetization transfer (MT) effects.

Theory and methods: By applying off-resonance irradiation between excitation and acquisition within an RF-spoiled gradient-echo scheme, in combination with multiple echo-time acquisitions, both Bloch-Siegert shift and magnetization transfer effects are simultaneously induced while relaxation and spin exchange processes occur concurrently. The spin dynamics are modeled using a three-pool framework, from which an analytical signal equation is derived and validated through numerical Bloch simulations. Monte Carlo simulations were further performed to analyze and compare the model's performance. Finally, the feasibility of this novel approach was investigated in vivo in human brain and knee tissues.

Results: Simulation results showed excellent agreement with the derived analytical signal equation across a wide range of flip angles and echo times. Monte Carlo analyses further validated that the three-pool parameter estimation pipeline performed robustly over various signal-to-noise ratio conditions. Multi-parameter fitting results from in vivo brain and knee studies yielded values consistent with previously reported literature. Collectively, these findings confirm that the proposed method can reliably characterize multi-component tissue parameters in macromolecule-rich environments while effectively compensating for $B_1^{+}$ inhomogeneity.

Conclusion: A signal acquisition and modeling framework for multi-component tissue quantification that accounts for magnetization transfer effects and $B_1^{+}$ inhomogeneity has been developed and validated. Both simulation and experimental results confirmed the robustness of this method and its applicability to various tissue types in the brain and knee.